This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM) Genomics
Combining multiple ChIP-seq peak detection systems using combinatorial fusion
1 Laboratory for Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY 10023, USA
2 Center for Health Informatics and Bioinformatics, New York University Langone Medical Center, New York, NY 10016, USA
Citation and License
BMC Genomics 2012, 13(Suppl 8):S12 doi:10.1186/1471-2164-13-S8-S12Published: 17 December 2012
Due to the recent rapid development in ChIP-seq technologies, which uses high-throughput next-generation DNA sequencing to identify the targets of Chromatin Immunoprecipitation, there is an increasing amount of sequencing data being generated that provides us with greater opportunity to analyze genome-wide protein-DNA interactions. In particular, we are interested in evaluating and enhancing computational and statistical techniques for locating protein binding sites. Many peak detection systems have been developed; in this study, we utilize the following six: CisGenome, MACS, PeakSeq, QuEST, SISSRs, and TRLocator.
We define two methods to merge and rescore the regions of two peak detection systems and analyze the performance based on average precision and coverage of transcription start sites. The results indicate that ChIP-seq peak detection can be improved by fusion using score or rank combination.
Our method of combination and fusion analysis would provide a means for generic assessment of available technologies and systems and assist researchers in choosing an appropriate system (or fusion method) for analyzing ChIP-seq data. This analysis offers an alternate approach for increasing true positive rates, while decreasing false positive rates and hence improving the ChIP-seq peak identification process.