From 6e369efb23b98b5cfcb55e54801d7be1fab1bb96 Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Wed, 3 Jul 2024 20:31:13 -0400
Subject: [PATCH 01/19] Refactored main function to pass config.ini as
 argument, instead of as global. DB paths separated into two separate ini
 files

---
 moalmanac/annotation-databases.ini  |  14 +
 moalmanac/annotator.py              | 161 ++++++------
 moalmanac/config.ini                |  38 +--
 moalmanac/config.py                 |   8 -
 moalmanac/datasources.py            |  59 +----
 moalmanac/evaluator.py              | 153 +++++------
 moalmanac/features.py               | 157 ++++++-----
 moalmanac/illustrator.py            |  39 +--
 moalmanac/investigator.py           |  54 ++--
 moalmanac/matchmaker.py             |  43 +--
 moalmanac/moalmanac.py              | 392 +++++++++++++++++-----------
 moalmanac/preclinical-databases.ini |  10 +
 moalmanac/reader.py                 |  31 ++-
 moalmanac/reporter.py               |  23 +-
 moalmanac/run_example.py            |  27 +-
 15 files changed, 638 insertions(+), 571 deletions(-)
 create mode 100644 moalmanac/annotation-databases.ini
 create mode 100644 moalmanac/preclinical-databases.ini

diff --git a/moalmanac/annotation-databases.ini b/moalmanac/annotation-databases.ini
new file mode 100644
index 0000000..65503a9
--- /dev/null
+++ b/moalmanac/annotation-databases.ini
@@ -0,0 +1,14 @@
+[databases]
+almanac_handle = datasources/moalmanac/molecular-oncology-almanac.json
+cancerhotspots_handle = datasources/cancerhotspots/hotspots_v2.txt
+3dcancerhotspots_handle = datasources/cancerhotspots/hotspots3d.txt
+cgc_handle = datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv
+cosmic_handle = datasources/cosmic/CosmicMutantExport_v97.lite.txt
+gsea_pathways_handle = datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt
+gsea_modules_handle = datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt
+exac_handle = datasources/exac/exac.expanded.r1.txt
+acmg_handle = datasources/acmg/acmg.secondaryfindings.v3.txt
+clinvar_handle = datasources/clinvar/variant_summary.lite.txt
+hereditary_handle = datasources/hereditary/hereditary.txt
+oncotree_handle = datasources/oncotree/oncotree.2023-03-09.txt
+lawrence_handle = datasources/lawrence/lawrence_mapped_ontology.txt
\ No newline at end of file
diff --git a/moalmanac/annotator.py b/moalmanac/annotator.py
index 2c1bf90..8bad42b 100644
--- a/moalmanac/annotator.py
+++ b/moalmanac/annotator.py
@@ -8,9 +8,6 @@
 import features
 
 from config import COLNAMES
-from config import CONFIG
-
-EXAC_CONFIG = CONFIG['exac']
 
 
 class Annotator:
@@ -45,28 +42,28 @@ def annotate(cls, df, dbs, importer, bin_name, comparison_columns):
         return df
 
     @classmethod
-    def annotate_almanac(cls, df, dbs, ontology):
+    def annotate_almanac(cls, df, dbs, ontology, config):
         df[cls.score_bin] = cls.preallocate_bin(cls.score_bin, df.index)
-        df = Almanac.annotate(df, dbs, ontology)
+        df = Almanac.annotate(df, dbs, ontology, config)
         return df
 
     @classmethod
-    def annotate_germline(cls, df, dbs, ontology):
+    def annotate_germline(cls, df, dbs, ontology, config):
         df[cls.score_bin] = cls.preallocate_bin(cls.score_bin, df.index)
-        df = Almanac.annotate(df, dbs, ontology)
+        df = Almanac.annotate(df, dbs, ontology, config)
         df = CancerHotspots.annotate(df, dbs)
         df = CancerGeneCensus.annotate(df, dbs)
         df = ACMG.annotate(df, dbs)
         df = ClinVar.annotate(df, dbs)
         df = Hereditary.annotate(df, dbs)
-        df = ExACExtended.annotate(df, dbs)
+        df = ExACExtended.annotate(df, dbs, config)
         df = MSI.annotate(df)
         return df
 
     @classmethod
-    def annotate_simple(cls, df, dbs, ontology):
+    def annotate_simple(cls, df, dbs, ontology, config):
         df[cls.score_bin] = cls.preallocate_bin(cls.score_bin, df.index)
-        df = Almanac.annotate(df, dbs, ontology)
+        df = Almanac.annotate(df, dbs, ontology, config)
         df = CancerHotspots.annotate(df, dbs)
         df = CancerHotspots3D.annotate(df, dbs)
         df = CancerGeneCensus.annotate(df, dbs)
@@ -79,23 +76,23 @@ def annotate_simple(cls, df, dbs, ontology):
         return df
 
     @classmethod
-    def annotate_somatic(cls, df, dbs, ontology):
+    def annotate_somatic(cls, df, dbs, ontology, config):
         df[cls.score_bin] = cls.preallocate_bin(cls.score_bin, df.index)
-        df = Almanac.annotate(df, dbs, ontology)
+        df = Almanac.annotate(df, dbs, ontology, config)
         df = CancerHotspots.annotate(df, dbs)
         df = CancerHotspots3D.annotate(df, dbs)
         df = CancerGeneCensus.annotate(df, dbs)
         df = Cosmic.annotate(df, dbs)
         df = GSEACancerPathways.annotate(df, dbs)
         df = GSEACancerModules.annotate(df, dbs)
-        df = ExAC.annotate(df, dbs)
+        df = ExAC.annotate(df, dbs, config)
         df = MSI.annotate(df)
         return df
 
     @classmethod
-    def annotate_somatic_no_exac(cls, df, dbs, ontology):
+    def annotate_somatic_no_exac(cls, df, dbs, ontology, config):
         df[cls.score_bin] = cls.preallocate_bin(cls.score_bin, df.index)
-        df = Almanac.annotate(df, dbs, ontology)
+        df = Almanac.annotate(df, dbs, ontology, config)
         df = CancerHotspots.annotate(df, dbs)
         df = CancerHotspots3D.annotate(df, dbs)
         df = CancerGeneCensus.annotate(df, dbs)
@@ -381,32 +378,21 @@ class Almanac:
         }
     }
 
-    feature_types_section = 'feature_types'
-    feature_types_config = CONFIG[feature_types_section]
-    aneuploidy = feature_types_config['aneuploidy']
-    burden = feature_types_config['burden']
-    copynumber_variant = feature_types_config['cna']
-    fusion = feature_types_config['fusion']
-    germline_variant = feature_types_config['germline']
-    microsatellite_status = feature_types_config['microsatellite']
-    signature = feature_types_config['signature']
-    somatic_variant = feature_types_config['mut']
-
     @classmethod
-    def annotate(cls, df, dbs, ontology):
+    def annotate(cls, df, dbs, ontology, config):
         db = datasources.Almanac.import_ds(dbs)
         ds = db['content']
         list_genes = db['genes']
 
         annotation_function_dict = {
-            cls.aneuploidy: cls.annotate_aneuploidy,
-            cls.burden: cls.annotate_burden,
-            cls.copynumber_variant: cls.annotate_copy_number,
-            cls.fusion: cls.annotate_fusion,
-            cls.germline_variant: cls.annotate_variants,
-            cls.microsatellite_status: cls.annotate_microsatellite_stability,
-            cls.signature: cls.annotate_signatures,
-            cls.somatic_variant: cls.annotate_variants
+            config['feature_types']['aneuploidy']: cls.annotate_aneuploidy,
+            config['feature_types']['burden']: cls.annotate_burden,
+            config['feature_types']['cna']: cls.annotate_copy_number,
+            config['feature_types']['fusion']: cls.annotate_fusion,
+            config['feature_types']['germline']: cls.annotate_variants,
+            config['feature_types']['microsatellite']: cls.annotate_microsatellite_stability,
+            config['feature_types']['signature']: cls.annotate_signatures,
+            config['feature_types']['mut']: cls.annotate_variants
         }
 
         for feature_type, group in df.groupby(cls.feature_type):
@@ -422,7 +408,13 @@ def annotate(cls, df, dbs, ontology):
                     .astype(float)
                 )
 
-            if feature_type in [cls.somatic_variant, cls.germline_variant, cls.copynumber_variant, cls.fusion]:
+            simple_biomarkers = [
+                config['feature_types']['mut'],
+                config['feature_types']['germline'],
+                config['feature_types']['cna'],
+                config['feature_types']['fusion']
+            ]
+            if feature_type in simple_biomarkers:
                 idx = group[cls.feature].isin(list_genes)
                 df.loc[group[~idx].index, cls.bin_name] = 0
                 group = group[group[cls.feature].isin(list_genes)]
@@ -981,18 +973,15 @@ class ExAC:
     af = datasources.ExAC.af
 
     bin_name = Annotator.exac_common_bin
-    exac_common_threshold = EXAC_CONFIG['exac_common_af_threshold']
 
     str_columns = [chr, ref, alt]
     int_columns = [start]
 
-    somatic = CONFIG['feature_types']['mut']
-    germline = CONFIG['feature_types']['germline']
     feature_type = features.Features.feature_type
 
     @classmethod
-    def append_exac_af(cls, df, ds, ds_columns):
-        variants, not_variants = cls.subset_for_variants(df)
+    def append_exac_af(cls, df, ds, ds_columns, variant_biomarker_types):
+        variants, not_variants = cls.subset_for_variants(df, variant_biomarker_types)
         ds = ds.loc[:, ds_columns]
 
         for column, data_type in [(cls.str_columns, str), (cls.int_columns, float), (cls.int_columns, int)]:
@@ -1017,15 +1006,24 @@ def append_exac_af(cls, df, ds, ds_columns):
         return result
 
     @classmethod
-    def annotate(cls, df, dbs):
+    def annotate(cls, df, dbs, config):
         df_dropped = cls.drop_existing_columns(df)
         ds = datasources.ExAC.import_ds(dbs)
-        df_annotated = cls.append_exac_af(df_dropped, ds, [cls.chr, cls.start, cls.ref, cls.alt, cls.af])
-        df_annotated[cls.bin_name] = cls.annotate_common_af(df_annotated[cls.af])
+        df_annotated = cls.append_exac_af(
+            df=df_dropped,
+            ds=ds,
+            ds_columns=[cls.chr, cls.start, cls.ref, cls.alt, cls.af],
+            variant_biomarker_types=[config['feature_types']['mut'], config['feature_types']['germline']]
+        )
+        common_allele_frequency_threshold=config['exac']['exac_common_af_threshold']
+        df_annotated[cls.bin_name] = cls.annotate_common_af(
+            series_exac_af=df_annotated[cls.af],
+            threshold=common_allele_frequency_threshold
+        )
         return features.Features.preallocate_missing_columns(df_annotated)
 
     @classmethod
-    def annotate_common_af(cls, series_exac_af):
+    def annotate_common_af(cls, series_exac_af, threshold):
         if not series_exac_af.empty:
             series = pd.Series(float(0.0), index=series_exac_af.index.tolist())
             condition = (series_exac_af
@@ -1034,7 +1032,7 @@ def annotate_common_af(cls, series_exac_af):
                          .astype(float).mean(axis=1)
                          .fillna(0.0)
                          )
-            idx = condition.astype(float) >= float(cls.exac_common_threshold)
+            idx = condition.astype(float) >= float(threshold)
             series[idx] = float(1.0)
             return series
         else:
@@ -1049,8 +1047,8 @@ def format_columns(cls, dataframe, column, data_type):
         return dataframe.loc[dataframe.index, column].astype(data_type)
 
     @classmethod
-    def subset_for_variants(cls, dataframe):
-        idx = dataframe[cls.feature_type].isin([cls.somatic, cls.germline])
+    def subset_for_variants(cls, dataframe, variant_biomarker_types):
+        idx = dataframe[cls.feature_type].isin(variant_biomarker_types)
         return dataframe[idx].copy(), dataframe[~idx].copy()
 
 
@@ -1083,11 +1081,20 @@ class ExACExtended:
                   an_afr, an_amr, an_eas, an_fin, an_nfe, an_sas, an_oth]
 
     @classmethod
-    def annotate(cls, df, dbs):
+    def annotate(cls, df, dbs, config):
         df_dropped = ExAC.drop_existing_columns(df)
         ds = datasources.ExACExtended.import_ds(dbs)
-        df_annotated = ExAC.append_exac_af(df_dropped, ds, cls.ds_columns)
-        df_annotated[ExAC.bin_name] = ExAC.annotate_common_af(df_annotated[ExAC.af])
+        df_annotated = ExAC.append_exac_af(
+            df=df_dropped,
+            ds=ds,
+            ds_columns=[cls.chr, cls.start, cls.ref, cls.alt, cls.af],
+            variant_biomarker_types=[config['feature_types']['mut'], config['feature_types']['germline']]
+        )
+        common_allele_frequency_threshold = config['exac']['exac_common_af_threshold']
+        df_annotated[ExAC.bin_name] = ExAC.annotate_common_af(
+            series_exac_af=df_annotated[ExAC.af],
+            threshold=common_allele_frequency_threshold
+        )
         return features.Features.preallocate_missing_columns(df_annotated)
 
 
@@ -1156,16 +1163,14 @@ class OverlapValidation:
     validation_coverage = COLNAMES[section]['validation_coverage']
     validation_detection_power = COLNAMES[section]['validation_detection_power']
 
-    somatic_variants = CONFIG['feature_types']['mut']
-
     merge_cols = [gene, alt_type, alt]
     fill_cols = [tumor_f, validation_tumor_f, validation_coverage]
 
     @classmethod
-    def append_validation(cls, primary, validation):
+    def append_validation(cls, primary, validation, biomarker_type):
         df = cls.drop_validation_columns(primary)
         df = cls.merge_data_frames(df, validation, cls.merge_cols)
-        idx = cls.get_mutation_index(df)
+        idx = cls.get_mutation_index(df, biomarker_type)
         for column in cls.fill_cols:
             df.loc[idx, column] = Annotator.fill_na(
                 dataframe=df.loc[idx, :],
@@ -1198,8 +1203,8 @@ def drop_validation_columns(cls, df):
         return df.drop([cls.validation_tumor_f, cls.validation_coverage], axis=1)
 
     @classmethod
-    def get_mutation_index(cls, df):
-        return df[df[cls.feature_type].eq(cls.somatic_variants)].index
+    def get_mutation_index(cls, df, biomarker_type):
+        return df[df[cls.feature_type].eq(biomarker_type)].index
 
     @classmethod
     def merge_data_frames(cls, df1, df2, columns):
@@ -1310,12 +1315,6 @@ class PreclinicalMatchmaking:
     feature_display = COLNAMES[section]['feature_display']
     predictive_implication = COLNAMES[section]['predictive_implication']
 
-    feature_types_section = 'feature_types'
-    feature_types_config = CONFIG[feature_types_section]
-    copy_number = feature_types_config['cna']
-    fusion = feature_types_config['fusion']
-    somatic_variant = feature_types_config['mut']
-
     evidence_map = {
         'FDA-Approved': 5, 'Guideline': 4, 'Clinical trial': 3,
         'Clinical evidence': 2, 'Preclinical': 1, 'Inferential': 0}
@@ -1328,14 +1327,18 @@ class PreclinicalMatchmaking:
     fusions_gene2 = 'fusions_gene2'
 
     @classmethod
-    def annotate(cls, input_dict, dbs):
-        input_variants = input_dict[cls.somatic_variant]
-        input_copy_number_alterations = input_dict[cls.copy_number]
-        input_fusions = input_dict[cls.fusion]
+    def annotate(cls, input_dict, dbs, config):
+        copy_number = config['feature_types']['cna']
+        fusion = config['feature_types']['fusion']
+        somatic_variant = config['feature_types']['mut']
+
+        input_variants = input_dict[somatic_variant]
+        input_copy_number_alterations = input_dict[copy_number]
+        input_fusions = input_dict[fusion]
 
-        variants = cls.annotate_somatic_variants(input_variants, dbs)
-        copy_number_alterations = cls.annotate_copy_numbers(input_copy_number_alterations, dbs)
-        fusions, fusions_gene1, fusions_gene2 = cls.annotate_fusions(input_fusions, dbs)
+        variants = cls.annotate_somatic_variants(input_variants, dbs, somatic_variant)
+        copy_number_alterations = cls.annotate_copy_numbers(input_copy_number_alterations, dbs, copy_number)
+        fusions, fusions_gene1, fusions_gene2 = cls.annotate_fusions(input_fusions, dbs, fusion)
         return {
             cls.variants: variants,
             cls.cnas: copy_number_alterations,
@@ -1345,12 +1348,12 @@ def annotate(cls, input_dict, dbs):
         }
 
     @classmethod
-    def annotate_copy_numbers(cls, df, dbs):
+    def annotate_copy_numbers(cls, df, dbs, biomarker_type_string):
         almanac = datasources.Almanac.import_ds(dbs)
         almanac_genes = datasources.Almanac.import_genes(dbs)
 
-        df = df[df[cls.feature_type].eq(cls.copy_number)]
-        db = Almanac.subset_records(almanac['content'], cls.feature_type, cls.copy_number)
+        df = df[df[cls.feature_type].eq(biomarker_type_string)]
+        db = Almanac.subset_records(almanac['content'], cls.feature_type, biomarker_type_string)
         db = pd.DataFrame(db)
 
         column_map = {cls.gene: cls.feature, cls.direction: cls.alteration_type}
@@ -1363,12 +1366,12 @@ def annotate_copy_numbers(cls, df, dbs):
         return df
 
     @classmethod
-    def annotate_fusions(cls, df, dbs):
+    def annotate_fusions(cls, df, dbs, biomarker_type_string):
         almanac = datasources.Almanac.import_ds(dbs)
         almanac_genes = datasources.Almanac.import_genes(dbs)
 
-        df = df[df[cls.feature_type].eq(cls.fusion)]
-        db = Almanac.subset_records(almanac['content'], cls.feature_type, cls.fusion)
+        df = df[df[cls.feature_type].eq(biomarker_type_string)]
+        db = Almanac.subset_records(almanac['content'], cls.feature_type, biomarker_type_string)
         db = pd.DataFrame(db)
 
         column_map = {cls.rearrangement_type: cls.alteration_type}
@@ -1503,12 +1506,12 @@ def annotate_fusions_matching(cls, df, db, db_genes, consider_partner=False):
         return df
 
     @classmethod
-    def annotate_somatic_variants(cls, df, dbs):
+    def annotate_somatic_variants(cls, df, dbs, biomarker_type_string):
         almanac = datasources.Almanac.import_ds(dbs)
         almanac_genes = datasources.Almanac.import_genes(dbs)
 
-        df = df[df[cls.feature_type].eq(cls.somatic_variant)]
-        db = Almanac.subset_records(almanac['content'], cls.feature_type, cls.somatic_variant)
+        df = df[df[cls.feature_type].eq(biomarker_type_string)]
+        db = Almanac.subset_records(almanac['content'], cls.feature_type, biomarker_type_string)
         db = pd.DataFrame(db)
 
         replacement_dictionary = {'Oncogenic Mutations': '', 'Activating mutation': ''}
diff --git a/moalmanac/config.ini b/moalmanac/config.ini
index 399c6aa..a22562b 100644
--- a/moalmanac/config.ini
+++ b/moalmanac/config.ini
@@ -1,11 +1,11 @@
 [function_toggle]
 ; Use this section to enable or disable functions performed by MOAlmanac by writing either 'on' or 'off'
-calculate_model_similarity = on
-calculate_preclinical_efficacy = on
+calculate_model_similarity = off
+calculate_preclinical_efficacy = off
 generate_actionability_report = on
-include_model_similarity_in_actionability_report = on
-include_preclinical_efficacy_in_actionability_report = on
-plot_preclinical_efficacy = on
+include_model_similarity_in_actionability_report = off
+include_preclinical_efficacy_in_actionability_report = off
+plot_preclinical_efficacy = off
 
 [versions]
 interpreter = 0.6.0
@@ -46,30 +46,4 @@ microsatellite = Microsatellite Stability
 burden = Mutational Burden
 signature = Mutational Signature
 aneuploidy = Aneuploidy
-knockdown = Knockdown
-
-[databases]
-almanac_handle = datasources/moalmanac/molecular-oncology-almanac.json
-cancerhotspots_handle = datasources/cancerhotspots/hotspots_v2.txt
-3dcancerhotspots_handle = datasources/cancerhotspots/hotspots3d.txt
-cgc_handle = datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv
-cosmic_handle = datasources/cosmic/CosmicMutantExport_v97.lite.txt
-gsea_pathways_handle = datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt
-gsea_modules_handle = datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt
-exac_handle = datasources/exac/exac.expanded.r1.txt
-acmg_handle = datasources/acmg/acmg.secondaryfindings.v3.txt
-clinvar_handle = datasources/clinvar/variant_summary.lite.txt
-hereditary_handle = datasources/hereditary/hereditary.txt
-oncotree_handle = datasources/oncotree/oncotree.2023-03-09.txt
-lawrence_handle = datasources/lawrence/lawrence_mapped_ontology.txt
-
-[preclinical]
-almanac_gdsc_mappings = datasources/preclinical/formatted/almanac-gdsc-mappings.json
-summary = datasources/preclinical/formatted/cell-lines.summary.txt
-variants = datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt
-copynumbers = datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt
-fusions = datasources/preclinical/annotated/cell-lines.fusions.annotated.txt
-fusions1 = datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt
-fusions2 = datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt
-gdsc = datasources/preclinical/formatted/sanger.gdsc.txt
-dictionary = datasources/preclinical/cell-lines.pkl
+knockdown = Knockdown
\ No newline at end of file
diff --git a/moalmanac/config.py b/moalmanac/config.py
index 42bada4..3a66f5d 100644
--- a/moalmanac/config.py
+++ b/moalmanac/config.py
@@ -1,15 +1,8 @@
 import configparser
 
-default_config = 'config.ini'
 default_colnames = 'colnames.ini'
 
 
-def create_config():
-    config = configparser.ConfigParser()
-    config.read(default_config)
-    return config
-
-
 def create_colnames_dict(config):
     dictionary = {}
     for section in config.sections():
@@ -25,5 +18,4 @@ def create_colnames():
     return create_colnames_dict(config)
 
 
-CONFIG = create_config()
 COLNAMES = create_colnames()
diff --git a/moalmanac/datasources.py b/moalmanac/datasources.py
index 741871f..60fa523 100644
--- a/moalmanac/datasources.py
+++ b/moalmanac/datasources.py
@@ -2,7 +2,6 @@
 
 from reader import Reader
 from config import COLNAMES
-from config import CONFIG
 
 
 class Datasources:
@@ -73,24 +72,6 @@ class Datasources:
     an_sas = COLNAMES[datasources_section]['exac_sas_an']
     an_oth = COLNAMES[datasources_section]['exac_oth_an']
 
-    @classmethod
-    def generate_db_dict(cls, config):
-        return {
-            'almanac_handle': config.get('databases', 'almanac_handle'),
-            'hotspots_handle': config.get('databases', 'cancerhotspots_handle'),
-            '3dhotspots_handle': config.get('databases', '3dcancerhotspots_handle'),
-            'clinvar_handle': config.get('databases', 'clinvar_handle'),
-            'cgc_handle': config.get('databases', 'cgc_handle'),
-            'cosmic_handle': config.get('databases', 'cosmic_handle'),
-            'gsea_pathways_handle': config.get('databases', 'gsea_pathways_handle'),
-            'gsea_modules_handle': config.get('databases', 'gsea_modules_handle'),
-            'exac_handle': config.get('databases', 'exac_handle'),
-            'acmg_handle': config.get('databases', 'acmg_handle'),
-            'hereditary_handle': config.get('databases', 'hereditary_handle'),
-            'oncotree_handle': config.get('databases', 'oncotree_handle'),
-            'lawrence_handle': config.get('databases', 'lawrence_handle')
-        }
-
 
 class ACMG:
     gene = Datasources.feature
@@ -196,7 +177,7 @@ def format_cancerhotspots(cls, df):
 
     @classmethod
     def import_ds(cls, dbs):
-        df = Reader.safe_read(dbs['hotspots_handle'], '\t', cls.column_map)
+        df = Reader.safe_read(dbs['cancerhotspots_handle'], '\t', cls.column_map)
         return cls.format_cancerhotspots(df)
 
 
@@ -211,7 +192,7 @@ class CancerHotspots3D:
 
     @classmethod
     def import_ds(cls, dbs):
-        return Reader.safe_read(dbs['3dhotspots_handle'], '\t', cls.column_map)
+        return Reader.safe_read(dbs['3dcancerhotspots_handle'], '\t', cls.column_map)
 
 
 class ClinVar:
@@ -408,16 +389,6 @@ def import_ds(cls, dbs):
 
 class Preclinical:
     section = 'preclinical'
-    summary_handle = CONFIG[section]['summary']
-    variants_handle = CONFIG[section]['variants']
-    cnas_handle = CONFIG[section]['copynumbers']
-    fusions_handle = CONFIG[section]['fusions']
-    fusions_gene1_handle = CONFIG[section]['fusions1']
-    fusions_gene2_handle = CONFIG[section]['fusions2']
-    gdsc_handle = CONFIG[section]['gdsc']
-    mappings_handle = CONFIG[section]['almanac_gdsc_mappings']
-    dictionary_handle = CONFIG[section]['dictionary']
-
     feature = COLNAMES[section]['feature']
     partner = COLNAMES[section]['partner']
     gene = COLNAMES[section]['gene']
@@ -437,12 +408,6 @@ class Preclinical:
     mappings = 'mappings'
     dictionary = 'dictionary'
 
-    feature_type = Datasources.feature_type
-    feature_types_section = 'feature_types'
-    variant_type = CONFIG[feature_types_section]['mut']
-    copy_number_type = CONFIG[feature_types_section]['cna']
-    fusion_type = CONFIG[feature_types_section]['fusion']
-
     @classmethod
     def create_convert_names_dict(cls, dataframe, map_from, map_to):
         return dataframe.loc[:, [map_from, map_to]].dropna().set_index(map_from)[map_to].to_dict()
@@ -452,16 +417,16 @@ def generate_sample_list(dataframe, use_column, sample_column):
         return dataframe[dataframe[use_column].astype(bool).astype(int).eq(1)][sample_column].sort_values().tolist()
 
     @classmethod
-    def import_dbs(cls):
-        summary = Reader.read(cls.summary_handle, delimiter='\t')
-        variants = Reader.read(cls.variants_handle, delimiter='\t', low_memory=False)
-        cnas = Reader.read(cls.cnas_handle, delimiter='\t', low_memory=False)
-        fusions = Reader.read(cls.fusions_handle, delimiter='\t', low_memory=False)
-        fusions1 = Reader.read(cls.fusions_gene1_handle, delimiter='\t', low_memory=False)
-        fusions2 = Reader.read(cls.fusions_gene2_handle, delimiter='\t', low_memory=False)
-        gdsc = Reader.read(cls.gdsc_handle, delimiter='\t', low_memory=False)
-        mappings = Reader.read_json(cls.mappings_handle)
-        dictionary = Reader.read_pickle(cls.dictionary_handle)
+    def import_dbs(cls, paths_dictionary):
+        summary = Reader.read(paths_dictionary['summary'], delimiter='\t')
+        variants = Reader.read(paths_dictionary['variants'], delimiter='\t', low_memory=False)
+        cnas = Reader.read(paths_dictionary['copynumbers'], delimiter='\t', low_memory=False)
+        fusions = Reader.read(paths_dictionary['fusions'], delimiter='\t', low_memory=False)
+        fusions1 = Reader.read(paths_dictionary['fusions1'], delimiter='\t', low_memory=False)
+        fusions2 = Reader.read(paths_dictionary['fusions2'], delimiter='\t', low_memory=False)
+        gdsc = Reader.read(paths_dictionary['gdsc'], delimiter='\t', low_memory=False)
+        mappings = Reader.read_json(paths_dictionary['almanac_gdsc_mappings'])
+        dictionary = Reader.read_pickle(paths_dictionary['dictionary'])
 
         ccle_map = cls.create_convert_names_dict(summary, cls.ccle_name, cls.broad)
         sanger_map = cls.create_convert_names_dict(summary, cls.sanger, cls.broad)
diff --git a/moalmanac/evaluator.py b/moalmanac/evaluator.py
index d541700..57d94d9 100644
--- a/moalmanac/evaluator.py
+++ b/moalmanac/evaluator.py
@@ -4,11 +4,10 @@
 
 import datasources
 import features
-from config import CONFIG
 from config import COLNAMES
 
 
-class Evaluator(object):
+class Evaluator:
     """
     Evaluate based on annotated bins
     """
@@ -81,20 +80,6 @@ class Evaluator(object):
     microsatellite_section = 'microsatellite'
     supporting_variants = COLNAMES[microsatellite_section]['supporting_variants']
 
-    feature_type_section = 'feature_types'
-    mut_type = CONFIG[feature_type_section]['mut']
-    copynumber_type = CONFIG[feature_type_section]['cna']
-    germline_type = CONFIG[feature_type_section]['germline']
-    fusion_type = CONFIG[feature_type_section]['fusion']
-    burden_type = CONFIG[feature_type_section]['burden']
-    microsatellite_type = CONFIG[feature_type_section]['microsatellite']
-    signature_type = CONFIG[feature_type_section]['signature']
-    aneuploidy_type = CONFIG[feature_type_section]['aneuploidy']
-
-    mutations_section = 'mutations'
-    min_coverage = CONFIG[mutations_section]['min_coverage']
-    min_af = CONFIG[mutations_section]['min_af']
-
     @classmethod
     def assign_bin(cls, df, bin_column, bin_label):
         series_score_bin = df.loc[:, cls.score_bin]
@@ -145,17 +130,18 @@ def remap_almanac_bins(series, old_value, new_value):
         return series.astype(int).replace(to_replace=old_value, value=new_value)
 
     @classmethod
-    def remove_low_allele_fraction_variants(cls, df):
-        idx_mut = df[df[cls.feature_type].isin([cls.mut_type, cls.germline_type])].index
-        idx_low_quality = df[df[cls.tumor_f].astype(float).lt(float(cls.min_af))].index
+    def remove_low_allele_fraction_variants(cls, df, minimum_allele_fraction=0.05):
+        # hard coding somatic and germline variant strings until future refactor
+        idx_mut = df[df[cls.feature_type].isin(['Somatic Variant', 'Germline Variant'])].index
+        idx_low_quality = df[df[cls.tumor_f].astype(float).lt(float(minimum_allele_fraction))].index
         idx_low_quality_muts = idx_mut.intersection(idx_low_quality)
         idx = df.index.difference(idx_low_quality_muts)
         return df.loc[idx, :]
 
     @classmethod
-    def remove_low_coverage_variants(cls, df):
-        idx_mut = df[df[cls.feature_type].isin([cls.mut_type, cls.germline_type])].index
-        idx_low_quality = df[df[cls.coverage].astype(float).le(float(cls.min_coverage))].index
+    def remove_low_coverage_variants(cls, df, minimum_coverage=15):
+        idx_mut = df[df[cls.feature_type].isin(['Somatic Variant', 'Germline Variant'])].index
+        idx_low_quality = df[df[cls.coverage].astype(float).le(float(minimum_coverage))].index
         idx_low_quality_muts = idx_mut.intersection(idx_low_quality)
         idx = df.index.difference(idx_low_quality_muts)
         return df.loc[idx, :]
@@ -193,46 +179,48 @@ def create_string_list(series):
         return ', '.join(map(str, series.unique()))
 
     @classmethod
-    def display_aneuploidy(cls, df, idx, feature):
-        return df.loc[idx, feature]
+    def display_aneuploidy(cls, df, idx):
+        return df.loc[idx, Evaluator.feature]
 
     @classmethod
-    def display_burden(cls, df, idx, alt):
-        return df.loc[idx, alt].astype(str)
+    def display_burden(cls, df, idx):
+        return df.loc[idx, Evaluator.alt].astype(str)
 
     @classmethod
-    def display_copynumber(cls, df, idx, feature, alt_type):
-        gene = df.loc[idx, feature]
-        direction = df.loc[idx, alt_type]
+    def display_copynumber(cls, df, idx):
+        gene = df.loc[idx, Evaluator.feature]
+        direction = df.loc[idx, Evaluator.alt_type]
         # Copy Number: CDKN2A Deletion
         return gene + ' ' + direction
 
     @classmethod
-    def display_fusion(cls, df, idx, alt):
-        fusion = df.loc[idx, alt]
+    def display_fusion(cls, df, idx):
+        fusion = df.loc[idx, Evaluator.alt]
         # Rearrangement: BCR--ABL1 Fusion
         return fusion + ' Fusion'
 
     @classmethod
-    def display_microsatellite_stability(cls, df, idx, feature):
-        return df.loc[idx, feature]
+    def display_microsatellite_stability(cls, df, idx):
+        return df.loc[idx, Evaluator.feature]
 
     @classmethod
-    def display_microsatellite_variants(cls, df, idx, feature, alt):
-        return df.loc[idx, feature] + ': ' + df.loc[idx, alt]
+    def display_microsatellite_variants(cls, df, idx):
+        return df.loc[idx, Evaluator.feature] + ': ' + df.loc[idx, Evaluator.alt]
 
     @classmethod
-    def display_signature(cls, df, idx, feature, alt):
-        signature = df.loc[idx, feature].str.replace('COSMIC Signature', 'COSMIC Signature (version 2)')
-        contribution = df.loc[idx, alt].astype(float).multiply(100).round(0).astype(int).astype(str)
+    def display_signature(cls, df, idx):
+        #before_string = "COSMIC Signature"
+        #after_string = f"COSMIC Signature (version {version})"
+        signature = df.loc[idx, Evaluator.feature]#.str.replace(before_string, after_string)
+        contribution = df.loc[idx, Evaluator.alt].astype(float).multiply(100).round(0).astype(int).astype(str)
         # Signature: Cosmic Signature 7 (65%)
         return signature + ' (' + contribution + '%)'
 
     @classmethod
-    def display_variant(cls, df, idx, feature, alt_type, alt):
-        gene = df.loc[idx, feature]
-        protein_change = df.loc[idx, alt]
-        variant_class = df.loc[idx, alt_type]
+    def display_variant(cls, df, idx):
+        gene = df.loc[idx, Evaluator.feature]
+        protein_change = df.loc[idx, Evaluator.alt]
+        variant_class = df.loc[idx, Evaluator.alt_type]
         # exon, pathogenic, cDNA, linebreaks
         # Gene p.Foo (c.DNA)
         # Exon 12 Missense
@@ -240,7 +228,7 @@ def display_variant(cls, df, idx, feature, alt_type, alt):
         return gene + ' ' + protein_change + ' (' + variant_class + ')'
 
     @classmethod
-    def evaluate(cls, somatic, germline, ms_variants, ms_status, burden, signatures, wgd):
+    def evaluate(cls, somatic, germline, ms_variants, ms_status, burden, signatures, wgd, config):
         somatic = cls.format_mutations(somatic)
         germline = cls.format_mutations(germline)
 
@@ -257,48 +245,43 @@ def evaluate(cls, somatic, germline, ms_variants, ms_status, burden, signatures,
             actionable_list.append(Evaluator.subset_almanac_bin(dataframe))
         df = features.Features.concat_list_of_dataframes(list_of_dataframes=actionable_list)
 
-        df[Evaluator.feature_display] = cls.format_feature_display(
-            df, Evaluator.feature_display,
-            Evaluator.feature_type, Evaluator.feature,
-            Evaluator.alt_type, Evaluator.alt)
+        df[Evaluator.feature_display] = cls.format_feature_display(df=df, config=config)
+        #    df, Evaluator.feature_display,
+        #    Evaluator.feature_type, Evaluator.feature,
+        #    Evaluator.alt_type, Evaluator.alt)
         return df.sort_values(cls.sort_columns, ascending=False)
 
     @classmethod
-    def format_feature_display(cls, df, feature_display_column,
-                               feature_type_column, feature_column,
-                               alt_type_column, alt_column):
-        idx_somatic = df[feature_type_column].isin([Evaluator.mut_type])
-        idx_germline = df[feature_type_column].isin([Evaluator.germline_type])
-        idx_cn = df[feature_type_column].isin([Evaluator.copynumber_type])
-        idx_fusion = df[feature_type_column].isin([Evaluator.fusion_type])
-        idx_msi = df[feature_type_column].isin([Evaluator.microsatellite_type])
+    def format_feature_display(cls, df, config):
+        display_column = Evaluator.feature_display
+        feature_type_column = Evaluator.feature_type
+        feature_column = Evaluator.feature
+        # alt_type_column = Evaluator.alt_type
+        # alt_column = Evaluator.alt
+        #sig_version = config['signatures']['version']
+
+        biomarker_types = config['feature_types']
+        idx_somatic = df[feature_type_column].isin([biomarker_types['mut']])
+        idx_germline = df[feature_type_column].isin([biomarker_types['germline']])
+        idx_cn = df[feature_type_column].isin([biomarker_types['cna']])
+        idx_fusion = df[feature_type_column].isin([biomarker_types['fusion']])
+        idx_msi = df[feature_type_column].isin([biomarker_types['microsatellite']])
         idx_msi_variants = df[feature_column].isin([Evaluator.supporting_variants])
         idx_msi = idx_msi & ~idx_msi_variants
-        idx_burden = df[feature_type_column].isin([Evaluator.burden_type])
-        idx_signature = df[feature_type_column].isin([Evaluator.signature_type])
-        idx_wgd = df[feature_column].isin([Evaluator.aneuploidy_type])
-
-        df.loc[idx_wgd, feature_display_column] = cls.display_aneuploidy(
-            df, idx_wgd, feature_column)
-        df.loc[idx_somatic, feature_display_column] = cls.display_variant(
-            df, idx_somatic, feature_column, alt_type_column, alt_column)
-        df.loc[idx_germline, feature_display_column] = cls.display_variant(
-            df, idx_germline, feature_column, alt_type_column, alt_column)
-        df.loc[idx_cn, feature_display_column] = cls.display_copynumber(
-            df, idx_cn, feature_column, alt_type_column)
-        df.loc[idx_fusion, feature_display_column] = cls.display_fusion(
-            df, idx_fusion, alt_column)
-        df.loc[idx_msi, feature_display_column] = cls.display_microsatellite_stability(
-            df, idx_msi, feature_column)
-        df.loc[idx_msi_variants, feature_display_column] = cls.display_microsatellite_variants(
-            df, idx_msi_variants, feature_column, alt_column)
-        df.loc[idx_burden, feature_display_column] = cls.display_burden(
-            df, idx_burden, alt_column)
-        df.loc[idx_signature, feature_display_column] = cls.display_signature(
-            df, idx_signature, feature_column, alt_column)
-        df.loc[idx_wgd, feature_display_column] = cls.display_aneuploidy(
-            df, idx_wgd, feature_column)
-        return df.loc[:, feature_display_column]
+        idx_burden = df[feature_type_column].isin([biomarker_types['burden']])
+        idx_signature = df[feature_type_column].isin([biomarker_types['signature']])
+        idx_wgd = df[feature_column].isin([biomarker_types['aneuploidy']])
+
+        df.loc[idx_somatic, display_column] = cls.display_variant(df=df, idx=idx_somatic)
+        df.loc[idx_germline, display_column] = cls.display_variant(df=df, idx=idx_germline)
+        df.loc[idx_cn, display_column] = cls.display_copynumber(df=df, idx=idx_cn)
+        df.loc[idx_fusion, display_column] = cls.display_fusion(df=df, idx=idx_fusion)
+        df.loc[idx_msi, display_column] = cls.display_microsatellite_stability(df=df, idx=idx_msi)
+        df.loc[idx_msi_variants, display_column] = cls.display_microsatellite_variants(df=df, idx=idx_msi_variants)
+        df.loc[idx_burden, display_column] = cls.display_burden(df=df, idx=idx_burden)
+        df.loc[idx_signature, display_column] = cls.display_signature(df=df, idx=idx_signature)
+        df.loc[idx_wgd, display_column] = cls.display_aneuploidy(df=df, idx=idx_wgd)
+        return df.loc[:, display_column]
 
     @classmethod
     def format_mutations(cls, df):
@@ -336,7 +319,7 @@ def summarize_ms_variants(cls, df):
         return msi_summary
 
 
-class Integrative(object):
+class Integrative:
     feature = datasources.Datasources.feature
     feature_type = datasources.Datasources.feature_type
     alt_type = datasources.Datasources.alt_type
@@ -357,13 +340,13 @@ def create_integrated_df(cls, genes):
         return pd.DataFrame(None, columns=cls.columns, index=genes)
 
     @classmethod
-    def evaluate(cls, somatic, germline, dbs, feature_types):
+    def evaluate(cls, somatic, germline, dbs, config):
         genes = cls.return_datasource_genes(dbs)
         df = cls.create_integrated_df(genes)
 
-        somatic_mutations = cls.extract_feature_type(somatic, feature_types['mutation'])
-        somatic_copynumbers = cls.extract_feature_type(somatic, feature_types['copynumber'])
-        somatic_fusions = cls.extract_feature_type(somatic, feature_types['fusion'])
+        somatic_mutations = cls.extract_feature_type(somatic, config['feature_types']['mut'])
+        somatic_copynumbers = cls.extract_feature_type(somatic, config['feature_types']['cna'])
+        somatic_fusions = cls.extract_feature_type(somatic, config['feature_types']['fusion'])
 
         for gene in df.index:
             gene_muts = somatic_mutations[somatic_mutations[cls.feature].astype(str) == gene]
diff --git a/moalmanac/features.py b/moalmanac/features.py
index bc3ec3d..164068a 100644
--- a/moalmanac/features.py
+++ b/moalmanac/features.py
@@ -9,9 +9,6 @@
 from reader import Reader
 
 from config import COLNAMES
-from config import CONFIG
-
-SIGNATURES_CONFIG = CONFIG['signatures']
 
 
 class Features:
@@ -120,28 +117,21 @@ def preallocate_missing_columns(cls, df):
 
 class Aneuploidy:
     aneuploidy = 'aneuploidy'
-
-    feature_type_section = 'feature_types'
-    feature_type = CONFIG[feature_type_section][aneuploidy]
-
     aneuploidy_section = aneuploidy
     wgd = COLNAMES[aneuploidy]['wgd']
     wgd_string = COLNAMES[aneuploidy]['wgd_string']
 
     @classmethod
-    def summarize(cls, boolean):
+    def summarize(cls, boolean, config):
         df = Features.create_empty_dataframe()
+        feature_type = config['feature_types']['aneuploidy']
         if boolean:
-            df.loc[0, Features.feature_type] = cls.feature_type
+            df.loc[0, Features.feature_type] = feature_type
             df.loc[0, Features.feature] = cls.wgd_string
         return df
 
 
 class BurdenReader:
-    feature_type_section = 'feature_types'
-    feature_type = CONFIG[feature_type_section]['burden']
-    feature_type_mutations = CONFIG[feature_type_section]['mut']
-
     burden_section = 'burden'
     patient_id = COLNAMES[burden_section]['patient']
     tumor_type = COLNAMES[burden_section]['tumor_type']
@@ -212,15 +202,16 @@ def evaluate_high_burden_boolean(cls, boolean):
             return Features.not_high_burden
 
     @classmethod
-    def import_feature(cls, handle, patient, variants, dbs):
+    def import_feature(cls, handle, patient, variants, dbs, config):
         if os.path.exists(handle):
             bases_covered = float(Reader.read(handle, '\t', index_col=False).columns.tolist()[0])
         else:
             bases_covered = np.nan
         df = cls.create_burden_series(patient, bases_covered)
-        df[Features.feature_type] = cls.feature_type
 
-        mutations = variants[variants[Features.feature_type] == cls.feature_type_mutations].shape[0]
+        biomarker_type = config['feature_types']['burden']
+        df[Features.feature_type] = biomarker_type
+        mutations = variants[variants[Features.feature_type] == config['feature_types']['mut']].shape[0]
         mutational_burden = cls.calculate_burden(mutations, bases_covered)
 
         df[cls.n_nonsyn_mutations] = mutations
@@ -239,10 +230,10 @@ def import_feature(cls, handle, patient, variants, dbs):
 
 
 class CopyNumber:
-    config = CONFIG['seg']
-    amplification = config['amp']
-    deletion = config['del']
-    feature_type = CONFIG['feature_types']['cna']
+    #config = CONFIG['seg']
+    #amplification = config['amp']
+    #deletion = config['del']
+    #feature_type = CONFIG['feature_types']['cna']
 
     @staticmethod
     def format_cn_gene(series):
@@ -250,7 +241,7 @@ def format_cn_gene(series):
         return new_series
 
     @classmethod
-    def import_feature(cls, called_handle, not_called_handle):
+    def import_feature(cls, called_handle, not_called_handle, config):
         if called_handle:
             column_map = CopyNumberCalled.create_column_map()
             handle = called_handle
@@ -259,13 +250,17 @@ def import_feature(cls, called_handle, not_called_handle):
             handle = not_called_handle
 
         df = Features.import_if_path_exists(handle, '\t', column_map, comment_character="#")
+
+        amplification_string = config['seg']['amp']
+        deletion_string = config['seg']['del']
         if not df.empty:
-            df[Features.feature_type] = Features.annotate_feature_type(cls.feature_type, df.index)
+            biomarker_type = config['feature_types']['cna']
+            df[Features.feature_type] = Features.annotate_feature_type(biomarker_type, df.index)
             df[Features.feature] = cls.format_cn_gene(df[Features.feature])
             if called_handle:
-                seg_accept, seg_reject = CopyNumberCalled.process_calls(df)
+                seg_accept, seg_reject = CopyNumberCalled.process_calls(df, amplification_string, deletion_string)
             else:
-                seg_accept, seg_reject = CopyNumberTotal.process_calls(df)
+                seg_accept, seg_reject = CopyNumberTotal.process_calls(df, config)
         else:
             seg_accept = Features.create_empty_dataframe()
             seg_reject = Features.create_empty_dataframe()
@@ -283,21 +278,21 @@ def create_column_map():
         }
 
     @classmethod
-    def filter_calls(cls, series):
-        return series.fillna('').isin([cls.amplification, cls.deletion])
+    def filter_calls(cls, series, amp_string, del_string):
+        return series.fillna('').isin([amp_string, del_string])
 
     @classmethod
-    def process_calls(cls, dataframe):
-        idx = cls.filter_calls(dataframe[Features.alt_type])
+    def process_calls(cls, dataframe, amp_string, del_string):
+        idx = cls.filter_calls(dataframe[Features.alt_type], amp_string, del_string)
         return dataframe[idx], dataframe[~idx]
 
 
 class CopyNumberTotal(CopyNumber):
     @classmethod
-    def annotate_amp_del(cls, idx, idx_amp, idx_del):
+    def annotate_amp_del(cls, idx, idx_amp, idx_del, amp_string, del_string):
         series = pd.Series('', index=idx)
-        series[idx_amp] = cls.amplification
-        series[idx_del] = cls.deletion
+        series[idx_amp] = amp_string
+        series[idx_del] = del_string
         return series
 
     @staticmethod
@@ -323,7 +318,7 @@ def drop_duplicate_genes(cls, df):
         )
 
     @classmethod
-    def filter_by_threshold(cls, df, percentile_amp, percentile_del):
+    def filter_by_threshold(cls, df, percentile_amp, percentile_del, amp_string, del_string):
         unique_segments = cls.get_unique_segments(df)
         threshold_amp = Features.calculate_percentile(unique_segments, percentile_amp)
         threshold_del = Features.calculate_percentile(unique_segments, percentile_del)
@@ -331,7 +326,7 @@ def filter_by_threshold(cls, df, percentile_amp, percentile_del):
         idx_amp = df[df[Features.segment_mean].astype(float) >= float(threshold_amp)].index
         idx_del = df[df[Features.segment_mean].astype(float) <= float(threshold_del)].index
 
-        df[Features.alt_type] = cls.annotate_amp_del(df.index, idx_amp, idx_del)
+        df[Features.alt_type] = cls.annotate_amp_del(df.index, idx_amp, idx_del, amp_string, del_string)
         idx_accept = df[df[Features.alt_type] != ''].index
         idx_unique = Features.drop_duplicate_genes(df.loc[idx_accept, :], Features.segment_mean)
 
@@ -346,10 +341,12 @@ def get_unique_segments(df):
         return df.drop_duplicates([Features.chr, Features.start])[Features.segment_mean]
 
     @classmethod
-    def process_calls(cls, dataframe):
-        amp_percentile = cls.config['amp_percentile']
-        del_percentile = cls.config['del_percentile']
-        return cls.filter_by_threshold(dataframe, amp_percentile, del_percentile)
+    def process_calls(cls, dataframe, config):
+        amp_percentile = config['seg']['amp_percentile']
+        del_percentile = config['seg']['del_percentile']
+        amp_string = config['seg']['amp']
+        del_string = config['seg']['del']
+        return cls.filter_by_threshold(dataframe, amp_percentile, del_percentile, amp_string, del_string)
 
 
 class CoverageMetrics:
@@ -391,14 +388,9 @@ def split_counts(series):
 
 
 class CosmicSignatures:
-    feature_type_section = 'feature_types'
-    feature_type = CONFIG[feature_type_section]['signature']
-
     signature_section = 'signatures'
     patient_id = COLNAMES[signature_section]['patient']
 
-    min_contribution = SIGNATURES_CONFIG['min_contribution']
-
     input_section = 'mutational_signature_input'
     input_signature = COLNAMES[input_section]['signature']
     input_contribution = COLNAMES[input_section]['contribution']
@@ -413,21 +405,23 @@ def create_column_map(cls):
         }
 
     @classmethod
-    def import_feature(cls, path):
+    def import_feature(cls, path, config):
         """Loads and formats Cosmic Mutational Signatures based on provided file path."""
         column_map = cls.create_column_map()
         df = Features.import_if_path_exists(path, delimiter='\t', column_map=column_map)
         if not df.empty:
-            df[Features.feature_type] = cls.feature_type
+            biomarker_type = config['feature_types']['signature']
+            minimum_contribution = config['signatures']['min_contribution']
+            df[Features.feature_type] = biomarker_type
             df[Features.alt_type] = 'v3.4'
             df[Features.alt] = cls.round_contributions(df[Features.alt])
-            idx = cls.index_for_minimum_contribution(df[Features.alt])
+            idx = cls.index_for_minimum_contribution(series=df[Features.alt], minimum_value=minimum_contribution)
             return df[idx]
         else:
             return Features.create_empty_dataframe()
 
     @classmethod
-    def index_for_minimum_contribution(cls, series, minimum_value=min_contribution):
+    def index_for_minimum_contribution(cls, series, minimum_value=0.06):
         """Subsets the provided SBS signatures to those that pass the minimum contribution, specified in config.ini"""
         return series.astype(float) >= float(minimum_value)
 
@@ -438,41 +432,38 @@ def round_contributions(series, decimals=3):
 
 
 class Fusion:
-    config = CONFIG['fusion']
-    alt_type = config['alt_type']
-    leftbreakpoint = config['leftbreakpoint']
-    rightbreakpoint = config['rightbreakpoint']
-    spanningfrags_min = config['spanningfrags_min']
-
-    feature_type = CONFIG['feature_types']['fusion']
-
     @classmethod
-    def create_colmap(cls):
+    def create_colmap(cls, config):
         section = 'fusion_input'
         column_names = COLNAMES[section]
+
+        leftbreakpoint = config['fusion']['leftbreakpoint']
+        rightbreakpoint = config['fusion']['rightbreakpoint']
         return {
             column_names['name']: Features.feature,
             column_names['spanningfrags']: Features.spanningfrags,
-            column_names[cls.leftbreakpoint]: cls.leftbreakpoint,
-            column_names[cls.rightbreakpoint]: cls.rightbreakpoint
+            column_names[leftbreakpoint]: leftbreakpoint,
+            column_names[rightbreakpoint]: rightbreakpoint
         }
 
     @staticmethod
-    def filter_by_spanning_fragment_count(series, minimum=spanningfrags_min):
+    def filter_by_spanning_fragment_count(series, minimum=5.0):
         minimum = int(float(minimum))
         return series[series.astype(int).ge(minimum)].index
 
     @classmethod
-    def import_feature(cls, handle):
-        column_map = cls.create_colmap()
+    def import_feature(cls, handle, config):
+        column_map = cls.create_colmap(config)
         df = Features.import_if_path_exists(handle, '\t', column_map, index_col=False)
         if not df.empty:
             split_genes = cls.split_genes(df[Features.feature])
             df[Features.left_gene] = split_genes[Features.left_gene]
             df[Features.right_gene] = split_genes[Features.right_gene]
 
-            left = cls.split_breakpoint(df[cls.leftbreakpoint])
-            right = cls.split_breakpoint(df[cls.rightbreakpoint])
+            leftbreakpoint = config['fusion']['leftbreakpoint']
+            rightbreakpoint = config['fusion']['rightbreakpoint']
+            left = cls.split_breakpoint(df[leftbreakpoint])
+            right = cls.split_breakpoint(df[rightbreakpoint])
 
             df[Features.chr] = left[Features.chr]
             df[Features.start] = left[Features.start]
@@ -480,13 +471,18 @@ def import_feature(cls, handle):
             df[Features.left_start] = left[Features.start]
             df[Features.right_chr] = right[Features.chr]
             df[Features.right_start] = right[Features.start]
-            df.drop([cls.leftbreakpoint, cls.rightbreakpoint], axis=1, inplace=True)
+            df.drop([leftbreakpoint, rightbreakpoint], axis=1, inplace=True)
 
-            df[Features.feature_type] = Features.annotate_feature_type(cls.feature_type, df.index)
-            df[Features.alt_type] = cls.alt_type
+            biomarker_type = config['feature_types']['fusion']
+            df[Features.feature_type] = Features.annotate_feature_type(biomarker_type, df.index)
+            df[Features.alt_type] = config['fusion']['alt_Type']
             df[Features.alt] = df[Features.feature]
 
-            idx_min_spanning_fragments = cls.filter_by_spanning_fragment_count(df[Features.spanningfrags])
+            min_fragments = config['fusion']['spanningfrags_min']
+            idx_min_spanning_fragments = cls.filter_by_spanning_fragment_count(
+                series=df[Features.spanningfrags],
+                minimum=min_fragments
+            )
             idx_unique = Features.drop_duplicate_genes(df.loc[idx_min_spanning_fragments, :], Features.feature)
             fusions_unique = df.loc[idx_unique, :]
 
@@ -521,11 +517,7 @@ def split_breakpoint(series_breakpoint):
 
 
 class MicrosatelliteReader:
-    microsatellite = 'microsatellite'
-    feature_type_section = 'feature_types'
-    feature_type = CONFIG[feature_type_section][microsatellite]
-
-    microsatellite_section = microsatellite
+    microsatellite_section = 'microsatellite'
     msih = COLNAMES[microsatellite_section]['msih']
     msil = COLNAMES[microsatellite_section]['msil']
     mss = COLNAMES[microsatellite_section]['mss']
@@ -543,9 +535,10 @@ def map_status(cls, status):
         return cls.status_map[status]
 
     @classmethod
-    def summarize(cls, status):
+    def summarize(cls, status, config):
         df = Features.create_empty_dataframe()
-        df.loc[0, Features.feature_type] = cls.feature_type
+        biomarker_type = config['feature_types']['microsatellite']
+        df.loc[0, Features.feature_type] = biomarker_type
         df.loc[0, Features.feature] = cls.map_status(status)
         return df
 
@@ -636,13 +629,12 @@ def return_variants_non_coding(cls, df):
 
 
 class MAFGermline(MAF):
-    feature_type = CONFIG['feature_types']['germline']
-
     @classmethod
-    def import_feature(cls, handle):
+    def import_feature(cls, handle, config):
         df = cls.import_maf(handle)
+        biomarker_type = config['feature_types']['germline']
         if not df.empty:
-            df = cls.format_maf(df, cls.feature_type)
+            df = cls.format_maf(df, biomarker_type)
             coding_variants = cls.return_variants_coding(df)
         else:
             coding_variants = cls.create_empty_dataframe()
@@ -651,13 +643,12 @@ def import_feature(cls, handle):
 
 
 class MAFSomatic(MAF):
-    feature_type = CONFIG['feature_types']['mut']
-
     @classmethod
-    def import_feature(cls, handle):
+    def import_feature(cls, handle, config):
         df = cls.import_maf(handle)
+        biomarker_type = config['feature_types']['mut']
         if not df.empty:
-            df = cls.format_maf(df, cls.feature_type)
+            df = cls.format_maf(df, biomarker_type)
             coding_variants = cls.return_variants_coding(df)
             non_coding_variants = cls.return_variants_non_coding(df)
         else:
@@ -682,8 +673,8 @@ class MAFValidation(MAF):
     columns = [gene, alt, alt_type, validation_tumor_f, validation_coverage]
 
     @classmethod
-    def import_feature(cls, handle):
-        df, df_reject = MAFSomatic.import_feature(handle)
+    def import_feature(cls, handle, config):
+        df, df_reject = MAFSomatic.import_feature(handle, config)
         df = df.drop(df.columns[df.columns.str.contains('validation')], axis=1)
         df = df.rename(columns=cls.column_map).loc[:, cls.columns]
         return df, df_reject
diff --git a/moalmanac/illustrator.py b/moalmanac/illustrator.py
index 36ae986..3f6e120 100644
--- a/moalmanac/illustrator.py
+++ b/moalmanac/illustrator.py
@@ -1,5 +1,4 @@
 from config import COLNAMES
-from config import CONFIG
 
 import io
 import base64
@@ -137,13 +136,6 @@ def create_title(string):
 
 
 class ValidationOverlap(Illustrator):
-    config_section = 'validation_sequencing'
-    min_af = float(CONFIG[config_section]['min_af_for_annotation'])
-    min_power = float(CONFIG[config_section]['min_power'])
-
-    feature_type_section = 'feature_types'
-    feature_type_mutation = CONFIG[feature_type_section]['mut']
-
     section = 'validation_sequencing'
     gene = COLNAMES[section]['gene']
     feature_type = COLNAMES[section]['feature_type']
@@ -164,8 +156,8 @@ def create_gene_alt_string(cls, data):
         return data[cls.gene].astype(str) + ' ' + data[cls.alt].astype(str)
 
     @classmethod
-    def format_data(cls, df):
-        idx = df[df[cls.feature_type] == cls.feature_type_mutation].index
+    def format_data(cls, df, biomarker_type_string):
+        idx = df[df[cls.feature_type] == biomarker_type_string].index
         data = df.loc[idx, cls.columns].fillna(0.0)
         data[cls.coverage] = pd.to_numeric(data[cls.coverage])
         data[cls.tumor_f] = pd.to_numeric(data[cls.tumor_f])
@@ -175,7 +167,7 @@ def format_data(cls, df):
         return data
 
     @classmethod
-    def plot_overlap_af(cls, data, title=''):
+    def plot_overlap_af(cls, data, title='', minimum_power=0.95, minimum_allele_fraction=0.05):
         fig = plt.figure(figsize=(7, 7))
 
         ax = plt.subplot()
@@ -190,22 +182,22 @@ def plot_overlap_af(cls, data, title=''):
         plt.yticks(fontsize=14)
         plt.xticks(fontsize=14)
 
-        powered = data[data[cls.validation_detection_power].astype(float) >= cls.min_power]
-        lowpower = data[data[cls.validation_detection_power].astype(float) < cls.min_power]
+        powered = data[data[cls.validation_detection_power].astype(float) >= minimum_power]
+        lowpower = data[data[cls.validation_detection_power].astype(float) < minimum_power]
         labels = cls.create_gene_alt_string(data)
 
         for idx in data.index:
             primary_tumor_f = float(data.loc[idx, cls.tumor_f])
             validation_tumor_f = float(data.loc[idx, cls.validation_tumor_f])
-            if (primary_tumor_f >= cls.min_af) & (validation_tumor_f >= cls.min_af):
+            if (primary_tumor_f >= minimum_allele_fraction) & (validation_tumor_f >= minimum_allele_fraction):
                 ax.annotate(labels[idx], (primary_tumor_f, validation_tumor_f))
 
         plt.scatter(powered[cls.tumor_f].tolist(), powered[cls.validation_tumor_f].tolist(),
                     color=Illustrator.tableau10['blue'],
-                    label=''.join(['Detection power in validation sequencing >= {}'.format(cls.min_power)]))
+                    label=''.join(['Detection power in validation sequencing >= {}'.format(minimum_power)]))
         plt.scatter(lowpower[cls.tumor_f].tolist(), lowpower[cls.validation_tumor_f].tolist(),
                     color=Illustrator.tableau10['grey'],
-                    label=''.join(['Detection power in validation sequencing < {}'.format(cls.min_power)]))
+                    label=''.join(['Detection power in validation sequencing < {}'.format(minimum_power)]))
 
         plt.xlim(-0.01, 1.01)
         plt.ylim(-0.01, 1.01)
@@ -222,7 +214,16 @@ def plot_overlap_af(cls, data, title=''):
         return fig
 
     @classmethod
-    def generate_dna_rna_plot(cls, df, patient_id, folder):
-        data = cls.format_data(df)
-        figure = cls.plot_overlap_af(data, title=patient_id)
+    def generate_dna_rna_plot(cls, df, patient_id, folder, config):
+        biomarker_type = config['feature_types']['mut']
+        minimum_power = float(config['validation_sequencing']['min_power'])
+        minimum_allele_fraction = float(config['validation_sequencing']['min_af_for_annotation'])
+
+        data = cls.format_data(df=df, biomarker_type_string=biomarker_type)
+        figure = cls.plot_overlap_af(
+            data=data,
+            title=patient_id,
+            minimum_power=minimum_power,
+            minimum_allele_fraction=minimum_allele_fraction
+        )
         Illustrator.save_fig(figure, folder, patient_id, 'validation_overlap.png')
diff --git a/moalmanac/investigator.py b/moalmanac/investigator.py
index cfb4847..0c2d6fc 100644
--- a/moalmanac/investigator.py
+++ b/moalmanac/investigator.py
@@ -6,7 +6,6 @@
 from datasources import Preclinical
 from illustrator import PreclinicalEfficacy
 
-from config import CONFIG
 from config import COLNAMES
 
 
@@ -58,26 +57,6 @@ class Investigator(object):
     ic50 = COLNAMES[preclinical_section]['ic50']
     tested_subfeature = COLNAMES[preclinical_section]['tested_subfeature']
 
-    feature_type_section = 'feature_types'
-    feature_type_mut = CONFIG[feature_type_section]['mut']
-    feature_type_germline = CONFIG[feature_type_section]['germline']
-    feature_type_cna = CONFIG[feature_type_section]['cna']
-    feature_type_fusion = CONFIG[feature_type_section]['fusion']
-    feature_type_burden = CONFIG[feature_type_section]['burden']
-    feature_type_signature = CONFIG[feature_type_section]['signature']
-    feature_types = {
-        'variant': feature_type_mut,
-        'germline': feature_type_germline,
-        'copy_number': feature_type_cna,
-        'fusion': feature_type_fusion,
-        'burden': feature_type_burden,
-        'signature': feature_type_signature
-    }
-
-    input_dtypes = [feature_types['variant'],
-                    feature_types['copy_number'],
-                    feature_types['fusion']]
-
     @staticmethod
     def list_feature_combinations(split_feature, feature_length):
         return ['.'.join(split_feature[:i]) for i in range(1, feature_length + 1)]
@@ -122,7 +101,7 @@ def calculate_mann_whitney_u(series1, series2):
             return stats.mannwhitneyu(series1, series2, alternative='two-sided')
 
     @classmethod
-    def create(cls, dbs, df_actionable):
+    def create(cls, dbs, df_actionable, config):
         summary = dbs[cls.summary]
         variants = dbs[cls.variants]
         cnas = dbs[cls.cnas]
@@ -131,8 +110,14 @@ def create(cls, dbs, df_actionable):
         genes = dbs[cls.gene]
         mappings = dbs[cls.mappings]
 
+        input_dtypes = [
+            config['feature_types']['mut'],
+            config['feature_types']['cna'],
+            config['feature_types']['fusion']
+        ]
+
         samples = Preclinical.generate_sample_list(summary, cls.use_column, cls.model_id)
-        idx_feature_type = df_actionable[cls.feature_type].isin(cls.input_dtypes)
+        idx_feature_type = df_actionable[cls.feature_type].isin(input_dtypes)
         idx_sensitive = ~(df_actionable[cls.sensitive_therapy].isnull() | df_actionable[cls.sensitive_therapy].eq(''))
 
         dictionary = {}
@@ -228,12 +213,12 @@ def populate_feature_dictionary(cls, groups, all_samples):
         return dictionary
 
     @classmethod
-    def select_split_function(cls, feature_type):
-        if feature_type == cls.feature_types['variant']:
+    def select_split_function(cls, feature_type, variant_string, copy_number_string, fusion_string):
+        if feature_type == variant_string:
             return cls.split_samples_for_variants
-        elif feature_type == cls.feature_types['copy_number']:
+        elif feature_type == copy_number_string:
             return cls.split_samples_for_copy_numbers
-        elif feature_type == cls.feature_types['fusion']:
+        elif feature_type == fusion_string:
             return cls.split_samples_for_fusions
         else:
             return cls.split_exit
@@ -245,10 +230,19 @@ def split_exit(cls, dbs, series, samples):
         exit()
 
     @classmethod
-    def split_samples_by_wt_mut(cls, series, dbs, samples):
+    def split_samples_by_wt_mut(cls, series, dbs, samples, config):
         feature_type = series.loc[cls.feature_type]
-        split_function = cls.select_split_function(feature_type)
-        return split_function(dbs, series, samples)
+        split_function = cls.select_split_function(
+            feature_type=feature_type,
+            variant_string=config['feature_types']['mut'],
+            copy_number_string=config['feature_types']['cna'],
+            fusion_string=config['feature_types']['fusion']
+        )
+        return split_function(
+            dbs=dbs,
+            series=series,
+            all_samples=samples
+        )
 
     @classmethod
     def split_samples_for_copy_numbers(cls, dbs, series, all_samples):
diff --git a/moalmanac/matchmaker.py b/moalmanac/matchmaker.py
index 542e9ed..2320d61 100644
--- a/moalmanac/matchmaker.py
+++ b/moalmanac/matchmaker.py
@@ -10,7 +10,6 @@
 from datasources import Preclinical as DatasourcePreclinical
 
 from config import COLNAMES
-from config import CONFIG
 
 
 class Matchmaker:
@@ -27,20 +26,14 @@ class Matchmaker:
     feature_columns = [feature_type, feature, alt_type, alt]
 
     cgc_bin = 'cgc_bin'
-
-    feature_types_section = 'feature_types'
-    feature_types_config = CONFIG[feature_types_section]
-    cn = feature_types_config['cna']
-    rearrangement = feature_types_config['fusion']
-    variant = feature_types_config['mut']
     fusion = 'Fusion'
 
     @classmethod
-    def concat_case_comparisons(cls, somatic, dbs):
+    def concat_case_comparisons(cls, somatic, dbs, variant_string, copy_number_string, rearrangement_string):
         somatic[cls.model_id] = cls.case_profile
-        case_variants = cls.subset_dataframe_eq(somatic, cls.feature_type, cls.variant)
-        case_cns = cls.subset_dataframe_eq(somatic, cls.feature_type, cls.cn)
-        case_fusions = cls.subset_dataframe_eq(somatic, cls.feature_type, cls.rearrangement)
+        case_variants = cls.subset_dataframe_eq(somatic, cls.feature_type, variant_string)
+        case_cns = cls.subset_dataframe_eq(somatic, cls.feature_type, copy_number_string)
+        case_fusions = cls.subset_dataframe_eq(somatic, cls.feature_type, rearrangement_string)
         if case_fusions.shape[0] > 0:
             case_fusions = cls.format_fusions(case_fusions)
 
@@ -54,24 +47,34 @@ def concat_case_comparisons(cls, somatic, dbs):
         fusion_columns = [cls.feature, cls.partner, cls.model_id]
         fusions = cls.concat_dataframes(case_fusions, comparison_fusions, fusion_columns)
 
-        variants[cls.feature_type] = cls.variant
-        copy_numbers[cls.feature_type] = cls.cn
-        fusions[cls.feature_type] = cls.rearrangement
+        variants[cls.feature_type] = variant_string
+        copy_numbers[cls.feature_type] = copy_number_string
+        fusions[cls.feature_type] = rearrangement_string
         fusions[cls.alt_type] = cls.fusion
 
         return {
-            cls.variant: variants,
-            cls.cn: copy_numbers,
-            cls.rearrangement: fusions
+            variant_string: variants,
+            copy_number_string: copy_numbers,
+            rearrangement_string: fusions
         }
 
     @classmethod
-    def compare(cls, dbs, dbs_preclinical, somatic, case_sample_id):
+    def compare(cls, dbs, dbs_preclinical, somatic, case_sample_id, config):
         cgc = DatasourceCGC.import_ds(dbs)
         almanac = DatasourceAlmanac.import_ds(dbs)
 
-        merged = cls.concat_case_comparisons(somatic, dbs_preclinical)
-        annotated = AnnotatorPreclinicalMatchmaking.annotate(merged, dbs)
+        somatic_variant_biomarker_type_string = config['feature_types']['mut']
+        copy_number_variant_biomarker_type_string = config['feature_types']['cna']
+        fusion_biomarker_type_string = config['feature_types']['fusion']
+
+        merged = cls.concat_case_comparisons(
+            somatic = somatic,
+            dbs = dbs_preclinical,
+            variant_string = somatic_variant_biomarker_type_string,
+            copy_number_string = copy_number_variant_biomarker_type_string,
+            rearrangement_string = fusion_biomarker_type_string
+        )
+        annotated = AnnotatorPreclinicalMatchmaking.annotate(merged, dbs, config)
         samples_to_use = cls.subset_samples(dbs_preclinical)
 
         calculated = SNFTypesCGCwithEvidence.calculate(annotated, samples_to_use, cgc, almanac)
diff --git a/moalmanac/moalmanac.py b/moalmanac/moalmanac.py
index b89223b..c47ff4c 100644
--- a/moalmanac/moalmanac.py
+++ b/moalmanac/moalmanac.py
@@ -1,7 +1,6 @@
 import time
 import argparse
 import os
-import pandas as pd
 import subprocess
 
 import annotator
@@ -16,7 +15,7 @@
 import writer
 
 from config import COLNAMES
-from config import CONFIG
+from reader import Ini
 
 snv_handle = 'snv_handle'
 indel_handle = 'indel_handle'
@@ -50,28 +49,7 @@
 ontology = COLNAMES[oncotree_section]['ontology']
 code = COLNAMES[oncotree_section]['code']
 
-feature_type_section = 'feature_types'
-feature_type_mut = CONFIG[feature_type_section]['mut']
-feature_type_germline = CONFIG[feature_type_section]['germline']
-feature_type_cna = CONFIG[feature_type_section]['cna']
-feature_type_fusion = CONFIG[feature_type_section]['fusion']
-feature_type_burden = CONFIG[feature_type_section]['burden']
-feature_type_signature = CONFIG[feature_type_section]['signature']
-feature_type_microsatellite = CONFIG[feature_type_section]['microsatellite']
-feature_type_aneuploidy = CONFIG[feature_type_section]['aneuploidy']
-feature_types = {
-    'mutation': feature_type_mut,
-    'germline': feature_type_germline,
-    'copynumber': feature_type_cna,
-    'fusion': feature_type_fusion,
-    'burden': feature_type_burden,
-    'signature': feature_type_signature,
-    'microsatellite': feature_type_microsatellite,
-    'aneuploidy': feature_type_aneuploidy
-}
-
 generate_illustrations = 'generate_illustrations'
-TOGGLE_FEATURES = CONFIG['function_toggle']
 
 
 def create_metadata_dictionary(input_dictionary):
@@ -95,9 +73,9 @@ def format_output_directory(directory):
         return directory
 
 
-def load_and_process_mutational_signatures(input, dbs, tumor_type):
-    signatures = features.CosmicSignatures.import_feature(input)
-    annotated = annotator.Annotator.annotate_almanac(signatures, dbs, tumor_type)
+def load_and_process_mutational_signatures(input, dbs, tumor_type, config):
+    signatures = features.CosmicSignatures.import_feature(input, config)
+    annotated = annotator.Annotator.annotate_almanac(signatures, dbs, tumor_type, config)
     evaluated = evaluator.Evaluator.evaluate_almanac(annotated)
     return evaluated
 
@@ -110,18 +88,17 @@ def plot_preclinical_efficacy(dictionary, folder, label):
             writer.Illustrations.write(figure, folder, label, f"{figure_name}.png")
 
 
-def process_preclinical_efficacy(dbs, dataframe, folder, label, plot: bool = False):
-    efficacy_dictionary = investigator.SensitivityDictionary.create(dbs, dataframe)
+def process_preclinical_efficacy(dbs, dataframe, folder, label, config, plot: bool = False):
+    efficacy_dictionary = investigator.SensitivityDictionary.create(dbs, dataframe, config)
     if plot:
         plot_preclinical_efficacy(efficacy_dictionary, folder, label)
     efficacy_summary = investigator.SummaryDataFrame.create(efficacy_dictionary, dataframe, label)
     return efficacy_dictionary, efficacy_summary
 
 
-def main(patient, inputs, output_folder):
+def main(patient, inputs, output_folder, config, dbs, dbs_preclinical=None):
     metadata_dictionary = create_metadata_dictionary(patient)
 
-    dbs = datasources.Datasources.generate_db_dict(CONFIG)
     output_folder = format_output_directory(output_folder)
     if output_folder != "":
         execute_cmd(f"mkdir -p {output_folder}")
@@ -132,47 +109,81 @@ def main(patient, inputs, output_folder):
     metadata_dictionary[ontology] = mapped_ontology[ontology]
     metadata_dictionary[code] = mapped_ontology[code]
 
-    df_snv, df_snv_reject = features.MAFSomatic.import_feature(inputs[snv_handle])
-    df_indel, df_indel_reject = features.MAFSomatic.import_feature(inputs[indel_handle])
-    df_cnv, df_cnv_reject = features.CopyNumber.import_feature(inputs[called_cn_handle], inputs[cnv_handle])
-    df_fusion, df_fusion_reject = features.Fusion.import_feature(inputs[fusion_handle])
+    df_snv, df_snv_reject = features.MAFSomatic.import_feature(inputs[snv_handle], config)
+    df_indel, df_indel_reject = features.MAFSomatic.import_feature(inputs[indel_handle], config)
+    df_cnv, df_cnv_reject = features.CopyNumber.import_feature(inputs[called_cn_handle], inputs[cnv_handle], config)
+    df_fusion, df_fusion_reject = features.Fusion.import_feature(inputs[fusion_handle], config)
 
     accepted_variants = [df_snv, df_indel, df_cnv, df_fusion]
     filtered_variants = [df_snv_reject, df_indel_reject, df_cnv_reject, df_fusion_reject]
     somatic_variants = features.Features.concat_list_of_dataframes(accepted_variants)
     somatic_filtered = features.Features.concat_list_of_dataframes(filtered_variants)
 
-    germline_variants, germline_reject = features.MAFGermline.import_feature(inputs[germline_handle])
+    germline_variants, germline_reject = features.MAFGermline.import_feature(inputs[germline_handle], config)
 
     if not somatic_variants.empty:
-        annotated_somatic = annotator.Annotator.annotate_somatic(somatic_variants, dbs, metadata_dictionary[code])
+        annotated_somatic = annotator.Annotator.annotate_somatic(
+            df=somatic_variants,
+            dbs=dbs,
+            ontology=metadata_dictionary[code],
+            config=config
+        )
         evaluated_somatic = evaluator.Evaluator.evaluate_somatic(annotated_somatic)
 
-        validation_accept, validation_reject = features.MAFValidation.import_feature(inputs[validation_handle])
+        validation_accept, validation_reject = features.MAFValidation.import_feature(inputs[validation_handle], config)
         if not validation_accept.empty:
-            evaluated_somatic = annotator.OverlapValidation.append_validation(evaluated_somatic, validation_accept)
-            illustrator.ValidationOverlap.generate_dna_rna_plot(evaluated_somatic, string_id, output_folder)
+            evaluated_somatic = annotator.OverlapValidation.append_validation(
+                primary=evaluated_somatic,
+                validation=validation_accept,
+                biomarker_type=config['feature_types']['mut'])
+            illustrator.ValidationOverlap.generate_dna_rna_plot(evaluated_somatic, string_id, output_folder, config)
     else:
         evaluated_somatic = features.Features.create_empty_dataframe()
 
     if not germline_variants.empty:
-        annotated_germline = annotator.Annotator.annotate_germline(germline_variants, dbs, metadata_dictionary[code])
+        annotated_germline = annotator.Annotator.annotate_germline(
+            germline_variants,
+            dbs,
+            metadata_dictionary[code],
+            config=config
+        )
         evaluated_germline = evaluator.Evaluator.evaluate_germline(annotated_germline)
     else:
         evaluated_germline = features.Features.create_empty_dataframe()
 
     evaluated_somatic = annotator.OverlapSomaticGermline.append_germline_hits(evaluated_somatic, evaluated_germline)
-    integrated = evaluator.Integrative.evaluate(evaluated_somatic, evaluated_germline, dbs, feature_types)
+    integrated = evaluator.Integrative.evaluate(evaluated_somatic, evaluated_germline, dbs, config)
 
-    somatic_burden = features.BurdenReader.import_feature(inputs[bases_covered_handle], metadata_dictionary, somatic_variants, dbs)
+    somatic_burden = features.BurdenReader.import_feature(
+        handle=inputs[bases_covered_handle],
+        patient=metadata_dictionary,
+        variants=somatic_variants,
+        dbs=dbs,
+        config=config
+    )
 
-    patient_wgd = features.Aneuploidy.summarize(metadata_dictionary[wgd])
-    patient_ms_status = features.MicrosatelliteReader.summarize(metadata_dictionary[ms_status])
+    patient_wgd = features.Aneuploidy.summarize(metadata_dictionary[wgd], config)
+    patient_ms_status = features.MicrosatelliteReader.summarize(metadata_dictionary[ms_status], config)
     metadata_dictionary[ms_status] = features.MicrosatelliteReader.map_status(metadata_dictionary[ms_status])
 
-    annotated_burden = annotator.Annotator.annotate_almanac(somatic_burden, dbs, metadata_dictionary[code])
-    annotated_wgd = annotator.Annotator.annotate_almanac(patient_wgd, dbs, metadata_dictionary[code])
-    annotated_ms_status = annotator.Annotator.annotate_almanac(patient_ms_status, dbs, metadata_dictionary[code])
+    annotated_burden = annotator.Annotator.annotate_almanac(
+        df=somatic_burden,
+        dbs=dbs,
+        ontology=metadata_dictionary[code],
+        config=config
+    )
+    annotated_wgd = annotator.Annotator.annotate_almanac(
+        df=patient_wgd,
+        dbs=dbs,
+        ontology=metadata_dictionary[code],
+        config=config
+    )
+    annotated_ms_status = annotator.Annotator.annotate_almanac(
+        df=patient_ms_status,
+        dbs=dbs,
+        ontology=metadata_dictionary[code],
+        config=config
+    )
 
     evaluated_burden = evaluator.Evaluator.evaluate_almanac(annotated_burden)
     evaluated_wgd = evaluator.Evaluator.evaluate_almanac(annotated_wgd)
@@ -182,55 +193,65 @@ def main(patient, inputs, output_folder):
     evaluated_mutational_signatures = load_and_process_mutational_signatures(
         input=inputs[mutational_signatures_path],
         dbs=dbs,
-        tumor_type=code
+        tumor_type=code,
+        config=config
     )
 
     actionable = evaluator.Actionable.evaluate(
-        evaluated_somatic,
-        evaluated_germline,
-        evaluated_ms_variants,
-        evaluated_ms_status,
-        evaluated_burden,
-        evaluated_mutational_signatures,
-        evaluated_wgd
+        somatic=evaluated_somatic,
+        germline=evaluated_germline,
+        ms_variants=evaluated_ms_variants,
+        ms_status=evaluated_ms_status,
+        burden=evaluated_burden,
+        signatures=evaluated_mutational_signatures,
+        wgd=evaluated_wgd,
+        config=config
     )
 
     strategies = evaluator.Strategies.report_therapy_strategies(actionable)
 
+    function_toggle = config['function_toggle']
+
     efficacy_summary = investigator.SummaryDataFrame.create_empty_dataframe()
-    efficacy_dictionary = {}
-    cell_lines_dictionary = {}
-    preclinical_efficacy_on = TOGGLE_FEATURES.getboolean('calculate_preclinical_efficacy')
+    # efficacy_dictionary = {}
+    # cell_lines_dictionary = {}
+    preclinical_efficacy_on = function_toggle.getboolean('calculate_preclinical_efficacy')
 
     # The input argument --disable_matchmaking will be removed in the next non-backwards compatible release
-    model_similarity_on = TOGGLE_FEATURES.getboolean('calculate_model_similarity') and not inputs[disable_matchmaking]
+    model_similarity_on = function_toggle.getboolean('calculate_model_similarity') and not inputs[disable_matchmaking]
     similarity_results = matchmaker.Matchmaker.create_empty_output()
     similarity_summary = {}
 
-    if preclinical_efficacy_on or model_similarity_on:
-        dbs_preclinical = datasources.Preclinical.import_dbs()
-        cell_lines_dictionary = dbs_preclinical['dictionary']
-        if preclinical_efficacy_on:
-            plot_preclinical = TOGGLE_FEATURES.getboolean('plot_preclinical_efficacy')
-            efficacy_results = process_preclinical_efficacy(
-                dbs_preclinical,
-                actionable,
-                output_folder,
-                string_id,
-                plot=plot_preclinical
-            )
-            efficacy_dictionary = efficacy_results[0]
-            efficacy_summary = efficacy_results[1]
-
-            actionable = annotator.PreclinicalEfficacy.annotate(
-                actionable,
-                efficacy_summary,
-                efficacy_dictionary,
-                append_lookup=TOGGLE_FEATURES.getboolean('include_preclinical_efficacy_in_actionability_report')
-            )
-        if model_similarity_on:
-            similarity_results = matchmaker.Matchmaker.compare(dbs, dbs_preclinical, evaluated_somatic, string_id)
-            similarity_summary = matchmaker.Report.create_report_dictionary(similarity_results, cell_lines_dictionary)
+    if dbs_preclinical is not None:
+        if preclinical_efficacy_on or model_similarity_on:
+            dbs_preclinical = datasources.Preclinical.import_dbs(dbs_preclinical)
+            cell_lines_dictionary = dbs_preclinical['dictionary']
+            if preclinical_efficacy_on:
+                plot_preclinical = function_toggle.getboolean('plot_preclinical_efficacy')
+                efficacy_results = process_preclinical_efficacy(
+                    dbs=dbs_preclinical,
+                    dataframe=actionable,
+                    folder=output_folder,
+                    label=string_id,
+                    config=config,
+                    plot=plot_preclinical
+                )
+                efficacy_dictionary = efficacy_results[0]
+                efficacy_summary = efficacy_results[1]
+
+                actionable = annotator.PreclinicalEfficacy.annotate(
+                    actionable,
+                    efficacy_summary,
+                    efficacy_dictionary,
+                    append_lookup=function_toggle.getboolean('include_preclinical_efficacy_in_actionability_report')
+                )
+
+            if model_similarity_on:
+                similarity_results = matchmaker.Matchmaker.compare(dbs, dbs_preclinical, evaluated_somatic, string_id)
+                similarity_summary = matchmaker.Report.create_report_dictionary(
+                    similarity_results,
+                    cell_lines_dictionary
+                )
 
     writer.Actionable.write(actionable, string_id, output_folder)
     writer.GermlineACMG.write(evaluated_germline, string_id, output_folder)
@@ -245,81 +266,137 @@ def main(patient, inputs, output_folder):
     writer.PreclinicalEfficacy.write(efficacy_summary, string_id, output_folder)
     writer.PreclinicalMatchmaking.write(similarity_results, string_id, output_folder)
 
-    if TOGGLE_FEATURES.getboolean('generate_actionability_report'):
+    if function_toggle.getboolean('generate_actionability_report'):
         report_dictionary = reporter.Reporter.generate_dictionary(evaluated_somatic, metadata_dictionary)
 
-        include_similarity = TOGGLE_FEATURES.getboolean('include_model_similarity_in_actionability_report')
+        include_similarity = function_toggle.getboolean('include_model_similarity_in_actionability_report')
         reporter.Reporter.generate_actionability_report(
-            actionable,
-            report_dictionary,
-            similarity=similarity_summary if include_similarity else None,
-            output_directory=output_folder
+            actionable = actionable,
+            report_dictionary = report_dictionary,
+            config = config,
+            similarity = similarity_summary if include_similarity else None,
+            output_directory = output_folder
         )
 
 
 if __name__ == "__main__":
     start_time = time.time()
 
-    arg_parser = argparse.ArgumentParser(prog='Molecular Oncology Almanac',
-                                         description='A clinical interpretation algorithm for cancer genomics.')
-    arg_parser.add_argument('--patient_id',
-                            help='patient id label',
-                            required=True)
-    arg_parser.add_argument('--description',
-                            default='',
-                            help='description of patient')
-    arg_parser.add_argument('--tumor_type',
-                            default='Unknown',
-                            help='reported tumor type')
-    arg_parser.add_argument('--stage',
-                            default='Unknown',
-                            help='disease stage')
-    arg_parser.add_argument('--snv_handle',
-                            default='',
-                            help='handle for SNV MAF')
-    arg_parser.add_argument('--indel_handle',
-                            default='',
-                            help='handle for InDel MAF')
-    arg_parser.add_argument('--bases_covered_handle',
-                            default='',
-                            help='handle for a text file which contains the numeric number of somatic bases')
-    arg_parser.add_argument('--called_cn_handle',
-                            default='',
-                            help='handle for called copy number alterations file, used over --cnv_handle')
-    arg_parser.add_argument('--cnv_handle',
-                            default='',
-                            help='handle for annotated seg file')
-    arg_parser.add_argument('--fusion_handle',
-                            default='',
-                            help='handle for STAR Fusion output, .final.abridged')
-    arg_parser.add_argument('--germline_handle',
-                            default='',
-                            help='handle for Germline MAF')
-    arg_parser.add_argument('--validation_handle',
-                            default='',
-                            help='handle for SNV MAF called from validation sequencing')
-    arg_parser.add_argument('--ms_status',
-                            default='unk',
-                            choices=['msih', 'msil', 'mss', 'unk'],
-                            help='microsatellite instability status')
-    arg_parser.add_argument('--mutational_signatures',
-                            default='',
-                            help='file for SBS signature contributions, version 3.4')
-    arg_parser.add_argument('--purity',
-                            default='Unknown',
-                            help='Tumor purity')
-    arg_parser.add_argument('--ploidy',
-                            default='Unknown',
-                            help='Tumor ploidy')
-    arg_parser.add_argument('--wgd',
-                            action='store_true',
-                            help='Specify the occurrence of whole genome duplication')
-    arg_parser.add_argument('--disable_matchmaking',
-                            action='store_true',
-                            help='Disable matchmaking in report')
-    arg_parser.add_argument('--output_directory',
-                            default=None,
-                            help='Output directory for generated files')
+    arg_parser = argparse.ArgumentParser(
+        prog='Molecular Oncology Almanac',
+        description='A clinical interpretation algorithm for cancer genomics.'
+    )
+    arg_parser.add_argument(
+        '--patient_id',
+        help='patient id label',
+        required=True
+    )
+    arg_parser.add_argument(
+        '--description',
+        default='',
+        help='description of patient'
+    )
+    arg_parser.add_argument(
+        '--tumor_type',
+        default='Unknown',
+        help='reported tumor type'
+    )
+    arg_parser.add_argument(
+        '--stage',
+        default='Unknown',
+        help='disease stage'
+    )
+    arg_parser.add_argument(
+        '--snv_handle',
+        default='',
+        help='handle for SNV MAF'
+    )
+    arg_parser.add_argument(
+        '--indel_handle',
+        default='',
+        help='handle for InDel MAF'
+    )
+    arg_parser.add_argument(
+        '--bases_covered_handle',
+        default='',
+        help='handle for a text file which contains the numeric number of somatic bases'
+    )
+    arg_parser.add_argument(
+        '--called_cn_handle',
+        default='',
+        help='handle for called copy number alterations file, used over --cnv_handle'
+    )
+    arg_parser.add_argument(
+        '--cnv_handle',
+        default='',
+        help='handle for annotated seg file'
+    )
+    arg_parser.add_argument(
+        '--fusion_handle',
+        default='',
+        help='handle for STAR Fusion output, .final.abridged'
+    )
+    arg_parser.add_argument(
+        '--germline_handle',
+        default='',
+        help='handle for Germline MAF'
+    )
+    arg_parser.add_argument(
+        '--validation_handle',
+        default='',
+        help='handle for SNV MAF called from validation sequencing'
+    )
+    arg_parser.add_argument(
+        '--ms_status',
+        default='unk',
+        choices=['msih', 'msil', 'mss', 'unk'],
+        help='microsatellite instability status'
+    )
+    arg_parser.add_argument(
+        '--mutational_signatures',
+        default='',
+        help='file for SBS signature contributions, version 3.4'
+    )
+    arg_parser.add_argument(
+        '--purity',
+        default='Unknown',
+        help='Tumor purity'
+    )
+    arg_parser.add_argument(
+        '--ploidy',
+        default='Unknown',
+        help='Tumor ploidy'
+    )
+    arg_parser.add_argument(
+        '--wgd',
+        action='store_true',
+        help='Specify the occurrence of whole genome duplication'
+    )
+    arg_parser.add_argument(
+        '--disable_matchmaking',
+        action='store_true',
+        help='Disable matchmaking in report'
+    )
+    arg_parser.add_argument(
+        '--output_directory',
+        default=None,
+        help='Output directory for generated files'
+    )
+    arg_parser.add_argument(
+        '--config', '-c',
+        required=True,
+        help='ini file that contains configuration details '
+    )
+    arg_parser.add_argument(
+        '--dbs',
+        required=True,
+        help='ini file that contains database paths '
+    )
+    arg_parser.add_argument(
+        '--preclinical-dbs',
+        required=False,
+        help='ini file that contains preclinical file paths'
+    )
     args = arg_parser.parse_args()
 
     patient_dict = {
@@ -348,7 +425,22 @@ def main(patient, inputs, output_folder):
 
     output_directory = args.output_directory if args.output_directory else os.getcwd()
 
-    main(patient_dict, inputs_dict, output_directory)
+    config_ini = Ini.read(args.config, extended_interpolation=False, convert_to_dictionary=False)
+
+    db_paths = Ini.read(args.dbs, extended_interpolation=False, convert_to_dictionary=True)
+    if args.preclinical_dbs:
+        preclinical_db_paths = Ini.read(args.preclinical_dbs, extended_interpolation=False, convert_to_dictionary=True)
+    else:
+        preclinical_db_paths = None
+
+    main(
+        patient=patient_dict,
+        inputs=inputs_dict,
+        output_folder=output_directory,
+        config=config_ini,
+        dbs=db_paths['databases'],
+        dbs_preclinical=preclinical_db_paths['preclinical']
+    )
 
     end_time = time.time()
     time_statement = "Molecular Oncology Almanac runtime: %s seconds" % round((end_time - start_time), 4)
diff --git a/moalmanac/preclinical-databases.ini b/moalmanac/preclinical-databases.ini
new file mode 100644
index 0000000..de60614
--- /dev/null
+++ b/moalmanac/preclinical-databases.ini
@@ -0,0 +1,10 @@
+[preclinical]
+almanac_gdsc_mappings = datasources/preclinical/formatted/almanac-gdsc-mappings.json
+summary = datasources/preclinical/formatted/cell-lines.summary.txt
+variants = datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt
+copynumbers = datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt
+fusions = datasources/preclinical/annotated/cell-lines.fusions.annotated.txt
+fusions1 = datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt
+fusions2 = datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt
+gdsc = datasources/preclinical/formatted/sanger.gdsc.txt
+dictionary = datasources/preclinical/cell-lines.pkl
\ No newline at end of file
diff --git a/moalmanac/reader.py b/moalmanac/reader.py
index c935ff7..ce61219 100644
--- a/moalmanac/reader.py
+++ b/moalmanac/reader.py
@@ -1,9 +1,38 @@
+import configparser
 import json
 import pandas as pd
 import pickle
 
 
-class Reader(object):
+class Ini:
+    @classmethod
+    def read(cls, path, extended_interpolation=False, convert_to_dictionary=False):
+        ini = cls.load(path, extended_interpolation=extended_interpolation)
+        if convert_to_dictionary:
+            return cls.convert_ini_to_dictionary(ini)
+        else:
+            return ini
+
+    @staticmethod
+    def convert_ini_to_dictionary(ini):
+        dictionary = {}
+        for section in ini.sections():
+            dictionary[section] = {}
+            for (key, value) in ini.items(section):
+                dictionary[section][key] = value
+        return dictionary
+
+    @staticmethod
+    def load(path, extended_interpolation=False):
+        if extended_interpolation:
+            config = configparser.ConfigParser(interpolation=configparser.ExtendedInterpolation())
+        else:
+            config = configparser.ConfigParser()
+        config.read(path)
+        return config
+
+
+class Reader:
     @staticmethod
     def check_comment_rows(handle, comment_character):
         skip_rows = 0
diff --git a/moalmanac/reporter.py b/moalmanac/reporter.py
index bb48023..e0c0ef8 100644
--- a/moalmanac/reporter.py
+++ b/moalmanac/reporter.py
@@ -5,7 +5,7 @@
 import os
 
 from config import COLNAMES
-from config import CONFIG
+#from config import CONFIG
 
 
 class Reporter:
@@ -23,23 +23,22 @@ class Reporter:
     ms_status = COLNAMES[report_section]['ms_status']
 
     @classmethod
-    def drop_double_fusion(cls, dataframe):
+    def drop_double_fusion(cls, dataframe, biomarker_type_string):
         feature_type = COLNAMES[cls.report_section]['feature_type']
         alt = COLNAMES[cls.report_section]['alteration']
-        rearrangement = CONFIG['feature_types']['fusion']
 
-        idx_rearrangement = dataframe[dataframe[feature_type].eq(rearrangement)].index
+        idx_rearrangement = dataframe[dataframe[feature_type].eq(biomarker_type_string)].index
         idx_rearrangement_keep = dataframe.loc[idx_rearrangement, :].drop_duplicates([alt], keep='first').index
         idx_rearrangement_drop = idx_rearrangement.difference(idx_rearrangement_keep)
         idx_keep = dataframe.index.difference(idx_rearrangement_drop)
         return dataframe.loc[idx_keep, :]
 
     @classmethod
-    def format_alterations(cls, dataframe):
+    def format_alterations(cls, dataframe, config):
         if dataframe.empty:
             return dataframe
 
-        dataframe = cls.drop_double_fusion(dataframe)
+        dataframe = cls.drop_double_fusion(dataframe, biomarker_type_string=config['feature_types']['fusion'])
 
         lookup = COLNAMES['datasources']
         columns = [lookup['sensitivity'], lookup['resistance'], lookup['prognosis']]
@@ -73,7 +72,7 @@ def format_clinical_columns(series, convert_to_float=False):
             return series
 
     @classmethod
-    def generate_actionability_report(cls, actionable, report_dictionary, similarity=None, output_directory=None):
+    def generate_actionability_report(cls, actionable, report_dictionary, config, similarity=None, output_directory=None):
         report = ActionabilityReport()
         report.add_metadata(
             name=report_dictionary['patient_id'],
@@ -89,13 +88,13 @@ def generate_actionability_report(cls, actionable, report_dictionary, similarity
             msi=report_dictionary['microsatellite_status']
         )
 
-        versions = cls.generate_version_dictionary()
+        versions = cls.generate_version_dictionary(config)
         report.add_versions(
             software=versions['software'],
             database=versions['database']
         )
 
-        actionable = cls.format_alterations(actionable)
+        actionable = cls.format_alterations(dataframe=actionable, config=config)
         report.add_alterations(actionable)
         report.add_similar_profiles(similarity)
 
@@ -120,10 +119,10 @@ def generate_date():
         return datetime.date.today().strftime("%b %d %Y")
 
     @classmethod
-    def generate_version_dictionary(cls):
+    def generate_version_dictionary(cls, config):
         version_section = 'versions'
-        software_version = CONFIG[version_section]['interpreter']
-        database_version = CONFIG[version_section]['database']
+        software_version = config[version_section]['interpreter']
+        database_version = config[version_section]['database']
         return {
             'software': software_version,
             'database': database_version
diff --git a/moalmanac/run_example.py b/moalmanac/run_example.py
index a8400f5..87c12c4 100644
--- a/moalmanac/run_example.py
+++ b/moalmanac/run_example.py
@@ -3,7 +3,9 @@
 import time
 import subprocess
 
-patient_dict = {
+from reader import Ini
+
+metadata_dictionary = {
     'patient_id': 'example',
     'reported_tumor_type': 'MEL',
     'stage': 'Metastatic',
@@ -14,7 +16,7 @@
     'microsatellite_status': 'msih'
 }
 
-empty_dict = {
+input_dictionary_empty = {
     'snv_handle': '',
     'indel_handle': '',
     'bases_covered_handle': '',
@@ -27,7 +29,7 @@
     'disable_matchmaking': False
 }
 
-example_dict = {
+input_dictionary = {
     'snv_handle': '../example_data/example_patient.capture.somatic.snvs.maf',
     'indel_handle': '../example_data/example_patient.capture.somatic.indels.maf',
     'bases_covered_handle': '../example_data/example_patient.capture.somatic.coverage.txt',
@@ -40,12 +42,20 @@
     'disable_matchmaking': False
 }
 
+config_ini_path = "config.ini"
+dbs_ini_path = "annotation-databases.ini"
+dbs_preclinical_ini_path = "preclinical-databases.ini"
+
+config_ini = Ini.read(config_ini_path, extended_interpolation=False, convert_to_dictionary=False)
+db_paths = Ini.read(dbs_ini_path, extended_interpolation=False, convert_to_dictionary=True)
+preclinical_db_paths = Ini.read(dbs_preclinical_ini_path, extended_interpolation=False, convert_to_dictionary=True)
+
 
 def execute_cmd(command):
     subprocess.call(command, shell=True)
 
 
-output_directory = "example"
+output_directory = "2024-07-03-config-edits"
 if output_directory != "":
     cmd = f"mkdir -p {output_directory}"
     execute_cmd(cmd)
@@ -53,7 +63,14 @@ def execute_cmd(command):
     output_directory = os.getcwd()
 
 start_time = time.time()
-moalmanac.main(patient_dict, example_dict, output_directory)
+moalmanac.main(
+    patient=metadata_dictionary,
+    inputs=input_dictionary,
+    output_folder=output_directory,
+    config=config_ini,
+    dbs=db_paths['databases'],
+    dbs_preclinical=preclinical_db_paths['preclinical']
+)
 end_time = time.time()
 
 time_statement = "Molecular Oncology Almanac runtime: %s seconds" % round((end_time - start_time), 4)

From 670d7042911ac88d1ccca712dcac4d14da0d5602 Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Wed, 3 Jul 2024 21:03:49 -0400
Subject: [PATCH 02/19] Test revisions in progress

---
 moalmanac/evaluator.py              | 19 +++++----------
 moalmanac/test/README.md            |  0
 moalmanac/test/annotator_tests.py   | 36 +++++++++++++++++++++++++----
 moalmanac/test/datasources_tests.py | 13 ++++++++++-
 moalmanac/test/evaluator_tests.py   | 34 +++++++++++++--------------
 5 files changed, 67 insertions(+), 35 deletions(-)
 create mode 100644 moalmanac/test/README.md

diff --git a/moalmanac/evaluator.py b/moalmanac/evaluator.py
index 57d94d9..3031baf 100644
--- a/moalmanac/evaluator.py
+++ b/moalmanac/evaluator.py
@@ -209,8 +209,8 @@ def display_microsatellite_variants(cls, df, idx):
 
     @classmethod
     def display_signature(cls, df, idx):
-        #before_string = "COSMIC Signature"
-        #after_string = f"COSMIC Signature (version {version})"
+        # before_string = "COSMIC Signature"
+        # after_string = f"COSMIC Signature (version {version})"
         signature = df.loc[idx, Evaluator.feature]#.str.replace(before_string, after_string)
         contribution = df.loc[idx, Evaluator.alt].astype(float).multiply(100).round(0).astype(int).astype(str)
         # Signature: Cosmic Signature 7 (65%)
@@ -235,7 +235,7 @@ def evaluate(cls, somatic, germline, ms_variants, ms_status, burden, signatures,
         germline = Evaluator.remove_benign_variants(germline)
         germline = Evaluator.remove_common_variants(germline)
 
-        ms_variants_summary = cls.summarize_ms_variants(ms_variants)
+        ms_variants_summary = cls.summarize_ms_variants(ms_variants, config)
 
         if not burden.loc[0, Evaluator.high_burden_boolean]:
             burden = burden.drop(burden.index[0])
@@ -295,22 +295,15 @@ def format_variant_classification(cls, series):
         return series.str.replace('_Mutation', '')
 
     @classmethod
-    def summarize_ms_variants(cls, df):
+    def summarize_ms_variants(cls, df, config):
         df = cls.format_mutations(df)
         msi_summary = features.Features.create_empty_dataframe()
         if not df.empty:
             feature = Evaluator.supporting_variants
-            feature_displays = cls.format_feature_display(
-                df,
-                Evaluator.feature_display,
-                Evaluator.feature_type,
-                Evaluator.feature,
-                Evaluator.alt_type,
-                Evaluator.alt
-            )
+            feature_displays = cls.format_feature_display(df=df, config=config)
             feature_displays_list = cls.create_string_list(feature_displays)
 
-            msi_summary.loc[0, Evaluator.feature_type] = Evaluator.microsatellite_type
+            msi_summary.loc[0, Evaluator.feature_type] = config['feature_types']['microsatellite']
             msi_summary.loc[0, Evaluator.feature] = feature
             msi_summary.loc[0, Evaluator.alt] = feature_displays_list
             msi_summary.loc[0, Evaluator.almanac_bin] = 1
diff --git a/moalmanac/test/README.md b/moalmanac/test/README.md
new file mode 100644
index 0000000..e69de29
diff --git a/moalmanac/test/annotator_tests.py b/moalmanac/test/annotator_tests.py
index 286028b..7a8cb47 100644
--- a/moalmanac/test/annotator_tests.py
+++ b/moalmanac/test/annotator_tests.py
@@ -9,7 +9,8 @@
 from datasources import Preclinical as datasources_Preclinical
 from features import Features
 from investigator import SensitivityDictionary
-from config import CONFIG
+# from reader import Ini
+# from config import CONFIG
 
 
 class UnitTestAnnotator(unittest.TestCase):
@@ -58,7 +59,9 @@ def test_annotate(self):
         gene = ACMG.gene
         bin_name = ACMG.bin_name
         df = pd.DataFrame({gene: ['TP53', 'FOO', 'PMS2', 'TSC1', 'AR']})
-        dbs = Datasources.generate_db_dict(CONFIG)
+        dbs = {
+            'acmg_handle': '../datasources/acmg.secondaryfindings.v3.txt'
+        }
 
         annotated = ACMG.annotate(df, dbs)
         expected_result = pd.Series([1, 0, 1, 1, 0], name=bin_name)
@@ -327,7 +330,18 @@ class UnitTestPreclinicalEfficacy(unittest.TestCase):
         'pvalue_mww': [2.322E-12, 7.627E-17, 0.835]
     }
     df2 = pd.DataFrame(data_dictionary, index=[0, 1, 2])
-    dbs_preclinical = datasources_Preclinical.import_dbs()
+    dbs_dictionary = {
+        'almanac_gdsc_mappings': '../datasources/preclinical/formatted/almanac-gdsc-mappings.json',
+        'summary': '../datasources/preclinical/formatted/cell-lines-summary.txt',
+        'variants': '../datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt',
+        'copynumbers': '../datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt',
+        'fusions': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.txt',
+        'fusions1': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt',
+        'fusions2': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt',
+        'gdsc': '../datasources/preclinical/formatted/sanger.gdsc.txt',
+        'dictionary': '../datasources/preclinical/cell-lines.pkl'
+    }
+    dbs_preclinical = datasources_Preclinical.import_dbs(dbs_dictionary)
     efficacy_dictionary = SensitivityDictionary.create(dbs_preclinical, df1)
 
     def test_annotate(self):
@@ -352,7 +366,21 @@ def test_series_for_significance(self):
 
 class UnitTestPreclinicalMatchmaking(unittest.TestCase):
     def test_annotate_copy_numbers(self):
-        dbs = Datasources.generate_db_dict(CONFIG)
+        dbs = {
+            'almanac_handle': '../datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': '../datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': '../datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': '../datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': '../datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': '../datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': '../datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': '../datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': '../datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': '../datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': '../datasources/hereditary/hereditary.txt',
+            'oncotree_handle': '../datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': '../datasources/lawrence/lawrence_mapped_ontology.txt'
+        }
         feature = PreclinicalMatchmaking.feature
         feature_type = PreclinicalMatchmaking.feature_type
         alteration_type = PreclinicalMatchmaking.alteration_type
diff --git a/moalmanac/test/datasources_tests.py b/moalmanac/test/datasources_tests.py
index 8e5a8db..027a9b9 100644
--- a/moalmanac/test/datasources_tests.py
+++ b/moalmanac/test/datasources_tests.py
@@ -28,7 +28,18 @@ def test_import_dbs(self):
         fusions = Preclinical.fusions
         gdsc = Preclinical.gdsc
         mappings = Preclinical.mappings
-        dbs = Preclinical.import_dbs()
+        dbs_dictionary = {
+            'almanac_gdsc_mappings': '../datasources/preclinical/formatted/almanac-gdsc-mappings.json',
+            'summary': '../datasources/preclinical/formatted/cell-lines-summary.txt',
+            'variants': '../datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt',
+            'copynumbers': '../datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt',
+            'fusions': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.txt',
+            'fusions1': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt',
+            'fusions2': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt',
+            'gdsc': '../datasources/preclinical/formatted/sanger.gdsc.txt',
+            'dictionary': '../datasources/preclinical/cell-lines.pkl'
+        }
+        dbs = Preclinical.import_dbs(dbs_dictionary)
 
         for label in [summary, variants, cnas, fusions, gdsc]:
             self.assertEqual(type(dbs[label]), type(pd.DataFrame()))
diff --git a/moalmanac/test/evaluator_tests.py b/moalmanac/test/evaluator_tests.py
index afceb40..ce7f7ad 100644
--- a/moalmanac/test/evaluator_tests.py
+++ b/moalmanac/test/evaluator_tests.py
@@ -110,32 +110,32 @@ def test_remap_almanac_bins(self):
 
     def test_remove_low_allele_fraction_variants(self):
         feature_type = Evaluator.feature_type
-        mut_type = Evaluator.mut_type
-        germline_type = Evaluator.germline_type
+        mut_type = 'Somatic Variant'
+        germline_type = 'Germline Variant'
         tumor_f = Evaluator.tumor_f
-        min_af = Evaluator.min_af
+        min_af = 0.05
 
         low_af = float(min_af) - 0.01
         high_af = float(min_af) + 0.01
 
         df = pd.DataFrame({feature_type: [mut_type, mut_type, germline_type, germline_type, 'Aneuploidy'],
                            tumor_f: [low_af, high_af, low_af, high_af, np.nan]})
-        subsetted = Evaluator.remove_low_allele_fraction_variants(df)
+        subsetted = Evaluator.remove_low_allele_fraction_variants(df, minimum_allele_fraction=min_af)
         self.assertEqual([1, 3, 4], subsetted.index.tolist())
 
     def test_remove_low_coverage_variants(self):
         feature_type = Evaluator.feature_type
-        mut_type = Evaluator.mut_type
-        germline_type = Evaluator.germline_type
+        mut_type = 'Somatic Variant'
+        germline_type = 'Germline Variant'
         coverage = Evaluator.coverage
-        min_coverage = Evaluator.min_coverage
+        min_coverage = 15
 
         low_coverage = float(min_coverage) - 10
         high_coverage = float(min_coverage) + 10
 
         df = pd.DataFrame({feature_type: [mut_type, mut_type, germline_type, germline_type, 'Aneuploidy'],
                            coverage: [low_coverage, high_coverage, low_coverage, high_coverage, np.nan]})
-        subsetted = Evaluator.remove_low_coverage_variants(df)
+        subsetted = Evaluator.remove_low_coverage_variants(df, minimum_coverage=min_coverage)
         self.assertEqual([1, 3, 4], subsetted.index.tolist())
 
     def test_remove_benign_variants(self):
@@ -166,17 +166,17 @@ def test_create_string_list(self):
         self.assertEqual('the, quick, fox', Actionable.create_string_list(series))
 
     def test_display_aneuploidy(self):
-        feature = Evaluator.aneuploidy_type
+        feature = 'Aneuploidy'
         df = pd.DataFrame({feature: ['A', 'B', 'C']})
         idx = [0, 2]
-        series = Actionable.display_aneuploidy(df, idx, feature)
+        series = Actionable.display_aneuploidy(df, idx)
         self.assertEqual(['A', 'C'], series.tolist())
 
     def test_display_burden(self):
         alt = Evaluator.alt
         df = pd.DataFrame({alt: ["10 mutations per Mb", "20", "30 mutations per Mb"]})
         idx = [0, 2]
-        series = Actionable.display_burden(df, idx, alt)
+        series = Actionable.display_burden(df, idx)
         self.assertEqual(['10 mutations per Mb', '30 mutations per Mb'], series.tolist())
 
     def test_display_copynumber(self):
@@ -185,21 +185,21 @@ def test_display_copynumber(self):
         df = pd.DataFrame({feature: ['Foo', 'Bar', 'FooBar'],
                            alt_type: ['Amp', 'Amp', 'Del']})
         idx = [0, 2]
-        series = Actionable.display_copynumber(df, idx, feature, alt_type)
+        series = Actionable.display_copynumber(df, idx)
         self.assertEqual(['Foo Amp', 'FooBar Del'], series.tolist())
 
     def test_display_fusion(self):
         alt = Evaluator.alt
         df = pd.DataFrame({alt: ['Foo--Bar', 'Bar--Foo', 'FooBar--Alpha']})
         idx = [0, 2]
-        series = Actionable.display_fusion(df, idx, alt)
+        series = Actionable.display_fusion(df, idx)
         self.assertEqual(['Foo--Bar Fusion', 'FooBar--Alpha Fusion'], series.tolist())
 
     def test_display_microsatellite_stability(self):
         feature = Evaluator.feature
         df = pd.DataFrame({feature: ['A', 'B', 'C']})
         idx = [0, 2]
-        series = Actionable.display_microsatellite_stability(df, idx, feature)
+        series = Actionable.display_microsatellite_stability(df, idx)
         self.assertEqual(['A', 'C'], series.tolist())
 
     def test_display_microsatellite_variants(self):
@@ -208,7 +208,7 @@ def test_display_microsatellite_variants(self):
         df = pd.DataFrame({feature: ['Foo', '', 'Bar'],
                            alt: ['Amp', '', 'Del']})
         idx = [0, 2]
-        series = Actionable.display_microsatellite_variants(df, idx, feature, alt)
+        series = Actionable.display_microsatellite_variants(df, idx)
         self.assertEqual(['Foo: Amp', 'Bar: Del'], series.tolist())
 
     def test_display_signature(self):
@@ -216,7 +216,7 @@ def test_display_signature(self):
         alt = Evaluator.alt
         df = pd.DataFrame({feature: ['Signature 1', '', 'Signature 2'], alt: [0.523, '', 0.0145]})
         idx = [0, 2]
-        series = Actionable.display_signature(df, idx, feature, alt)
+        series = Actionable.display_signature(df, idx)
         self.assertEqual(['Signature 1 (52%)', 'Signature 2 (1%)'],
                          series.tolist())
 
@@ -228,7 +228,7 @@ def test_display_variant(self):
                            alt_type: ['Missense', 'Nonsense', 'Frameshift'],
                            alt: ['p.V600E', 'p.N500*', 'p.L151fs*']})
         idx = [0, 2]
-        series = Actionable.display_variant(df, idx, feature, alt_type, alt)
+        series = Actionable.display_variant(df, idx)
         self.assertEqual(['Foo p.V600E (Missense)', 'FooBar p.L151fs* (Frameshift)'], series.tolist())
 
     def test_format_variant_classification(self):

From b4520ee361711a6e7988bbdb42222713f77ebc8f Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Tue, 9 Jul 2024 21:23:04 -0400
Subject: [PATCH 03/19] Regression tests match

---
 moalmanac/annotator.py              |  4 ++-
 moalmanac/config.ini                | 10 +++----
 moalmanac/features.py               |  5 ----
 moalmanac/investigator.py           |  2 +-
 moalmanac/test/datasources_tests.py | 18 ++++++-------
 moalmanac/test/evaluator_tests.py   |  2 +-
 moalmanac/test/features_tests.py    | 41 ++++++++++++++++++++++-------
 7 files changed, 51 insertions(+), 31 deletions(-)

diff --git a/moalmanac/annotator.py b/moalmanac/annotator.py
index 8bad42b..b7b22e4 100644
--- a/moalmanac/annotator.py
+++ b/moalmanac/annotator.py
@@ -1015,6 +1015,8 @@ def annotate(cls, df, dbs, config):
             ds_columns=[cls.chr, cls.start, cls.ref, cls.alt, cls.af],
             variant_biomarker_types=[config['feature_types']['mut'], config['feature_types']['germline']]
         )
+        print(df_annotated['exac_af'].head())
+        print(df_annotated['exac_af'].isnull().value_counts())
         common_allele_frequency_threshold=config['exac']['exac_common_af_threshold']
         df_annotated[cls.bin_name] = cls.annotate_common_af(
             series_exac_af=df_annotated[cls.af],
@@ -1087,7 +1089,7 @@ def annotate(cls, df, dbs, config):
         df_annotated = ExAC.append_exac_af(
             df=df_dropped,
             ds=ds,
-            ds_columns=[cls.chr, cls.start, cls.ref, cls.alt, cls.af],
+            ds_columns=cls.ds_columns,
             variant_biomarker_types=[config['feature_types']['mut'], config['feature_types']['germline']]
         )
         common_allele_frequency_threshold = config['exac']['exac_common_af_threshold']
diff --git a/moalmanac/config.ini b/moalmanac/config.ini
index a22562b..9e21e3c 100644
--- a/moalmanac/config.ini
+++ b/moalmanac/config.ini
@@ -1,11 +1,11 @@
 [function_toggle]
 ; Use this section to enable or disable functions performed by MOAlmanac by writing either 'on' or 'off'
-calculate_model_similarity = off
-calculate_preclinical_efficacy = off
+calculate_model_similarity = on
+calculate_preclinical_efficacy = on
 generate_actionability_report = on
-include_model_similarity_in_actionability_report = off
-include_preclinical_efficacy_in_actionability_report = off
-plot_preclinical_efficacy = off
+include_model_similarity_in_actionability_report = on
+include_preclinical_efficacy_in_actionability_report = on
+plot_preclinical_efficacy = on
 
 [versions]
 interpreter = 0.6.0
diff --git a/moalmanac/features.py b/moalmanac/features.py
index 164068a..b0195e2 100644
--- a/moalmanac/features.py
+++ b/moalmanac/features.py
@@ -230,11 +230,6 @@ def import_feature(cls, handle, patient, variants, dbs, config):
 
 
 class CopyNumber:
-    #config = CONFIG['seg']
-    #amplification = config['amp']
-    #deletion = config['del']
-    #feature_type = CONFIG['feature_types']['cna']
-
     @staticmethod
     def format_cn_gene(series):
         new_series = series.str.split(' ', expand=True).loc[:, 0]
diff --git a/moalmanac/investigator.py b/moalmanac/investigator.py
index 0c2d6fc..9b3c762 100644
--- a/moalmanac/investigator.py
+++ b/moalmanac/investigator.py
@@ -129,7 +129,7 @@ def create(cls, dbs, df_actionable, config):
             feature_display = df_actionable.loc[index, cls.feature_display]
             index_dict = {}
             if mapped:
-                feature_dictionary = cls.split_samples_by_wt_mut(df_actionable.loc[index, :], dbs, samples)
+                feature_dictionary = cls.split_samples_by_wt_mut(df_actionable.loc[index, :], dbs, samples, config)
                 features = list(feature_dictionary)
                 for therapy in mapped:
                     therapy_dict = {}
diff --git a/moalmanac/test/datasources_tests.py b/moalmanac/test/datasources_tests.py
index 027a9b9..82f0cd4 100644
--- a/moalmanac/test/datasources_tests.py
+++ b/moalmanac/test/datasources_tests.py
@@ -29,15 +29,15 @@ def test_import_dbs(self):
         gdsc = Preclinical.gdsc
         mappings = Preclinical.mappings
         dbs_dictionary = {
-            'almanac_gdsc_mappings': '../datasources/preclinical/formatted/almanac-gdsc-mappings.json',
-            'summary': '../datasources/preclinical/formatted/cell-lines-summary.txt',
-            'variants': '../datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt',
-            'copynumbers': '../datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt',
-            'fusions': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.txt',
-            'fusions1': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt',
-            'fusions2': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt',
-            'gdsc': '../datasources/preclinical/formatted/sanger.gdsc.txt',
-            'dictionary': '../datasources/preclinical/cell-lines.pkl'
+            'almanac_gdsc_mappings': 'datasources/preclinical/formatted/almanac-gdsc-mappings.json',
+            'summary': 'datasources/preclinical/formatted/cell-lines.summary.txt',
+            'variants': 'datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt',
+            'copynumbers': 'datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt',
+            'fusions': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.txt',
+            'fusions1': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt',
+            'fusions2': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt',
+            'gdsc': 'datasources/preclinical/formatted/sanger.gdsc.txt',
+            'dictionary': 'datasources/preclinical/cell-lines.pkl'
         }
         dbs = Preclinical.import_dbs(dbs_dictionary)
 
diff --git a/moalmanac/test/evaluator_tests.py b/moalmanac/test/evaluator_tests.py
index ce7f7ad..4c27b4b 100644
--- a/moalmanac/test/evaluator_tests.py
+++ b/moalmanac/test/evaluator_tests.py
@@ -166,7 +166,7 @@ def test_create_string_list(self):
         self.assertEqual('the, quick, fox', Actionable.create_string_list(series))
 
     def test_display_aneuploidy(self):
-        feature = 'Aneuploidy'
+        feature = Evaluator.feature
         df = pd.DataFrame({feature: ['A', 'B', 'C']})
         idx = [0, 2]
         series = Actionable.display_aneuploidy(df, idx)
diff --git a/moalmanac/test/features_tests.py b/moalmanac/test/features_tests.py
index 4143d43..a591582 100644
--- a/moalmanac/test/features_tests.py
+++ b/moalmanac/test/features_tests.py
@@ -2,8 +2,8 @@
 import pandas as pd
 
 from moalmanac import features
-from config import CONFIG, COLNAMES
-
+from config import COLNAMES
+from reader import Ini
 
 class UnitTestFeatures(unittest.TestCase):
     def test_annotate_feature_type(self):
@@ -69,19 +69,30 @@ def test_create_column_map(self):
         self.assertEqual(column_map['call'], features.Features.alt_type)
 
     def test_filter_calls(self):
+        amp_string = 'Amplification'
+        del_string = 'Deletion'
         tmp = pd.Series(['Amplification', 'Deletion', '', 'Deletion'])
-        idx = features.CopyNumberCalled.filter_calls(tmp)
+        idx = features.CopyNumberCalled.filter_calls(series=tmp, amp_string=amp_string, del_string=del_string)
         self.assertEqual([0, 1, 3], idx[idx].index.tolist())
         self.assertEqual([2], idx[~idx].index.tolist())
 
 
 class UnitTestCopyNumberTotal(unittest.TestCase):
     def test_annotate_amp_del(self):
+        amp_string = 'Amplification'
+        del_string = 'Deletion'
         index = pd.Index([0, 1, 2])
         index_amp = pd.Index([0])
         index_del = pd.Index([2])
-        expected = [features.CopyNumberTotal.amplification, '', features.CopyNumberTotal.deletion]
-        self.assertEqual(expected, features.CopyNumberTotal.annotate_amp_del(index, index_amp, index_del).tolist())
+        expected = [amp_string, '', del_string]
+        result = features.CopyNumberTotal.annotate_amp_del(
+            idx=index,
+            idx_amp=index_amp,
+            idx_del=index_del,
+            amp_string=amp_string,
+            del_string=del_string
+        )
+        self.assertEqual(expected, result.tolist())
 
     def test_create_column_map(self):
         column_map = features.CopyNumberTotal.create_column_map()
@@ -102,12 +113,20 @@ def test_drop_duplicate_genes(self):
             self.assertEqual(True, idx in features.CopyNumberTotal.drop_duplicate_genes(df))
 
     def test_filter_by_threshold(self):
+        amp_string = 'Amplification'
+        del_string = 'Deletion'
         values = pd.Series(range(1, 101))
         df = pd.DataFrame({features.Features.feature: values,
                            features.Features.chr: values,
                            features.Features.start: values,
                            features.Features.segment_mean: values})
-        accept, reject = features.CopyNumberTotal.filter_by_threshold(df, 97.5, 2.5)
+        accept, reject = features.CopyNumberTotal.filter_by_threshold(
+            df=df,
+            percentile_amp=97.5,
+            percentile_del=2.5,
+            amp_string=amp_string,
+            del_string=del_string
+        )
         expected = pd.Index([0, 1, 2, 97, 98, 99])
         for idx in expected:
             self.assertEqual(True, idx in accept.index)
@@ -157,13 +176,17 @@ def test_subset_significant_signatures(self):
 
 class UnitTestFusion(unittest.TestCase):
     def test_create_column_map(self):
-        column_map = features.Fusion.create_colmap()
+        config = Ini.read('config.ini', extended_interpolation=False, convert_to_dictionary=False)
+        column_map = features.Fusion.create_colmap(config)
+        leftbreakpoint = 'leftbreakpoint'
+        rightbreakpoint = 'rightbreakpoint'
+
         values = list(column_map.values())
         self.assertEqual(4, len(column_map))
         self.assertEqual(features.Features.feature, values[0])
         self.assertEqual(features.Features.spanningfrags, values[1])
-        self.assertEqual(features.Fusion.leftbreakpoint, values[2])
-        self.assertEqual(features.Fusion.rightbreakpoint, values[3])
+        self.assertEqual(leftbreakpoint, values[2])
+        self.assertEqual(rightbreakpoint, values[3])
 
     def test_filter_by_spanning_fragment_count(self):
         series = pd.Series([4, 5, 6])

From 9e6766864b2a39b17d5b5184ce410c6f4301db51 Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Tue, 9 Jul 2024 21:48:08 -0400
Subject: [PATCH 04/19] 1 unit test still failing

---
 moalmanac/moalmanac.py               |  20 ++-
 moalmanac/test/annotator_tests.py    | 231 ++++++++++++++++++++-------
 moalmanac/test/investigator_tests.py |  57 +++++--
 3 files changed, 227 insertions(+), 81 deletions(-)

diff --git a/moalmanac/moalmanac.py b/moalmanac/moalmanac.py
index c47ff4c..05ff8c3 100644
--- a/moalmanac/moalmanac.py
+++ b/moalmanac/moalmanac.py
@@ -213,8 +213,6 @@ def main(patient, inputs, output_folder, config, dbs, dbs_preclinical=None):
     function_toggle = config['function_toggle']
 
     efficacy_summary = investigator.SummaryDataFrame.create_empty_dataframe()
-    # efficacy_dictionary = {}
-    # cell_lines_dictionary = {}
     preclinical_efficacy_on = function_toggle.getboolean('calculate_preclinical_efficacy')
 
     # The input argument --disable_matchmaking will be removed in the next non-backwards compatible release
@@ -247,7 +245,13 @@ def main(patient, inputs, output_folder, config, dbs, dbs_preclinical=None):
                 )
 
             if model_similarity_on:
-                similarity_results = matchmaker.Matchmaker.compare(dbs, dbs_preclinical, evaluated_somatic, string_id)
+                similarity_results = matchmaker.Matchmaker.compare(
+                    dbs=dbs,
+                    dbs_preclinical=dbs_preclinical,
+                    somatic=evaluated_somatic,
+                    case_sample_id=string_id,
+                    config=config
+                )
                 similarity_summary = matchmaker.Report.create_report_dictionary(
                     similarity_results,
                     cell_lines_dictionary
@@ -271,11 +275,11 @@ def main(patient, inputs, output_folder, config, dbs, dbs_preclinical=None):
 
         include_similarity = function_toggle.getboolean('include_model_similarity_in_actionability_report')
         reporter.Reporter.generate_actionability_report(
-            actionable = actionable,
-            report_dictionary = report_dictionary,
-            config = config,
-            similarity = similarity_summary if include_similarity else None,
-            output_directory = output_folder
+            actionable=actionable,
+            report_dictionary=report_dictionary,
+            config=config,
+            similarity=similarity_summary if include_similarity else None,
+            output_directory=output_folder
         )
 
 
diff --git a/moalmanac/test/annotator_tests.py b/moalmanac/test/annotator_tests.py
index 7a8cb47..b512eeb 100644
--- a/moalmanac/test/annotator_tests.py
+++ b/moalmanac/test/annotator_tests.py
@@ -60,7 +60,7 @@ def test_annotate(self):
         bin_name = ACMG.bin_name
         df = pd.DataFrame({gene: ['TP53', 'FOO', 'PMS2', 'TSC1', 'AR']})
         dbs = {
-            'acmg_handle': '../datasources/acmg.secondaryfindings.v3.txt'
+            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt'
         }
 
         annotated = ACMG.annotate(df, dbs)
@@ -164,7 +164,7 @@ def test_update_series_with_best_match(self):
                     "doi": "10.1126/science.1062538", "pmid": "11423618", "nct": '',
                     "last_updated": "6/13/19",
                     "feature_display": "ABL1 p.T315I (Missense)", "predictive_implication_map": 1.0}, {}]
-        somatic_variant = Almanac.somatic_variant
+        somatic_variant = 'Somatic Variant'
         series = pd.Series(dtype=object)
 
         for columns in [Almanac.column_map_sensitive, Almanac.column_map_resistance, Almanac.column_map_prognostic]:
@@ -197,9 +197,9 @@ def test_append_exac_af(self):
         alt = ExAC.alt
         af = ExAC.af
         feature_type = Features.feature_type
-        somatic = CONFIG['feature_types']['mut']
-        germline = CONFIG['feature_types']['germline']
-        cn = CONFIG['feature_types']['cna']
+        somatic = 'Somatic Variant'
+        germline = 'Germline Variant'
+        cn = 'Copy Number'
 
         df = pd.DataFrame({chr: [1, 2, 3, 1],
                            start: [100, 101, 103, 100],
@@ -212,13 +212,18 @@ def test_append_exac_af(self):
                              ref: ["C", "A", "T"],
                              alt: ["G", "G", "G"],
                              af: [1, 0.5, 0.001]})
-        result = ExAC.append_exac_af(df, exac, [chr, start, ref, alt, af])
+        biomarker_types = [somatic, germline]
+        result = ExAC.append_exac_af(
+            df=df,
+            ds=exac,
+            ds_columns=[chr, start, ref, alt, af],
+            variant_biomarker_types=biomarker_types)
         self.assertEqual([1, 0, 0, 0], result[af].tolist())
 
     def test_annotate_common_af(self):
-        exac_common_threshold = ExAC.exac_common_threshold
+        exac_common_threshold = 0.001
         series = pd.Series([float(exac_common_threshold) - 0.01, float(exac_common_threshold) + 0.01])
-        result = ExAC.annotate_common_af(series)
+        result = ExAC.annotate_common_af(series, threshold=exac_common_threshold)
         self.assertEqual(0.0, result.loc[0])
         self.assertEqual(1.0, result.loc[1])
 
@@ -248,7 +253,11 @@ class UnitTestValidation(unittest.TestCase):
     })
 
     def test_append_validation(self):
-        result = OverlapValidation.append_validation(UnitTestValidation.dataframe1, UnitTestValidation.dataframe2)
+        result = OverlapValidation.append_validation(
+            UnitTestValidation.dataframe1,
+            UnitTestValidation.dataframe2,
+            biomarker_type='Somatic Variant'
+        )
         result = result.fillna('')
         self.assertEqual(UnitTestValidation.dataframe1['feature'].tolist(), result['feature'].tolist())
         self.assertEqual([0.20, '', 0.66, 0.0], result['validation_tumor_f'].tolist())
@@ -287,7 +296,7 @@ def test_get_mutation_index(self):
         dataframe = pd.DataFrame(['Somatic Variant', 'bar', 'foo'], columns=[OverlapValidation.feature_type])
         solution = ['Somatic Variant']
         solution_index = pd.Index([0])
-        result = OverlapValidation.get_mutation_index(dataframe)
+        result = OverlapValidation.get_mutation_index(dataframe, biomarker_type='Somatic Variant')
         self.assertEqual(solution[0], dataframe.loc[result[0], OverlapValidation.feature_type])
         self.assertEqual(solution_index[0], result[0])
 
@@ -331,18 +340,25 @@ class UnitTestPreclinicalEfficacy(unittest.TestCase):
     }
     df2 = pd.DataFrame(data_dictionary, index=[0, 1, 2])
     dbs_dictionary = {
-        'almanac_gdsc_mappings': '../datasources/preclinical/formatted/almanac-gdsc-mappings.json',
-        'summary': '../datasources/preclinical/formatted/cell-lines-summary.txt',
-        'variants': '../datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt',
-        'copynumbers': '../datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt',
-        'fusions': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.txt',
-        'fusions1': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt',
-        'fusions2': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt',
-        'gdsc': '../datasources/preclinical/formatted/sanger.gdsc.txt',
-        'dictionary': '../datasources/preclinical/cell-lines.pkl'
+        'almanac_gdsc_mappings': 'datasources/preclinical/formatted/almanac-gdsc-mappings.json',
+        'summary': 'datasources/preclinical/formatted/cell-lines.summary.txt',
+        'variants': 'datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt',
+        'copynumbers': 'datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt',
+        'fusions': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.txt',
+        'fusions1': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt',
+        'fusions2': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt',
+        'gdsc': 'datasources/preclinical/formatted/sanger.gdsc.txt',
+        'dictionary': 'datasources/preclinical/cell-lines.pkl'
+    }
+    config = {
+        'feature_types': {
+            'mut': 'Somatic Variant',
+            'cna': 'Copy Number',
+            'fusion': 'Rearrangement'
+        }
     }
     dbs_preclinical = datasources_Preclinical.import_dbs(dbs_dictionary)
-    efficacy_dictionary = SensitivityDictionary.create(dbs_preclinical, df1)
+    efficacy_dictionary = SensitivityDictionary.create(dbs_preclinical, df1, config=config)
 
     def test_annotate(self):
         result = PreclinicalEfficacy.annotate(
@@ -367,26 +383,26 @@ def test_series_for_significance(self):
 class UnitTestPreclinicalMatchmaking(unittest.TestCase):
     def test_annotate_copy_numbers(self):
         dbs = {
-            'almanac_handle': '../datasources/moalmanac/molecular-oncology-almanac.json',
-            'cancerhotspots_handle': '../datasources/cancerhotspots/hotspots_v2.txt',
-            '3dcancerhotspots_handle': '../datasources/cancerhotspots/hotspots3d.txt',
-            'cgc_handle': '../datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
-            'cosmic_handle': '../datasources/cosmic/CosmicMutantExport_v97.lite.txt',
-            'gsea_pathways_handle': '../datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
-            'gsea_modules_handle': '../datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
-            'exac_handle': '../datasources/exac/exac.expanded.r1.txt',
-            'acmg_handle': '../datasources/acmg/acmg.secondaryfindings.v3.txt',
-            'clinvar_handle': '../datasources/clinvar/variant_summary.lite.txt',
-            'hereditary_handle': '../datasources/hereditary/hereditary.txt',
-            'oncotree_handle': '../datasources/oncotree/oncotree.2023-03-09.txt',
-            'lawrence_handle': '../datasources/lawrence/lawrence_mapped_ontology.txt'
+            'almanac_handle': 'datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': 'datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': 'datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': 'datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': 'datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': 'datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': 'datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': 'datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': 'datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': 'datasources/hereditary/hereditary.txt',
+            'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
         }
         feature = PreclinicalMatchmaking.feature
         feature_type = PreclinicalMatchmaking.feature_type
         alteration_type = PreclinicalMatchmaking.alteration_type
         alteration = PreclinicalMatchmaking.alteration
 
-        copy_number = PreclinicalMatchmaking.copy_number
+        copy_number = 'Copy Number'
 
         df = pd.DataFrame({
             feature: ['CDKN2A', 'CDKN2A', 'KRAS'],
@@ -394,7 +410,7 @@ def test_annotate_copy_numbers(self):
         })
         df[feature_type] = copy_number
         df[alteration] = pd.NA
-        result = PreclinicalMatchmaking.annotate_copy_numbers(df, dbs)
+        result = PreclinicalMatchmaking.annotate_copy_numbers(df, dbs, biomarker_type_string=copy_number)
 
         expected_cdkn2a_del = {
             'feature_match_1': 1,
@@ -448,12 +464,26 @@ def test_annotate_copy_numbers(self):
             self.assertEqual(result.loc[2, key], expected_kras_amp[key])
 
     def test_annotate_fusions(self):
-        dbs = Datasources.generate_db_dict(CONFIG)
+        dbs = {
+            'almanac_handle': 'datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': 'datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': 'datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': 'datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': 'datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': 'datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': 'datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': 'datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': 'datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': 'datasources/hereditary/hereditary.txt',
+            'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
+        }
         feature = PreclinicalMatchmaking.feature
         feature_type = PreclinicalMatchmaking.feature_type
         alteration_type = PreclinicalMatchmaking.alteration_type
         partner = PreclinicalMatchmaking.partner
-        fusion = PreclinicalMatchmaking.fusion
+        fusion = 'Rearrangement'
         model_id = PreclinicalMatchmaking.model_id
 
         df = pd.DataFrame({
@@ -463,7 +493,7 @@ def test_annotate_fusions(self):
         df[alteration_type] = 'Fusion'
         df[model_id] = 'case'
 
-        result, group1, group2 = PreclinicalMatchmaking.annotate_fusions(df, dbs)
+        result, group1, group2 = PreclinicalMatchmaking.annotate_fusions(df, dbs, biomarker_type_string=fusion)
 
         expected_index_0 = {
             'feature_match_1': 1,
@@ -696,12 +726,26 @@ def test_annotate_fusions(self):
         self.assertEqual(group2.loc[1, 'gsea_modules_bin'], expected_index_1_group2['gsea_modules_bin'])
 
     def test_annotate_fusions_matching(self):
-        dbs = Datasources.generate_db_dict(CONFIG)
+        dbs = {
+            'almanac_handle': 'datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': 'datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': 'datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': 'datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': 'datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': 'datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': 'datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': 'datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': 'datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': 'datasources/hereditary/hereditary.txt',
+            'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
+        }
         feature = PreclinicalMatchmaking.feature
         feature_type = PreclinicalMatchmaking.feature_type
         alteration_type = PreclinicalMatchmaking.alteration_type
         partner = PreclinicalMatchmaking.partner
-        fusion = PreclinicalMatchmaking.fusion
+        fusion = 'Fusion'
         model_id = PreclinicalMatchmaking.model_id
         evidence_map_str = PreclinicalMatchmaking.evidence_map_str
         merged = PreclinicalMatchmaking.merged
@@ -743,13 +787,27 @@ def test_annotate_fusions_matching(self):
                 self.assertEqual(result.loc[index, key], value)
 
     def test_annotate_somatic_variants(self):
-        dbs = Datasources.generate_db_dict(CONFIG)
+        dbs = {
+            'almanac_handle': 'datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': 'datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': 'datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': 'datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': 'datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': 'datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': 'datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': 'datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': 'datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': 'datasources/hereditary/hereditary.txt',
+            'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
+        }
         feature = PreclinicalMatchmaking.feature
         feature_type = PreclinicalMatchmaking.feature_type
         alteration_type = PreclinicalMatchmaking.alteration_type
         alteration = PreclinicalMatchmaking.alteration
 
-        somatic_variant = PreclinicalMatchmaking.somatic_variant
+        somatic_variant = 'Somatic Variant'
 
         df = pd.DataFrame({
             feature: ['BRAF', 'BRAF', 'IDH1', 'CDKN2A'],
@@ -758,7 +816,7 @@ def test_annotate_somatic_variants(self):
         })
         df[feature_type] = somatic_variant
 
-        result = PreclinicalMatchmaking.annotate_somatic_variants(df, dbs)
+        result = PreclinicalMatchmaking.annotate_somatic_variants(df, dbs, biomarker_type_string=somatic_variant)
 
         expected_braf_1 = {
             'feature_match_1': 1,
@@ -844,11 +902,25 @@ def test_annotate_match_1(self):
     def test_annotate_match_2(self):
         match_2 = PreclinicalMatchmaking.match_2
 
-        dbs = Datasources.generate_db_dict(CONFIG)
+        dbs = {
+            'almanac_handle': 'datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': 'datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': 'datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': 'datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': 'datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': 'datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': 'datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': 'datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': 'datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': 'datasources/hereditary/hereditary.txt',
+            'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
+        }
         almanac = datasource_Almanac.import_ds(dbs)
-        copy_number = PreclinicalMatchmaking.copy_number
-        fusion = PreclinicalMatchmaking.fusion
-        somatic_variant = PreclinicalMatchmaking.somatic_variant
+        copy_number = 'Copy Number'
+        fusion = 'Rearrangement'
+        somatic_variant = 'Somatic Variant'
         
         feature = PreclinicalMatchmaking.feature
         alteration_type = PreclinicalMatchmaking.alteration_type
@@ -908,11 +980,25 @@ def test_annotate_match_2(self):
     def test_annotate_match_3(self):
         match_3 = PreclinicalMatchmaking.match_3
 
-        dbs = Datasources.generate_db_dict(CONFIG)
+        dbs = {
+            'almanac_handle': 'datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': 'datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': 'datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': 'datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': 'datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': 'datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': 'datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': 'datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': 'datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': 'datasources/hereditary/hereditary.txt',
+            'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
+        }
         almanac = datasource_Almanac.import_ds(dbs)
-        copy_number = PreclinicalMatchmaking.copy_number
-        fusion = PreclinicalMatchmaking.fusion
-        somatic_variant = PreclinicalMatchmaking.somatic_variant
+        copy_number = 'Copy Number'
+        fusion = 'Rearrangement'
+        somatic_variant = 'Somatic Variant'
 
         feature = PreclinicalMatchmaking.feature
         alteration_type = PreclinicalMatchmaking.alteration_type
@@ -981,10 +1067,25 @@ def test_annotate_match_3(self):
     def test_annotate_match_4(self):
         match_4 = PreclinicalMatchmaking.match_4
 
-        dbs = Datasources.generate_db_dict(CONFIG)
+        dbs = {
+            'almanac_handle': 'datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': 'datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': 'datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': 'datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': 'datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': 'datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': 'datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': 'datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': 'datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': 'datasources/hereditary/hereditary.txt',
+            'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
+        }
         almanac = datasource_Almanac.import_ds(dbs)
-        fusion = PreclinicalMatchmaking.fusion
-        somatic_variant = PreclinicalMatchmaking.somatic_variant
+        copy_number = 'Copy Number'
+        fusion = 'Rearrangement'
+        somatic_variant = 'Somatic Variant'
 
         feature = PreclinicalMatchmaking.feature
         alteration_type = PreclinicalMatchmaking.alteration_type
@@ -1030,11 +1131,25 @@ def test_annotate_match_4(self):
         self.assertEqual(result.loc[3, match_4], result.loc[3, 'expectation1'])
 
     def test_format_db(self):
-        dbs = Datasources.generate_db_dict(CONFIG)
+        dbs = {
+            'almanac_handle': 'datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': 'datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': 'datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': 'datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': 'datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': 'datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': 'datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': 'datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': 'datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': 'datasources/hereditary/hereditary.txt',
+            'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
+        }
         almanac = datasource_Almanac.import_ds(dbs)
-        copy_number = PreclinicalMatchmaking.copy_number
-        fusion = PreclinicalMatchmaking.fusion
-        somatic_variant = PreclinicalMatchmaking.somatic_variant
+        copy_number = 'Copy Number'
+        fusion = 'Rearrangement'
+        somatic_variant = 'Somatic Variant'
 
         feature = PreclinicalMatchmaking.feature
         alteration_type = PreclinicalMatchmaking.alteration_type
diff --git a/moalmanac/test/investigator_tests.py b/moalmanac/test/investigator_tests.py
index dc636db..f8a9a50 100644
--- a/moalmanac/test/investigator_tests.py
+++ b/moalmanac/test/investigator_tests.py
@@ -4,7 +4,7 @@
 
 from datasources import Preclinical
 from investigator import Investigator, SensitivityDictionary
-
+from reader import Ini
 
 class UnitTestSensitivityDictionary(unittest.TestCase):
     def test_calculate_series_exp(self):
@@ -54,7 +54,18 @@ def test_calculate_mann_whitney_u(self):
         self.assertTrue(math.isnan(statistic))
 
     def test_create(self):
-        dbs = Preclinical.import_dbs()
+        dbs_paths = {
+            'almanac_gdsc_mappings': 'datasources/preclinical/formatted/almanac-gdsc-mappings.json',
+            'summary': 'datasources/preclinical/formatted/cell-lines.summary.txt',
+            'variants': 'datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt',
+            'copynumbers': 'datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt',
+            'fusions': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.txt',
+            'fusions1': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt',
+            'fusions2': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt',
+            'gdsc': 'datasources/preclinical/formatted/sanger.gdsc.txt',
+            'dictionary': 'datasources/preclinical/cell-lines.pkl'
+        }
+        dbs = Preclinical.import_dbs(dbs_paths)
         data_dictionary = {
             'feature_type': ['Somatic Variant'],
             'feature': ['BRAF'],
@@ -67,7 +78,8 @@ def test_create(self):
         actionable = pd.DataFrame(data_dictionary, index=[0])
         expected_dabrafenib = '2.322e-12'
         expected_trametinib = '2.344e-09'
-        result = SensitivityDictionary.create(dbs, actionable)
+        config = Ini.read('config.ini', extended_interpolation=False, convert_to_dictionary=False)
+        result = SensitivityDictionary.create(dbs, actionable, config)
         self.assertEqual(result[0]['Dabrafenib']['BRAF']['comparison']['pvalue_mww'], expected_dabrafenib)
         self.assertEqual(result[0]['Trametinib']['BRAF']['comparison']['pvalue_mww'], expected_trametinib)
 
@@ -104,17 +116,27 @@ def test_generate_feature_strings(self):
         self.assertEqual(result, ['CDKN2A Copy Number Deletion', 'CDKN2A Copy Number', 'CDKN2A'])
 
     def test_select_split_function(self):
-        var = Investigator.feature_types['variant']
-        cn = Investigator.feature_types['copy_number']
-        fusion = Investigator.feature_types['fusion']
+        config = Ini.read('config.ini', extended_interpolation=False, convert_to_dictionary=False)
+        var_string = config['feature_types']['mut']
+        cn_string = config['feature_types']['cna']
+        fusion_string = config['feature_types']['fusion']
 
         var_function = SensitivityDictionary.split_samples_for_variants
         cn_function = SensitivityDictionary.split_samples_for_copy_numbers
         fusion_function = SensitivityDictionary.split_samples_for_fusions
 
-        self.assertEqual(SensitivityDictionary.select_split_function(var), var_function)
-        self.assertEqual(SensitivityDictionary.select_split_function(cn), cn_function)
-        self.assertEqual(SensitivityDictionary.select_split_function(fusion), fusion_function)
+        self.assertEqual(
+            SensitivityDictionary.select_split_function(var_string, var_string, cn_string, fusion_string),
+            var_function
+        )
+        self.assertEqual(
+            SensitivityDictionary.select_split_function(cn_string, var_string, cn_string, fusion_string),
+            cn_function
+        )
+        self.assertEqual(
+            SensitivityDictionary.select_split_function(fusion_string, var_string, cn_string, fusion_string),
+            fusion_function
+        )
 
     def test_split_samples_by_wt_mut(self):
         gene = SensitivityDictionary.gene
@@ -161,9 +183,11 @@ def test_split_samples_by_wt_mut(self):
             fusions: db_fusions
         }
 
-        results_variants = SensitivityDictionary.split_samples_by_wt_mut(data.loc[0, :], dbs, samples)
-        results_cnas = SensitivityDictionary.split_samples_by_wt_mut(data.loc[1, :], dbs, samples)
-        results_fusions = SensitivityDictionary.split_samples_by_wt_mut(data.loc[2, :], dbs, samples)
+        config = Ini.read('config.ini', extended_interpolation=False, convert_to_dictionary=False)
+
+        results_variants = SensitivityDictionary.split_samples_by_wt_mut(data.loc[0, :], dbs, samples, config)
+        results_cnas = SensitivityDictionary.split_samples_by_wt_mut(data.loc[1, :], dbs, samples, config)
+        results_fusions = SensitivityDictionary.split_samples_by_wt_mut(data.loc[2, :], dbs, samples, config)
         self.assertEqual(results_variants['BRAF']['samples'][0], ['B', 'C', 'D'])
         self.assertEqual(results_variants['BRAF']['samples'][1], ['A', 'E'])
         self.assertEqual(results_variants['BRAF Somatic Variant']['samples'][0], ['E'])
@@ -217,7 +241,8 @@ def test_split_samples_for_copy_numbers(self):
             cnas: db_cnas,
         }
 
-        results_cnas = SensitivityDictionary.split_samples_by_wt_mut(data.loc[1, :], dbs, samples)
+        config = Ini.read('config.ini', extended_interpolation=False, convert_to_dictionary=False)
+        results_cnas = SensitivityDictionary.split_samples_by_wt_mut(data.loc[1, :], dbs, samples, config)
         self.assertEqual(results_cnas['CDKN2A']['samples'][0], ['A', 'C', 'D', 'E'])
         self.assertEqual(results_cnas['CDKN2A']['samples'][1], ['B'])
         self.assertEqual(results_cnas['CDKN2A Copy Number']['samples'][0], ['A', 'D', 'E'])
@@ -253,7 +278,8 @@ def test_split_samples_for_fusions(self):
             fusions: db_fusions
         }
 
-        results_fusions = SensitivityDictionary.split_samples_by_wt_mut(data.loc[2, :], dbs, samples)
+        config = Ini.read('config.ini', extended_interpolation=False, convert_to_dictionary=False)
+        results_fusions = SensitivityDictionary.split_samples_by_wt_mut(data.loc[2, :], dbs, samples, config)
         self.assertEqual(results_fusions['TMPRSS2']['samples'][0], ['A', 'B', 'E'])
         self.assertEqual(results_fusions['TMPRSS2']['samples'][1], ['C', 'D'])
         self.assertEqual(results_fusions['ERG']['samples'][0], ['A', 'B', 'D', 'E'])
@@ -294,7 +320,8 @@ def test_split_samples_for_variants(self):
             variants: db_variants
         }
 
-        results_variants = SensitivityDictionary.split_samples_by_wt_mut(data.loc[0, :], dbs, samples)
+        config = Ini.read('config.ini', extended_interpolation=False, convert_to_dictionary=False)
+        results_variants = SensitivityDictionary.split_samples_by_wt_mut(data.loc[0, :], dbs, samples, config)
         self.assertEqual(results_variants['BRAF']['samples'][0], ['B', 'C', 'D'])
         self.assertEqual(results_variants['BRAF']['samples'][1], ['A', 'E'])
         self.assertEqual(results_variants['BRAF Somatic Variant']['samples'][0], ['E'])

From 367374bffb81dfde1ee0660371f67dade27968c2 Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Tue, 9 Jul 2024 22:20:50 -0400
Subject: [PATCH 05/19] Annotator unit test resolved

---
 moalmanac/annotator.py            | 2 --
 moalmanac/test/annotator_tests.py | 3 ++-
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/moalmanac/annotator.py b/moalmanac/annotator.py
index b7b22e4..51b181e 100644
--- a/moalmanac/annotator.py
+++ b/moalmanac/annotator.py
@@ -1015,8 +1015,6 @@ def annotate(cls, df, dbs, config):
             ds_columns=[cls.chr, cls.start, cls.ref, cls.alt, cls.af],
             variant_biomarker_types=[config['feature_types']['mut'], config['feature_types']['germline']]
         )
-        print(df_annotated['exac_af'].head())
-        print(df_annotated['exac_af'].isnull().value_counts())
         common_allele_frequency_threshold=config['exac']['exac_common_af_threshold']
         df_annotated[cls.bin_name] = cls.annotate_common_af(
             series_exac_af=df_annotated[cls.af],
diff --git a/moalmanac/test/annotator_tests.py b/moalmanac/test/annotator_tests.py
index b512eeb..fd4afce 100644
--- a/moalmanac/test/annotator_tests.py
+++ b/moalmanac/test/annotator_tests.py
@@ -741,11 +741,12 @@ def test_annotate_fusions_matching(self):
             'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
             'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
         }
+
         feature = PreclinicalMatchmaking.feature
         feature_type = PreclinicalMatchmaking.feature_type
         alteration_type = PreclinicalMatchmaking.alteration_type
         partner = PreclinicalMatchmaking.partner
-        fusion = 'Fusion'
+        fusion = 'Rearrangement'
         model_id = PreclinicalMatchmaking.model_id
         evidence_map_str = PreclinicalMatchmaking.evidence_map_str
         merged = PreclinicalMatchmaking.merged

From b7e3ff9973b6dc05c9976927bd5a8e5f3bf125e3 Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Tue, 9 Jul 2024 22:52:45 -0400
Subject: [PATCH 06/19] Added time to default output folder from run_example

---
 moalmanac/annotation-databases.ini   |  27 ++--
 moalmanac/preclinical-databases.ini  |  19 +--
 moalmanac/run_example.py             |  11 +-
 moalmanac/test/annotator_tests.py    | 228 +++++++++++++--------------
 moalmanac/test/datasources_tests.py  |  18 +--
 moalmanac/test/investigator_tests.py |  18 +--
 6 files changed, 163 insertions(+), 158 deletions(-)

diff --git a/moalmanac/annotation-databases.ini b/moalmanac/annotation-databases.ini
index 65503a9..200f099 100644
--- a/moalmanac/annotation-databases.ini
+++ b/moalmanac/annotation-databases.ini
@@ -1,14 +1,15 @@
 [databases]
-almanac_handle = datasources/moalmanac/molecular-oncology-almanac.json
-cancerhotspots_handle = datasources/cancerhotspots/hotspots_v2.txt
-3dcancerhotspots_handle = datasources/cancerhotspots/hotspots3d.txt
-cgc_handle = datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv
-cosmic_handle = datasources/cosmic/CosmicMutantExport_v97.lite.txt
-gsea_pathways_handle = datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt
-gsea_modules_handle = datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt
-exac_handle = datasources/exac/exac.expanded.r1.txt
-acmg_handle = datasources/acmg/acmg.secondaryfindings.v3.txt
-clinvar_handle = datasources/clinvar/variant_summary.lite.txt
-hereditary_handle = datasources/hereditary/hereditary.txt
-oncotree_handle = datasources/oncotree/oncotree.2023-03-09.txt
-lawrence_handle = datasources/lawrence/lawrence_mapped_ontology.txt
\ No newline at end of file
+root = ../datasources
+almanac_handle = ${root}/moalmanac/molecular-oncology-almanac.json
+cancerhotspots_handle = ${root}/cancerhotspots/hotspots_v2.txt
+3dcancerhotspots_handle = ${root}/cancerhotspots/hotspots3d.txt
+cgc_handle = ${root}/cancergenecensus/cancer_gene_census_v97.genes.tsv
+cosmic_handle = ${root}/cosmic/CosmicMutantExport_v97.lite.txt
+gsea_pathways_handle = ${root}/gsea_gene_sets/GSEA_cancer_gene_sets.txt
+gsea_modules_handle = ${root}/gsea_gene_sets/c4.cm.v6.0.symbols.txt
+exac_handle = ${root}/exac/exac.expanded.r1.txt
+acmg_handle = ${root}/acmg/acmg.secondaryfindings.v3.txt
+clinvar_handle = ${root}/clinvar/variant_summary.lite.txt
+hereditary_handle = ${root}/hereditary/hereditary.txt
+oncotree_handle = ${root}/oncotree/oncotree.2023-03-09.txt
+lawrence_handle = ${root}/lawrence/lawrence_mapped_ontology.txt
\ No newline at end of file
diff --git a/moalmanac/preclinical-databases.ini b/moalmanac/preclinical-databases.ini
index de60614..d6e2086 100644
--- a/moalmanac/preclinical-databases.ini
+++ b/moalmanac/preclinical-databases.ini
@@ -1,10 +1,11 @@
 [preclinical]
-almanac_gdsc_mappings = datasources/preclinical/formatted/almanac-gdsc-mappings.json
-summary = datasources/preclinical/formatted/cell-lines.summary.txt
-variants = datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt
-copynumbers = datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt
-fusions = datasources/preclinical/annotated/cell-lines.fusions.annotated.txt
-fusions1 = datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt
-fusions2 = datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt
-gdsc = datasources/preclinical/formatted/sanger.gdsc.txt
-dictionary = datasources/preclinical/cell-lines.pkl
\ No newline at end of file
+root = ../datasources
+almanac_gdsc_mappings = ${root}/preclinical/formatted/almanac-gdsc-mappings.json
+summary = ${root}/preclinical/formatted/cell-lines.summary.txt
+variants = ${root}/preclinical/annotated/cell-lines.somatic-variants.annotated.txt
+copynumbers = ${root}/preclinical/annotated/cell-lines.copy-numbers.annotated.txt
+fusions = ${root}/preclinical/annotated/cell-lines.fusions.annotated.txt
+fusions1 = ${root}/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt
+fusions2 = ${root}/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt
+gdsc = ${root}/preclinical/formatted/sanger.gdsc.txt
+dictionary = ${root}/preclinical/cell-lines.pkl
\ No newline at end of file
diff --git a/moalmanac/run_example.py b/moalmanac/run_example.py
index 87c12c4..75718b4 100644
--- a/moalmanac/run_example.py
+++ b/moalmanac/run_example.py
@@ -1,7 +1,9 @@
 import moalmanac
 import os
-import time
 import subprocess
+import time
+
+from datetime import date
 
 from reader import Ini
 
@@ -47,15 +49,16 @@
 dbs_preclinical_ini_path = "preclinical-databases.ini"
 
 config_ini = Ini.read(config_ini_path, extended_interpolation=False, convert_to_dictionary=False)
-db_paths = Ini.read(dbs_ini_path, extended_interpolation=False, convert_to_dictionary=True)
-preclinical_db_paths = Ini.read(dbs_preclinical_ini_path, extended_interpolation=False, convert_to_dictionary=True)
+db_paths = Ini.read(dbs_ini_path, extended_interpolation=True, convert_to_dictionary=True)
+preclinical_db_paths = Ini.read(dbs_preclinical_ini_path, extended_interpolation=True, convert_to_dictionary=True)
 
 
 def execute_cmd(command):
     subprocess.call(command, shell=True)
 
 
-output_directory = "2024-07-03-config-edits"
+today = date.today().isoformat()
+output_directory = f"{today}-example-outputs"
 if output_directory != "":
     cmd = f"mkdir -p {output_directory}"
     execute_cmd(cmd)
diff --git a/moalmanac/test/annotator_tests.py b/moalmanac/test/annotator_tests.py
index fd4afce..399a806 100644
--- a/moalmanac/test/annotator_tests.py
+++ b/moalmanac/test/annotator_tests.py
@@ -60,7 +60,7 @@ def test_annotate(self):
         bin_name = ACMG.bin_name
         df = pd.DataFrame({gene: ['TP53', 'FOO', 'PMS2', 'TSC1', 'AR']})
         dbs = {
-            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt'
+            'acmg_handle': '../datasources/acmg/acmg.secondaryfindings.v3.txt'
         }
 
         annotated = ACMG.annotate(df, dbs)
@@ -340,15 +340,15 @@ class UnitTestPreclinicalEfficacy(unittest.TestCase):
     }
     df2 = pd.DataFrame(data_dictionary, index=[0, 1, 2])
     dbs_dictionary = {
-        'almanac_gdsc_mappings': 'datasources/preclinical/formatted/almanac-gdsc-mappings.json',
-        'summary': 'datasources/preclinical/formatted/cell-lines.summary.txt',
-        'variants': 'datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt',
-        'copynumbers': 'datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt',
-        'fusions': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.txt',
-        'fusions1': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt',
-        'fusions2': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt',
-        'gdsc': 'datasources/preclinical/formatted/sanger.gdsc.txt',
-        'dictionary': 'datasources/preclinical/cell-lines.pkl'
+        'almanac_gdsc_mappings': '../datasources/preclinical/formatted/almanac-gdsc-mappings.json',
+        'summary': '../datasources/preclinical/formatted/cell-lines.summary.txt',
+        'variants': '../datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt',
+        'copynumbers': '../datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt',
+        'fusions': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.txt',
+        'fusions1': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt',
+        'fusions2': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt',
+        'gdsc': '../datasources/preclinical/formatted/sanger.gdsc.txt',
+        'dictionary': '../datasources/preclinical/cell-lines.pkl'
     }
     config = {
         'feature_types': {
@@ -383,19 +383,19 @@ def test_series_for_significance(self):
 class UnitTestPreclinicalMatchmaking(unittest.TestCase):
     def test_annotate_copy_numbers(self):
         dbs = {
-            'almanac_handle': 'datasources/moalmanac/molecular-oncology-almanac.json',
-            'cancerhotspots_handle': 'datasources/cancerhotspots/hotspots_v2.txt',
-            '3dcancerhotspots_handle': 'datasources/cancerhotspots/hotspots3d.txt',
-            'cgc_handle': 'datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
-            'cosmic_handle': 'datasources/cosmic/CosmicMutantExport_v97.lite.txt',
-            'gsea_pathways_handle': 'datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
-            'gsea_modules_handle': 'datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
-            'exac_handle': 'datasources/exac/exac.expanded.r1.txt',
-            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt',
-            'clinvar_handle': 'datasources/clinvar/variant_summary.lite.txt',
-            'hereditary_handle': 'datasources/hereditary/hereditary.txt',
-            'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
-            'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
+            'almanac_handle': '../datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': '../datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': '../datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': '../datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': '../datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': '../datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': '../datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': '../datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': '../datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': '../datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': '../datasources/hereditary/hereditary.txt',
+            'oncotree_handle': '../datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': '../datasources/lawrence/lawrence_mapped_ontology.txt'
         }
         feature = PreclinicalMatchmaking.feature
         feature_type = PreclinicalMatchmaking.feature_type
@@ -465,19 +465,19 @@ def test_annotate_copy_numbers(self):
 
     def test_annotate_fusions(self):
         dbs = {
-            'almanac_handle': 'datasources/moalmanac/molecular-oncology-almanac.json',
-            'cancerhotspots_handle': 'datasources/cancerhotspots/hotspots_v2.txt',
-            '3dcancerhotspots_handle': 'datasources/cancerhotspots/hotspots3d.txt',
-            'cgc_handle': 'datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
-            'cosmic_handle': 'datasources/cosmic/CosmicMutantExport_v97.lite.txt',
-            'gsea_pathways_handle': 'datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
-            'gsea_modules_handle': 'datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
-            'exac_handle': 'datasources/exac/exac.expanded.r1.txt',
-            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt',
-            'clinvar_handle': 'datasources/clinvar/variant_summary.lite.txt',
-            'hereditary_handle': 'datasources/hereditary/hereditary.txt',
-            'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
-            'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
+            'almanac_handle': '../datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': '../datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': '../datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': '../datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': '../datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': '../datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': '../datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': '../datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': '../datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': '../datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': '../datasources/hereditary/hereditary.txt',
+            'oncotree_handle': '../datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': '../datasources/lawrence/lawrence_mapped_ontology.txt'
         }
         feature = PreclinicalMatchmaking.feature
         feature_type = PreclinicalMatchmaking.feature_type
@@ -727,19 +727,19 @@ def test_annotate_fusions(self):
 
     def test_annotate_fusions_matching(self):
         dbs = {
-            'almanac_handle': 'datasources/moalmanac/molecular-oncology-almanac.json',
-            'cancerhotspots_handle': 'datasources/cancerhotspots/hotspots_v2.txt',
-            '3dcancerhotspots_handle': 'datasources/cancerhotspots/hotspots3d.txt',
-            'cgc_handle': 'datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
-            'cosmic_handle': 'datasources/cosmic/CosmicMutantExport_v97.lite.txt',
-            'gsea_pathways_handle': 'datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
-            'gsea_modules_handle': 'datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
-            'exac_handle': 'datasources/exac/exac.expanded.r1.txt',
-            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt',
-            'clinvar_handle': 'datasources/clinvar/variant_summary.lite.txt',
-            'hereditary_handle': 'datasources/hereditary/hereditary.txt',
-            'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
-            'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
+            'almanac_handle': '../datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': '../datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': '../datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': '../datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': '../datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': '../datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': '../datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': '../datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': '../datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': '../datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': '../datasources/hereditary/hereditary.txt',
+            'oncotree_handle': '../datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': '../datasources/lawrence/lawrence_mapped_ontology.txt'
         }
 
         feature = PreclinicalMatchmaking.feature
@@ -789,19 +789,19 @@ def test_annotate_fusions_matching(self):
 
     def test_annotate_somatic_variants(self):
         dbs = {
-            'almanac_handle': 'datasources/moalmanac/molecular-oncology-almanac.json',
-            'cancerhotspots_handle': 'datasources/cancerhotspots/hotspots_v2.txt',
-            '3dcancerhotspots_handle': 'datasources/cancerhotspots/hotspots3d.txt',
-            'cgc_handle': 'datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
-            'cosmic_handle': 'datasources/cosmic/CosmicMutantExport_v97.lite.txt',
-            'gsea_pathways_handle': 'datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
-            'gsea_modules_handle': 'datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
-            'exac_handle': 'datasources/exac/exac.expanded.r1.txt',
-            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt',
-            'clinvar_handle': 'datasources/clinvar/variant_summary.lite.txt',
-            'hereditary_handle': 'datasources/hereditary/hereditary.txt',
-            'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
-            'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
+            'almanac_handle': '../datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': '../datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': '../datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': '../datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': '../datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': '../datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': '../datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': '../datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': '../datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': '../datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': '../datasources/hereditary/hereditary.txt',
+            'oncotree_handle': '../datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': '../datasources/lawrence/lawrence_mapped_ontology.txt'
         }
         feature = PreclinicalMatchmaking.feature
         feature_type = PreclinicalMatchmaking.feature_type
@@ -904,19 +904,19 @@ def test_annotate_match_2(self):
         match_2 = PreclinicalMatchmaking.match_2
 
         dbs = {
-            'almanac_handle': 'datasources/moalmanac/molecular-oncology-almanac.json',
-            'cancerhotspots_handle': 'datasources/cancerhotspots/hotspots_v2.txt',
-            '3dcancerhotspots_handle': 'datasources/cancerhotspots/hotspots3d.txt',
-            'cgc_handle': 'datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
-            'cosmic_handle': 'datasources/cosmic/CosmicMutantExport_v97.lite.txt',
-            'gsea_pathways_handle': 'datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
-            'gsea_modules_handle': 'datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
-            'exac_handle': 'datasources/exac/exac.expanded.r1.txt',
-            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt',
-            'clinvar_handle': 'datasources/clinvar/variant_summary.lite.txt',
-            'hereditary_handle': 'datasources/hereditary/hereditary.txt',
-            'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
-            'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
+            'almanac_handle': '../datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': '../datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': '../datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': '../datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': '../datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': '../datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': '../datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': '../datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': '../datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': '../datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': '../datasources/hereditary/hereditary.txt',
+            'oncotree_handle': '../datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': '../datasources/lawrence/lawrence_mapped_ontology.txt'
         }
         almanac = datasource_Almanac.import_ds(dbs)
         copy_number = 'Copy Number'
@@ -982,19 +982,19 @@ def test_annotate_match_3(self):
         match_3 = PreclinicalMatchmaking.match_3
 
         dbs = {
-            'almanac_handle': 'datasources/moalmanac/molecular-oncology-almanac.json',
-            'cancerhotspots_handle': 'datasources/cancerhotspots/hotspots_v2.txt',
-            '3dcancerhotspots_handle': 'datasources/cancerhotspots/hotspots3d.txt',
-            'cgc_handle': 'datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
-            'cosmic_handle': 'datasources/cosmic/CosmicMutantExport_v97.lite.txt',
-            'gsea_pathways_handle': 'datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
-            'gsea_modules_handle': 'datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
-            'exac_handle': 'datasources/exac/exac.expanded.r1.txt',
-            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt',
-            'clinvar_handle': 'datasources/clinvar/variant_summary.lite.txt',
-            'hereditary_handle': 'datasources/hereditary/hereditary.txt',
-            'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
-            'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
+            'almanac_handle': '../datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': '../datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': '../datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': '../datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': '../datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': '../datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': '../datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': '../datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': '../datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': '../datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': '../datasources/hereditary/hereditary.txt',
+            'oncotree_handle': '../datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': '../datasources/lawrence/lawrence_mapped_ontology.txt'
         }
         almanac = datasource_Almanac.import_ds(dbs)
         copy_number = 'Copy Number'
@@ -1069,19 +1069,19 @@ def test_annotate_match_4(self):
         match_4 = PreclinicalMatchmaking.match_4
 
         dbs = {
-            'almanac_handle': 'datasources/moalmanac/molecular-oncology-almanac.json',
-            'cancerhotspots_handle': 'datasources/cancerhotspots/hotspots_v2.txt',
-            '3dcancerhotspots_handle': 'datasources/cancerhotspots/hotspots3d.txt',
-            'cgc_handle': 'datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
-            'cosmic_handle': 'datasources/cosmic/CosmicMutantExport_v97.lite.txt',
-            'gsea_pathways_handle': 'datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
-            'gsea_modules_handle': 'datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
-            'exac_handle': 'datasources/exac/exac.expanded.r1.txt',
-            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt',
-            'clinvar_handle': 'datasources/clinvar/variant_summary.lite.txt',
-            'hereditary_handle': 'datasources/hereditary/hereditary.txt',
-            'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
-            'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
+            'almanac_handle': '../datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': '../datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': '../datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': '../datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': '../datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': '../datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': '../datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': '../datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': '../datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': '../datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': '../datasources/hereditary/hereditary.txt',
+            'oncotree_handle': '../datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': '../datasources/lawrence/lawrence_mapped_ontology.txt'
         }
         almanac = datasource_Almanac.import_ds(dbs)
         copy_number = 'Copy Number'
@@ -1133,19 +1133,19 @@ def test_annotate_match_4(self):
 
     def test_format_db(self):
         dbs = {
-            'almanac_handle': 'datasources/moalmanac/molecular-oncology-almanac.json',
-            'cancerhotspots_handle': 'datasources/cancerhotspots/hotspots_v2.txt',
-            '3dcancerhotspots_handle': 'datasources/cancerhotspots/hotspots3d.txt',
-            'cgc_handle': 'datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
-            'cosmic_handle': 'datasources/cosmic/CosmicMutantExport_v97.lite.txt',
-            'gsea_pathways_handle': 'datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
-            'gsea_modules_handle': 'datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
-            'exac_handle': 'datasources/exac/exac.expanded.r1.txt',
-            'acmg_handle': 'datasources/acmg/acmg.secondaryfindings.v3.txt',
-            'clinvar_handle': 'datasources/clinvar/variant_summary.lite.txt',
-            'hereditary_handle': 'datasources/hereditary/hereditary.txt',
-            'oncotree_handle': 'datasources/oncotree/oncotree.2023-03-09.txt',
-            'lawrence_handle': 'datasources/lawrence/lawrence_mapped_ontology.txt'
+            'almanac_handle': '../datasources/moalmanac/molecular-oncology-almanac.json',
+            'cancerhotspots_handle': '../datasources/cancerhotspots/hotspots_v2.txt',
+            '3dcancerhotspots_handle': '../datasources/cancerhotspots/hotspots3d.txt',
+            'cgc_handle': '../datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv',
+            'cosmic_handle': '../datasources/cosmic/CosmicMutantExport_v97.lite.txt',
+            'gsea_pathways_handle': '../datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt',
+            'gsea_modules_handle': '../datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt',
+            'exac_handle': '../datasources/exac/exac.expanded.r1.txt',
+            'acmg_handle': '../datasources/acmg/acmg.secondaryfindings.v3.txt',
+            'clinvar_handle': '../datasources/clinvar/variant_summary.lite.txt',
+            'hereditary_handle': '../datasources/hereditary/hereditary.txt',
+            'oncotree_handle': '../datasources/oncotree/oncotree.2023-03-09.txt',
+            'lawrence_handle': '../datasources/lawrence/lawrence_mapped_ontology.txt'
         }
         almanac = datasource_Almanac.import_ds(dbs)
         copy_number = 'Copy Number'
diff --git a/moalmanac/test/datasources_tests.py b/moalmanac/test/datasources_tests.py
index 82f0cd4..7d95676 100644
--- a/moalmanac/test/datasources_tests.py
+++ b/moalmanac/test/datasources_tests.py
@@ -29,15 +29,15 @@ def test_import_dbs(self):
         gdsc = Preclinical.gdsc
         mappings = Preclinical.mappings
         dbs_dictionary = {
-            'almanac_gdsc_mappings': 'datasources/preclinical/formatted/almanac-gdsc-mappings.json',
-            'summary': 'datasources/preclinical/formatted/cell-lines.summary.txt',
-            'variants': 'datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt',
-            'copynumbers': 'datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt',
-            'fusions': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.txt',
-            'fusions1': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt',
-            'fusions2': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt',
-            'gdsc': 'datasources/preclinical/formatted/sanger.gdsc.txt',
-            'dictionary': 'datasources/preclinical/cell-lines.pkl'
+            'almanac_gdsc_mappings': '../datasources/preclinical/formatted/almanac-gdsc-mappings.json',
+            'summary': '../datasources/preclinical/formatted/cell-lines.summary.txt',
+            'variants': '../datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt',
+            'copynumbers': '../datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt',
+            'fusions': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.txt',
+            'fusions1': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt',
+            'fusions2': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt',
+            'gdsc': '../datasources/preclinical/formatted/sanger.gdsc.txt',
+            'dictionary': '../datasources/preclinical/cell-lines.pkl'
         }
         dbs = Preclinical.import_dbs(dbs_dictionary)
 
diff --git a/moalmanac/test/investigator_tests.py b/moalmanac/test/investigator_tests.py
index f8a9a50..30dc662 100644
--- a/moalmanac/test/investigator_tests.py
+++ b/moalmanac/test/investigator_tests.py
@@ -55,15 +55,15 @@ def test_calculate_mann_whitney_u(self):
 
     def test_create(self):
         dbs_paths = {
-            'almanac_gdsc_mappings': 'datasources/preclinical/formatted/almanac-gdsc-mappings.json',
-            'summary': 'datasources/preclinical/formatted/cell-lines.summary.txt',
-            'variants': 'datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt',
-            'copynumbers': 'datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt',
-            'fusions': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.txt',
-            'fusions1': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt',
-            'fusions2': 'datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt',
-            'gdsc': 'datasources/preclinical/formatted/sanger.gdsc.txt',
-            'dictionary': 'datasources/preclinical/cell-lines.pkl'
+            'almanac_gdsc_mappings': '../datasources/preclinical/formatted/almanac-gdsc-mappings.json',
+            'summary': '../datasources/preclinical/formatted/cell-lines.summary.txt',
+            'variants': '../datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt',
+            'copynumbers': '../datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt',
+            'fusions': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.txt',
+            'fusions1': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt',
+            'fusions2': '../datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt',
+            'gdsc': '../datasources/preclinical/formatted/sanger.gdsc.txt',
+            'dictionary': '../datasources/preclinical/cell-lines.pkl'
         }
         dbs = Preclinical.import_dbs(dbs_paths)
         data_dictionary = {

From 92e9759f4eb8eac65b84dad4ed912c87ee81b1d4 Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Tue, 9 Jul 2024 22:54:51 -0400
Subject: [PATCH 07/19] Update to example actionable output

---
 example_output/example_output.actionable.txt | 44 ++++++++++----------
 1 file changed, 22 insertions(+), 22 deletions(-)

diff --git a/example_output/example_output.actionable.txt b/example_output/example_output.actionable.txt
index 863f2f4..909dca8 100644
--- a/example_output/example_output.actionable.txt
+++ b/example_output/example_output.actionable.txt
@@ -1,22 +1,22 @@
-score_bin	sensitive_predictive_implication	resistance_predictive_implication	prognostic_predictive_implication	feature_type	feature	alteration_type	alteration	tumor_f	total_coverage	exac_af	exac_common	clinvar	sensitive_score_bin	sensitive_therapy_name	sensitive_therapy_strategy	sensitive_therapy_type	sensitive_oncotree_code	sensitive_description	sensitive_citation	sensitive_url	resistance_score_bin	resistance_therapy_name	resistance_therapy_strategy	resistance_therapy_type	resistance_oncotree_code	resistance_description	resistance_citation	resistance_url	prognostic_score_bin	favorable_prognosis	prognostic_oncotree_code	prognostic_description	prognostic_citation	prognostic_url	number_germline_mutations_in_gene	validation_total_coverage	validation_tumor_f	validation_detection_power	feature_display	preclinical_efficacy_observed	patient_id	tumor_sample_barcode	normal_sample_barcode
-Putatively Actionable	FDA-Approved	Guideline	Guideline	Somatic Variant	BRAF	Missense	p.V600E	0.6316	152	1.6e-05	0.0		Putatively Actionable	Binimetinib + Encorafenib	MEK inhibition + B-RAF inhibition	Targeted therapy	NSCLC	The U.S. Food and Drug Administration (FDA) approved encorafenib in combination with binimetinib for the treatment of adult patients with metastatic non-small cell lung cancer (NSCLC) with BRAF V600E mutation, as detected by an FDA-approved test.	Array BioPharma Inc. Braftovi (encorafenib) [package insert]. U.S. Food and Drug Administration website. https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/210496s014lbl.pdf. Revised October 2023. Accessed November 1, 2023.	https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/210496s014lbl.pdf	Putatively Actionable	Panitumumab	EGFR inhibition	Targeted therapy	COADREAD	Panitumumab (Vectibix) is not recommended by the National Comprehensive Cancer Network® (NCCN®) as a treatment option for patients with metastatic colorectal cancer, BRAF V600E makes response to panitumumab or cetuximab highly unlikely unless given with a BRAF inhibitor. 	Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Colon Cancer V.4.2018. © National Comprehensive Cancer Network, Inc. 2018. All rights reserved. Accessed March 20 2019. To view the most recent and complete version of the guideline, go online to NCCN.org.	https://www.nccn.org/professionals/physician_gls/pdf/colon_blocks.pdf	Putatively Actionable	0	COADREAD	BRAF V600E alterations are associated with an unfavorable prognosis in MSI-low and microsatellite-stable patients.	Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Colon Cancer V.2.2016. © National Comprehensive Cancer Network, Inc. 2016. All rights reserved. Accessed March 20 2019. To view the most recent and complete version of the guideline, go online to NCCN.org.	https://www.nccn.org/professionals/physician_gls/pdf/colon_blocks.pdf	1.0	4.0	0.0	0.161	BRAF p.V600E (Missense)		example	example_tumor_profile	example_normal_profile
-Putatively Actionable	Guideline			Copy Number	CDK4	Amplification				0.0	0.0		Putatively Actionable	Palbociclib	CDK4/6 inhibition	Targeted therapy	WDLS	Palbociclib (Ibrance) is recommended by the National Comprehensive Cancer Network® (NCCN®) as a treatment option for patients with well-differentiated liposarcoma and CDK4 amplification. CDK4 amplification is characteristic of well-differentiated and dedifferentiated liposarcomas, and palbociclib shows activity in this context.	Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Soft Tissue Sarcoma V.1.2021. © National Comprehensive Cancer Network, Inc. 2019. All rights reserved. Accessed November 19th, 2019. To view the most recent and complete version of the guideline, go online to NCCN.org.	https://www.nccn.org/professionals/physician_gls/pdf/sarcoma_blocks.pdf																			CDK4 Amplification	1	example		
-Putatively Actionable	Clinical evidence			Copy Number	CDKN2A	Deletion				0.0	0.0		Putatively Actionable	Palbociclib	CDK4/6 inhibition	Targeted therapy	ULMS	A patient with uterine leiomyosarcoma whose tumor harbored a CDKN2A mutant which inactivated p16INK4a experienced clinical benefit from treatment with palbociclib.	Elvin JA, Gay LM, Ort R, et al. Clinical Benefit in Response to Palbociclib Treatment in Refractory Uterine Leiomyosarcomas with a Common CDKN2A Alteration. Oncologist. 2017;22(4):416-421.	https://doi.org/10.1634/theoncologist.2016-0310																			CDKN2A Deletion	1	example		
-Putatively Actionable	Preclinical		Guideline	Copy Number	TP53	Deletion				0.0	0.0		Putatively Actionable	Talazoparib	PARP inhibition	Targeted therapy	OS	Response to talazoparib in osteosarcoma cell lines was associated with homologous recombination deficiency in a study of 5 cancer cell lines. Osteosarcoma cell lines MG63 and ZK-58 displayed the highest sensitivity to talazoparib, SaOS-2 and MNNG-HOS displayed intermediate sensitivity, and U2OS cells remained resistant. Cell lines MG63, ZK-58, and MNNG-HOS scored HRD-LOH positive according to a score (Abkevich et al. 2012). MG63 cells harbored copy losses in BAP1, FANCA, and FANCD2 while ZK-58 carried disruptive copy loss in BARD1 and copy gain in FANCD2. SaOS-2 cells harbored copy losses in CHEK2 and TP53 and MNNG-HOS cells have copy loss of ATM and disruptive copy gains in PTEN and FANCD2. The talazoparib-resistant cell line, U2OS, carried a heterozygous BRCA2 copy loss and one intact BRCA2 alelle.	Engert F, Kovac M, Baumhoer D, Nathrath M, Fulda S. Osteosarcoma cells with genetic signatures of BRCAness are susceptible to the PARP inhibitor talazoparib alone or in combination with chemotherapeutics. Oncotarget. 2016;8(30):48794-48806.	https://www.clinicalkey.com/#!/content/playContent/1-s2.0-S1470204513700494									Putatively Actionable	0	MM	Deletion of 17p13 leads to LoH of TP53 and is considered a high-risk feature of multiple myeloma.	Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Multiple Myeloma V.2.2016. © National Comprehensive Cancer Network, Inc. 2016. All rights reserved. Accessed November 5 2016. To view the most recent and complete version of the guideline, go online to NCCN.org.	https://www.nccn.org/professionals/physician_gls/pdf/myeloma_blocks.pdf	1.0				TP53 Deletion	1	example		
-Putatively Actionable		Clinical evidence		Copy Number	BRAF	Amplification				0.0	0.0										Putatively Actionable	Vemurafenib	B-RAF inhibition	Targeted therapy	MEL	Amplification of BRAF may predict resistance to RAF inhibition.	Wagle, Nikhil, et al. MAP kinase pathway alterations in BRAF-mutant melanoma patients with acquired resistance to combined RAF/MEK inhibition. Cancer discovery 4.1 (2014): 61-68.	https://doi.org/10.1158/2159-8290.CD-13-0631							1.0				BRAF Amplification		example		
-Investigate Actionability	FDA-Approved	Guideline	Guideline	Microsatellite Stability	MSI-High								Investigate Actionability	Dostarlimab-gxly	PD-1/PD-L1 inhibition	Immunotherapy	UCEC	The U.S. Food and Drug Administration (FDA) granted approval to dostarlimab-gxly in combination with carboplatin and paclitaxel, followed by single agent dostarlimab-gxly, for the treatment of adult patients primary advanced or recurrent endometrial cancer that is mismatch repair deficient (dMMR), as determined by an FDA-approved test, or microsatellite-instability-high.	GlaxoSmithKline LLC. Jemperli (dostarlimab-gxly) [package insert]. U.S. Food and Drug Administration website. https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/761174s006lbl.pdf. Revised July 2023. Accessed September 6, 2023.	https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/761174s006lbl.pdf	Investigate Actionability	5-Fluorouracil	Thymidylate synthase inhibition	Chemotherapy	COADREAD	5-Fluorouracil is not recommended by the National Comprehensive Cancer Network® (NCCN®) as a treatment option for patients with MSI-High colorectal cancer. These patients appear to not benefit from, and may be resistant to, 5-fluorouracil therapy.	Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Colon Cancer V.2.2016. © National Comprehensive Cancer Network, Inc. 2016. All rights reserved. Accessed November 5 2016. To view the most recent and complete version of the guideline, go online to NCCN.org.	https://www.nccn.org/professionals/physician_gls/pdf/colon_blocks.pdf	Investigate Actionability	1	COADREAD	Patients with MSI-High colorectal cancer often have a favorable prognosis.	Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Colon Cancer V.2.2016. © National Comprehensive Cancer Network, Inc. 2016. All rights reserved. Accessed November 5 2016. To view the most recent and complete version of the guideline, go online to NCCN.org.	https://www.nccn.org/professionals/physician_gls/pdf/colon_blocks.pdf					MSI-High		example		
-Investigate Actionability	Guideline			Rearrangement	COL1A1	Fusion	COL1A1--CITED4			0.0	0.0		Investigate Actionability	Imatinib	PDGF-R inhibition	Targeted therapy	DFSP	Imatinib (Gleevec) is recommended by the National Comprehensive Cancer Network® (NCCN®) as a treatment option for patients with localized or metastatic dermatofibrosarcoma tumors containing t(17;22)(q22;q13).	Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Dermatofibrosarcoma V.1.2017. © National Comprehensive Cancer Network, Inc. 2017. All rights reserved. Accessed November 5 2016. To view the most recent and complete version of the guideline, go online to NCCN.org.	https://www.nccn.org/professionals/physician_gls/pdf/dfsp_blocks.pdf																			COL1A1--CITED4 Fusion	0	example		
-Investigate Actionability	Inferential			Mutational Signature	SBS5	v3.4	0.224						Investigate Actionability	Cisplatin	Platinum-based chemotherapy	Chemotherapy	BLCA	COSMIC Signature 5 is associated with somatic ERCC2 mutations, which may suggest sensitivity to cisplatin based chemotherapy.	Kim J, Mouw KW, Polak P, et al. Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors. Nat Genet. 2016;48(6):600-606.	https://doi.org/10.1038/ng.3557																			SBS5 (22%)		example		
-Investigate Actionability	Inferential			Mutational Signature	SBS10b	v3.4	0.119						Investigate Actionability	Durvalumab	PD-1/PD-L1 inhibition	Immunotherapy		COSMIC Signature 10 is observed in some of the most hypermutant samples and recurrent POLE mutations. POLE mutant tumors are being included along with MMR-deficient tumors in several ongoing trials for sensitivity to immunotherapy.	Mouw KW, Goldberg MS, Konstantinopoulos PA, D'Andrea AD. DNA Damage and Repair Biomarkers of Immunotherapy Response Cancer Discov. 2017; 7(7):675-693.	https://doi.org/10.1158/2159-8290.CD-17-0226																			SBS10b (12%)		example		
-Investigate Actionability			Inferential	Aneuploidy	Whole genome doubling																								Investigate Actionability	0		WGD was associated with adverse survival pan-cancer in patients with advanced disease and in cancers with heterogeneous clinical outcomes, even following the development of metastasis.	Bielski CM, Zehir A, Penson AV, et al. Genome doubling shapes the evolution and prognosis of advanced cancers Nat Genet. 2018; 50(8):1189-1195.	https://doi.org/10.1038/s41588-018-0165-1							example		
-Investigate Actionability	FDA-Approved			Germline Variant	BRCA2	Frameshift	p.S1982fs	0.5	100	0.0	0.0	Pathogenic	Investigate Actionability	Abiraterone acetate + Niraparib	Antiandrogen + PARP inhibition	Combination therapy	PRAD	The U.S. Food and Drug Administration (FDA) granted approval to niraparib, a poly (ADP-ribose) polymerase (PARP) inhibitor, and abiraterone acetate, a CYP17 inhibitor indicated with prednisone for the treatment of adult patients with deleterious or suspected deleterious BRCA-mutated (BRCAm) metastatic castration-resistant prostate cancer (mCRPC).	Janssen Biotech, Inc. Akeega (niraparib and abiraterone acetate) [package insert]. U.S. Food and Drug Administration website. https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/216793s000lbl.pdf. Revised August 2023. Accessed September 6, 2023.	https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/216793s000lbl.pdf																			BRCA2 p.S1982fs (Frameshift)		example	example_tumor_profile	example_normal_profile
-Investigate Actionability	Clinical evidence			Somatic Variant	MSH2	Missense	p.D887N	0.8039	51	0.0	0.0		Investigate Actionability	Pembrolizumab	PD-1/PD-L1 inhibition	Immunotherapy	COADREAD	Patients with defects in DNA mismatch repair genes may have enhanced sensitivity to immune checkpoint blockade.	Le DT, Uram JN, Wang H, et al. PD-1 Blockade in Tumors with Mismatch-Repair Deficiency. N Engl J Med. 2015;372(26):2509-20.	https://doi.org/10.1056/NEJMoa1500596																0.0	0.0	0.0	MSH2 p.D887N (Missense)		example	example_tumor_profile	example_normal_profile
-Investigate Actionability	Preclinical			Copy Number	FGFR2	Deletion				0.0	0.0		Investigate Actionability	Infigratinib	FGFR inhibition	Targeted therapy	COADREAD	A study of 32 Cancer Cell Line Encyclopedia cell lines demonstrating FGFR1 and FGFR2 amplification demonstrated sensitivity to Infigratinib.	Guagnano V, Kauffmann A, Wohrle S, et al. FGFR genetic alterations predict for sensitivity to NVP-BGJ398, a selective pan-FGFR inhibitor. Cancer Discov. 2012;2(12):1118-33.	https://doi.org/10.1158/2159-8290.CD-12-0210																			FGFR2 Deletion		example		
-Investigate Actionability			Guideline	Somatic Variant	STAG2	Missense	p.F467I	0.3571	56	0.0	0.0																		Investigate Actionability	0	MDS	The National Comprehensive Cancer Network® (NCCN®) highlights STAG2 nonsense, frameshift, and splice site variants as being associated with a poor prognosis in patients with myelodysplastic syndromes.	Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Myelodysplastic Syndromes V.2.2023. © National Comprehensive Cancer Network, Inc. 2023. All rights reserved. Accessed November 2, 2023. To view the most recent and complete version of the guideline, go online to NCCN.org.	https://www.nccn.org/professionals/physician_gls/pdf/mds_blocks.pdf		27.0	0.0	0.9881	STAG2 p.F467I (Missense)		example	example_tumor_profile	example_normal_profile
-Investigate Actionability			Guideline	Somatic Variant	ZRSR2	Missense	p.N261I	0.4019	107	0.0	0.0																		Investigate Actionability	0	MDS	The National Comprehensive Cancer Network® (NCCN®) highlights ZRSR2 nonsense and frameshift variants as being associated with a poor prognosis in patients with myelodysplastic syndromes.	Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Myelodysplastic Syndromes V.2.2023. © National Comprehensive Cancer Network, Inc. 2023. All rights reserved. Accessed November 2, 2023. To view the most recent and complete version of the guideline, go online to NCCN.org.	https://www.nccn.org/professionals/physician_gls/pdf/mds_blocks.pdf		29.0	0.0	0.9987	ZRSR2 p.N261I (Missense)		example	example_tumor_profile	example_normal_profile
-Biologically Relevant				Somatic Variant	NTRK2	Missense	p.H300Y	0.6136	44	0.0	0.0																									17.0	0.0	0.9982	NTRK2 p.H300Y (Missense)		example	example_tumor_profile	example_normal_profile
-Biologically Relevant				Somatic Variant	PDGFRB	Missense	p.G674E	0.4	40	0.0	0.0																									222.0	0.0	1.0	PDGFRB p.G674E (Missense)		example	example_tumor_profile	example_normal_profile
-Biologically Relevant				Germline Variant	BRAF	Nonsense	p.R509*	0.5	100	0.0	0.0																												BRAF p.R509* (Nonsense)		example	example_tumor_profile	example_normal_profile
-Biologically Relevant				Microsatellite Stability	Supporting variants		PRDM2 p.E282del (Deletion)																																Supporting variants: PRDM2 p.E282del (Deletion)		example		
-Biologically Relevant				Mutational Signature	SBS7a	v3.4	0.424																																SBS7a (42%)		example		
-Biologically Relevant				Mutational Signature	SBS30	v3.4	0.231																																SBS30 (23%)		example		
+score_bin	sensitive_predictive_implication	resistance_predictive_implication	prognostic_predictive_implication	feature_type	feature	alteration_type	alteration	tumor_f	total_coverage	exac_af	exac_common	clinvar	sensitive_score_bin	sensitive_therapy_name	sensitive_therapy_strategy	sensitive_therapy_type	sensitive_oncotree_code	sensitive_description	sensitive_citation	sensitive_url	resistance_score_bin	resistance_therapy_name	resistance_therapy_strategy	resistance_therapy_type	resistance_oncotree_code	resistance_description	resistance_citation	resistance_url	prognostic_score_bin	favorable_prognosis	prognostic_oncotree_code	prognostic_description	prognostic_citation	prognostic_url	number_germline_mutations_in_gene	validation_total_coverage	validation_tumor_f	validation_detection_power	feature_display	preclinical_efficacy_observed	patient_id	tumor_sample_barcode	normal_sample_barcode
+Putatively Actionable	FDA-Approved	Guideline	Guideline	Somatic Variant	BRAF	Missense	p.V600E	0.6316	152	1.60E-05	0		Putatively Actionable	Binimetinib + Encorafenib	MEK inhibition + B-RAF inhibition	Targeted therapy	NSCLC	"The U.S. Food and Drug Administration (FDA) approved encorafenib in combination with binimetinib for the treatment of adult patients with metastatic non-small cell lung cancer (NSCLC) with BRAF V600E mutation, as detected by an FDA-approved test."	"Array BioPharma Inc. Braftovi (encorafenib) [package insert]. U.S. Food and Drug Administration website. https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/210496s014lbl.pdf. Revised October 2023. Accessed November 1, 2023."	https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/210496s014lbl.pdf	Putatively Actionable	Panitumumab	EGFR inhibition	Targeted therapy	COADREAD	"Panitumumab (Vectibix) is not recommended by the National Comprehensive Cancer Network® (NCCN®) as a treatment option for patients with metastatic colorectal cancer, BRAF V600E makes response to panitumumab or cetuximab highly unlikely unless given with a BRAF inhibitor. "	"Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Colon Cancer V.4.2018. © National Comprehensive Cancer Network, Inc. 2018. All rights reserved. Accessed March 20 2019. To view the most recent and complete version of the guideline, go online to NCCN.org."	https://www.nccn.org/professionals/physician_gls/pdf/colon_blocks.pdf	Putatively Actionable	0	COADREAD	BRAF V600E alterations are associated with an unfavorable prognosis in MSI-low and microsatellite-stable patients.	"Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Colon Cancer V.2.2016. © National Comprehensive Cancer Network, Inc. 2016. All rights reserved. Accessed March 20 2019. To view the most recent and complete version of the guideline, go online to NCCN.org."	https://www.nccn.org/professionals/physician_gls/pdf/colon_blocks.pdf	1	4	0	0.161	BRAF p.V600E (Missense)		example	example_tumor_profile	example_normal_profile
+Putatively Actionable	Guideline			Copy Number	CDK4	Amplification				0	0		Putatively Actionable	Palbociclib	CDK4/6 inhibition	Targeted therapy	WDLS	"Palbociclib (Ibrance) is recommended by the National Comprehensive Cancer Network® (NCCN®) as a treatment option for patients with well-differentiated liposarcoma and CDK4 amplification. CDK4 amplification is characteristic of well-differentiated and dedifferentiated liposarcomas, and palbociclib shows activity in this context."	"Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Soft Tissue Sarcoma V.1.2021. © National Comprehensive Cancer Network, Inc. 2019. All rights reserved. Accessed November 19th, 2019. To view the most recent and complete version of the guideline, go online to NCCN.org."	https://www.nccn.org/professionals/physician_gls/pdf/sarcoma_blocks.pdf																			CDK4 Amplification		example		
+Putatively Actionable	Clinical evidence			Copy Number	CDKN2A	Deletion				0	0		Putatively Actionable	Palbociclib	CDK4/6 inhibition	Targeted therapy	ULMS	A patient with uterine leiomyosarcoma whose tumor harbored a CDKN2A mutant which inactivated p16INK4a experienced clinical benefit from treatment with palbociclib.	"Elvin JA, Gay LM, Ort R, et al. Clinical Benefit in Response to Palbociclib Treatment in Refractory Uterine Leiomyosarcomas with a Common CDKN2A Alteration. Oncologist. 2017;22(4):416-421."	https://doi.org/10.1634/theoncologist.2016-0310																			CDKN2A Deletion		example		
+Putatively Actionable	Preclinical		Guideline	Copy Number	TP53	Deletion				0	0		Putatively Actionable	Talazoparib	PARP inhibition	Targeted therapy	OS	"Response to talazoparib in osteosarcoma cell lines was associated with homologous recombination deficiency in a study of 5 cancer cell lines. Osteosarcoma cell lines MG63 and ZK-58 displayed the highest sensitivity to talazoparib, SaOS-2 and MNNG-HOS displayed intermediate sensitivity, and U2OS cells remained resistant. Cell lines MG63, ZK-58, and MNNG-HOS scored HRD-LOH positive according to a score (Abkevich et al. 2012). MG63 cells harbored copy losses in BAP1, FANCA, and FANCD2 while ZK-58 carried disruptive copy loss in BARD1 and copy gain in FANCD2. SaOS-2 cells harbored copy losses in CHEK2 and TP53 and MNNG-HOS cells have copy loss of ATM and disruptive copy gains in PTEN and FANCD2. The talazoparib-resistant cell line, U2OS, carried a heterozygous BRCA2 copy loss and one intact BRCA2 alelle."	"Engert F, Kovac M, Baumhoer D, Nathrath M, Fulda S. Osteosarcoma cells with genetic signatures of BRCAness are susceptible to the PARP inhibitor talazoparib alone or in combination with chemotherapeutics. Oncotarget. 2016;8(30):48794-48806."	https://www.clinicalkey.com/#!/content/playContent/1-s2.0-S1470204513700494									Putatively Actionable	0	MM	Deletion of 17p13 leads to LoH of TP53 and is considered a high-risk feature of multiple myeloma.	"Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Multiple Myeloma V.2.2016. © National Comprehensive Cancer Network, Inc. 2016. All rights reserved. Accessed November 5 2016. To view the most recent and complete version of the guideline, go online to NCCN.org."	https://www.nccn.org/professionals/physician_gls/pdf/myeloma_blocks.pdf	1				TP53 Deletion		example		
+Putatively Actionable		Clinical evidence		Copy Number	BRAF	Amplification				0	0										Putatively Actionable	Vemurafenib	B-RAF inhibition	Targeted therapy	MEL	Amplification of BRAF may predict resistance to RAF inhibition.	"Wagle, Nikhil, et al. MAP kinase pathway alterations in BRAF-mutant melanoma patients with acquired resistance to combined RAF/MEK inhibition. Cancer discovery 4.1 (2014): 61-68."	https://doi.org/10.1158/2159-8290.CD-13-0631							1				BRAF Amplification		example		
+Investigate Actionability	FDA-Approved	Guideline	Guideline	Microsatellite Stability	MSI-High								Investigate Actionability	Dostarlimab-gxly	PD-1/PD-L1 inhibition	Immunotherapy	UCEC	"The U.S. Food and Drug Administration (FDA) granted approval to dostarlimab-gxly in combination with carboplatin and paclitaxel, followed by single agent dostarlimab-gxly, for the treatment of adult patients primary advanced or recurrent endometrial cancer that is mismatch repair deficient (dMMR), as determined by an FDA-approved test, or microsatellite-instability-high."	"GlaxoSmithKline LLC. Jemperli (dostarlimab-gxly) [package insert]. U.S. Food and Drug Administration website. https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/761174s006lbl.pdf. Revised July 2023. Accessed September 6, 2023."	https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/761174s006lbl.pdf	Investigate Actionability	5-Fluorouracil	Thymidylate synthase inhibition	Chemotherapy	COADREAD	"5-Fluorouracil is not recommended by the National Comprehensive Cancer Network® (NCCN®) as a treatment option for patients with MSI-High colorectal cancer. These patients appear to not benefit from, and may be resistant to, 5-fluorouracil therapy."	"Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Colon Cancer V.2.2016. © National Comprehensive Cancer Network, Inc. 2016. All rights reserved. Accessed November 5 2016. To view the most recent and complete version of the guideline, go online to NCCN.org."	https://www.nccn.org/professionals/physician_gls/pdf/colon_blocks.pdf	Investigate Actionability	1	COADREAD	Patients with MSI-High colorectal cancer often have a favorable prognosis.	"Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Colon Cancer V.2.2016. © National Comprehensive Cancer Network, Inc. 2016. All rights reserved. Accessed November 5 2016. To view the most recent and complete version of the guideline, go online to NCCN.org."	https://www.nccn.org/professionals/physician_gls/pdf/colon_blocks.pdf					MSI-High		example		
+Investigate Actionability	Guideline			Rearrangement	COL1A1	Fusion	COL1A1--CITED4			0	0		Investigate Actionability	Imatinib	PDGF-R inhibition	Targeted therapy	DFSP	Imatinib (Gleevec) is recommended by the National Comprehensive Cancer Network® (NCCN®) as a treatment option for patients with localized or metastatic dermatofibrosarcoma tumors containing t(17;22)(q22;q13).	"Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Dermatofibrosarcoma V.1.2017. © National Comprehensive Cancer Network, Inc. 2017. All rights reserved. Accessed November 5 2016. To view the most recent and complete version of the guideline, go online to NCCN.org."	https://www.nccn.org/professionals/physician_gls/pdf/dfsp_blocks.pdf																			COL1A1--CITED4 Fusion		example		
+Investigate Actionability	Inferential			Mutational Signature	SBS5	v3.4	0.224						Investigate Actionability	Cisplatin	Platinum-based chemotherapy	Chemotherapy	BLCA	"COSMIC Signature 5 is associated with somatic ERCC2 mutations, which may suggest sensitivity to cisplatin based chemotherapy."	"Kim J, Mouw KW, Polak P, et al. Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors. Nat Genet. 2016;48(6):600-606."	https://doi.org/10.1038/ng.3557																			SBS5 (22%)		example		
+Investigate Actionability	Inferential			Mutational Signature	SBS10b	v3.4	0.119						Investigate Actionability	Durvalumab	PD-1/PD-L1 inhibition	Immunotherapy		COSMIC Signature 10 is observed in some of the most hypermutant samples and recurrent POLE mutations. POLE mutant tumors are being included along with MMR-deficient tumors in several ongoing trials for sensitivity to immunotherapy.	"Mouw KW, Goldberg MS, Konstantinopoulos PA, D'Andrea AD. DNA Damage and Repair Biomarkers of Immunotherapy Response Cancer Discov. 2017; 7(7):675-693."	https://doi.org/10.1158/2159-8290.CD-17-0226																			SBS10b (12%)		example		
+Investigate Actionability			Inferential	Aneuploidy	Whole genome doubling																								Investigate Actionability	0		"WGD was associated with adverse survival pan-cancer in patients with advanced disease and in cancers with heterogeneous clinical outcomes, even following the development of metastasis."	"Bielski CM, Zehir A, Penson AV, et al. Genome doubling shapes the evolution and prognosis of advanced cancers Nat Genet. 2018; 50(8):1189-1195."	https://doi.org/10.1038/s41588-018-0165-1							example		
+Investigate Actionability	FDA-Approved			Germline Variant	BRCA2	Frameshift	p.S1982fs	0.5	100	0	0	Pathogenic	Investigate Actionability	Abiraterone acetate + Niraparib	Antiandrogen + PARP inhibition	Combination therapy	PRAD	"The U.S. Food and Drug Administration (FDA) granted approval to niraparib, a poly (ADP-ribose) polymerase (PARP) inhibitor, and abiraterone acetate, a CYP17 inhibitor indicated with prednisone for the treatment of adult patients with deleterious or suspected deleterious BRCA-mutated (BRCAm) metastatic castration-resistant prostate cancer (mCRPC)."	"Janssen Biotech, Inc. Akeega (niraparib and abiraterone acetate) [package insert]. U.S. Food and Drug Administration website. https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/216793s000lbl.pdf. Revised August 2023. Accessed September 6, 2023."	https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/216793s000lbl.pdf																			BRCA2 p.S1982fs (Frameshift)		example	example_tumor_profile	example_normal_profile
+Investigate Actionability	Clinical evidence			Somatic Variant	MSH2	Missense	p.D887N	0.8039	51	0	0		Investigate Actionability	Pembrolizumab	PD-1/PD-L1 inhibition	Immunotherapy	COADREAD	Patients with defects in DNA mismatch repair genes may have enhanced sensitivity to immune checkpoint blockade.	"Le DT, Uram JN, Wang H, et al. PD-1 Blockade in Tumors with Mismatch-Repair Deficiency. N Engl J Med. 2015;372(26):2509-20."	https://doi.org/10.1056/NEJMoa1500596																0	0	0	MSH2 p.D887N (Missense)		example	example_tumor_profile	example_normal_profile
+Investigate Actionability	Preclinical			Copy Number	FGFR2	Deletion				0	0		Investigate Actionability	Infigratinib	FGFR inhibition	Targeted therapy	COADREAD	A study of 32 Cancer Cell Line Encyclopedia cell lines demonstrating FGFR1 and FGFR2 amplification demonstrated sensitivity to Infigratinib.	"Guagnano V, Kauffmann A, Wohrle S, et al. FGFR genetic alterations predict for sensitivity to NVP-BGJ398, a selective pan-FGFR inhibitor. Cancer Discov. 2012;2(12):1118-33."	https://doi.org/10.1158/2159-8290.CD-12-0210																			FGFR2 Deletion		example		
+Investigate Actionability			Guideline	Somatic Variant	STAG2	Missense	p.F467I	0.3571	56	0	0																		Investigate Actionability	0	MDS	"The National Comprehensive Cancer Network® (NCCN®) highlights STAG2 nonsense, frameshift, and splice site variants as being associated with a poor prognosis in patients with myelodysplastic syndromes."	"Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Myelodysplastic Syndromes V.2.2023. © National Comprehensive Cancer Network, Inc. 2023. All rights reserved. Accessed November 2, 2023. To view the most recent and complete version of the guideline, go online to NCCN.org."	https://www.nccn.org/professionals/physician_gls/pdf/mds_blocks.pdf		27	0	0.9881	STAG2 p.F467I (Missense)		example	example_tumor_profile	example_normal_profile
+Investigate Actionability			Guideline	Somatic Variant	ZRSR2	Missense	p.N261I	0.4019	107	0	0																		Investigate Actionability	0	MDS	The National Comprehensive Cancer Network® (NCCN®) highlights ZRSR2 nonsense and frameshift variants as being associated with a poor prognosis in patients with myelodysplastic syndromes.	"Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Myelodysplastic Syndromes V.2.2023. © National Comprehensive Cancer Network, Inc. 2023. All rights reserved. Accessed November 2, 2023. To view the most recent and complete version of the guideline, go online to NCCN.org."	https://www.nccn.org/professionals/physician_gls/pdf/mds_blocks.pdf		29	0	0.9987	ZRSR2 p.N261I (Missense)		example	example_tumor_profile	example_normal_profile
+Biologically Relevant				Somatic Variant	NTRK2	Missense	p.H300Y	0.6136	44	0	0																									17	0	0.9982	NTRK2 p.H300Y (Missense)		example	example_tumor_profile	example_normal_profile
+Biologically Relevant				Somatic Variant	PDGFRB	Missense	p.G674E	0.4	40	0	0																									222	0	1	PDGFRB p.G674E (Missense)		example	example_tumor_profile	example_normal_profile
+Biologically Relevant				Germline Variant	BRAF	Nonsense	p.R509*	0.5	100	0	0																												BRAF p.R509* (Nonsense)		example	example_tumor_profile	example_normal_profile
+Biologically Relevant				Microsatellite Stability	Supporting variants		PRDM2 p.E282del (Deletion)																																Supporting variants: PRDM2 p.E282del (Deletion)		example		
+Biologically Relevant				Mutational Signature	SBS7a	v3.4	0.424																																SBS7a (42%)		example		
+Biologically Relevant				Mutational Signature	SBS30	v3.4	0.231																																SBS30 (23%)		example		
\ No newline at end of file

From 8dba89f7bdf407c51914705246a8145354c85ba8 Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Tue, 9 Jul 2024 22:57:24 -0400
Subject: [PATCH 08/19] Moved datasources to root dir of repo

---
 .../datasources => datasources}/README.md     |   0
 .../acmg/README.md                            |   0
 .../acmg/acmg.secondaryfindings.v3.txt        |   0
 .../cancergenecensus/README.md                |   0
 .../cancer_gene_census_v97.genes.tsv          |   0
 .../cancergenecensus/diff_versions.py         |   0
 .../cancergenecensus/extract_genes.py         |   0
 .../cancerhotspots/README.md                  |   0
 .../cancerhotspots/hotspots.txt               |   0
 .../cancerhotspots/hotspots3d.txt             |   0
 .../cancerhotspots/hotspots_v2.txt            |   0
 .../prep_3dhotspots/3d_hotspots.xls           | Bin
 .../prep_3dhotspots/3d_hotspots_T2.txt        |   0
 .../prep_3dhotspots/3d_hotspots_T5.txt        |   0
 .../prep_3dhotspots/prep3dhotspots.py         |   0
 .../clinvar/README.md                         |   0
 .../clinvar/prepare_clinvar.py                |   0
 .../clinvar/variant_summary.lite.txt          |   0
 .../cosmic/README.md                          |   0
 .../cosmic/prepare_cosmic.py                  |   0
 .../exac/README.md                            |   0
 .../exac/build_exac.sh                        |   0
 .../exac/expand_exac.py                       |   0
 .../gsea_gene_sets/GSEA_cancer_gene_sets.txt  |   0
 .../gsea_gene_sets/README.md                  |   0
 .../gsea_gene_sets/c4.cm.v6.0.symbols.txt     |   0
 .../hereditary/README.md                      |   0
 .../hereditary/hereditary.txt                 |   0
 .../lawrence/README.md                        |   0
 .../lawrence/lawrence_ST2.txt                 |   0
 .../lawrence/lawrence_mapped_ontology.txt     |   0
 .../lawrence/map_oncotree_lawrence.ipynb      |   0
 .../moalmanac/README.md                       |   0
 .../moalmanac/create_almanac_db.py            |   0
 .../moalmanac/molecular-oncology-almanac.json |   0
 .../oncotree/README.md                        |   0
 datasources/oncotree/get-oncotree.ipynb       |  76 ++++++++++++++++++
 .../oncotree/get_oncotree.py                  |   0
 .../oncotree/oncotree.2023-03-09.txt          |   0
 .../preclinical/README.md                     |   0
 .../preclinical/almanac-gdsc-mappings.json    |   0
 .../preclinical/annotated/README.md           |   0
 .../annotated/annotate-copy-numbers.py        |   0
 .../preclinical/annotated/annotate-fusions.py |   0
 .../annotated/annotate-molecular-features.sh  |   0
 .../annotated/annotate-variants.py            |   0
 .../cell-lines.copy-numbers.annotated.txt     |   0
 .../cell-lines.fusions.annotated.gene1.txt    |   0
 .../cell-lines.fusions.annotated.gene2.txt    |   0
 .../cell-lines.fusions.annotated.txt          |   0
 .../cell-lines.somatic-variants.annotated.txt |   0
 .../preclinical/cell-lines.pkl                | Bin
 .../formatted/0.map-almanac-to-gdsc.ipynb     |   0
 ...process-cell-line-molecular-features.ipynb |   0
 .../preclinical/formatted/README.md           |   0
 .../formatted/almanac-gdsc-mappings.json      |   0
 .../formatted/cell-line-names.formatted.txt   |   0
 .../formatted/cell-lines.copy-numbers.txt     |   0
 .../formatted/cell-lines.fusions.txt          |   0
 .../formatted/cell-lines.somatic-variants.txt |   0
 .../formatted/cell-lines.summary.txt          |   0
 .../preclinical/formatted/sanger.gdsc.txt     |   0
 .../preclinical/generate-dictionary.ipynb     |   0
 .../preclinical/source/README.md              |   0
 .../preclinical/source/ccle-2019/README.md    |   0
 .../preclinical/source/depmap/README.md       |   0
 .../preclinical/source/gdsc/README.md         |   0
 67 files changed, 76 insertions(+)
 rename {moalmanac/datasources => datasources}/README.md (100%)
 rename {moalmanac/datasources => datasources}/acmg/README.md (100%)
 rename {moalmanac/datasources => datasources}/acmg/acmg.secondaryfindings.v3.txt (100%)
 rename {moalmanac/datasources => datasources}/cancergenecensus/README.md (100%)
 rename {moalmanac/datasources => datasources}/cancergenecensus/cancer_gene_census_v97.genes.tsv (100%)
 rename {moalmanac/datasources => datasources}/cancergenecensus/diff_versions.py (100%)
 rename {moalmanac/datasources => datasources}/cancergenecensus/extract_genes.py (100%)
 rename {moalmanac/datasources => datasources}/cancerhotspots/README.md (100%)
 rename {moalmanac/datasources => datasources}/cancerhotspots/hotspots.txt (100%)
 rename {moalmanac/datasources => datasources}/cancerhotspots/hotspots3d.txt (100%)
 rename {moalmanac/datasources => datasources}/cancerhotspots/hotspots_v2.txt (100%)
 rename {moalmanac/datasources => datasources}/cancerhotspots/prep_3dhotspots/3d_hotspots.xls (100%)
 rename {moalmanac/datasources => datasources}/cancerhotspots/prep_3dhotspots/3d_hotspots_T2.txt (100%)
 rename {moalmanac/datasources => datasources}/cancerhotspots/prep_3dhotspots/3d_hotspots_T5.txt (100%)
 rename {moalmanac/datasources => datasources}/cancerhotspots/prep_3dhotspots/prep3dhotspots.py (100%)
 rename {moalmanac/datasources => datasources}/clinvar/README.md (100%)
 rename {moalmanac/datasources => datasources}/clinvar/prepare_clinvar.py (100%)
 rename {moalmanac/datasources => datasources}/clinvar/variant_summary.lite.txt (100%)
 rename {moalmanac/datasources => datasources}/cosmic/README.md (100%)
 rename {moalmanac/datasources => datasources}/cosmic/prepare_cosmic.py (100%)
 rename {moalmanac/datasources => datasources}/exac/README.md (100%)
 rename {moalmanac/datasources => datasources}/exac/build_exac.sh (100%)
 rename {moalmanac/datasources => datasources}/exac/expand_exac.py (100%)
 rename {moalmanac/datasources => datasources}/gsea_gene_sets/GSEA_cancer_gene_sets.txt (100%)
 rename {moalmanac/datasources => datasources}/gsea_gene_sets/README.md (100%)
 rename {moalmanac/datasources => datasources}/gsea_gene_sets/c4.cm.v6.0.symbols.txt (100%)
 rename {moalmanac/datasources => datasources}/hereditary/README.md (100%)
 rename {moalmanac/datasources => datasources}/hereditary/hereditary.txt (100%)
 rename {moalmanac/datasources => datasources}/lawrence/README.md (100%)
 rename {moalmanac/datasources => datasources}/lawrence/lawrence_ST2.txt (100%)
 rename {moalmanac/datasources => datasources}/lawrence/lawrence_mapped_ontology.txt (100%)
 rename {moalmanac/datasources => datasources}/lawrence/map_oncotree_lawrence.ipynb (100%)
 rename {moalmanac/datasources => datasources}/moalmanac/README.md (100%)
 rename {moalmanac/datasources => datasources}/moalmanac/create_almanac_db.py (100%)
 rename {moalmanac/datasources => datasources}/moalmanac/molecular-oncology-almanac.json (100%)
 rename {moalmanac/datasources => datasources}/oncotree/README.md (100%)
 create mode 100644 datasources/oncotree/get-oncotree.ipynb
 rename {moalmanac/datasources => datasources}/oncotree/get_oncotree.py (100%)
 rename {moalmanac/datasources => datasources}/oncotree/oncotree.2023-03-09.txt (100%)
 rename {moalmanac/datasources => datasources}/preclinical/README.md (100%)
 rename {moalmanac/datasources => datasources}/preclinical/almanac-gdsc-mappings.json (100%)
 rename {moalmanac/datasources => datasources}/preclinical/annotated/README.md (100%)
 rename {moalmanac/datasources => datasources}/preclinical/annotated/annotate-copy-numbers.py (100%)
 rename {moalmanac/datasources => datasources}/preclinical/annotated/annotate-fusions.py (100%)
 rename {moalmanac/datasources => datasources}/preclinical/annotated/annotate-molecular-features.sh (100%)
 rename {moalmanac/datasources => datasources}/preclinical/annotated/annotate-variants.py (100%)
 rename {moalmanac/datasources => datasources}/preclinical/annotated/cell-lines.copy-numbers.annotated.txt (100%)
 rename {moalmanac/datasources => datasources}/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt (100%)
 rename {moalmanac/datasources => datasources}/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt (100%)
 rename {moalmanac/datasources => datasources}/preclinical/annotated/cell-lines.fusions.annotated.txt (100%)
 rename {moalmanac/datasources => datasources}/preclinical/annotated/cell-lines.somatic-variants.annotated.txt (100%)
 rename {moalmanac/datasources => datasources}/preclinical/cell-lines.pkl (100%)
 rename {moalmanac/datasources => datasources}/preclinical/formatted/0.map-almanac-to-gdsc.ipynb (100%)
 rename {moalmanac/datasources => datasources}/preclinical/formatted/1.process-cell-line-molecular-features.ipynb (100%)
 rename {moalmanac/datasources => datasources}/preclinical/formatted/README.md (100%)
 rename {moalmanac/datasources => datasources}/preclinical/formatted/almanac-gdsc-mappings.json (100%)
 rename {moalmanac/datasources => datasources}/preclinical/formatted/cell-line-names.formatted.txt (100%)
 rename {moalmanac/datasources => datasources}/preclinical/formatted/cell-lines.copy-numbers.txt (100%)
 rename {moalmanac/datasources => datasources}/preclinical/formatted/cell-lines.fusions.txt (100%)
 rename {moalmanac/datasources => datasources}/preclinical/formatted/cell-lines.somatic-variants.txt (100%)
 rename {moalmanac/datasources => datasources}/preclinical/formatted/cell-lines.summary.txt (100%)
 rename {moalmanac/datasources => datasources}/preclinical/formatted/sanger.gdsc.txt (100%)
 rename {moalmanac/datasources => datasources}/preclinical/generate-dictionary.ipynb (100%)
 rename {moalmanac/datasources => datasources}/preclinical/source/README.md (100%)
 rename {moalmanac/datasources => datasources}/preclinical/source/ccle-2019/README.md (100%)
 rename {moalmanac/datasources => datasources}/preclinical/source/depmap/README.md (100%)
 rename {moalmanac/datasources => datasources}/preclinical/source/gdsc/README.md (100%)

diff --git a/moalmanac/datasources/README.md b/datasources/README.md
similarity index 100%
rename from moalmanac/datasources/README.md
rename to datasources/README.md
diff --git a/moalmanac/datasources/acmg/README.md b/datasources/acmg/README.md
similarity index 100%
rename from moalmanac/datasources/acmg/README.md
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diff --git a/moalmanac/datasources/acmg/acmg.secondaryfindings.v3.txt b/datasources/acmg/acmg.secondaryfindings.v3.txt
similarity index 100%
rename from moalmanac/datasources/acmg/acmg.secondaryfindings.v3.txt
rename to datasources/acmg/acmg.secondaryfindings.v3.txt
diff --git a/moalmanac/datasources/cancergenecensus/README.md b/datasources/cancergenecensus/README.md
similarity index 100%
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diff --git a/moalmanac/datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv b/datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv
similarity index 100%
rename from moalmanac/datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv
rename to datasources/cancergenecensus/cancer_gene_census_v97.genes.tsv
diff --git a/moalmanac/datasources/cancergenecensus/diff_versions.py b/datasources/cancergenecensus/diff_versions.py
similarity index 100%
rename from moalmanac/datasources/cancergenecensus/diff_versions.py
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diff --git a/moalmanac/datasources/cancergenecensus/extract_genes.py b/datasources/cancergenecensus/extract_genes.py
similarity index 100%
rename from moalmanac/datasources/cancergenecensus/extract_genes.py
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diff --git a/moalmanac/datasources/cancerhotspots/README.md b/datasources/cancerhotspots/README.md
similarity index 100%
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diff --git a/moalmanac/datasources/cancerhotspots/hotspots.txt b/datasources/cancerhotspots/hotspots.txt
similarity index 100%
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diff --git a/moalmanac/datasources/cancerhotspots/hotspots3d.txt b/datasources/cancerhotspots/hotspots3d.txt
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diff --git a/moalmanac/datasources/cancerhotspots/hotspots_v2.txt b/datasources/cancerhotspots/hotspots_v2.txt
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diff --git a/moalmanac/datasources/cancerhotspots/prep_3dhotspots/3d_hotspots.xls b/datasources/cancerhotspots/prep_3dhotspots/3d_hotspots.xls
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rename from moalmanac/datasources/cancerhotspots/prep_3dhotspots/3d_hotspots.xls
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diff --git a/moalmanac/datasources/cancerhotspots/prep_3dhotspots/3d_hotspots_T2.txt b/datasources/cancerhotspots/prep_3dhotspots/3d_hotspots_T2.txt
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rename from moalmanac/datasources/cancerhotspots/prep_3dhotspots/3d_hotspots_T2.txt
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diff --git a/moalmanac/datasources/cancerhotspots/prep_3dhotspots/3d_hotspots_T5.txt b/datasources/cancerhotspots/prep_3dhotspots/3d_hotspots_T5.txt
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diff --git a/moalmanac/datasources/clinvar/README.md b/datasources/clinvar/README.md
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diff --git a/moalmanac/datasources/clinvar/variant_summary.lite.txt b/datasources/clinvar/variant_summary.lite.txt
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diff --git a/moalmanac/datasources/cosmic/README.md b/datasources/cosmic/README.md
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diff --git a/moalmanac/datasources/exac/README.md b/datasources/exac/README.md
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diff --git a/moalmanac/datasources/exac/build_exac.sh b/datasources/exac/build_exac.sh
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diff --git a/moalmanac/datasources/exac/expand_exac.py b/datasources/exac/expand_exac.py
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diff --git a/moalmanac/datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt b/datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt
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rename from moalmanac/datasources/gsea_gene_sets/GSEA_cancer_gene_sets.txt
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diff --git a/moalmanac/datasources/gsea_gene_sets/README.md b/datasources/gsea_gene_sets/README.md
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diff --git a/moalmanac/datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt b/datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt
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rename from moalmanac/datasources/gsea_gene_sets/c4.cm.v6.0.symbols.txt
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diff --git a/moalmanac/datasources/hereditary/README.md b/datasources/hereditary/README.md
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diff --git a/moalmanac/datasources/lawrence/README.md b/datasources/lawrence/README.md
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rename from moalmanac/datasources/lawrence/README.md
rename to datasources/lawrence/README.md
diff --git a/moalmanac/datasources/lawrence/lawrence_ST2.txt b/datasources/lawrence/lawrence_ST2.txt
similarity index 100%
rename from moalmanac/datasources/lawrence/lawrence_ST2.txt
rename to datasources/lawrence/lawrence_ST2.txt
diff --git a/moalmanac/datasources/lawrence/lawrence_mapped_ontology.txt b/datasources/lawrence/lawrence_mapped_ontology.txt
similarity index 100%
rename from moalmanac/datasources/lawrence/lawrence_mapped_ontology.txt
rename to datasources/lawrence/lawrence_mapped_ontology.txt
diff --git a/moalmanac/datasources/lawrence/map_oncotree_lawrence.ipynb b/datasources/lawrence/map_oncotree_lawrence.ipynb
similarity index 100%
rename from moalmanac/datasources/lawrence/map_oncotree_lawrence.ipynb
rename to datasources/lawrence/map_oncotree_lawrence.ipynb
diff --git a/moalmanac/datasources/moalmanac/README.md b/datasources/moalmanac/README.md
similarity index 100%
rename from moalmanac/datasources/moalmanac/README.md
rename to datasources/moalmanac/README.md
diff --git a/moalmanac/datasources/moalmanac/create_almanac_db.py b/datasources/moalmanac/create_almanac_db.py
similarity index 100%
rename from moalmanac/datasources/moalmanac/create_almanac_db.py
rename to datasources/moalmanac/create_almanac_db.py
diff --git a/moalmanac/datasources/moalmanac/molecular-oncology-almanac.json b/datasources/moalmanac/molecular-oncology-almanac.json
similarity index 100%
rename from moalmanac/datasources/moalmanac/molecular-oncology-almanac.json
rename to datasources/moalmanac/molecular-oncology-almanac.json
diff --git a/moalmanac/datasources/oncotree/README.md b/datasources/oncotree/README.md
similarity index 100%
rename from moalmanac/datasources/oncotree/README.md
rename to datasources/oncotree/README.md
diff --git a/datasources/oncotree/get-oncotree.ipynb b/datasources/oncotree/get-oncotree.ipynb
new file mode 100644
index 0000000..188c6fb
--- /dev/null
+++ b/datasources/oncotree/get-oncotree.ipynb
@@ -0,0 +1,76 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "15aef7c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9006f380",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "request = 'http://oncotree.mskcc.org/api/tumorTypes/tree?version=oncotree_latest_stable'\n",
+    "r = requests.get(request)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "875a3e89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['OVARY', 'LYMPH', 'SOFT_TISSUE', 'THYROID', 'PLEURA', 'PANCREAS', 'BILIARY_TRACT', 'BREAST', 'HEAD_NECK', 'EYE', 'AMPULLA_OF_VATER', 'THYMUS', 'CERVIX', 'PNS', 'BOWEL', 'BONE', 'SKIN', 'BLADDER', 'BRAIN', 'ADRENAL_GLAND', 'PROSTATE', 'LUNG', 'PENIS', 'UTERUS', 'OTHER', 'TESTIS', 'LIVER', 'PERITONEUM', 'MYELOID', 'VULVA', 'KIDNEY', 'STOMACH'])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r.json()['TISSUE']['children'].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0c4d27c3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "moalmanac",
+   "language": "python",
+   "name": "moalmanac"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/moalmanac/datasources/oncotree/get_oncotree.py b/datasources/oncotree/get_oncotree.py
similarity index 100%
rename from moalmanac/datasources/oncotree/get_oncotree.py
rename to datasources/oncotree/get_oncotree.py
diff --git a/moalmanac/datasources/oncotree/oncotree.2023-03-09.txt b/datasources/oncotree/oncotree.2023-03-09.txt
similarity index 100%
rename from moalmanac/datasources/oncotree/oncotree.2023-03-09.txt
rename to datasources/oncotree/oncotree.2023-03-09.txt
diff --git a/moalmanac/datasources/preclinical/README.md b/datasources/preclinical/README.md
similarity index 100%
rename from moalmanac/datasources/preclinical/README.md
rename to datasources/preclinical/README.md
diff --git a/moalmanac/datasources/preclinical/almanac-gdsc-mappings.json b/datasources/preclinical/almanac-gdsc-mappings.json
similarity index 100%
rename from moalmanac/datasources/preclinical/almanac-gdsc-mappings.json
rename to datasources/preclinical/almanac-gdsc-mappings.json
diff --git a/moalmanac/datasources/preclinical/annotated/README.md b/datasources/preclinical/annotated/README.md
similarity index 100%
rename from moalmanac/datasources/preclinical/annotated/README.md
rename to datasources/preclinical/annotated/README.md
diff --git a/moalmanac/datasources/preclinical/annotated/annotate-copy-numbers.py b/datasources/preclinical/annotated/annotate-copy-numbers.py
similarity index 100%
rename from moalmanac/datasources/preclinical/annotated/annotate-copy-numbers.py
rename to datasources/preclinical/annotated/annotate-copy-numbers.py
diff --git a/moalmanac/datasources/preclinical/annotated/annotate-fusions.py b/datasources/preclinical/annotated/annotate-fusions.py
similarity index 100%
rename from moalmanac/datasources/preclinical/annotated/annotate-fusions.py
rename to datasources/preclinical/annotated/annotate-fusions.py
diff --git a/moalmanac/datasources/preclinical/annotated/annotate-molecular-features.sh b/datasources/preclinical/annotated/annotate-molecular-features.sh
similarity index 100%
rename from moalmanac/datasources/preclinical/annotated/annotate-molecular-features.sh
rename to datasources/preclinical/annotated/annotate-molecular-features.sh
diff --git a/moalmanac/datasources/preclinical/annotated/annotate-variants.py b/datasources/preclinical/annotated/annotate-variants.py
similarity index 100%
rename from moalmanac/datasources/preclinical/annotated/annotate-variants.py
rename to datasources/preclinical/annotated/annotate-variants.py
diff --git a/moalmanac/datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt b/datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt
similarity index 100%
rename from moalmanac/datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt
rename to datasources/preclinical/annotated/cell-lines.copy-numbers.annotated.txt
diff --git a/moalmanac/datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt b/datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt
similarity index 100%
rename from moalmanac/datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt
rename to datasources/preclinical/annotated/cell-lines.fusions.annotated.gene1.txt
diff --git a/moalmanac/datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt b/datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt
similarity index 100%
rename from moalmanac/datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt
rename to datasources/preclinical/annotated/cell-lines.fusions.annotated.gene2.txt
diff --git a/moalmanac/datasources/preclinical/annotated/cell-lines.fusions.annotated.txt b/datasources/preclinical/annotated/cell-lines.fusions.annotated.txt
similarity index 100%
rename from moalmanac/datasources/preclinical/annotated/cell-lines.fusions.annotated.txt
rename to datasources/preclinical/annotated/cell-lines.fusions.annotated.txt
diff --git a/moalmanac/datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt b/datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt
similarity index 100%
rename from moalmanac/datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt
rename to datasources/preclinical/annotated/cell-lines.somatic-variants.annotated.txt
diff --git a/moalmanac/datasources/preclinical/cell-lines.pkl b/datasources/preclinical/cell-lines.pkl
similarity index 100%
rename from moalmanac/datasources/preclinical/cell-lines.pkl
rename to datasources/preclinical/cell-lines.pkl
diff --git a/moalmanac/datasources/preclinical/formatted/0.map-almanac-to-gdsc.ipynb b/datasources/preclinical/formatted/0.map-almanac-to-gdsc.ipynb
similarity index 100%
rename from moalmanac/datasources/preclinical/formatted/0.map-almanac-to-gdsc.ipynb
rename to datasources/preclinical/formatted/0.map-almanac-to-gdsc.ipynb
diff --git a/moalmanac/datasources/preclinical/formatted/1.process-cell-line-molecular-features.ipynb b/datasources/preclinical/formatted/1.process-cell-line-molecular-features.ipynb
similarity index 100%
rename from moalmanac/datasources/preclinical/formatted/1.process-cell-line-molecular-features.ipynb
rename to datasources/preclinical/formatted/1.process-cell-line-molecular-features.ipynb
diff --git a/moalmanac/datasources/preclinical/formatted/README.md b/datasources/preclinical/formatted/README.md
similarity index 100%
rename from moalmanac/datasources/preclinical/formatted/README.md
rename to datasources/preclinical/formatted/README.md
diff --git a/moalmanac/datasources/preclinical/formatted/almanac-gdsc-mappings.json b/datasources/preclinical/formatted/almanac-gdsc-mappings.json
similarity index 100%
rename from moalmanac/datasources/preclinical/formatted/almanac-gdsc-mappings.json
rename to datasources/preclinical/formatted/almanac-gdsc-mappings.json
diff --git a/moalmanac/datasources/preclinical/formatted/cell-line-names.formatted.txt b/datasources/preclinical/formatted/cell-line-names.formatted.txt
similarity index 100%
rename from moalmanac/datasources/preclinical/formatted/cell-line-names.formatted.txt
rename to datasources/preclinical/formatted/cell-line-names.formatted.txt
diff --git a/moalmanac/datasources/preclinical/formatted/cell-lines.copy-numbers.txt b/datasources/preclinical/formatted/cell-lines.copy-numbers.txt
similarity index 100%
rename from moalmanac/datasources/preclinical/formatted/cell-lines.copy-numbers.txt
rename to datasources/preclinical/formatted/cell-lines.copy-numbers.txt
diff --git a/moalmanac/datasources/preclinical/formatted/cell-lines.fusions.txt b/datasources/preclinical/formatted/cell-lines.fusions.txt
similarity index 100%
rename from moalmanac/datasources/preclinical/formatted/cell-lines.fusions.txt
rename to datasources/preclinical/formatted/cell-lines.fusions.txt
diff --git a/moalmanac/datasources/preclinical/formatted/cell-lines.somatic-variants.txt b/datasources/preclinical/formatted/cell-lines.somatic-variants.txt
similarity index 100%
rename from moalmanac/datasources/preclinical/formatted/cell-lines.somatic-variants.txt
rename to datasources/preclinical/formatted/cell-lines.somatic-variants.txt
diff --git a/moalmanac/datasources/preclinical/formatted/cell-lines.summary.txt b/datasources/preclinical/formatted/cell-lines.summary.txt
similarity index 100%
rename from moalmanac/datasources/preclinical/formatted/cell-lines.summary.txt
rename to datasources/preclinical/formatted/cell-lines.summary.txt
diff --git a/moalmanac/datasources/preclinical/formatted/sanger.gdsc.txt b/datasources/preclinical/formatted/sanger.gdsc.txt
similarity index 100%
rename from moalmanac/datasources/preclinical/formatted/sanger.gdsc.txt
rename to datasources/preclinical/formatted/sanger.gdsc.txt
diff --git a/moalmanac/datasources/preclinical/generate-dictionary.ipynb b/datasources/preclinical/generate-dictionary.ipynb
similarity index 100%
rename from moalmanac/datasources/preclinical/generate-dictionary.ipynb
rename to datasources/preclinical/generate-dictionary.ipynb
diff --git a/moalmanac/datasources/preclinical/source/README.md b/datasources/preclinical/source/README.md
similarity index 100%
rename from moalmanac/datasources/preclinical/source/README.md
rename to datasources/preclinical/source/README.md
diff --git a/moalmanac/datasources/preclinical/source/ccle-2019/README.md b/datasources/preclinical/source/ccle-2019/README.md
similarity index 100%
rename from moalmanac/datasources/preclinical/source/ccle-2019/README.md
rename to datasources/preclinical/source/ccle-2019/README.md
diff --git a/moalmanac/datasources/preclinical/source/depmap/README.md b/datasources/preclinical/source/depmap/README.md
similarity index 100%
rename from moalmanac/datasources/preclinical/source/depmap/README.md
rename to datasources/preclinical/source/depmap/README.md
diff --git a/moalmanac/datasources/preclinical/source/gdsc/README.md b/datasources/preclinical/source/gdsc/README.md
similarity index 100%
rename from moalmanac/datasources/preclinical/source/gdsc/README.md
rename to datasources/preclinical/source/gdsc/README.md

From abdd8a0f0aa838c465391b6234c69270a9b4efce Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Tue, 9 Jul 2024 23:00:35 -0400
Subject: [PATCH 09/19] removed

---
 datasources/oncotree/get-oncotree.ipynb | 76 -------------------------
 1 file changed, 76 deletions(-)
 delete mode 100644 datasources/oncotree/get-oncotree.ipynb

diff --git a/datasources/oncotree/get-oncotree.ipynb b/datasources/oncotree/get-oncotree.ipynb
deleted file mode 100644
index 188c6fb..0000000
--- a/datasources/oncotree/get-oncotree.ipynb
+++ /dev/null
@@ -1,76 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "15aef7c6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "import pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "9006f380",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "request = 'http://oncotree.mskcc.org/api/tumorTypes/tree?version=oncotree_latest_stable'\n",
-    "r = requests.get(request)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "875a3e89",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['OVARY', 'LYMPH', 'SOFT_TISSUE', 'THYROID', 'PLEURA', 'PANCREAS', 'BILIARY_TRACT', 'BREAST', 'HEAD_NECK', 'EYE', 'AMPULLA_OF_VATER', 'THYMUS', 'CERVIX', 'PNS', 'BOWEL', 'BONE', 'SKIN', 'BLADDER', 'BRAIN', 'ADRENAL_GLAND', 'PROSTATE', 'LUNG', 'PENIS', 'UTERUS', 'OTHER', 'TESTIS', 'LIVER', 'PERITONEUM', 'MYELOID', 'VULVA', 'KIDNEY', 'STOMACH'])"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r.json()['TISSUE']['children'].keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0c4d27c3",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "moalmanac",
-   "language": "python",
-   "name": "moalmanac"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From d412bdab1095d2a6c57f622f46f445b5c002a444 Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Tue, 9 Jul 2024 23:01:03 -0400
Subject: [PATCH 10/19] Removed deconstructSigs R wrapper

---
 moalmanac/run_deconstructsigs.R | 41 ---------------------------------
 1 file changed, 41 deletions(-)
 delete mode 100644 moalmanac/run_deconstructsigs.R

diff --git a/moalmanac/run_deconstructsigs.R b/moalmanac/run_deconstructsigs.R
deleted file mode 100644
index 89900a3..0000000
--- a/moalmanac/run_deconstructsigs.R
+++ /dev/null
@@ -1,41 +0,0 @@
-#!/usr/bin/Rscript
-library("deconstructSigs")
-
-args = commandArgs(trailingOnly=TRUE)
-patient_id = args[1]
-snv_handle = args[2]
-sample = args[3]
-ref = args[4]
-alt = args[5]
-chr = args[6]
-pos = args[7]
-folder = args[8]
-
-maf = read.csv(snv_handle, sep = '\t', comment.char = '#')
-names(maf) <- tolower(names(maf))
-cols = c(sample, ref, alt, chr, pos)
-maf <- maf[colnames(maf) %in% cols]
-
-maf$tumor_sample_barcode <- sapply(maf$tumor_sample_barcode, as.factor)
-maf$reference_allele <- sapply(maf$reference_allele, as.factor)
-maf$tumor_seq_allele2 <- sapply(maf$tumor_seq_allele2, as.factor)
-maf$chromosome <- sapply(maf$chromosome, as.factor)
-
-unique.samples = unique(maf$tumor_sample_barcode)
-
-sigs.input <- mut.to.sigs.input(mut.ref = maf,
-    sample.id = sample, chr = chr,
-    pos = pos, ref = ref,
-    alt = alt)
-
-temp.filename <- paste(folder, patient_id, ".sigs.context.txt", sep = "")
-write.table(sigs.input, file = temp.filename, sep = '\t', row.names = FALSE)
-
-for (sample_ in unique.samples) {
-    output.sigs <- whichSignatures(tumor.ref = sigs.input,
-        signatures.ref = signatures.cosmic, sample.id = sample_, 
-        context = TRUE, tri.counts.method = 'default')
-    
-    temp.filename = paste(folder, patient_id, ".sigs.cosmic.txt", sep = "")
-    write.table(output.sigs, file = temp.filename, sep = '\t', row.names = FALSE)
-}

From 64a22a74f6f5f0c3c2419dcf23287334d74307e0 Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Tue, 9 Jul 2024 23:01:19 -0400
Subject: [PATCH 11/19] Removed deconstructSigs wrapper

---
 moalmanac/wrapper_deconstructsigs.sh | 12 ------------
 1 file changed, 12 deletions(-)
 delete mode 100644 moalmanac/wrapper_deconstructsigs.sh

diff --git a/moalmanac/wrapper_deconstructsigs.sh b/moalmanac/wrapper_deconstructsigs.sh
deleted file mode 100644
index 1b6307c..0000000
--- a/moalmanac/wrapper_deconstructsigs.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/bin/bash
-
-patient_id=$1
-snv_handle=$2
-sample=$3
-ref=$4
-alt=$5
-chr=$6
-pos=$7
-folder=$8
-
-Rscript run_deconstructsigs.R ${patient_id} ${snv_handle} ${sample} ${ref} ${alt} ${chr} ${pos} ${folder}

From b92c27b5d21ff3f52ba05b5064923d7f0de9fba4 Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Tue, 9 Jul 2024 23:24:44 -0400
Subject: [PATCH 12/19] Updated documentation

---
 moalmanac/README.md           |  30 ++++--
 moalmanac/moalmanac.py        |   4 +-
 moalmanac/simplified_input.py | 169 +++++++++++++++++++++++-----------
 3 files changed, 139 insertions(+), 64 deletions(-)

diff --git a/moalmanac/README.md b/moalmanac/README.md
index a65e169..72d6cd1 100644
--- a/moalmanac/README.md
+++ b/moalmanac/README.md
@@ -2,11 +2,13 @@
 Molecular Oncology Almanac can be run by executing either `moalmanac.py` with [standard input formats](#standard-usage) or `simplified_input.py` with [simplified inputs](#simplified-input). Please follow the [installation instructions](../README.md#installation) before use.
 
 ## Standard usage
-Molecular Oncology Almanac may be executed on any combination of input data but does require a patient_id to label output files. Additional settings can be set by modifying the [config.ini](#configini) file and column names may be modified by editing the [colnames.ini](#colnamesini) file.
+Molecular Oncology Almanac may be executed on any combination of input data but does require a patient_id to label output files. Additional settings can be set by modifying the [config.ini](#configini) file and column names may be modified by editing the [colnames.ini](#colnamesini) file. The [config.ini](config.ini) and [annotation-databases.ini](annotation-databases.ini) must be passed as arguments to moalmanac.py.
 
 Required arguments:
 ```
     --patient_id            <string>    patient identifier
+    --config                <string>    ini file that contains configuration details, config.ini
+    --dbs                   <string>    ini file that contains database paths for annotation, annotation-databases.ini
 ```
 
 Optional arguments:
@@ -28,6 +30,7 @@ Optional arguments:
     --disable_matchmaking   <boolean>   remove patient-to-cell line matchmaking from report
     --description           <string>    description of patient
     --output-directory      <string>    specify location of produced outputs
+    --preclinical-dbs       <string>    path to preclinical-databases.ini file
 ```
 
 Example:
@@ -46,8 +49,11 @@ python moalmanac.py \
     --validation_handle "../example_data/example_patient.rna.somatic.snvs.maf" \
     --purity 0.85 \
     --ploidy 4.02 \
-    --ms_status "msih"
-    --wgd
+    --ms_status "msih" \
+    --wgd \
+    --config config.ini \
+    --dbs annotation-databases.ini \
+    --preclinical-dbs preclinical-databases.ini
 ```
 
 These example inputs may also be processed by executing `run_example.py`. 
@@ -85,6 +91,8 @@ This is also described in the [description of inputs](../docs/description-of-inp
 Required arguments:
 ```
     --patient_id            <string>    patient identifier
+    --config                <string>    ini file that contains configuration details, config.ini
+    --dbs                   <string>    ini file that contains database paths for annotation, annotation-databases.ini
 ```
 
 Optional arguments:
@@ -98,6 +106,7 @@ Optional arguments:
     --wgd                   <boolean>   specify the occurence of whole genome duplication
     --description           <string>    description of patient
     --output-directory      <string>    specify location of produced outputs
+    --preclinical-dbs       <string>    path to preclinical-databases.ini file
 ```
 
 Example:
@@ -110,8 +119,11 @@ python simplified_input.py \
     --input "../example_data/example_patient.simplified_input.txt" \
     --purity 0.85 \
     --ploidy 4.02 \
-    --ms_status "msih"
-    --wgd
+    --ms_status "msih" \
+    --wgd \
+    --config config.ini \
+    --dbs annotation-databases.ini \
+    --preclinical-dbs preclinical-databases.ini
 ```
 
 ## Configuration
@@ -128,8 +140,6 @@ The configuration file [config.ini](config.ini) lets users change settings, thre
 - `signatures` allows users to specify the minimum required contribution to consider mutational signatures
 - `validation_sequencing` allows users to specify minimum power and allelic fraction to consider for variants from validation sequencing
 - `feature_types` allows users to specify strings for considered feature types
-- `databases` specifies file paths for databases used for annotation, found in the `moalmanac/databases/` folder
-- `preclinical` specifies file paths for datasources used for preclinical functions, [model_similarity](../docs/description-of-outputs.md#profile-to-cell-line-matchmaking) and [preclinical efficacy](../docs/description-of-outputs.md#preclinical-efficacy)
 
 ### colnames.ini
 The configuration file [colnames.ini](colnames.ini) lets users change strings associated with column names for input and output files. The file contains the following relevant sections,
@@ -137,6 +147,12 @@ The configuration file [colnames.ini](colnames.ini) lets users change strings as
 
 Other sections in this configuration file are used internally to MOAlmanac for processing.
 
+### annotation-databases.ini
+The configuration file [annotation-databases.ini](annotation-databases.ini) lets users change paths to datasources being used to annotate genomic variants within the algorithm. This file contains a single `databases` section, with either a relative or absolute path being set to the `root` variable. By default, this points to the [datasources/](../datasources/) folder in the root directory of this repository.
+
+### preclinical-databases.ini
+Similar to `annotation-databases.ini`, the configuration file [preclinical-databases.ini](preclinical-databases.ini) lets users change paths to datasources being used to for preclinical comparison functions,  [model_similarity](../docs/description-of-outputs.md#profile-to-cell-line-matchmaking) and [preclinical efficacy](../docs/description-of-outputs.md#preclinical-efficacy).
+
 ## Citation
 If you find this tool or any code herein useful, please cite:  
 > [Reardon, B., Moore, N.D., Moore, N.S., *et al*. Integrating molecular profiles into clinical frameworks through the Molecular Oncology Almanac to prospectively guide precision oncology. *Nat Cancer* (2021). https://doi.org/10.1038/s43018-021-00243-3](https://www.nature.com/articles/s43018-021-00243-3)
diff --git a/moalmanac/moalmanac.py b/moalmanac/moalmanac.py
index 05ff8c3..1b5572a 100644
--- a/moalmanac/moalmanac.py
+++ b/moalmanac/moalmanac.py
@@ -389,12 +389,12 @@ def main(patient, inputs, output_folder, config, dbs, dbs_preclinical=None):
     arg_parser.add_argument(
         '--config', '-c',
         required=True,
-        help='ini file that contains configuration details '
+        help='ini file that contains configuration details'
     )
     arg_parser.add_argument(
         '--dbs',
         required=True,
-        help='ini file that contains database paths '
+        help='ini file that contains database paths'
     )
     arg_parser.add_argument(
         '--preclinical-dbs',
diff --git a/moalmanac/simplified_input.py b/moalmanac/simplified_input.py
index 5bdfc09..51d57a8 100644
--- a/moalmanac/simplified_input.py
+++ b/moalmanac/simplified_input.py
@@ -17,7 +17,7 @@
 import writer
 
 from config import COLNAMES
-from config import CONFIG
+from reader import Ini
 
 snv_handle = 'snv_handle'
 indel_handle = 'indel_handle'
@@ -84,34 +84,41 @@ def subset_by_feature_type(dataframe):
     return somatic, germline
 
 
-def main(patient, input_file, output_folder):
-    dbs = datasources.Datasources.generate_db_dict(CONFIG)
+def main(patient, input_file, output_folder, config, dbs, dbs_preclinical=None):
+    metadata_dictionary = moalmanac.create_metadata_dictionary(patient)
+
     output_folder = moalmanac.format_output_directory(output_folder)
     if output_folder != "":
         moalmanac.execute_cmd(f"mkdir -p {output_folder}")
 
-    string_id = patient[patient_id]
+    string_id = metadata_dictionary[patient_id]
 
-    mapped_ontology = ontologymapper.OntologyMapper.map(dbs, patient[tumor_type])
-    patient[ontology] = mapped_ontology[ontology]
-    patient[code] = mapped_ontology[code]
+    mapped_ontology = ontologymapper.OntologyMapper.map(dbs, metadata_dictionary[tumor_type])
+    metadata_dictionary[ontology] = mapped_ontology[ontology]
+    metadata_dictionary[code] = mapped_ontology[code]
 
     alterations = features.Simple.import_feature(input_file)
-    annotated_alterations = annotator.Annotator.annotate_simple(alterations, dbs, patient[code])
+    annotated_alterations = annotator.Annotator.annotate_simple(alterations, dbs, patient[code], config=config)
     evaluated_alterations = evaluator.Evaluator.evaluate_somatic(annotated_alterations)
 
     evaluated_somatic, evaluated_germline = subset_by_feature_type(evaluated_alterations)
     evaluated_somatic = annotator.OverlapSomaticGermline.append_germline_hits(evaluated_somatic, evaluated_germline)
     integrated = evaluator.Integrative.evaluate(evaluated_somatic, evaluated_germline, dbs, feature_types)
 
-    somatic_burden = features.BurdenReader.import_feature('', patient, evaluated_somatic, dbs)
-    patient_wgd = features.Aneuploidy.summarize(patient[wgd])
-    patient_ms_status = features.MicrosatelliteReader.summarize(patient[ms_status])
+    somatic_burden = features.BurdenReader.import_feature(
+        handle='',
+        patient=metadata_dictionary,
+        variants=evaluated_somatic,
+        dbs=dbs,
+        config=config
+    )
+    patient_wgd = features.Aneuploidy.summarize(patient[wgd], config=config)
+    patient_ms_status = features.MicrosatelliteReader.summarize(patient[ms_status], config=config)
     patient[ms_status] = features.MicrosatelliteReader.map_status(patient[ms_status])
 
-    annotated_burden = annotator.Annotator.annotate_almanac(somatic_burden, dbs, patient[code])
-    annotated_wgd = annotator.Annotator.annotate_almanac(patient_wgd, dbs, patient[code])
-    annotated_ms_status = annotator.Annotator.annotate_almanac(patient_ms_status, dbs, patient[code])
+    annotated_burden = annotator.Annotator.annotate_almanac(somatic_burden, dbs, patient[code], config=config)
+    annotated_wgd = annotator.Annotator.annotate_almanac(patient_wgd, dbs, patient[code], config=config)
+    annotated_ms_status = annotator.Annotator.annotate_almanac(patient_ms_status, dbs, patient[code], config=config)
 
     evaluated_burden = evaluator.Evaluator.evaluate_almanac(annotated_burden)
     evaluated_wgd = evaluator.Evaluator.evaluate_almanac(annotated_wgd)
@@ -125,14 +132,14 @@ def main(patient, input_file, output_folder):
         ms_status=evaluated_ms_status,
         burden=evaluated_burden,
         signatures=features.Features.create_empty_dataframe(),
-        wgd=evaluated_wgd
+        wgd=evaluated_wgd,
+        config=config
     )
 
     strategies = evaluator.Strategies.report_therapy_strategies(actionable)
 
+    function_toggle = config['function_toggle']
     efficacy_summary = investigator.SummaryDataFrame.create_empty_dataframe()
-    efficacy_dictionary = {}
-    cell_lines_dictionary = {}
     preclinical_efficacy_on = TOGGLE_FEATURES.getboolean('calculate_preclinical_efficacy')
 
     # The input argument --disable_matchmaking will be removed in the next non-backwards compatible release
@@ -146,10 +153,11 @@ def main(patient, input_file, output_folder):
         if preclinical_efficacy_on:
             plot_preclinical = TOGGLE_FEATURES.getboolean('plot_preclinical_efficacy')
             efficacy_results = moalmanac.process_preclinical_efficacy(
-                dbs_preclinical,
-                actionable,
-                output_folder,
-                string_id,
+                dbs=dbs_preclinical,
+                dataframe=actionable,
+                folder=output_folder,
+                label=string_id,
+                config=config,
                 plot=plot_preclinical
             )
             efficacy_dictionary = efficacy_results[0]
@@ -162,8 +170,17 @@ def main(patient, input_file, output_folder):
                 append_lookup=TOGGLE_FEATURES.getboolean('include_preclinical_efficacy_in_actionability_report')
             )
         if model_similarity_on:
-            similarity_results = matchmaker.Matchmaker.compare(dbs, dbs_preclinical, evaluated_somatic, string_id)
-            similarity_summary = matchmaker.Report.create_report_dictionary(similarity_results, cell_lines_dictionary)
+            similarity_results = matchmaker.Matchmaker.compare(
+                dbs=dbs,
+                dbs_preclinical=dbs_preclinical,
+                somatic=evaluated_somatic,
+                case_sample_id=string_id,
+                config=config
+            )
+            similarity_summary = matchmaker.Report.create_report_dictionary(
+                similarity_results,
+                cell_lines_dictionary
+            )
 
     writer.Actionable.write(actionable, string_id, output_folder)
     writer.GermlineACMG.write(evaluated_germline, string_id, output_folder)
@@ -177,13 +194,14 @@ def main(patient, input_file, output_folder):
     writer.PreclinicalEfficacy.write(efficacy_summary, string_id, output_folder)
     writer.PreclinicalMatchmaking.write(similarity_results, string_id, output_folder)
 
-    if TOGGLE_FEATURES.getboolean('generate_actionability_report'):
-        report_dictionary = reporter.Reporter.generate_dictionary(evaluated_somatic, patient)
+    if function_toggle.getboolean('generate_actionability_report'):
+        report_dictionary = reporter.Reporter.generate_dictionary(evaluated_somatic, metadata_dictionary)
 
-        include_similarity = TOGGLE_FEATURES.getboolean('include_model_similarity_in_actionability_report')
+        include_similarity = function_toggle.getboolean('include_model_similarity_in_actionability_report')
         reporter.Reporter.generate_actionability_report(
-            actionable,
-            report_dictionary,
+            actionable=actionable,
+            report_dictionary=report_dictionary,
+            config=config,
             similarity=similarity_summary if include_similarity else None,
             output_directory=output_folder
         )
@@ -194,33 +212,66 @@ def main(patient, input_file, output_folder):
 
     arg_parser = argparse.ArgumentParser(prog='Molecular Oncology Almanac using simplified input',
                                          description='Annotates only using the Molecular Oncology Almanac database')
-    arg_parser.add_argument('--patient_id',
-                            help='patient id label',
-                            required=True)
-    arg_parser.add_argument('--stage',
-                            default='Unknown',
-                            help='disease stage')
-    arg_parser.add_argument('--tumor_type',
-                            default='Unknown',
-                            help='reported tumor type')
-    arg_parser.add_argument('--input',
-                            help='Tab delimited file of observed alterations')
-    arg_parser.add_argument('--ms_status',
-                            default='unk',
-                            choices=['msih', 'msil', 'mss', 'unk'],
-                            help='microsatellite instability status')
-    arg_parser.add_argument('--purity',
-                            default='Unknown',
-                            help='Tumor purity')
-    arg_parser.add_argument('--ploidy',
-                            default='Unknown',
-                            help='Tumor ploidy')
-    arg_parser.add_argument('--wgd',
-                            action='store_true',
-                            help='Specify the occurrence of whole genome duplication')
-    arg_parser.add_argument('--output_directory',
-                            default=None,
-                            help='Output directory for generated files')
+    arg_parser.add_argument(
+        '--patient_id',
+        help='patient id label',
+        required=True
+    )
+    arg_parser.add_argument(
+        '--stage',
+        default='Unknown',
+        help='disease stage'
+    )
+    arg_parser.add_argument(
+        '--tumor_type',
+        default='Unknown',
+        help='reported tumor type'
+    )
+    arg_parser.add_argument(
+        '--input',
+        help='Tab delimited file of observed alterations'
+    )
+    arg_parser.add_argument(
+        '--ms_status',
+        default='unk',
+        choices=['msih', 'msil', 'mss', 'unk'],
+        help='microsatellite instability status'
+    )
+    arg_parser.add_argument(
+        '--purity',
+        default='Unknown',
+        help='Tumor purity'
+    )
+    arg_parser.add_argument(
+        '--ploidy',
+        default='Unknown',
+        help='Tumor ploidy'
+    )
+    arg_parser.add_argument(
+        '--wgd',
+        action='store_true',
+        help='Specify the occurrence of whole genome duplication'
+    )
+    arg_parser.add_argument(
+        '--output_directory',
+        default=None,
+        help='Output directory for generated files'
+    )
+    arg_parser.add_argument(
+        '--config', '-c',
+        required=True,
+        help='ini file that contains configuration details'
+    )
+    arg_parser.add_argument(
+        '--dbs',
+        required=True,
+        help='ini file that contains database paths '
+    )
+    arg_parser.add_argument(
+        '--preclinical-dbs',
+        required=False,
+        help='ini file that contains preclinical file paths'
+    )
     args = arg_parser.parse_args()
 
     patient_dict = {
@@ -236,6 +287,14 @@ def main(patient, input_file, output_folder):
 
     output_directory = args.output_directory if args.output_directory else os.getcwd()
 
+    config_ini = Ini.read(args.config, extended_interpolation=False, convert_to_dictionary=False)
+
+    db_paths = Ini.read(args.dbs, extended_interpolation=False, convert_to_dictionary=True)
+    if args.preclinical_dbs:
+        preclinical_db_paths = Ini.read(args.preclinical_dbs, extended_interpolation=False, convert_to_dictionary=True)
+    else:
+        preclinical_db_paths = None
+
     main(patient_dict, args.input, output_directory)
 
     end_time = time.time()

From 7a34c9d03096ef847d8d564834c4a10f54754e77 Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Tue, 9 Jul 2024 23:35:10 -0400
Subject: [PATCH 13/19] Increased minor version of algorithm

---
 moalmanac/config.ini | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/moalmanac/config.ini b/moalmanac/config.ini
index 9e21e3c..6a38ea3 100644
--- a/moalmanac/config.ini
+++ b/moalmanac/config.ini
@@ -8,7 +8,7 @@ include_preclinical_efficacy_in_actionability_report = on
 plot_preclinical_efficacy = on
 
 [versions]
-interpreter = 0.6.0
+interpreter = 0.7.0
 database = v.2024-04-11
 
 [exac]

From bd7d377245dba455abaf3d66f084d783931529e1 Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Tue, 9 Jul 2024 23:42:59 -0400
Subject: [PATCH 14/19] Removed commented out code

---
 moalmanac/evaluator.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/moalmanac/evaluator.py b/moalmanac/evaluator.py
index 3031baf..d684c89 100644
--- a/moalmanac/evaluator.py
+++ b/moalmanac/evaluator.py
@@ -246,9 +246,6 @@ def evaluate(cls, somatic, germline, ms_variants, ms_status, burden, signatures,
         df = features.Features.concat_list_of_dataframes(list_of_dataframes=actionable_list)
 
         df[Evaluator.feature_display] = cls.format_feature_display(df=df, config=config)
-        #    df, Evaluator.feature_display,
-        #    Evaluator.feature_type, Evaluator.feature,
-        #    Evaluator.alt_type, Evaluator.alt)
         return df.sort_values(cls.sort_columns, ascending=False)
 
     @classmethod
@@ -256,9 +253,6 @@ def format_feature_display(cls, df, config):
         display_column = Evaluator.feature_display
         feature_type_column = Evaluator.feature_type
         feature_column = Evaluator.feature
-        # alt_type_column = Evaluator.alt_type
-        # alt_column = Evaluator.alt
-        #sig_version = config['signatures']['version']
 
         biomarker_types = config['feature_types']
         idx_somatic = df[feature_type_column].isin([biomarker_types['mut']])

From 40bcbb77570a8a76847740bbe4fb9bd028d7667f Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Tue, 9 Jul 2024 23:48:25 -0400
Subject: [PATCH 15/19] Removed additional commented code

---
 moalmanac/reporter.py             | 1 -
 moalmanac/test/annotator_tests.py | 2 --
 2 files changed, 3 deletions(-)

diff --git a/moalmanac/reporter.py b/moalmanac/reporter.py
index e0c0ef8..21f8828 100644
--- a/moalmanac/reporter.py
+++ b/moalmanac/reporter.py
@@ -5,7 +5,6 @@
 import os
 
 from config import COLNAMES
-#from config import CONFIG
 
 
 class Reporter:
diff --git a/moalmanac/test/annotator_tests.py b/moalmanac/test/annotator_tests.py
index 399a806..5a43b13 100644
--- a/moalmanac/test/annotator_tests.py
+++ b/moalmanac/test/annotator_tests.py
@@ -9,8 +9,6 @@
 from datasources import Preclinical as datasources_Preclinical
 from features import Features
 from investigator import SensitivityDictionary
-# from reader import Ini
-# from config import CONFIG
 
 
 class UnitTestAnnotator(unittest.TestCase):

From d69eaa6d2fe9874616a8f489c9f33b91f73ad26a Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Wed, 10 Jul 2024 00:02:19 -0400
Subject: [PATCH 16/19] Removed unused ini reader

---
 moalmanac/reader.py | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/moalmanac/reader.py b/moalmanac/reader.py
index ce61219..74ff8df 100644
--- a/moalmanac/reader.py
+++ b/moalmanac/reader.py
@@ -5,14 +5,6 @@
 
 
 class Ini:
-    @classmethod
-    def read(cls, path, extended_interpolation=False, convert_to_dictionary=False):
-        ini = cls.load(path, extended_interpolation=extended_interpolation)
-        if convert_to_dictionary:
-            return cls.convert_ini_to_dictionary(ini)
-        else:
-            return ini
-
     @staticmethod
     def convert_ini_to_dictionary(ini):
         dictionary = {}

From 6ec4477cffd3e33f218884b2f0b99ea679afb809 Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Wed, 10 Jul 2024 00:03:00 -0400
Subject: [PATCH 17/19] just kidding

---
 moalmanac/reader.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/moalmanac/reader.py b/moalmanac/reader.py
index 74ff8df..ce61219 100644
--- a/moalmanac/reader.py
+++ b/moalmanac/reader.py
@@ -5,6 +5,14 @@
 
 
 class Ini:
+    @classmethod
+    def read(cls, path, extended_interpolation=False, convert_to_dictionary=False):
+        ini = cls.load(path, extended_interpolation=extended_interpolation)
+        if convert_to_dictionary:
+            return cls.convert_ini_to_dictionary(ini)
+        else:
+            return ini
+
     @staticmethod
     def convert_ini_to_dictionary(ini):
         dictionary = {}

From 3e400bfd450200ec8eb61bc84c24724a9317fe11 Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Wed, 10 Jul 2024 00:07:36 -0400
Subject: [PATCH 18/19] Set extended interpolation to True for db loading

---
 moalmanac/moalmanac.py        | 5 +++--
 moalmanac/simplified_input.py | 4 ++--
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/moalmanac/moalmanac.py b/moalmanac/moalmanac.py
index 1b5572a..5470b66 100644
--- a/moalmanac/moalmanac.py
+++ b/moalmanac/moalmanac.py
@@ -431,12 +431,13 @@ def main(patient, inputs, output_folder, config, dbs, dbs_preclinical=None):
 
     config_ini = Ini.read(args.config, extended_interpolation=False, convert_to_dictionary=False)
 
-    db_paths = Ini.read(args.dbs, extended_interpolation=False, convert_to_dictionary=True)
+    db_paths = Ini.read(args.dbs, extended_interpolation=True, convert_to_dictionary=True)
     if args.preclinical_dbs:
-        preclinical_db_paths = Ini.read(args.preclinical_dbs, extended_interpolation=False, convert_to_dictionary=True)
+        preclinical_db_paths = Ini.read(args.preclinical_dbs, extended_interpolation=True, convert_to_dictionary=True)
     else:
         preclinical_db_paths = None
 
+    print(db_paths)
     main(
         patient=patient_dict,
         inputs=inputs_dict,
diff --git a/moalmanac/simplified_input.py b/moalmanac/simplified_input.py
index 51d57a8..7264c20 100644
--- a/moalmanac/simplified_input.py
+++ b/moalmanac/simplified_input.py
@@ -289,9 +289,9 @@ def main(patient, input_file, output_folder, config, dbs, dbs_preclinical=None):
 
     config_ini = Ini.read(args.config, extended_interpolation=False, convert_to_dictionary=False)
 
-    db_paths = Ini.read(args.dbs, extended_interpolation=False, convert_to_dictionary=True)
+    db_paths = Ini.read(args.dbs, extended_interpolation=True, convert_to_dictionary=True)
     if args.preclinical_dbs:
-        preclinical_db_paths = Ini.read(args.preclinical_dbs, extended_interpolation=False, convert_to_dictionary=True)
+        preclinical_db_paths = Ini.read(args.preclinical_dbs, extended_interpolation=True, convert_to_dictionary=True)
     else:
         preclinical_db_paths = None
 

From 5e5d773288e4714937e33ec520b78be34bfd2828 Mon Sep 17 00:00:00 2001
From: Brendan Reardon <breardon@broadinstitute.org>
Date: Thu, 11 Jul 2024 10:14:02 -0400
Subject: [PATCH 19/19] revised input args for simplified input

---
 moalmanac/simplified_input.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/moalmanac/simplified_input.py b/moalmanac/simplified_input.py
index 7264c20..ba87257 100644
--- a/moalmanac/simplified_input.py
+++ b/moalmanac/simplified_input.py
@@ -295,7 +295,14 @@ def main(patient, input_file, output_folder, config, dbs, dbs_preclinical=None):
     else:
         preclinical_db_paths = None
 
-    main(patient_dict, args.input, output_directory)
+    main(
+        patient=patient_dict,
+        input_file=args.input,
+        output_folder=output_directory,
+        config=config_ini,
+        dbs=db_paths,
+        dbs_preclinical=preclinical_db_paths
+    )
 
     end_time = time.time()
     time_statement = "Molecular Oncology Almanac runtime: %s seconds" % round((end_time - start_time), 4)