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

ABSTRACT:

Motivation

Chromatin immune-precipitation 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 many 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

SUBMITTER: Koh PW 

PROVIDER: S-EPMC5870713 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature