Improved detection of epigenomic marks with mixed-effects hidden Markov models

Area of Science:

• Genomics
• Epigenetics
• Bioinformatics

Background:

• Chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) identifies protein-DNA interactions.
• Accurate identification of enriched genomic loci is crucial for understanding epigenomic marks and gene regulation.
• Detecting consensus enrichment regions across multiple ChIP-seq samples is increasingly important due to reduced sequencing costs.

Purpose of the Study:

• To develop a statistical model for detecting broad consensus regions of enrichment from ChIP-seq replicates.
• To improve the accuracy of peak calling in ChIP-seq data analysis.

Main Methods:

• A statistical model based on a class of zero-inflated mixed-effects hidden Markov models was developed.
• The model was designed to detect consensus regions of enrichment from technical or biological ChIP-seq replicates.
• The method was applied to data from the Encyclopedia of DNA Elements and Roadmap Epigenomics projects and simulation studies.

Main Results:

• The proposed model outperforms existing methods for consensus peak calling in common epigenomic marks.
• The model effectively accounts for excess zeros and sample-specific biases in ChIP-seq data.
• Demonstrated superior performance in identifying consensus regions of enrichment.

Conclusions:

• The developed statistical model provides a robust approach for analyzing ChIP-seq data.
• This method enhances the understanding of epigenomic marks and gene regulatory mechanisms by accurately identifying consensus protein-DNA interaction sites.
• The model offers an improved solution for consensus peak calling in ChIP-seq experiments.