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Evaluation of Algorithm Performance in ChIP-Seq Peak Detection

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  • Elizabeth G Wilbanks
  • Marc T Facciotti

Abstract

Next-generation DNA sequencing coupled with chromatin immunoprecipitation (ChIP-seq) is revolutionizing our ability to interrogate whole genome protein-DNA interactions. Identification of protein binding sites from ChIP-seq data has required novel computational tools, distinct from those used for the analysis of ChIP-Chip experiments. The growing popularity of ChIP-seq spurred the development of many different analytical programs (at last count, we noted 31 open source methods), each with some purported advantage. Given that the literature is dense and empirical benchmarking challenging, selecting an appropriate method for ChIP-seq analysis has become a daunting task. Herein we compare the performance of eleven different peak calling programs on common empirical, transcription factor datasets and measure their sensitivity, accuracy and usability. Our analysis provides an unbiased critical assessment of available technologies, and should assist researchers in choosing a suitable tool for handling ChIP-seq data.

Suggested Citation

  • Elizabeth G Wilbanks & Marc T Facciotti, 2010. "Evaluation of Algorithm Performance in ChIP-Seq Peak Detection," PLOS ONE, Public Library of Science, vol. 5(7), pages 1-12, July.
    • Handle: RePEc:plo:pone00:0011471
    DOI: 10.1371/journal.pone.0011471
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    Citations

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    Cited by:

    1. Timothy Bailey & Pawel Krajewski & Istvan Ladunga & Celine Lefebvre & Qunhua Li & Tao Liu & Pedro Madrigal & Cenny Taslim & Jie Zhang, 2013. "Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-8, November.
    2. Weronika Sikora-Wohlfeld & Marit Ackermann & Eleni G Christodoulou & Kalaimathy Singaravelu & Andreas Beyer, 2013. "Assessing Computational Methods for Transcription Factor Target Gene Identification Based on ChIP-seq Data," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-11, November.
    3. Apratim Mitra & Jiuzhou Song, 2012. "WaveSeq: A Novel Data-Driven Method of Detecting Histone Modification Enrichments Using Wavelets," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-11, September.
    4. Tatsunori B Hashimoto & Matthew D Edwards & David K Gifford, 2014. "Universal Count Correction for High-Throughput Sequencing," PLOS Computational Biology, Public Library of Science, vol. 10(3), pages 1-11, March.
    5. Dongjun Chung & Dan Park & Kevin Myers & Jeffrey Grass & Patricia Kiley & Robert Landick & Sündüz Keleş, 2013. "dPeak: High Resolution Identification of Transcription Factor Binding Sites from PET and SET ChIP-Seq Data," PLOS Computational Biology, Public Library of Science, vol. 9(10), pages 1-13, October.
    6. Yuzhuo Wang & Chengzhi Zhang & Kai Li, 2022. "A review on method entities in the academic literature: extraction, evaluation, and application," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2479-2520, May.
    7. Caiyan Jia & Matthew B Carson & Yang Wang & Youfang Lin & Hui Lu, 2014. "A New Exhaustive Method and Strategy for Finding Motifs in ChIP-Enriched Regions," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-13, January.
    8. Anthony Mathelier & Wyeth W Wasserman, 2013. "The Next Generation of Transcription Factor Binding Site Prediction," PLOS Computational Biology, Public Library of Science, vol. 9(9), pages 1-18, September.
    9. Haipeng Xing & Yifan Mo & Will Liao & Michael Q Zhang, 2012. "Genome-Wide Localization of Protein-DNA Binding and Histone Modification by a Bayesian Change-Point Method with ChIP-seq Data," PLOS Computational Biology, Public Library of Science, vol. 8(7), pages 1-12, July.

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