Nishanth Ulhas Nair1, Sunil Kumar1, Bernard M E Moret2, Philipp Bucher2. 1. Laboratory for Computational Biology and Bioinformatics, School of Computer and Communication Sciences, Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne and Swiss Institute for Bioinformatics, 1015 Lausanne, Switzerland. 2. Laboratory for Computational Biology and Bioinformatics, School of Computer and Communication Sciences, Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne and Swiss Institute for Bioinformatics, 1015 Lausanne, Switzerland Laboratory for Computational Biology and Bioinformatics, School of Computer and Communication Sciences, Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne and Swiss Institute for Bioinformatics, 1015 Lausanne, Switzerland.
Abstract
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 improvements, 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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 improvements, 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Agata Page; Pier P Paoli; Stephen J Hill; Rachel Howarth; Raymond Wu; Soo-Mi Kweon; Jeremy French; Steve White; Hidekazu Tsukamoto; Derek A Mann; Jelena Mann Journal: J Hepatol Date: 2014-10-20 Impact factor: 25.083