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Open Access Highly Accessed Methodology article

Shape-based peak identification for ChIP-Seq

Valerie Hower1*, Steven N Evans12 and Lior Pachter13*

Author Affiliations

1 Department of Mathematics, University of California, Berkeley, California, USA

2 Department of Statistics, University of California, Berkeley, California, USA

3 Department of Molecular and Cell Biology, University of California, Berkeley, California, USA

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BMC Bioinformatics 2011, 12:15  doi:10.1186/1471-2105-12-15

Published: 12 January 2011



The identification of binding targets for proteins using ChIP-Seq has gained popularity as an alternative to ChIP-chip. Sequencing can, in principle, eliminate artifacts associated with microarrays, and cheap sequencing offers the ability to sequence deeply and obtain a comprehensive survey of binding. A number of algorithms have been developed to call "peaks" representing bound regions from mapped reads. Most current algorithms incorporate multiple heuristics, and despite much work it remains difficult to accurately determine individual peaks corresponding to distinct binding events.


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 statistically sound and 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 validate our approach using previously published data and show that it can discover previously missed regions.


The difficulty in accurately calling peaks for ChIP-Seq data is partly due to the difficulty in defining peaks, and we demonstrate a novel method that improves on the accuracy of previous methods in resolving peaks. Our introduction of a robust statistical test based on ideas from topological data analysis is also novel. Our methods are implemented in a program called T-PIC (

ree shape
dentification for
hIP-Seq) is available at webcite