dbNSFP: A Lightweight Database of Human Non-synonymous SNPs and Their Functional Predictions

By Xiaoming Liu, Xueqiu Jian, Eric Boerwinkle. Human Mutation, 2011

817 citations recorded by the source corpus.

With the advance of sequencing technologies, whole exome sequencing has increasingly been used to identify mutations that cause human diseases, especially rare Mendelian diseases. Among the analysis steps, functional prediction (of being deleterious) plays an important role in filtering or prioritizing nonsynonymous SNP (NS) for further analysis. Unfortunately, different prediction algorithms use different information and each has its own strength and weakness. It has been suggested that investigators should use predictions from multiple algorithms instead of relying on a single one. However, querying predictions from different databases/Web‐servers for different algorithms is both tedious and time consuming, especially when dealing with a huge number of NSs identified by exome sequencing. To facilitate the process, we developed dbNSFP (database for nonsynonymous SNPs' functional predictions). It compiles prediction scores from four new and popular algorithms (SIFT, Polyphen2, LRT, and MutationTaster), along with a conservation score (PhyloP) and other related information, for every potential NS in the human genome (a total of 75,931,005). It is the first integrated database of functional predictions from multiple algorithms for the comprehensive collection of human NSs. dbNSFP is freely available for download at http://sites.google.com/site/jpopgen/dbNSFP. Hum Mutat 32:894–899, 2011. © 2011 Wiley‐Liss, Inc.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/79547907be10fc922b58d7f8e08c47ad91d41ef8

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An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge (2014, methodology): “There were a large variety of annotation sources (see Table 4), including but not limited to: OMIM [50], Uniprot [51], SeattleSeq [52], SNPedia [53], NCBI ClinVar, PharmGKB [54], Human Gene Mutation Database [55], dbNSFP [56], and in-house annotations.”
dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs (2020, methodology): “Previously, we developed dbNSFP version 1 [11], 2 [12], and 3 [13] to provide a “ one-stop-shop ” for functional annotations for non-synonymous SNVs (nsSNVs) and splice site SNVs (ssSNVs), top candidate variant types for Mendelian diseases.”
dbNSFP v2.0: A Database of Human Non-synonymous SNVs and Their Functional Predictions and Annotations (2013, neutral): “0 (Liu et al. 2011 Based on the Consensus Coding Sequence (CCDS) version 20090327 (Pruitt et al. 2009), dbNSFP v1.”
ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants. (2018, background): “5 Hence, recently, new ‘‘ensemble’’ predictors have combined individual predictors in order to achieve higher classiﬁcation accuracy.”
Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models (2012, methodology): “Figure S3. than any individual method [Liu et al., 2011; Olatubosun et al., 2012].”
wANNOVAR: annotating genetic variants for personal genomes via the web (2012, methodology): “The wANNOVAR server already incorporates SIFT and PolyPhen2 scores with additional scoring systems (table 1).(4) The wANNOVAR server differs from SeattleSeq in that: (1) it allows flexibility by permitting the users to select gene definition systems, including RefSeq genes,(22) ENSEMBL genes,(23) UC”
PERCH: a unified framework for disease gene prioritization (2017, methodology): “These scores were obtained from the dbNSFP version 2.3 [Liu et al., 2011].”
A general framework for estimating the relative pathogenicity of human genetic variants (2014, methodology): “5% of the coverage distribution for each sample), (2) positions surrounding insertions/deletions (± 5 bp of an insertion/deletion), (3) positions identified as prone to systematic error in Illumina sequencing, (4) positions marked by soft masking in the human reference sequence, (5) positions with a”
Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. (2015, methodology): “scores using LR and SVM algorithms and made the prediction scores for 87 347 044 possible variants in the whole-exome publicly available in ANNOVAR (35) software and dbNSFP database (36,37).”
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines (2017, methodology): “” For a given missense variant, predictions by numerous algorithms are publicly available, e.g. via dbNSFP [2] or Variant Effect Predictor [3] from which a few algorithms are typically chosen for variant interpretation and are often used without additional calibration.”