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Abstract: We present a new algorithm for the identification of bound regions fromChIP-seq experiments. Our method for identifying statistically significantpeaks from read coverage is inspired by the notion of persistence intopological data analysis and provides a non-parametric approach that is robustto noise in experiments. Specifically, our method reduces the peak callingproblem to the study of tree-based statistics derived from the data. Wedemonstrate the accuracy of our method on existing datasets, and we show thatit can discover previously missed regions and can more clearly discriminatebetween multiple binding events. The software T-PIC Tree shape PeakIdentification for ChIP-Seq is available atthis http URL

Autor: Valerie Hower, Steven N. Evans, Lior Pachter


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