Evaluation of Algorithm Performance in ChIP-Seq Peak Detection
By Elizabeth G. Wilbanks, Marc T. Facciotti. PLoS ONE, 2010
307 citations recorded by the source corpus.
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.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/5169f2bb6486e983fe7a2679467817b0fea4a8c3
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Citations in the corpus
- Inferring direct DNA binding from ChIP-seq (2012, methodology): “When all goes well, the actual ChIP-ed binding site lies somewhere within a region of about 100 bp (1), centered on the ‘peak’, and with increasing probability closer to the center.”
- A protocol for RNA methylation differential analysis with MeRIP-Seq data and exomePeak R/Bioconductor package. (2014, methodology): “The detection of methylation sites has been mainly formulated as the peak detection problem in ChIP-Seq [19, 20].”
- JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles (2013, methodology): “As part of the curation of ChIP-seq data, and as introduced earlier, we computed a centrality score as described in (20), based on our expectation that the positions where the maximum number of ChIP-seq reads map on the genome of reference will be strongly enriched for binding sites corresponding to”