Title: Shape-based peak identification for ChIP-Seq
Author: Valerie Hower, Steven N. Evans, & Lior Pachter
Abstract: We present a new algorithm for the identification of bound
regions from ChIP-seq experiments. Our method for identifying statistically 
significant peaks from read coverage is inspired by the notion of
persistence in topological data analysis and provides a non-parametric
approach that is robust to noise in experiments. Specifically, our method
reduces the peak calling problem to the study of tree-based statistics
derived from the data. We demonstrate the accuracy of our method on
existing datasets, and we show that it can discover previously missed regions 
and can more clearly discriminate between multiple binding events.
The software T-PIC (Tree shape Peak Identification for ChIP-Seq) 
is available at
http://math.berkeley.edu/~vhower/tpic.html
Date: May 2010