Companion code for the paper:
'A Chinese hamster transcription start site atlas that enables targeted editing of CHO cells.' Isaac Shamie, Sascha H Duttke, Karen J la Cour Karottki, Claudia Z Han, Anders H Hansen, Hooman Hefzi, Kai Xiong, Shangzhong Li, Samuel J Roth, Jenhan Tao, Gyun Min Lee, Christopher K Glass, Helene Faustrup Kildegaard, Christopher Benner, Nathan E Lewis, NAR Genomics and Bioinformatics, Volume 3, Issue 3, September 2021, lqab061, https://doi.org/10.1093/nargab/lqab061

Data

All sequencing data are submitted to the Gene Expression Omnibus (GEO) with GEO ID GSE159044. You can run the full pipeline using the raw sequencing data, The Supplementary Data provided in the manuscript is also uploaded to Synapse (synapse.org), with ID syn22969187. This includes our revised protein-coding promoter TSS annotation, in which each of TSS has an associated RefSeq transcript and gene association. This is done for both NCBI RefSeq (Supplementary Data S2) and with RefSeq in conjunction with the proteogenomics annotation reported in (42) (Supplementary Data S3). Open-chromatin regions merged across samples are provided on synapse as a bed file as well. The genome is taken from NCBI.

Required Software

• Homer (http://homer.ucsd.edu/homer/index.html)
• Snakemake (min version 6)
• numpanpar - (https://github.com/isshamie/parallel_helper) parallel utility package for parallelizing over pandas dataframes and numpy arrays
• Lewis' Lab repo https://github.com/LewisLabUCSD/NGS-Pipeline (branch isaac)

Steps to run

1. Create new conda environment (https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html). Install required software.
2. Download repo and install repo package by running

git clone git@github.com:isshamie/CH_TSS.git
cd CH_TSS 
pip install -e . #local & editable installation. Can drop the -e`

** Can run the sequencing pipeline or download the processed data from this Google Drive folder.
If you want to run the full pipeline, run steps 3-6. Otherwise downnload data and continue to 7.

Sequence alignment and peak detection: 3. Download data from GEO Accession GSE159044 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE159044)
4. Install both the NGS-Pipeline (https://github.com/LewisLabUCSD/NGS-Pipeline) and https://github.com/kundajelab/atac_dnase_pipelines for ATAC-seq
5. Update run_pipeline.sh and run_atac.sh to have the full path to the data.
6. Run both ./run_pipeline.sh and ./run_atac.sh to run the TSS sequencing and ATAC-seq, respectively.

Downstream analysis (snakemake) 7. The parameter file is parameters/params.yaml. Update parameters/params.yaml to have proper paths.
8. Download required software (see above) and Python packages (pandas, snakemake>6.0, matplotlib-venn, scipy)
9. Create the new TSS annotation and figures using the snakefile by running snakemake -s snakefile
10. Run total-RNA pipeline: First, use NGS-pipeline to get bam files for the total-RNA data. Then, get counts and process by runninng the jupyter notebooks found in notebooks/csRNA_pipeline in the numbered order. Step can be skipped, but won't be able to run a few notebooks below (F1d and F3b, below)

11. Run additional notebooks to finish additional figures.
Notebooks:

• F1d_sankey.ipynb: Generates the Sankey diagram
• F1e_gene_count.ipynb: Generates Figure 1E, total genes captured
• F2ab_histograms_tss: Read histograms around annotation, using Homer
• F2c_refTSS_Nuc: Generates nucleotide plots around TSSs for F2c.
• F3a_barplots.01 and F3a_barplots.02: Run in succession. These merge the bone-marrow WT and 1hKLA and then gets the barplots for number of genes expressed in X number of tissues.
• F3b_RNASeq_Gene_TPM_CDF: Gets the CDF for the total-RNA-seq genes. Requires to run the total-RNA pipeline (#10 above)
• F3ef_homer_motifs: Wrapper to run Homer motif detection
• F4a_silenced_glycosyltransferases: Detection of TSSs for important gene class
• SF2_compare_experiments: Compares the different TSS experiments
• SF4a_ATAC.ipynb: Open-chromatin around CHO TSSs.
• SF5_RNA_CHO: RNA-seq expression from CHO public data in genes grouped by TSS status
• SF6_promoter_usage: Promoter usage in all annotated genes and conserved genes
  *Note that some notebooks may have some hard-coded paths. This should be minimal, but there may be some re-running while fixing the location paths. Additionally, extra figures are made which involve varied parameters.