Cross-species regulatory sequence activity prediction

PLoS Computational Biology
David R Kelley

Abstract

Machine learning algorithms trained to predict the regulatory activity of nucleic acid sequences have revealed principles of gene regulation and guided genetic variation analysis. While the human genome has been extensively annotated and studied, model organisms have been less explored. Model organism genomes offer both additional training sequences and unique annotations describing tissue and cell states unavailable in humans. Here, we develop a strategy to train deep convolutional neural networks simultaneously on multiple genomes and apply it to learn sequence predictors for large compendia of human and mouse data. Training on both genomes improves gene expression prediction accuracy on held out and variant sequences. We further demonstrate a novel and powerful approach to apply mouse regulatory models to analyze human genetic variants associated with molecular phenotypes and disease. Together these techniques unleash thousands of non-human epigenetic and transcriptional profiles toward more effective investigation of how gene regulation affects human disease.

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Citations

Sep 19, 2020·Nature Communications·Kushal K DeyAlkes L Price
Oct 14, 2020·Nature Methods·Geoff FudenbergKatherine S Pollard
Apr 24, 2021·Genome Research·Evan M CoferOlga G Troyanskaya
Jun 10, 2021·Current Opinion in Chemical Biology·Jacob Schreiber, Ritambhara Singh
Jun 29, 2021·Frontiers in Molecular Biosciences·Jan ZrimecAleksej Zelezniak
Jul 29, 2021·Trends in Genetics : TIG·Melina Claussnitzer, Katalin Susztak

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Methods Mentioned

BETA
ChIP-seq
ChIP
immunoprecipitation
Hi-C

Software Mentioned

bam
fastp
CAGE
GENCODE
Hi
TensorFlow
RepeatMasker
FANTOM
learn
SLDP

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