Somepalli, G and Sahoo, S and Singh, A and Hannenhalli, S (2021) Prioritizing and characterizing functionally relevant genes across human tissues. In: PLoS Computational Biology, 17 (7).
|
PDF
plo_com_17-07_2021.pdf - Published Version Download (2MB) | Preview |
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. © 2021 This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
| Item Type: | Journal Article |
|---|---|
| Publication: | PLoS Computational Biology |
| Publisher: | Public Library of Science |
| Additional Information: | The copyright for this article belongs to Public Library of Science |
| Department/Centre: | UG Programme |
| Date Deposited: | 15 Nov 2021 11:33 |
| Last Modified: | 15 Nov 2021 11:33 |
| URI: | http://eprints.iisc.ac.in/id/eprint/69662 |
Actions (login required)
 |
View Item |
