histoneHMM

HistoneHMM is a bioinformatics software tool designed to aid in the differential analysis of histone modifications with broad genomic footprints. One crucial experimental goal in high-throughput sequencing is to compare the ChIP-seq profiles between an experimental sample and a reference sample and identify regions that exhibit differential enrichment. However, this remains challenging for histone modifications with broad domains, such as heterochromatin-associated H3K27me3, as most ChIP-seq algorithms are designed to detect well-defined peak-like features.

To address this limitation, the paper's authors introduce histoneHMM, a powerful bivariate Hidden Markov Model that works with histone modifications with broad genomic footprints. The model aggregates short reads over larger regions and takes the resulting bivariate read counts as inputs for an unsupervised classification procedure, requiring no further tuning parameters. histoneHMM outputs probabilistic classifications of genomic regions as either modified in both samples, unmodified in both samples, or differentially modified between samples.

The authors comprehensively tested histoneHMM in the context of two broad repressive marks, H3K27me3 and H3K9me3, and evaluated region calls with follow-up qPCR and RNA-seq data. The results show that histoneHMM outperforms competing methods in detecting functionally relevant differentially modified regions. The software tool is particularly useful in the differential analysis of ChIP-seq data, as it provides a more effective and efficient solution to overcome the limitations of other existing methods.

HistoneHMM is a fast algorithm written in C++ and compiled as an R package. It runs in the R computing environment, seamlessly integrating with the extensive bioinformatic tool sets available through Bioconductor. This makes histoneHMM an attractive choice for scientists performing differential analysis of ChIP-seq data.