Abstract:
:A gene is considered essential if loss of function results in loss of viability, fitness or in disease. This concept is well established for coding genes; however, non-coding regions are thought less likely to be determinants of critical functions. Here we train a machine learning model using functional, mutational and structural features, including new genome essentiality metrics, 3D genome organization and enhancer reporter data to identify deleterious variants in non-coding regions. We assess the model for functional correlates by using data from tiling-deletion-based and CRISPR interference screens of activity of cis-regulatory elements in over 3 Mb of genome sequence. Finally, we explore two user cases that involve indels and the disruption of enhancers associated with a developmental disease. We rank variants in the non-coding genome according to their predicted deleteriousness. The model prioritizes non-coding regions associated with regulation of important genes and with cell viability, an in vitro surrogate of essentiality.
journal_name
Nat Communjournal_title
Nature communicationsauthors
Wells A,Heckerman D,Torkamani A,Yin L,Sebat J,Ren B,Telenti A,di Iulio Jdoi
10.1038/s41467-019-13212-3subject
Has Abstractpub_date
2019-11-20 00:00:00pages
5241issue
1issn
2041-1723pii
10.1038/s41467-019-13212-3journal_volume
10pub_type
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