PT  - JOURNAL ARTICLE
AU  - Alex Wells
AU  - David Heckerman
AU  - Ali Torkamani
AU  - Li Yin
AU  - Bing Ren
AU  - Amalio Telenti
AU  - Julia di Iulio
TI  - Identification of essential regulatory elements in the human genome
AID  - 10.1101/444562
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 444562
4099  - http://biorxiv.org/content/early/2018/10/16/444562.short
4100  - http://biorxiv.org/content/early/2018/10/16/444562.full
AB  - The identification of essential regulatory elements is central to the understanding of the consequences of genetic variation. Here we use novel genomic data and machine learning techniques to map essential regulatory elements and to guide functional validation. We train an XGBoost model using 38 functional and structural features, including genome essentiality metrics, 3D genome organization and enhancer reporter STARR-seq data to differentiate between pathogenic and control non-coding genetic variants. We validate the accuracy of prediction by using data from tiling-deletion-based and CRISPR interference screens of activity of cis-regulatory elements. In neurodevelopmental disorders, the model (ncER, non-coding Essential Regulation) maps essential genomic segments within deletions and rearranged topologically associated domains linked to human disease. We show that the approach successfully identifies essential regulatory elements in the human genome.