PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants
Abstract 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 in...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-10-01
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| Series: | Genome Biology |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13059-018-1546-6 |
| _version_ | 1818309643271667712 |
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| author | Corneliu A. Bodea Adele A. Mitchell Alex Bloemendal Aaron G. Day-Williams Heiko Runz Shamil R. Sunyaev |
| author_facet | Corneliu A. Bodea Adele A. Mitchell Alex Bloemendal Aaron G. Day-Williams Heiko Runz Shamil R. Sunyaev |
| author_sort | Corneliu A. Bodea |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-13T07:33:26Z |
| format | Article |
| id | doaj.art-01dfcfcedd2d4a578c8bac8e1ec8674c |
| institution | Directory Open Access Journal |
| issn | 1474-760X |
| language | English |
| last_indexed | 2024-12-13T07:33:26Z |
| publishDate | 2018-10-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj.art-01dfcfcedd2d4a578c8bac8e1ec8674c2022-12-21T23:55:08ZengBMCGenome Biology1474-760X2018-10-0119111710.1186/s13059-018-1546-6PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variantsCorneliu A. Bodea0Adele A. Mitchell1Alex Bloemendal2Aaron G. Day-Williams3Heiko Runz4Shamil R. Sunyaev5Department of Genetics and Pharmacogenomics, MRLDepartment of Genetics and Pharmacogenomics, MRLThe Broad Institute of MIT and HarvardDepartment of Genetics and Pharmacogenomics, MRLDepartment of Genetics and Pharmacogenomics, MRLDivision of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical SchoolAbstract 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.http://link.springer.com/article/10.1186/s13059-018-1546-6Noncoding variantComputational functionality predictionEpigenetic regulationCell type specificityFunctional scoringVariant prioritization |
| spellingShingle | Corneliu A. Bodea Adele A. Mitchell Alex Bloemendal Aaron G. Day-Williams Heiko Runz Shamil R. Sunyaev PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants Genome Biology Noncoding variant Computational functionality prediction Epigenetic regulation Cell type specificity Functional scoring Variant prioritization |
| title | PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants |
| title_full | PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants |
| title_fullStr | PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants |
| title_full_unstemmed | PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants |
| title_short | PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants |
| title_sort | pines phenotype informed tissue weighting improves prediction of pathogenic noncoding variants |
| topic | Noncoding variant Computational functionality prediction Epigenetic regulation Cell type specificity Functional scoring Variant prioritization |
| url | http://link.springer.com/article/10.1186/s13059-018-1546-6 |
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