Dataset Information

HistoneHMM: Differential analysis of histone modifications with broad genomic footprints.

ABSTRACT: BACKGROUND:ChIP-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. RESULTS:To 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. CONCLUSION:histoneHMM 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 .

SUBMITTER: Heinig M

PROVIDER: S-EPMC4347972 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

histoneHMM: Differential analysis of histone modifications with broad genomic footprints.

Heinig Matthias M Colomé-Tatché Maria M Taudt Aaron A Rintisch Carola C Schafer Sebastian S Pravenec Michal M Hubner Norbert N Vingron Martin M Johannes Frank F

BMC bioinformatics 20150222

<h4>Background</h4>ChIP-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 ar ...[more]

PMID: 25884684

Similar Datasets

Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (mailto:nshoresh@broad.mit.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track displays maps of chromatin state generated by the Broad/MGH ENCODE group using ChIP-seq. Chemical modifications (methylation, acetylation) to the histone proteins present in chromatin influence gene expression by changing how accessible the chromatin is to transcription. The ChIP-seq method involves first using formaldehyde to cross-link histones and other DNA-associated proteins to genomic DNA within cells. The cross-linked chromatin is subsequently extracted, mechanically sheared, and immunoprecipitated using specific antibodies. After reversal of cross-links, the immunoprecipitated DNA is sequenced and mapped to the human reference genome. The relative enrichment of each antibody-target (epitope) across the genome is inferred from the density of mapped fragments. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf ChIP-seq: Cells were grown according to the approved ENCODE cell culture protocols. Cells were fixed in 1% formaldehyde and resuspended in lysis buffer. Chromatin was sheared to 200-700 bp using a Diagenode Bioruptor. Solubilized chromatin was immunoprecipitated with antibodies against each of the histone antibodies listed above. Antibody-chromatin complexes were pulled-down using protein A-sepharose (or anti-IgM-conjugated agarose for RNA polymerase II), washed and then eluted. After cross-link reversal and proteinase K treatment, immunoprecipitated DNA was extracted with phenol-chloroform, ethanol precipitated, treated with RNAse and purified. One to ten nanograms of DNA were end-repaired, adapter-ligated and sequenced by Illumina Genome Analyzers as recommended by the manufacturer. Alignment: Sequence reads from each IP experiment were aligned to the human reference genome (GRCh37/hg19) using MAQ with default parameters, except '-C 11' and '-H output_file', which outputs up to 11 additional best matches for each read (if any are found) to a file. This information was used to filter out any read that had more than 10 best matches on the genome. Note: It is likely that instances where multiple reads align to the same position and with the same orientation are due to enhanced PCR amplification of a single DNA fragment. No attempt has been made, however, to remove such artifacts from the data, following ENCODE practices. Signal: Fragment densities were computed by counting the number of reads overlapping each 25 bp bin along the genome. Densities were computed using igvtools count with default parameters (in particular, '-w 25' to set window size of 25 bp and '-f mean' to report the mean value across the window), except for '-e' set to extend the reads to 200 bp, and the .wig output was converted to bigWig using wigToBigWig from the UCSC Kent software package. Peaks: Discrete intervals of ChIP-seq fragment enrichment were identified using Scripture, a scan statistics approach, under the assumption of uniform background signal. All data sets where processed with '-task chip', and with '-windows 100,200,500,1000,5000,10000,100000'. (No mask file nor the '-trim' option have been used.) The resulting called segments were then further filtered to remove intervals that are significantly enriched only because they contain smaller enriched intervals within them. This post-processing step has been implemented using Matlab. The use of the post-processing step allowed very large enriched intervals (of the order of Mbps for H3K27me3, for instance) to be detected, as well as much smaller intervals, without the need to tailor the parameters of Scripture based on prior expectations.

Dataset Information

HistoneHMM: Differential analysis of histone modifications with broad genomic footprints.

Publications

histoneHMM: Differential analysis of histone modifications with broad genomic footprints.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets