Skip to content

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

55 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Automation Pipeline for Transcription Factor Binding Site Identification in E. coli ChIP-Seq Data

Python 3.10+ License: MIT

This project aims to automate the exact identification of transcriptional regulator binding sites for 144 transcription factors (TFs) across the complete Escherichia coli genome, utilizing regions determined via ChIP-seq experiments.


Available Data

Peak File

This file contains the binding region information for the 144 transcription factors. To ensure structured data processing, it is organized into the following columns:

Column Name Description
Dataset_Ids Unique identifiers representing the specific experiment or condition under which the binding sites were identified.
TF_name Name of the transcription factor binding to the specified DNA sequence.
Peak_start Starting position of the binding peak within the genome.
Peak_end Ending position of the binding peak within the genome.
Peak_center Central position of the binding peak within the genome.
Peak_number Sequential peak number (useful for internal reference within the same dataset).
Max_Fold_Enrichment Maximum enrichment observed at the peak.
Max_Norm_Fold_Enrichment Normalized maximum enrichment.
Proximal_genes Genes located in proximity to the binding site.
Center_position_type Genomic classification of the peak's central position (e.g., intergenic, intronic).

Full Genome

The complete E. coli genome is provided and utilized in FASTA format.


Project Objectives

FASTA File Generation

Develop a command-line tool to extract and compile the peak sequences for each TF into individual FASTA files. Each output file will comprehensively represent a specific transcriptional regulator.

# Example execution command for the sequence extraction pipeline
python3 src/main.py -g doc/E_coli_K12_MG1655_U00096.3.txt -p data/union_peaks_file.tsv -s results/

Collaboration & Resources

This pipeline is designed to generate the raw inputs required for downstream motif discovery using the meme software suite. The required assets include:

  • FASTA Sequences: Compiled sequence files for all TFs.
  • Genome File: The U00096.3.fna reference file.
  • Version Control: The GitHub repository URL hosting the project and source code, designed to facilitate feedback, issue tracking, and contributions from all collaborators.

Development Best Practices

To ensure high software quality, reproducibility, and maintainability, this project adheres to the following standards:

  • Version Control: Strict use of Git for tracking changes, managing branches, and ensuring seamless collaboration.
  • Code Review: Implementation of periodic peer code reviews to elevate software quality and foster knowledge sharing among the team.
  • Comprehensive Documentation: Maintaining clear, up-to-date documentation for both the source code and operational workflows, ensuring a smooth onboarding process for new contributors.
  • Automated Testing: Development of automated testing pipelines to validate the functionality, accuracy, and robustness of the software.

Implementation Plan

  1. Sequence Extractor Development: Program the core module responsible for generating FASTA files from the peak data. As an automated pipeline, all required parameters must be fully configurable via a Command Line Interface (CLI).
  2. Integration and Testing: Merge the developed modules and execute comprehensive integration tests to ensure flawless end-to-end functionality.
  3. Deployment and Training: Deploy the system on the collaborator's server and conduct user training sessions for the target end-users.

About

Project on TFBS Search in the Python Course for the Bachelor of Science in Genomic Sciences, UNAM (2025-2 semester).

Resources

Stars

0 stars

Watchers

1 watching

Forks

Releases

Packages

Contributors

Languages