Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models

By Hashem A. Shihab, Julian Gough, David N. Cooper, Peter D. Stenson, Gary L. A. Barker, Keith J. Edwards, Ian N. M. Day, Tom R. Gaunt. Human Mutation, 2012

1,269 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0065647c537b4beb0ab52e158a7f94bc015f9755

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Citations in the corpus

An integrative approach to predicting the functional effects of non-coding and coding sequence variation (2015, methodology): “In this article, we propose a method for predicting the functional impact of both coding and non-coding SNVs. Existing protein-based prediction algorithms tend to exploit evolutionary conservation when determining whether or not variants are functional.”
Assessing the Pathogenicity of Insertion and Deletion Variants with the Variant Effect Scoring Tool (VEST‐Indel) (2015, background): “Given that poor specificity also limits the utility of methods aimed at assessing the pathogenicity of missense variants [Chan et al., 2007; Hicks et al., 2011; Thusberg et al., 2011; Shihab et al., 2013], the approach presented here might prove beneficial for variant classification in general.”
Ensembl 2015 (2014, methodology): “The VEP off-line script version has been extended to include additional data types (PubMed IDs, Exome Sequencing Project frequencies, UniProt IDs, HGNC IDs) with new plugins for popular data sets, such as FATHMM (34), dbNSFP (35) and CADD (36).”
Comprehensive Characterization of Cancer Driver Genes and Mutations (2018, methodology): “…designed to distinguish between driver and passenger somatic mutations (CHASM [Wong et al., 2011], CanDrA [Carter et al., 2013], fathmm [Shihab et al., 2013] and transFIC [Gonzalez-Perez et al., 2012]) and four tools that leverage information from protein structures (HotSpot3D [Niu et…”
dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Non-synonymous and Splice Site SNVs (2016, background): “It added two functional prediction scores, MutationAssessor [Reva et al., 2011] and FATHMM [Shihab et al., 2013]; two conservation scores, GERP++ [Davydov et al., 2010] and SiPhy C© 2015 WILEY PERIODICALS, INC. [Garber et al., 2009; Lindblad-Toh et al., 2011]; and allele frequencies from the 1000…”
The Ensembl Variant Effect Predictor (2016, methodology): “Other pathogenicity predictor scores such as Condel (Gonzalez-Perez et al. 2012), FATHMM (Shihab et al. 2013) and MutationTaster (Schwarz et al. 2014) are available for human data via VEP plugins (see Table 4).”
The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies (2017, neutral): “2013), FATHMM (Shihab et al. 2013), FATHMM-MKL (Shihab et al.”
Development of Bioinformatics Infrastructure for Genomics Research in H3Africa (2017, positive): “Additionally, HUMA includes Vapor, a variant analysis workflow, which combines the predictions of Provean [23], PolyPhen-2 [24], PhD-SNP [25], FATHMM [26], AUTO-MUTE [27], MuPro [28], and I-Mutant 2.”
Ranking non-synonymous single nucleotide polymorphisms based on disease concepts (2014, methodology): “One of these methods is the Functional Analysis through Hidden Markov Models (FATHMM) algorithm [3]: a sequence-based method which combines evolutionary conservation in homologous (both”
PERCH: a unified framework for disease gene prioritization (2017, methodology): “2009), FATHMM (Shihab et al. 2013), LRT (Chun and Fay 2009), Mutation Taster (Schwarz et al.”
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines (2017, result): “Consistent with earlier results [31], we found that FATHMM [32] is particularly sensitive to this type of circularity.”
DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins (2017, background): “Similar to (12,13,17), the training set was used in cross-validation settings to learn the log-odd ratio of the probability of observing a deleterious or a neutral variant on each PFAM entry (see Supplementary Section S2 for details).”