Prioritizing and characterizing functionally relevant genes across human tissues.
Journal:
PLoS computational biology
PMID:
34270548
Abstract
Knowledge of genes that are critical to a tissue's function remains difficult to ascertain and presents a major bottleneck toward a mechanistic understanding of genotype-phenotype links. Here, we present the first machine learning model-FUGUE-combining transcriptional and network features, to predict tissue-relevant genes across 30 human tissues. FUGUE achieves an average cross-validation auROC of 0.86 and auPRC of 0.50 (expected 0.09). In independent datasets, FUGUE accurately distinguishes tissue or cell type-specific genes, significantly outperforming the conventional metric based on tissue-specific expression alone. Comparison of tissue-relevant transcription factors across tissue recapitulate their developmental relationships. Interestingly, the tissue-relevant genes cluster on the genome within topologically associated domains and furthermore, are highly enriched for differentially expressed genes in the corresponding cancer type. We provide the prioritized gene lists in 30 human tissues and an open-source software to prioritize genes in a novel context given multi-sample transcriptomic data.
Authors
Keywords
Computational Biology
Female
Gene Expression Regulation, Developmental
Gene Regulatory Networks
Genetic Association Studies
Genome-Wide Association Study
Genome, Human
Humans
Machine Learning
Male
Models, Genetic
Multigene Family
Neoplasms
Protein Interaction Maps
Software
Tissue Distribution
Transcription Factors
Transcriptome