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Denoising genome-wide histone ChIP-seq with convolutional neural networks

Pang Wei Koh, Emma Pierson, Anshul Kundaje
doi: https://doi.org/10.1101/052118
Pang Wei Koh
Departments of Computer Science and Genetics, Stanford University
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Emma Pierson
Departments of Computer Science and Genetics, Stanford University
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Anshul Kundaje
Departments of Computer Science and Genetics, Stanford University
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Abstract

Chromatin immunoprecipitation sequencing (ChIP-seq) experiments targeting histone modifications are commonly used to characterize the dynamic epigenomes of diverse cell types and tissues. However, suboptimal experimental parameters such as poor ChIP enrichment, low cell input, low library complexity, and low sequencing depth can significantly affect the quality and sensitivity of histone ChIP-seq experiments. We show that a convolutional neural network trained to learn a mapping between suboptimal and high-quality histone ChIP-seq data in reference cell types can overcome various sources of noise and substantially enhance signal when applied to low-quality samples across individuals, cell types, and species. This approach allows us to reduce cost and increase data quality. More broadly, our approach – using a high-dimensional discriminative model to encode a generative noise process – is generally applicable to biological problems where it is easy to generate noisy data but difficult to analytically characterize the noise or underlying data distribution.

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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 4.0 International license.
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Posted May 07, 2016.
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Denoising genome-wide histone ChIP-seq with convolutional neural networks
Pang Wei Koh, Emma Pierson, Anshul Kundaje
bioRxiv 052118; doi: https://doi.org/10.1101/052118
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Denoising genome-wide histone ChIP-seq with convolutional neural networks
Pang Wei Koh, Emma Pierson, Anshul Kundaje
bioRxiv 052118; doi: https://doi.org/10.1101/052118

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