000203236 001__ 203236
000203236 005__ 20181203023702.0
000203236 0247_ $$2doi$$a10.1093/bioinformatics/btu318
000203236 022__ $$a1367-4803
000203236 02470 $$2ISI$$a000342912400044
000203236 02470 $$2PMID$$a24812341
000203236 037__ $$aARTICLE
000203236 245__ $$aProbabilistic partitioning methods to find significant patterns in ChIP-Seq data
000203236 260__ $$aOxford$$bOxford University Press$$c2014
000203236 269__ $$a2014
000203236 300__ $$a8
000203236 336__ $$aJournal Articles
000203236 520__ $$aMotivation: 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.
000203236 700__ $$aNair, Nishanth Ulhas$$uEcole Polytech Fed Lausanne, Sch Comp & Commun Sci, Lab Computat Biol & Bioinformat, CH-1015 Lausanne, Switzerland
000203236 700__ $$aKumar, Sunil$$uEcole Polytech Fed Lausanne, Sch Life Sci, Swiss Inst Expt Canc Res ISREC, CH-1015 Lausanne, Switzerland
000203236 700__ $$0241987$$aMoret, Bernard M. E.$$g172233$$uEcole Polytech Fed Lausanne, Sch Comp & Commun Sci, Lab Computat Biol & Bioinformat, CH-1015 Lausanne, Switzerland
000203236 700__ $$0244404$$aBucher, Philipp$$g113607$$uEcole Polytech Fed Lausanne, Sch Life Sci, Swiss Inst Expt Canc Res ISREC, CH-1015 Lausanne, Switzerland
000203236 773__ $$j30$$k17$$q2406-2413$$tBioinformatics
000203236 909C0 $$0252244$$pGR-BUCHER$$xU11780
000203236 909C0 $$0252020$$pLCBB$$xU11274
000203236 909CO $$ooai:infoscience.tind.io:203236$$pSV$$pIC$$particle
000203236 917Z8 $$x172233
000203236 917Z8 $$x182396
000203236 937__ $$aEPFL-ARTICLE-203236
000203236 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000203236 980__ $$aARTICLE