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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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Article Information

doi 
https://doi.org/10.1101/090704
History 
  • January 15, 2017.

Article Versions

  • Version 1 (November 30, 2016 - 18:25).
  • Version 2 (December 1, 2016 - 08:05).
  • You are viewing Version 3, the most recent version of this article.
Copyright 
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.

Author Information

  1. Mingxiang Teng1,2 and
  2. Rafael A. Irizarry1,2,*
  1. 1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, United States
  2. 2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
  1. ↵*To whom correspondence should be addressed: Rafael A. Irizarry: rafa{at}jimmy.harvard.edu
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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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