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Ranking of non-coding pathogenic variants and putative essential regions of the human genome

Authors :
Alex Wells
David Heckerman
Ali Torkamani
Li Yin
Jonathan Sebat
Bing Ren
Amalio Telenti
Julia di Iulio
Source :
Nature Communications, Vol 10, Iss 1, Pp 1-9 (2019)
Publication Year :
2019
Publisher :
Nature Portfolio, 2019.

Abstract

Whole genome sequencing (WGS) holds promise to solve a subset of Mendelian disease cases for which exome sequencing did not provide a genetic diagnosis. Here, Wells et al. report a supervised machine learning model trained on functional, mutational and structural features for rank-scoring and interpreting variants in non-coding regions from WGS.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
10
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.55b664ef26374b7793165c7ee6e62223
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-019-13212-3