RegEvol

RegEvol is a computational framework for detecting directional selection in regulatory sequences through phenotypic predictions and phenotype-to-fitness models. This ongoing project aims to identify signatures of positive selection acting on transcription factor binding sites across mammals and drosophila, using both statistical and evolutionary approaches.

---

Project Overview

The pipeline includes the following key steps:

Regulatory Data Processing

• TF ChIP-seq Peak Calling: Extraction and processing of transcription factor binding sites from publicly available ChIP-seq datasets in mammals and drosophila.
• Homologous Sequence Retrieval: Identification of orthologous regulatory regions using whole-genome alignments from the Zoonomia Consortium.

Predictive Modeling

• gkm-SVM Training: Training of gapped k-mer support vector machine (gkm-SVM) models on species-specific regulatory sequences to predict binding affinity changes.

Positive Selection Detection

• Permutation Test: Application of the method from Liu & Robinson-Rechavi, 2020 to detect extreme change in binding affinity.
• RegSel Test: A novel maximum likelihood-based test (RegEvol) to infer evolutionary regimes of regulatory regions from predicted binding affinity shifts.

Comparative and Functional Analyses

• Simulations: Sequence evolution simulations under controlled parameters to validate the performance and robustness of selection detection methods.
• Conservation Metrics: Comparison of selection signals with evolutionary conservation scores (phastCons and phyloP).
• Functional Enrichment: Gene Ontology enrichment analysis of genes associated with positively selected regulatory regions.
• Divergence vs. Polymorphism: Integration of interspecies divergence and intraspecies polymorphism data to interpret evolutionary dynamics.

---

Repository structure

├── 1_chipseq/                    # Scripts to run nf-core ChIP-seq pipeline
│   ├── run.nextflow.sh           # Main wrapper script
│   └── logs/                     # Logs generated by pipeline runs
│
├── 2_selection_tests/            # Scripts and Snakemake workflow for RegEvol and Permutation tests 
│   ├── Snakemake/                # Snakefile and workflow rules
│   ├── envs/                     # Conda environment YAML files
│   └── scripts/                  # Python/R scripts used by the workflow
│
├── 3_analysis/                   # Scripts for downstream analyses and figures
│
├── config/                       # Global configuration files
│
├── docs/                         # ChiP-seq samples description and download files
│
└── README.md                     # Project documentation

---

Requirements

Before running the pipelines, ensure the following tools are installed on your system:

• Conda / Mamba – for managing environments and dependencies.
• Singularity – required to use Docker/Singularity containers in Snakemake rules.

This workflow also depends on:

• Snakemake ≥ X.Y : to run the RegEvol pipeline.
• Nextflow : to run the ChIP-seq calling pipeline.

Option 1: Use your existing installation

If you already have Snakemake and Nextflow installed on your system, you can skip creating the bundled environment and run the workflow directly.

Option 2: Create the provided environment

If you don’t, you can create the environment with:

mamba env create -f config/workflows.yaml

---

Usage

1. Run the ChIP-seq pipeline (Nextflow)

The workflow uses the nf-core/chipseq (2.1.0) Nextflow pipeline to process ChIP-seq data.

Required inputs

The pipeline requires the following three input files:

1. Reference genome sequence (FASTA format)
   → Located in ../data/genome_sequences/<species>/.

2. Sample sheet (CSV format)
   → Describes the FASTQ files, sample groups, and metadata (see the nf-core/chipseq sample sheet specification).
   → Located in ../docs/ChIP-seq/<species>/<sample>_samples_input.csv.

3. Annotations (GTF or GFF file)
   → Located in ../data/genome_sequences/<species>/.

Data download

If FASTQ files are not already present in ../data/ChIP-seq/<species>/<sample>/, the pipeline will automatically download them using the list of URLs provided in: docs/ChIP-seq/<sp>/DL_<sample>.txt

Run

./1_chipseq/run.nextflow.sh --sp <species> --sample <sample> [options]

Required arguments:

• --sp → species name (must match those in config/params.sh)
• --sample → sample name (used for result subfolders)

Optional arguments (can be passed in any order using --option value):

• --threads → number of threads to allocate (default: 1)
• --peaksType → "Narrow" for transcription factors, "Broad" for histone marks (default: "Narrow")
• --system → Execution mode: local or SLURM (default: "local")
• --container → Container type: "conda" or "singularity" (default: "conda")
• --resume → "resume" to resume a previous run, "false" to start fresh (default: "false")
• --skip → "true" to skip SPP and MultiQC steps, "false" to run all steps (default: "false")
• --help → display usage and exit

Example:

./1_chipseq/run.nextflow.sh --sp human --sample Wilson --threads 16 --system SLURM --container singularity

2. Run the RegEvol pipeline (Snakemake)

./2_selection_tests/run.snakemake.sh --sp <species> --sample <sample> [options]

Required arguments:

• --sp → species name (must match those in config/params.sh)
• --sample → sample name (used for result subfolders)

Optional arguments (can be passed in any order using --option value):

• --threads → number of threads to allocate (default: 1)
• --dryRun → Run Snakemake in dry-run mode: true/false (default: false)
• --help → display usage and exit

Example:

./2_selection_tests/run.snakemake.sh --sp human --sample Wilson --threads 10 --dryRun true