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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Innovative strategies for annotating the "relationSNP" between variants and molecular phenotypes.
TL;DR: The history behind SNP annotation, commonly used tools, and newer strategies for SNP annotation are discussed, to help illustrate how each SNP annotation method impacts the way in which the genetic and molecular etiology of a disease is explored in-silico.
Journal ArticleDOI
Type IV Collagen Variants in CKD: Performance of Computational Predictions for Identifying Pathogenic Variants.
Cole Shulman,Cole Shulman,Emerald Liang,Emerald Liang,Misato Kamura,Khalil Udwan,Khalil Udwan,Tony Yao,Tony Yao,Daniel C. Cattran,Heather N. Reich,Michelle Hladunewich,York Pei,Judy Savige,Andrew D. Paterson,Mary Ann Suico,Hirofumi Kai,Moumita Barua +17 more
TL;DR: In this paper, the authors evaluated the performance of in silico programs for COL4A3/A4/A5 variants in disease cohorts, including a local focal segmental glomerulosclerosis (FSGS) cohort, in which they are categorized as pathogenic or benign based on clinical criteria.
Journal ArticleDOI
Molecular characterization, homology modeling and docking studies of the R2787H missense variation in BRCA2 gene: Association with breast cancer.
Aouatef Riahi,Abdelmonem Messaoudi,Ridha Mrad,Asma Fourati,Habiba Chabouni-Bouhamed,Maher Kharrat +5 more
TL;DR: A combined molecular and computational approach to classify BRCA UVs missense variations suggest that R2787H variant could have potential functional impact and additional functional analyzes may provide appropriate assessment to classify such variants.
Journal ArticleDOI
Molecular dynamics approach to identification of new OGG1 cancer-associated somatic variants with impaired activity.
Aleksandr V. Popov,Anton V. Endutkin,Darya D. Yatsenko,Anna V. Yudkina,Alexander E. Barmatov,Kristina A. Makasheva,Darya Yu. Raspopova,Evgeniia A. Diatlova,Dmitry O. Zharkov +8 more
TL;DR: In this article, the authors used molecular dynamics (MD) to model the structures of 20 clinically observed variants of a DNA repair enzyme, 8-oxoguanine DNA glycosylase, and experimentally characterized the activity, thermostability and DNA binding in a subset of these mutant proteins.
References
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