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
Reads0
Chats0
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
Citations
More filters
Journal ArticleDOI
The Ensembl Variant Effect Predictor.
William M. McLaren,Laurent Gil,Sarah E. Hunt,Harpreet Singh Riat,Graham R. S. Ritchie,Anja Thormann,Paul Flicek,Fiona Cunningham +7 more
TL;DR: The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.
Journal ArticleDOI
Comprehensive Characterization of Cancer Driver Genes and Mutations.
Matthew H. Bailey,Collin Tokheim,Eduard Porta-Pardo,Sohini Sengupta,Denis Bertrand,Amila Weerasinghe,Antonio Colaprico,Michael C. Wendl,Jaegil Kim,Brendan Reardon,Patrick Kwok Shing Ng,Kang Jin Jeong,Song Cao,Zixing Wang,Jianjiong Gao,Qingsong Gao,Fang Wang,Eric Minwei Liu,Loris Mularoni,Carlota Rubio-Perez,Niranjan Nagarajan,Isidro Cortes-Ciriano,Daniel Cui Zhou,Wen-Wei Liang,Julian M. Hess,Venkata Yellapantula,David Tamborero,Abel Gonzalez-Perez,Chayaporn Suphavilai,Jia Yu Ko,Ekta Khurana,Peter J. Park,Eliezer M. Van Allen,Eliezer M. Van Allen,Han Liang,Michael S. Lawrence,Adam Godzik,Nuria Lopez-Bigas,Josh Stuart,David A. Wheeler,Gad Getz,Ken Chen,Alexander J. Lazar,Gordon B. Mills,Rachel Karchin,Li Ding +45 more
TL;DR: This study reports a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations, identifying 299 driver genes with implications regarding their anatomical sites and cancer/cell types.
Journal ArticleDOI
REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants
Nilah M. Ioannidis,Joseph H. Rothstein,Joseph H. Rothstein,Vikas Pejaver,Sumit Middha,Shannon K. McDonnell,Saurabh Baheti,Anthony M. Musolf,Qing Li,Emily R. Holzinger,Danielle M. Karyadi,Lisa A. Cannon-Albright,Craig C. Teerlink,Janet L. Stanford,William B. Isaacs,Jianfeng Xu,Kathleen A. Cooney,Kathleen A. Cooney,Ethan M. Lange,Johanna Schleutker,John D. Carpten,Isaac J. Powell,Olivier Cussenot,Geraldine Cancel-Tassin,Graham G. Giles,Graham G. Giles,Robert J. MacInnis,Robert J. MacInnis,Christiane Maier,Chih-Lin Hsieh,Fredrik Wiklund,William J. Catalona,William D. Foulkes,Diptasri Mandal,Rosalind A. Eeles,Zsofia Kote-Jarai,Carlos Bustamante,Daniel J. Schaid,Trevor Hastie,Elaine A. Ostrander,Joan E. Bailey-Wilson,Predrag Radivojac,Stephen N. Thibodeau,Alice S. Whittemore,Weiva Sieh,Weiva Sieh +45 more
TL;DR: This work developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, LRT, GERP, SiPhy, phyloP, and phastCons.
Journal ArticleDOI
The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine
Peter D. Stenson,Matthew Mort,Edward V. Ball,Katy Shaw,Andrew David Phillips,David Neil Cooper +5 more
TL;DR: The Human Gene Mutation Database (HGMD®) is a comprehensive collection of germline mutations in nuclear genes that underlie, or are associated with, human inherited disease.
Journal ArticleDOI
The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies
Peter D. Stenson,Matthew Mort,Edward V. Ball,Katy Evans,Matthew J. Hayden,Sally Heywood,Michelle Hussain,Andrew David Phillips,David Neil Cooper +8 more
TL;DR: The Human Gene Mutation Database constitutes de facto the central unified gene/disease-oriented repository of heritable mutations causing human genetic disease used worldwide by researchers, clinicians, diagnostic laboratories and genetic counsellors, and is an essential tool for the annotation of next-generation sequencing data.
References
More filters
Journal ArticleDOI
Predicting the functional impact of protein mutations: application to cancer genomics
TL;DR: A new functional impact score (FIS) for amino acid residue changes using evolutionary conservation patterns is introduced, estimating that at least 5% of cancer-relevant mutations involve switch of function, rather than simply loss or gain of function.
Hidden Markov Models
TL;DR: 'Profiles' of protein structures and sequence alignments can detect subtle homologies and are beginning to appear in the fields of protein-structure prediction and large-scale genome-sequence analysis.
Journal ArticleDOI
Pfam : a comprehensive database of protein domain families based on seed alignments
TL;DR: A database based on hidden Markov model profiles (HMMs), which combines high quality and completeness, and a large number of previously unannotated proteins from the Caenorhabditis elegans genome project were classified.
Journal ArticleDOI
Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure.
TL;DR: A new procedure is described for detecting and correcting those errors that arise at the model-building stage of the procedure and a good procedure for creating HMMs for sequences of proteins of known structure are determined.
Journal ArticleDOI
Hidden Markov models
TL;DR: HMM-based profiles have been used in the fields of protein-structure prediction and large-scale genome-sequence analysis as discussed by the authors, and have been applied in protein-seq analysis.