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Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-Seq data

View ORCID ProfileMingxiang Teng, View ORCID ProfileRafael A. Irizarry
doi: https://doi.org/10.1101/090704
Mingxiang Teng
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, United States
2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
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Rafael A. Irizarry
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, United States
2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
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  • For correspondence: rafa@jimmy.harvard.edu
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Abstract

The main application of ChIP-seq technology is the detection of genomic regions that bind to a protein of interest. A large part of functional genomics public catalogs are based on ChIP-seq data. These catalogs rely on peak calling algorithms that infer protein-binding sites by detecting genomic regions associated with more mapped reads (coverage) than expected by chance as a result of the experimental protocol's lack of perfect specificity. We find that GC-content bias accounts for substantial variability in the observed coverage for ChIP-Seq experiments and that this variability leads to false-positive peak calls. More concerning is that GC-effect varies across experiments, with the effect strong enough to result in a substantial number of peaks called differently when different laboratories perform experiments on the same cell-line. However, accounting for GC-content in ChIP-Seq is challenging because the binding sites of interest tend to be more common in high GC-content regions, which confounds real biological signal with the unwanted variability. To account for this challenge we introduce a statistical approach that accounts for GC-effects on both non-specific noise and signal induced by the binding site. The method can be used to account for this bias in binding quantification as well to improve existing peak calling algorithms. We use this approach to show a reduction in false positive peaks as well as improved consistency across laboratories.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 15, 2017.
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Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-Seq data
Mingxiang Teng, Rafael A. Irizarry
bioRxiv 090704; doi: https://doi.org/10.1101/090704
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Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-Seq data
Mingxiang Teng, Rafael A. Irizarry
bioRxiv 090704; doi: https://doi.org/10.1101/090704

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