dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymou
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dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs Xiaoming Liu1* , Chang Li1, Chengcheng Mou2, Yibo Dong1 and Yicheng Tu2
Abstract Whole exome sequencing has been increasingly used in human disease studies. Prioritization based on appropriate functional annotations has been used as an indispensable step to select candidate variants. Here we present the latest updates to dbNSFP (version 4.1), a database designed to facilitate this step by providing deleteriousness prediction and functional annotation for all potential nonsynonymous and splice-site SNVs (a total of 84,013,093) in the human genome. The current version compiled 36 deleteriousness prediction scores, including 12 transcriptspecific scores, and other variant and gene-level functional annotations. The database is available at http://database. liulab.science/dbNSFP with a downloadable version and a web-service. Keywords: Whole exome sequencing, Database, Nonsynonymous SNV, Deleteriousness prediction, Functional annotation
Background Whole-exome sequencing (WES) and whole-genome sequencing (WGS) have been increasingly used in human disease studies in the research and clinical setting [1–3]. As a result, we witness a tsunami of DNA sequence data from both healthy individuals and those with Mendelian or complex diseases. Identifying variants that cause diseases or are associated with disease risks from a large number of DNA variants identified in sequencing requires an excessive amount of time and effort. To accomplish this daunting task, investigators have relied on functional annotations to filter or prioritize variants based on our current knowledge or prediction models. In more detail, functional annotations can be separated into general annotation and functional prediction: the * Correspondence: [email protected] 1 USF Genomics & College of Public Health, University of South Florida, Tampa, FL, USA Full list of author information is available at the end of the article
former provides qualitative or descriptive annotation of a variant indirectly related to its potential function, such as whether the variant is a nonsynonymous SNV; the latter typically provides direct quantitative or yes-or-no deleteriousness prediction of the variant based on a statistical model. Fast and comprehensive functional annotation tools will become even more critical in the near future because of three intertwined ongoing trends: the decreasing cost of DNA sequencing, the development and practice of precision medicine [4], and the adaptation of WES and WGS in small labs [5]. There have been several annotation tools available for large-scale DNA sequence data, such as UCSC Genome Browser’s Variant Annotation Integrator [6], Ensembl’s Variant Effect Predictor (VEP) [7], ANNOVAR [8], and SnpEff [9]. Most of these focused on general annotations based on given gene models. Although gene-model based annotations are handy, there are other important functional annotati
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