ChIP-BIT2: a software tool to detect weak binding events using a Bayesian integration approach

Xi Chen, Xu Shi, Andrew F. Neuwald, Leena Hilakivi-Clarke, Robert Clarke, Jianhua Xuan

Research output: Contribution to journalArticlepeer-review

Abstract

Background: ChIP-seq combines chromatin immunoprecipitation assays with sequencing and identifies genome-wide binding sites for DNA binding proteins. While many binding sites have strong ChIP-seq ‘peak’ observations and are well captured, there are still regions bound by proteins weakly, with a relatively low ChIP-seq signal enrichment. These weak binding sites, especially those at promoters and enhancers, are functionally important because they also regulate nearby gene expression. Yet, it remains a challenge to accurately identify weak binding sites in ChIP-seq data due to the ambiguity in differentiating these weak binding sites from the amplified background DNAs. Results: ChIP-BIT2 (http://sourceforge.net/projects/chipbitc/) is a software package for ChIP-seq peak detection. ChIP-BIT2 employs a mixture model integrating protein and control ChIP-seq data and predicts strong or weak protein binding sites at promoters, enhancers, or other genomic locations. For binding sites at gene promoters, ChIP-BIT2 simultaneously predicts their target genes. ChIP-BIT2 has been validated on benchmark regions and tested using large-scale ENCODE ChIP-seq data, demonstrating its high accuracy and wide applicability. Conclusion: ChIP-BIT2 is an efficient ChIP-seq peak caller. It provides a better lens to examine weak binding sites and can refine or extend the existing binding site collection, providing additional regulatory regions for decoding the mechanism of gene expression regulation.

Original languageEnglish (US)
Article number193
JournalBMC bioinformatics
Volume22
Issue number1
DOIs
StatePublished - Apr 15 2021

Bibliographical note

Funding Information:
This work was supported by National Institutes of Health (NIH) grants CA149653 (to JX), CA164384 (to LHC) and CA149147 (RC), and by NIH-NIGMS Grant R01GM125878 to AFN.

Publisher Copyright:
© 2021, The Author(s).

PubMed: MeSH publication types

  • Journal Article

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