The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Evaluation of Algorithm Performance in ChIP-Seq Peak Detection
Open accessAbstract
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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Cites
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
High-Resolution Profiling of Histone Methylations in the Human Genome
Barski, A., Cuddapah, S., Cui, K., Roh, T.-Y., Schones, D. E., Wang, Z., Wei, G., Chepelev, I., & Zhao, K. (2007) · Cell · doi:10.1016/j.cell.2007.05.009 · Cited by 6,845
ChIP–seq: advantages and challenges of a maturing technology
Park, P. J. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2641 · Cited by 2,217
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Cited by
ER-stress-induced transcriptional regulation increases protein synthesis leading to cell death
Han, J., Back, S. H., Hur, J., Lin, Y.-H., Gildersleeve, R., Shan, J., Yuan, C. L., Krokowski, D., Wang, S., Hatzoglou, M., et al. (2013) · Nature Cell Biology · doi:10.1038/ncb2738 · Cited by 1,691
Meng, J., Lu, Z., Liu, H., Zhang, L., Zhang, S., Chen, Y., Rao, M. K., & Huang, Y. (2014) · Methods · doi:10.1016/j.ymeth.2014.06.008 · Cited by 375
Features that define the best ChIP-seq peak calling algorithms
Thomas, R., Thomas, S., Holloway, A. K., & Pollard, K. S. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw035 · Cited by 112
Non-targeted transcription factors motifs are a systemic component of ChIP-seq datasets
Worsley Hunt, R., & Wasserman, W. W. (2014) · Genome Biology · doi:10.1186/s13059-014-0412-4 · Cited by 73
Metadata
- Authors
- Wilbanks, E. G., & Facciotti, M. T.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2010-07-08
- Volume
- 5
- Issue
- 7
- Pages
- e11471
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2044164623
- Cited by
- 285
- References
- 7
- FWCI
- 12.52
- Type
- journal article
- Language
- en
- Open access
- gold
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