Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models.
Hashem A. Shihab,Julian Gough,David Neil Cooper,Peter D. Stenson,Gary L A Barker,Keith J. Edwards,Ian N. M. Day,Tom R. Gaunt +7 more
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TLDR
The Functional Analysis Through Hidden Markov Models (FATHMM) software and server is described: a species‐independent method with optional species‐specific weightings for the prediction of the functional effects of protein missense variants, demonstrating that FATHMM can be efficiently applied to high‐throughput/large‐scale human and nonhuman genome sequencing projects with the added benefit of phenotypic outcome associations.Abstract:
The rate at which nonsynonymous single nucleotide polymorphisms (nsSNPs) are being identified in the human genome is increasing dramatically owing to advances in whole-genome/whole-exome sequencing technologies. Automated methods capable of accurately and reliably distinguishing between pathogenic and functionally neutral nsSNPs are therefore assuming ever-increasing importance. Here, we describe the Functional Analysis Through Hidden Markov Models (FATHMM) software and server: a species-independent method with optional species-specific weightings for the prediction of the functional effects of protein missense variants. Using a model weighted for human mutations, we obtained performance accuracies that outperformed traditional prediction methods (i.e., SIFT, PolyPhen, and PANTHER) on two separate benchmarks. Furthermore, in one benchmark, we achieve performance accuracies that outperform current state-of-the-art prediction methods (i.e., SNPs&GO and MutPred). We demonstrate that FATHMM can be efficiently applied to high-throughput/large-scale human and nonhuman genome sequencing projects with the added benefit of phenotypic outcome associations. To illustrate this, we evaluated nsSNPs in wheat (Triticum spp.) to identify some of the important genetic variants responsible for the phenotypic differences introduced by intense selection during domestication. A Web-based implementation of FATHMM, including a high-throughput batch facility and a downloadable standalone package, is available at http://fathmm.biocompute.org.uk.read more
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Massively parallel sequencing analysis of benign melanocytic naevi
John R. Lozada,Felipe C Geyer,Pier Selenica,David N Brown,Barbara Alemar,Barbara Alemar,Taha Merghoub,Michael F. Berger,Klaus J. Busam,Allan C. Halpern,Britta Weigelt,Jorge S. Reis-Filho,Travis J. Hollmann +12 more
TL;DR: The aim of this study was to investigate the repertoire of somatic genetic alterations in melanocytic naevi and find out whether these alterations are related to malignant melanoma or not.
Journal ArticleDOI
Functional analyses of a novel missense and other mutations of the vitamin D receptor in association with alopecia
Mayuko Tamura,Mayuko Tamura,Michiyasu Ishizawa,Tsuyoshi Isojima,Samim Özen,Akira Oka,Makoto Makishima,Sachiko Kitanaka +7 more
TL;DR: A novel missense mutation of VDR causing HVDRR with alopecia is identified, providing an additional evidence for the previous findings suggesting that DNA binding by the VDR/RXR heterodimer is essential for the function of the V DR in hair development.
Journal ArticleDOI
A pipeline combining multiple strategies for prioritizing heterozygous variants for the identification of candidate genes in exome datasets
TL;DR: This pipeline prioritizes a short list of heterozygous variants in exome datasets based on the top 10 concordant variants combining multiple systems, and confirms that the combination of systems with different approaches improves the prediction of candidate variants compared with the use of a single method.
Journal ArticleDOI
Inheritance-mode specific pathogenicity prioritization (ISPP) for human protein coding genes.
Jacob Shujui Hsu,Johnny S. H. Kwan,Zhicheng Pan,Maria-Mercè Garcia-Barceló,Pak C. Sham,Miaoxin Li +5 more
TL;DR: It is suggested that genes manifesting disease inheritance modes tend to have unique characteristics, and the inheritance-mode specific pathogenicity prioritization (ISPP) outperformed other well-known methods including Haploinsufficiency, Recessive, Network centrality, Genic Intolerance, Gene Damage Index and Gene Constraint scores.
Journal ArticleDOI
Genome interpretation using in silico predictors of variant impact
TL;DR: In this article , the authors present a review of in-silico variant impact prediction methods and illustrate representative types of experimental and clinical applications using various datasets that are not yet published.
References
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