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AIControl: Replacing matched control experiments with machine learning improves ChIP-seq peak identification

Nao Hiranuma, Scott M. Lundberg, Su-In Lee
doi: https://doi.org/10.1101/278762
Nao Hiranuma
1Paul G. Allen School of Computer Science and Engineering, University of Washington email:
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  • For correspondence: suinlee@cs.washington.edu
Scott M. Lundberg
1Paul G. Allen School of Computer Science and Engineering, University of Washington email:
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  • For correspondence: suinlee@cs.washington.edu
Su-In Lee
1Paul G. Allen School of Computer Science and Engineering, University of Washington email:
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  • For correspondence: suinlee@cs.washington.edu
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Abstract

ChIP-seq is a technique to determine binding locations of transcription factors, which remains a central challenge in molecular biology. Current practice is to use a “control” dataset to remove background signals from a immunoprecipitation (IP) target dataset. We introduce the AlControl framework, which eliminates the need to obtain a control dataset and instead identifies binding peaks by estimating the distributions of background signals from many publicly available control ChIP-seq datasets. We thereby avoid the cost of running control experiments while simultaneously increasing the accuracy of binding location identification. Specifically, AIControl can (1) estimate background signals at fine resolution, (2) systematically weigh the most appropriate control datasets in a data-driven way, (3) capture sources of potential biases that may be missed by one control dataset, and (4) remove the need for costly and time-consuming control experiments. We applied AIControl to 410 IP datasets in the ENCODE ChIP-seq database, using 440 control datasets from 107 cell types to impute background signal. Without using matched control datasets, AIControl identified peaks that were more enriched for putative binding sites than those identified by other popular peak callers that used a matched control dataset. We also demonstrated that our framework identifies binding sites that recover documented protein interactions more accurately.

Footnotes

  • We majorly revised our article by adding some supplementary results. We also improved the usability of our software and moved it to a different repository.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted December 26, 2018.
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AIControl: Replacing matched control experiments with machine learning improves ChIP-seq peak identification
Nao Hiranuma, Scott M. Lundberg, Su-In Lee
bioRxiv 278762; doi: https://doi.org/10.1101/278762
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AIControl: Replacing matched control experiments with machine learning improves ChIP-seq peak identification
Nao Hiranuma, Scott M. Lundberg, Su-In Lee
bioRxiv 278762; doi: https://doi.org/10.1101/278762

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