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A flexible simulation toolkit for designing and evaluating ChIP-sequencing experiments

An Zheng, Michael Lamkin, Yutong Qiu, Kevin Ren, View ORCID ProfileAlon Goren, Melissa Gymrek
doi: https://doi.org/10.1101/624486
An Zheng
1Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA USA
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Michael Lamkin
2Department of Bioengineering, University of California San Diego, La Jolla, CA USA
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Yutong Qiu
1Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA USA
3School of Computer Science, Carnegie Mellon University, Pittsburgh, PA USA
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Kevin Ren
4Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA USA
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Alon Goren
5Department of Medicine, University of California San Diego, La Jolla, CA USA
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  • ORCID record for Alon Goren
  • For correspondence: mgymrek@ucsd.edu agoren@ucsd.edu
Melissa Gymrek
1Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA USA
5Department of Medicine, University of California San Diego, La Jolla, CA USA
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  • For correspondence: mgymrek@ucsd.edu agoren@ucsd.edu
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Abstract

A major challenge in evaluating quantitative ChIP-seq analyses, such as peak calling and differential binding, is a lack of reliable ground truth data. We present Tulip, a toolkit for rapidly simulating ChIP-seq data using statistical models of the experimental steps. Tulip may be used for a range of applications, including power analysis for experimental design, benchmarking of analysis tools, and modeling effects of processes such as replication on ChIP-seq signals.

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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 May 01, 2019.
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A flexible simulation toolkit for designing and evaluating ChIP-sequencing experiments
An Zheng, Michael Lamkin, Yutong Qiu, Kevin Ren, Alon Goren, Melissa Gymrek
bioRxiv 624486; doi: https://doi.org/10.1101/624486
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A flexible simulation toolkit for designing and evaluating ChIP-sequencing experiments
An Zheng, Michael Lamkin, Yutong Qiu, Kevin Ren, Alon Goren, Melissa Gymrek
bioRxiv 624486; doi: https://doi.org/10.1101/624486

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