MotifGenie: a Python application for searching transcription factor binding sequences using ChIP-Seq datasets.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Next-generation sequencing has led to vast genomic data accumulation in public repositories.
• Chromatin immunoprecipitation sequencing (ChIP-Seq) is crucial for understanding gene expression regulation by transcription factors (TFs) and chromatin proteins.
• The Cistrome Project provides access to numerous ChIP-Seq experiments, but lacks integrative motif analysis for shared binding regions.

Purpose of the Study:

• To develop a computational tool for identifying common transcription factor binding sequences across multiple ChIP-Seq experiments.
• To enable integrative motif analysis on shared binding regions within the Cistrome database.

Main Methods:

• Implemented a Python command-line tool named MotifGenie.
• Utilized MACS 2.0 identified peaks from the Cistrome database for each experiment.
• Identified shared peak regions and scanned them for TF binding motifs from the JASPAR database.

Main Results:

• MotifGenie successfully identifies TF binding sequences common to multiple ChIP-Seq experiments.
• The tool facilitates the analysis of shared binding regions, enhancing motif discovery.
• Findings generated by MotifGenie have been validated through laboratory experiments.

Conclusions:

• MotifGenie provides a novel solution for integrative motif analysis of ChIP-Seq data.
• The tool enhances the utility of large-scale genomic repositories like Cistrome.
• MotifGenie is freely available, supporting broader research in gene regulation.