ChIPdig

ChIPdig analyzes multi-sample Chromatin Immunoprecipitation sequencing (ChIP-seq) datasets to identify, annotate, and compare genomic enrichment of transcription factor binding and histone modifications.


Key Features:

  • Multi-Sample Analysis: Processes multiple ChIP-seq datasets simultaneously for comparative analyses across conditions or experimental designs.
  • Genome-wide Differential Enrichment Analysis: Performs genome-wide differential enrichment analysis to identify differences in signal between samples.
  • Read Mapping: Aligns sequencing reads to a reference genome.
  • Peak Calling: Identifies regions of significant enrichment corresponding to potential protein-DNA interactions.
  • Annotation of Regions: Annotates peaks to genomic features including transcription start and termination sites, exons, introns, and untranslated regions (UTRs).
  • Visualization Tools: Generates heatmaps and metaplots to visualize coverage and enrichment patterns across genomic regions.

Scientific Applications:

  • Epigenetic Research: Mapping transcription factor binding sites and histone modifications to study chromatin regulation.
  • Regulatory Mechanism Analysis: Supporting studies that elucidate regulatory mechanisms underlying gene expression changes across different biological conditions or treatments.
  • Large-scale ChIP-seq Projects: Enabling processing and comparative analysis of datasets from large-scale projects such as modENCODE.

Methodology:

Performs read mapping to a reference genome, peak calling to detect enriched regions, annotation of peaks to genomic features (transcription start and termination sites, exons, introns, UTRs), generation of heatmaps and metaplots, and genome-wide differential enrichment analysis across multiple samples.

Topics

Details

License:
GPL-3.0
Programming Languages:
R
Added:
11/14/2019
Last Updated:
12/11/2020

Operations

Publications

Esse R. ChIPdig: a comprehensive user-friendly tool for mining multi-sample ChIP-seq data. F1000Research. 2019;8:1295. doi:10.12688/f1000research.20027.1.

Funding: - National Institutes of Health: GM107056