chromswitch: a flexible method to detect chromatin state switches

Summary Chromatin state plays a major role in controlling gene expression, and comparative analysis of ChIP‐seq data is key to understanding epigenetic regulation. We present chromswitch, an R/Bioconductor package to integrate epigenomic data in a defined window of interest to detect an overall switch in chromatin state. Chromswitch accurately classifies a benchmarking dataset, and when applied genome‐wide, the tool successfully detects chromatin changes that result in brain‐specific expression. Availability and implementation Chromswitch is implemented as an R package available from Bioconductor at https://bioconductor.org/packages/chromswitch. All data and code for reproducing the analysis presented in this paper are available at https://doi.org/10.5281/zenodo.1101260.

[1]  Michael Q. Zhang,et al.  Integrative analysis of 111 reference human epigenomes , 2015, Nature.

[2]  G. N. Lance,et al.  A General Theory of Classificatory Sorting Strategies: 1. Hierarchical Systems , 1967, Comput. J..

[3]  Sündüz Keles,et al.  Detecting differential binding of transcription factors with ChIP-seq , 2012, Bioinform..

[4]  Kevin C. Chen,et al.  Spectacle: fast chromatin state annotation using spectral learning , 2015, Genome Biology.

[5]  Emery H Bresnick,et al.  jMOSAiCS: joint analysis of multiple ChIP-seq datasets , 2013, Genome Biology.

[6]  Julia Hirschberg,et al.  V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure , 2007, EMNLP.

[7]  Hao Wu,et al.  A novel statistical method for quantitative comparison of multiple ChIP-seq datasets , 2015, Bioinform..

[8]  Rory Stark.  Differential Oestrogen Receptor Binding is Associated with Clinical Outcome in Breast Cancer , 2012, RECOMB.

[9]  Manolis Kellis,et al.  Systematic chromatin state comparison of epigenomes associated with diverse properties including sex and tissue type , 2015, Nature Communications.

[10]  Ho-Ryun Chung,et al.  Chromatin segmentation based on a probabilistic model for read counts explains a large portion of the epigenome , 2015, Genome Biology.

[11]  Manolis Kellis,et al.  ChromHMM: automating chromatin-state discovery and characterization , 2012, Nature Methods.

[12]  T. Furey.  ChIP – seq and beyond : new and improved methodologies to detect and characterize protein – DNA interactions , 2012 .

[13]  Peter J. Park,et al.  hiHMM: Bayesian non-parametric joint inference of chromatin state maps , 2015, Bioinform..

[14]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[15]  William Stafford Noble,et al.  Unsupervised pattern discovery in human chromatin structure through genomic segmentation , 2012, Nature Methods.

[16]  Joydeep Ghosh,et al.  Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions , 2002, J. Mach. Learn. Res..