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Deep sequencing technologies are routinely employed to probe protein-DNA interactions and other epigenetic modifications such as  DNA  methylation. These technologies return complex results in the form of binding profiles which can be extended over several kilobases and present characteristic spatial patterns. Such patterns appear to be reproducible among replicates, yet how they can be incorporated in a formal statistical analysis of the data is non-trivial. In this talk I will introduce MMDiff, a kernel-based statistical methodology to test for changes in spatial patterns in ChIP-Seq peaks. I will show how MMDiff analysis helped us formulate mechanistic hypotheses on the role of transcription factor proteins in the establishment of histone marks, which we later showed to be consistent with a large-scale analysis of the  ENCODE  data sets. Finally, if there is time, I will introduce  M3D , an extension of MMDiff to  DNA  methylation profiles obtained with bisulfite-sequencing experiments.

References: Schweikert et al, MMDiff: quantitative testing for shape changes in ChIP-Seq data sets,  BMC  Genomics 14:826 (2013) Benveniste et al, Transcription factor binding predicts histone modifications in human cell lines,  PNAS , in press (2014) Mayo et al,  M3D : a kernel based test for spatially correlated changes in methylation profiles, under review.

 If you have a question about this talk, please contact Giuseppe Gallone.