Snakemake pipeline for 3P-Seq Analysis

This repository contains a Snakemake pipeline for processing and analyzing sequencing data. The pipeline uses several tools, including ABS-Scan and 3PSeq Analysis, along with custom scripts for post-processing. It also takes care of sexual and asexual seperately.

Overview

This pipeline processes 3P-Seq data to analyze polyadenylation events in Schmidtea mediterranea. The workflow includes:

• Quality control with fastqc
• Read alignment using bowtie
• Peak detection for 3' polyadenylation sites
• Hexamer sequence analysis and secondary PAS signal detection

Installation and Running the Pipeline

Run the following command to install dependencies:

brew install fastqc bowtie bedtools samtools wget
pip3 install joblib scipy numpy

1. Environment Setup

Ensure you have Miniconda or Anaconda installed. Then, create and activate a conda environment for running Snakemake:

# Create the environment (if not already created)
conda create -n snakemake -c bioconda -c conda-forge snakemake

# Activate the environment
conda activate snakemake

(Note: If your environment is already named "snakemake", simply activate it with conda activate snakemake.)

2. Verifying the Installation

Confirm that Snakemake is installed by checking its version:

snakemake --version

A version number should be printed.

3. Configuring the Pipeline

Review the config.yaml file to ensure the sample data, genome paths, and output directories match your setup. Update any paths if needed.

4. Testing the Snakefile

Before running the full pipeline, perform a dry-run to detect any issues:

snakemake -n

This command will print the planned jobs without executing them. You can also check the workflow syntax with:

snakemake --lint

This command will generate graphical representation of the piepline

snakemake --dag | dot -Tsvg > dag.svg

5. Running the Pipeline

After verifying that the dry-run completes without errors, start the pipeline. Specify the number of cores (adjust based on your system’s capability):

snakemake --cores 4

If your workflow rules use individual conda environments, run:

snakemake --use-conda --cores 4

6. Monitoring and Output

• Logs and Output Directories:
  The pipeline writes output to directories specified in the config.yaml file (e.g., qc/, data/trimmed/, data/aligned/, results/). Check these directories to monitor the progress and results of your analysis.

• Interrupting and Resuming:
  You can interrupt the pipeline with Ctrl+C. Resume the analysis by rerunning the same snakemake command; it will pick up from where it left off.

Additional Notes

• Make sure all input files (FASTQ, genome FASTA, GFF) are in the correct locations as specified in config.yaml.
• Custom scripts (identify_last_exons.py, combine_results.py) and tools (ABS-Scan-master, 3PSeq_analysis-master) should have the proper permissions to execute.

making bowtie index

bash bowtie-build --wrapper basic-0 SmedSxl_genome_v4.0.nt.gz bowtie_index/genome

download fastq

Go to the 3P https://www.ncbi.nlm.nih.gov/sra/?term=SRP070102

Download Fastq files

1. SRR3168630 - Planaria Asexual Strain - single end Poly A transcriptomic - Illumina GAIIx

2. SRR3169012 - Planaria Sexual Strain - single end Poly A transcriptomic - Illumina GAIIx

3. SRR3168939 - Planaria Sexual Strain - single end Poly A transcriptomic - Illumina GAIIx

4. SRR3168624 - Planaria Sexual Strain - single end Poly A transcriptomic - Ion torrent PGM

fastqc look at the quality of the reads

brew install fastqc
sudo cp /opt/homebrew/bin/fastqc /usr/local/bin/
mkdir -p qc
fastqc -t 4 *fastq.gz -o qc/

By following these steps, you can install, test, and run the Snakemake-based workflow successfully.