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Evaluation of Algorithm Performance in ChIP-Seq Peak Detection

Overview of attention for article published in PLOS ONE, July 2010
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Title
Evaluation of Algorithm Performance in ChIP-Seq Peak Detection
Published in
PLOS ONE, July 2010
DOI 10.1371/journal.pone.0011471
Pubmed ID
Authors

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.

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Geographical breakdown

Country Count As %
United States 33 5%
Germany 17 2%
United Kingdom 15 2%
France 7 <1%
Italy 3 <1%
Austria 3 <1%
Sweden 3 <1%
Belgium 3 <1%
Japan 3 <1%
Other 22 3%
Unknown 605 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 218 31%
Researcher 214 30%
Student > Master 75 11%
Student > Bachelor 54 8%
Professor > Associate Professor 34 5%
Other 84 12%
Unknown 35 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 410 57%
Biochemistry, Genetics and Molecular Biology 115 16%
Computer Science 58 8%
Mathematics 21 3%
Medicine and Dentistry 17 2%
Other 45 6%
Unknown 48 7%