PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants

Functional characterization of the noncoding genome is essential for 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...

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Bibliographic Details
Published in:Genome Biology 2018-10, Vol.19 (1), p.173-173, Article 173
Main Authors: Bodea, Corneliu A, Mitchell, Adele A, Bloemendal, Alex, Day-Williams, Aaron G, Runz, Heiko, Sunyaev, Shamil R
Format: Article
Language:English
Subjects:
Algorithms
Annotations
Cell type specificity
Computational functionality prediction
Computer applications
Consortia
Deoxyribonucleic acid
Disease
DNA
DNA, Intergenic - genetics
Enhancer Elements, Genetic - genetics
Epigenesis, Genetic
Epigenetic regulation
Epigenetics
Functional scoring
Gene regulation
Genetic Loci
Genetic Predisposition to Disease
Genetic Variation
Genomes
Genotype & phenotype
Humans
Inflammatory Bowel Diseases - genetics
Molecular Sequence Annotation
Noncoding variant
Parkinson Disease - genetics
Phenotype
Phenotypes
Risk Factors
Software
Variant prioritization
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Summary:Functional characterization of the noncoding genome is essential for 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. PINES enables analyses to 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.
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-018-1546-6