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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Phenotypic Variability and mTOR Pathway Gene Aberrations in Familial Tuberous Sclerosis
Winnie S. Liang,Shobana Sekar,Sara Nasser,Jonathan Adkins,Lori Cuyugan,Daniel Enriquez,Sampath Rangasamy,Vinodh Narayanan +7 more
TL;DR: The first reported DNA and RNA analysis of TSC families demonstrating mTOR pathway aberrations in mild and severe forms of the disease is described.
Journal ArticleDOI
Machine learning techniques for pathogenicity prediction of non-synonymous single nucleotide polymorphisms in human body
Journal ArticleDOI
Potential Involvement of NSD1, KRT24 and ACACA in the Genetic Predisposition to Colorectal Cancer
Isabel Quintana,Pilar Mur,Mariona Terradas,Sandra García-Mulero,Gemma Aiza,Matilde Navarro,Virginia Piñol,Joan Brunet,Victor Moreno,Rebeca Sanz-Pamplona,Gabriel Capellá,Laura Valle +11 more
TL;DR: Most genes identified through the ALFRED in silico method were not relevant for CRC predisposition, although a possible association was detected for NSD1, KRT24 and ACACA.
Journal ArticleDOI
Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations
Kai Ye,Bimal Patel +1 more
TL;DR: The Structure-Based Pathogenicity Relationship Identifier (SPRI) as mentioned in this paper is a computational tool for accurate evaluation of pathological effects of missense single mutations and prediction of higher-order spatially organized units of mutational clusters.
Posted ContentDOI
Gene variants in ZGRF1 implicated for a rare sensory reflex epilepsy
TL;DR: These studies aimed at identifying a causative gene in a four-generation family with several of its members affected with the disorder, from the southern parts of India, suggest involvement of unanticipated molecular mechanisms underlying this neurobehavioral phenotype.
References
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