Literature DB >> 31283070

VIPdb, a genetic Variant Impact Predictor Database.

Zhiqiang Hu1, Changhua Yu1,2, Mabel Furutsuki1,3, Gaia Andreoletti1, Melissa Ly1,4, Roger Hoskins1, Aashish N Adhikari1, Steven E Brenner1.   

Abstract

Genome sequencing identifies vast number of genetic variants. Predicting these variants' molecular and clinical effects is one of the preeminent challenges in human genetics. Accurate prediction of the impact of genetic variants improves our understanding of how genetic information is conveyed to molecular and cellular functions, and is an essential step towards precision medicine. Over one hundred tools/resources have been developed specifically for this purpose. We summarize these tools as well as their characteristics, in the genetic Variant Impact Predictor Database (VIPdb). This database will help researchers and clinicians explore appropriate tools, and inform the development of improved methods. VIPdb can be browsed and downloaded at https://genomeinterpretation.org/vipdb.
© 2019 Wiley Periodicals, Inc.

Entities:  

Keywords:  SNV phenotype; SV impact; VIPdb; genotype-phenotype relationship; variant impact; variant impact prediction

Mesh：

Substances：

Year:  2019        PMID： 31283070      PMCID： PMC7288905          DOI： 10.1002/humu.23858

Source DB:  PubMed          Journal:  Hum Mutat        ISSN： 1059-7794            Impact factor:   4.878


  177 in total

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Authors:  Robert Vaser; Swarnaseetha Adusumalli; Sim Ngak Leng; Mile Sikic; Pauline C Ng
Journal:  Nat Protoc       Date:  2015-12-03       Impact factor: 13.491

Review 2.  Protein aggregation and amyloidosis: confusion of the kinds?

Authors:  Frederic Rousseau; Joost Schymkowitz; Luis Serrano
Journal:  Curr Opin Struct Biol       Date:  2006-01-24       Impact factor: 6.809

3.  A new disease-specific machine learning approach for the prediction of cancer-causing missense variants.

Authors:  Emidio Capriotti; Russ B Altman
Journal:  Genomics       Date:  2011-07-07       Impact factor: 5.736

4.  PANTHER-PSEP: predicting disease-causing genetic variants using position-specific evolutionary preservation.

Authors:  Haiming Tang; Paul D Thomas
Journal:  Bioinformatics       Date:  2016-05-18       Impact factor: 6.937

5.  Genomic features defining exonic variants that modulate splicing.

Authors:  Adam Woolfe; James C Mullikin; Laura Elnitski
Journal:  Genome Biol       Date:  2010-02-16       Impact factor: 13.583

6.  FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model.

Authors:  Mingjun Wang; Xing-Ming Zhao; Kazuhiro Takemoto; Haisong Xu; Yuan Li; Tatsuya Akutsu; Jiangning Song
Journal:  PLoS One       Date:  2012-08-24       Impact factor: 3.240

7.  SNPdbe: constructing an nsSNP functional impacts database.

Authors:  Christian Schaefer; Alice Meier; Burkhard Rost; Yana Bromberg
Journal:  Bioinformatics       Date:  2011-12-30       Impact factor: 6.937

8.  CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer.

Authors:  Mark D M Leiserson; Hsin-Ta Wu; Fabio Vandin; Benjamin J Raphael
Journal:  Genome Biol       Date:  2015-08-08       Impact factor: 13.583

9.  PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions.

Authors:  Jaroslav Bendl; Miloš Musil; Jan Štourač; Jaroslav Zendulka; Jiří Damborský; Jan Brezovský
Journal:  PLoS Comput Biol       Date:  2016-05-25       Impact factor: 4.475

10.  When loss-of-function is loss of function: assessing mutational signatures and impact of loss-of-function genetic variants.

Authors:  Kymberleigh A Pagel; Vikas Pejaver; Guan Ning Lin; Hyun-Jun Nam; Matthew Mort; David N Cooper; Jonathan Sebat; Lilia M Iakoucheva; Sean D Mooney; Predrag Radivojac
Journal:  Bioinformatics       Date:  2017-07-15       Impact factor: 6.937
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  10 in total

Review 1.  Strategies to Identify Genetic Variants Causing Infertility.

Authors:  Xinbao Ding; John C Schimenti
Journal:  Trends Mol Med       Date:  2021-01-08       Impact factor: 15.272

2.  Rhapsody: predicting the pathogenicity of human missense variants.

Authors:  Luca Ponzoni; Daniel A Peñaherrera; Zoltán N Oltvai; Ivet Bahar
Journal:  Bioinformatics       Date:  2020-05-01       Impact factor: 6.937

3.  VarSite: Disease variants and protein structure.

Authors:  Roman A Laskowski; James D Stephenson; Ian Sillitoe; Christine A Orengo; Janet M Thornton
Journal:  Protein Sci       Date:  2019-10-27       Impact factor: 6.725

4.  Molecular mechanics and dynamic simulations of well-known Kabuki syndrome-associated KDM6A variants reveal putative mechanisms of dysfunction.

Authors:  Young-In Chi; Timothy J Stodola; Thiago M De Assuncao; Elise N Leverence; Swarnendu Tripathi; Nikita R Dsouza; Angela J Mathison; Donald G Basel; Brian F Volkman; Brian C Smith; Gwen Lomberk; Michael T Zimmermann; Raul Urrutia
Journal:  Orphanet J Rare Dis       Date:  2021-02-05       Impact factor: 4.123

5.  DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction.

Authors:  Daniel Munro; Mona Singh
Journal:  Bioinformatics       Date:  2020-12-16       Impact factor: 6.937

6.  A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization.

Authors:  Giovanna Nicora; Susanna Zucca; Ivan Limongelli; Riccardo Bellazzi; Paolo Magni
Journal:  Sci Rep       Date:  2022-02-15       Impact factor: 4.379

Review 7.  Genome interpretation using in silico predictors of variant impact.

Authors:  Panagiotis Katsonis; Kevin Wilhelm; Amanda Williams; Olivier Lichtarge
Journal:  Hum Genet       Date:  2022-04-30       Impact factor: 5.881

8.  Structural bioinformatics enhances mechanistic interpretation of genomic variation, demonstrated through the analyses of 935 distinct RAS family mutations.

Authors:  Swarnendu Tripathi; Nikita R Dsouza; Raul Urrutia; Michael T Zimmermann
Journal:  Bioinformatics       Date:  2021-06-16       Impact factor: 6.937

9.  Analysis of protein missense alterations by combining sequence- and structure-based methods.

Authors:  Aram Gyulkhandanyan; Alireza R Rezaie; Lubka Roumenina; Nathalie Lagarde; Veronique Fremeaux-Bacchi; Maria A Miteva; Bruno O Villoutreix
Journal:  Mol Genet Genomic Med       Date:  2020-02-25       Impact factor: 2.183

Review 10.  The Use of Whole Genome and Exome Sequencing for Newborn Screening: Challenges and Opportunities for Population Health.

Authors:  Audrey C Woerner; Renata C Gallagher; Jerry Vockley; Aashish N Adhikari
Journal:  Front Pediatr       Date:  2021-07-19       Impact factor: 3.418
  10 in total

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