Probabilistic partitioning methods to find significant patterns in ChIP-Seq data

Nishanth Ulhas Nair, Sunil Kumar, Bernard M.E. Moret, Philipp Bucher
2014 Bioinformatics  
Motivation: We have witnessed an enormous increase in ChIP-Seq data for histone modifications in the past few years. Discovering significant patterns in these data is an important problem for understanding biological mechanisms. Results: We propose probabilistic partitioning methods to discover significant patterns in ChIP-Seq data. Our methods take into account signal magnitude, shape, strand orientation and shifts. We compare our methods with some current methods and demonstrate significant
more » ... provements, especially with sparse data. Besides pattern discovery and classification, probabilistic partitioning can serve other purposes in ChIP-Seq data analysis. Specifically, we exemplify its merits in the context of peak finding and partitioning of nucleosome positioning patterns in human promoters. Availability and implementation: The software and code are available in the supplementary material.
doi:10.1093/bioinformatics/btu318 pmid:24812341 fatcat:yqjl7qrgb5h63ih747zezdfwrm