Cross-species regulatory sequence activity prediction

作者：David R. Kelley · 发表于：PLoS Computational Biology · 年份：2020 · DOI：10.1371/journal.pcbi.1008050 · 被引用次数：259 · 研究领域：Genomics and Chromatin Dynamics、RNA and protein synthesis mechanisms、Genomics and Phylogenetic Studies

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 nonhuman epigenetic and transcriptional profiles toward more effective investigation of how gene regulation affects human disease. Author summaryHuman population genetic studies have highlighted thousands of genomic sites that correlate with traits and diseases that do not modify gene sequences directly, but instead modify where and when those genes are expressed.To better understand how these sites influence traits and diseases, and consider their relevance for drug development, we need better models for how DNA sequences determine gene expression.Rec...