ArticleBMC bioinformatics2015
histoneHMM: Differential analysis of histone modifications with broad genomic footprints.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Rescuing biologically relevant consensus regions across replicated samples.BMC bioinformatics · 2023Article
- Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection.Genome biology · 2022Article
- Complete loss of H3K9 methylation dissolves mouse heterochromatin organization.Nature communications · 2021Article
- The dynamic broad epigenetic (H3K4me3, H3K27ac) domain as a mark of essential genes.Clinical epigenetics · 2021Review
- Seq-ing answers: Current data integration approaches to uncover mechanisms of transcriptional regulation.Computational and structural biotechnology journal · 2020Review
- Genome-wide analysis of epigenetic dynamics across human developmental stages and tissues.BMC genomics · 2019Article
- RELACS nuclei barcoding enables high-throughput ChIP-seq.Communications biology · 2018Article
- Differential peak calling of ChIP-seq signals with replicates with THOR.Nucleic acids research · 2016Article
- csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows.Nucleic acids research · 2016Article
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9 authors.
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Abstract
backgroundChIP-seq has become a routine method for interrogating the genome-wide distribution of various histone modifications. An important experimental goal is to compare the ChIP-seq profiles between an experimental sample and a reference sample, and to identify regions that show differential enrichment. However, comparative analysis of samples 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.
resultsTo address this limitation we introduce histoneHMM, a powerful bivariate Hidden Markov Model for the differential analysis of histone modifications with broad genomic footprints. histoneHMM 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 being either modified in both samples, unmodified in both samples or differentially modified between samples. We extensively tested histoneHMM in the context of two broad repressive marks, H3K27me3 and H3K9me3, and evaluated region calls with follow up qPCR as well as RNA-seq data. Our results show that histoneHMM outperforms competing methods in detecting functionally relevant differentially modified regions.
conclusionhistoneHMM is a fast algorithm written in C++ and compiled as an R package. It runs in the popular R computing environment and thus seamlessly integrates with the extensive bioinformatic tool sets available through Bioconductor. This makeshistoneHMM an attractive choice for the differential analysis of ChIP-seq data. Software is available from http://histonehmm.molgen.mpg.de .
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