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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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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개체군 유전체 염기서열 및 변이의 변환데이터에 대한 인공지능 딥러닝 모델을 이용한 바이오마커 검출 방법
TL;DR: For instance, the authors reported that the number of people who responded to the call was more than 2.5 million and the response time was less than 1.5 minutes, respectively.
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AmazonForest: In-silico Meta-Prediction of Pathogenic Variants
Palheta H,Wanderson Gonçalves e Gonçalves,Leonardo Miranda de Brito,Ribeiro dos Santos A,Matsumoto M,Ândrea Ribeiro-dos-Santos,Gilderlanio S. Araújo +6 more
TL;DR: The performance of data pre-processing methods combined with classical prediction methods, such as Naive Bayes, Random Forest, and Support Vector Machine are evaluated to build a meta-prediction model aiming to improve genetic pathogenicity interpretation.
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Comprehensive in Silico Analyses of Single Nucleotide Variants of the Human Orthologues of 171 Murine Loci to Seek Novel Insights into the Genetics of Human Pigmentation
Kausik Ganguly,Debmalya Sengupta,Neelanjana Sarkar,Noyonika Mukherjee,Tithi Dutta,Arpan Saha,Tania Saha,Bhaswati Ghosh,Sujan Chatterjee,Pronay Brahmachari,Aritra Kundu,Mainak Sengupta +11 more
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Specifications of the acmg/amp variant curation guidelines for myocilin: recommendations from the clingen glaucoma expert panel
TL;DR: The Glaucoma VCEP was created to develop rule specifications for genes associated with primary glaucomA, including myocilin ( MYOC ), the most common cause of Mendelian glaucomba as mentioned in this paper .
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Rare variant association study of veteran twin whole-genomes links severe depression with a nonsynonymous change in the neuronal gene BHLHE22.
Daniel Hupalo,Christopher W. Forsberg,Jack Goldberg,Jack Goldberg,William S. Kremen,Michael J. Lyons,Anthony R. Soltis,Coralie Viollet,Robert J. Ursano,Murray B. Stein,Carol E. Franz,Yan V. Sun,Viola Vaccarino,Nicholas L. Smith,Nicholas L. Smith,Clifton L. Dalgard,Matthew D. Wilkerson,Harvey B. Pollard +17 more
TL;DR: In this paper, the authors used the optimised sequence kernel association test and Fisher's Exact test to fine map loci associated with severe depression using deep whole-genome sequencing data in an independent population.
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Basic Local Alignment Search Tool
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The Pfam protein families database
Marco Punta,Penny Coggill,Ruth Y. Eberhardt,Jaina Mistry,John Tate,Chris Boursnell,Ningze Pang,Kristoffer Forslund,Goran Ceric,Jody Clements,Andreas Heger,Liisa Holm,Erik L. L. Sonnhammer,Sean R. Eddy,Alex Bateman,Robert D. Finn +15 more
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