Cross-species regulatory sequence activity prediction.
Journal:
PLoS computational biology
PMID:
32687525
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.
Authors
Keywords
Algorithms
Animals
Computational Biology
Databases, Genetic
Epigenomics
Gene Expression Regulation
Genetic Variation
Genome, Human
Genomics
Hepatocytes
Humans
Machine Learning
Mice
Models, Genetic
Models, Statistical
Mutation
Neural Networks, Computer
Quantitative Trait Loci
Sequence Analysis, DNA
Software
Species Specificity