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bioRxiv · 10.1101/306621

HMMRATAC, the Hidden Markov ModeleR for ATAC-seq

Tarbell, E. D.·Liu, T.
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Abstract

ATAC-seq has been widely adopted to identify accessible chromatin regions across the genome. However, current data analysis still utilizes approaches initially designed for ChIP-seq or DNase-seq, without taking into account the transposase digested DNA fragments that contain additional nucleosome positioning information. We present the first dedicated ATAC-seq analysis tool, a semi-supervised machine learning approach named HMMRATAC. HMMRATAC splits a single ATAC-seq dataset into nucleosome-free and nucleosome-enriched signals, learns the unique chromatin structure around accessible regions, and then predicts accessible regions across the entire genome. We show that HMMRATAC outperforms the popular peak-calling algorithms on published human and mouse ATAC-seq datasets.

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Tarbell, E. D., Liu, T.. 2018-04-24. HMMRATAC, the Hidden Markov ModeleR for ATAC-seq. https://doi.org/10.1101/306621

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