PT  - JOURNAL ARTICLE
AU  - Corneliu A. Bodea
AU  - Adele A. Mitchell
AU  - Alex Bloemendal
AU  - Aaron G. Day-Williams
AU  - Heiko Runz
AU  - Shamil R. Sunyaev
TI  - Phenotype-specific information improves prediction of functional impact for noncoding variants
AID  - 10.1101/083642
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 083642
4099  - http://biorxiv.org/content/early/2018/05/03/083642.short
4100  - http://biorxiv.org/content/early/2018/05/03/083642.full
AB  - Functional characterization of the noncoding genome is essential for the biological understanding of gene regulation and disease. Here, we introduce the computational framework PINES (Phenotype-Informed Noncoding Element Scoring) which predicts the functional impact of noncoding variants by integrating epigenetic annotations in a phenotype-dependent manner. A unique feature of PINES is that analyses may be customized towards genomic annotations from cell types of the highest relevance given the phenotype of interest. We illustrate that PINES identifies functional noncoding variation more accurately than methods that do not use phenotype-weighted knowledge, while at the same time being flexible and easy to use via a dedicated web portal.