Shape-based peak identification for ChIP-Seq

Background: 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. Results: 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 treebased statistics derived from the data. We validate o...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Valerie Hower, Steven N. Evans, Lior Pachter
• Keywords: Artificial Intelligence, BMCBI 2011, Incorporate Multiple Heuristics, Most Current Algorithms, Topological Data Analysis