Denoising genome-wide histone ChIP-seq with convolutional neural networks
By Pang Wei Koh, Emma Pierson, Anshul B Kundaje. 2016
64 citations recorded by the source corpus.
Motivation Chromatin immunoprecipitation sequencing (ChIP-seq) experiments are commonly used to obtain genome-wide profiles of histone modifications associated with different types of functional genomic elements. However, the quality of histone ChIP-seq data is affected by a myriad of experimental parameters such as the amount of input DNA, antibody specificity, ChIP enrichment, and sequencing depth. Making accurate inferences from chromatin profiling experiments that involve diverse experimental parameters is challenging. Results We introduce a convolutional denoising algorithm, Coda, that uses convolutional neural networks to learn a mapping from suboptimal to high-quality histone ChIP-seq data. This overcomes various sources of noise and variability, substantially enhancing and recovering signal when applied to low-quality chromatin profiling datasets across individuals, cell types, and species. Our method has the potential to improve data quality at reduced costs. More broadly, this approach – using a high-dimensional discriminative model to encode a generative noise process – is generally applicable to other biological domains where it is easy to generate noisy data but difficult to analytically characterize the noise or underlying data distribution. Availability https://github.com/kundajelab/coda Contact akundaje@stanford.edu
Subfields: Molecular Biology
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In Molecular Biology
- Casey S. Greene
- Yifan Peng
- Brett K. Beaulieu-Jones
- G. Way
- Amr M. Alexandari
- Zhiyong Lu
- Avanti Shrikumar
- S. Joshua Swamidass
Citations in the corpus
- Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Similar deep learning methods have been applied to impute low-resolution ChIP-seq signal from bulk tissue with great success, and they could easily be adapted to singlecell data [155,217].”
- Biological Sequence Modeling with Convolutional Kernel Networks (2017, neutral): “…a DNA or RNA sequence binds to a protein [Alipanahi et al., 2015], its splicing outcome [Jha et al., 2017], or its chromatin accessibility [Kelley et al., 2016], predicting the resistance of a bacterial strain to a drug [Drouin et al., 2016], or denoising a ChIP-seq signal [Koh et al., 2017].”