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Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning

Author

Listed:
  • Dimitrios Vitsios

    (BioPharmaceuticals R&D, AstraZeneca)

  • Ryan S. Dhindsa

    (BioPharmaceuticals R&D, AstraZeneca)

  • Lawrence Middleton

    (BioPharmaceuticals R&D, AstraZeneca)

  • Ayal B. Gussow

    (National Library of Medicine)

  • Slavé Petrovski

    (BioPharmaceuticals R&D, AstraZeneca)

Abstract

Elucidating functionality in non-coding regions is a key challenge in human genomics. It has been shown that intolerance to variation of coding and proximal non-coding sequence is a strong predictor of human disease relevance. Here, we integrate intolerance to variation, functional genomic annotations and primary genomic sequence to build JARVIS: a comprehensive deep learning model to prioritize non-coding regions, outperforming other human lineage-specific scores. Despite being agnostic to evolutionary conservation, JARVIS performs comparably or outperforms conservation-based scores in classifying pathogenic single-nucleotide and structural variants. In constructing JARVIS, we introduce the genome-wide residual variation intolerance score (gwRVIS), applying a sliding-window approach to whole genome sequencing data from 62,784 individuals. gwRVIS distinguishes Mendelian disease genes from more tolerant CCDS regions and highlights ultra-conserved non-coding elements as the most intolerant regions in the human genome. Both JARVIS and gwRVIS capture previously inaccessible human-lineage constraint information and will enhance our understanding of the non-coding genome.

Suggested Citation

  • Dimitrios Vitsios & Ryan S. Dhindsa & Lawrence Middleton & Ayal B. Gussow & Slavé Petrovski, 2021. "Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21790-4
    DOI: 10.1038/s41467-021-21790-4
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    Cited by:

    1. Victor Lopez Soriano & Alfredo Dueñas Rey & Rajarshi Mukherjee & Frauke Coppieters & Miriam Bauwens & Andy Willaert & Elfride De Baere, 2024. "Multi-omics analysis in human retina uncovers ultraconserved cis-regulatory elements at rare eye disease loci," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Margaret Sunitha Selvaraj & Xihao Li & Zilin Li & Akhil Pampana & David Y. Zhang & Joseph Park & Stella Aslibekyan & Joshua C. Bis & Jennifer A. Brody & Brian E. Cade & Lee-Ming Chuang & Ren-Hua Chung, 2022. "Whole genome sequence analysis of blood lipid levels in >66,000 individuals," Nature Communications, Nature, vol. 13(1), pages 1-18, December.

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