An integrative approach to predicting the functional effects of non-coding and coding sequence variation
Motivation: Technological advances have enabled the identification of an increasingly large spectrum of single nucleotide variants within the human genome, many of which may be associated with monogenic disease or complex traits. Here, we propose an integrative approach, named FATHMM-MKL, to predict...
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| Published in | Bioinformatics Vol. 31; no. 10; pp. 1536 - 1543 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
15.05.2015
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Motivation: Technological advances have enabled the identification of an increasingly large spectrum of single nucleotide variants within the human genome, many of which may be associated with monogenic disease or complex traits. Here, we propose an integrative approach, named FATHMM-MKL, to predict the functional consequences of both coding and non-coding sequence variants. Our method utilizes various genomic annotations, which have recently become available, and learns to weight the significance of each component annotation source.
Results: We show that our method outperforms current state-of-the-art algorithms, CADD and GWAVA, when predicting the functional consequences of non-coding variants. In addition, FATHMM-MKL is comparable to the best of these algorithms when predicting the impact of coding variants. The method includes a confidence measure to rank order predictions.
Availability and implementation: The FATHMM-MKL webserver is available at: http://fathmm.biocompute.org.uk
Contact: H.Shihab@bristol.ac.uk or Mark.Rogers@bristol.ac.uk or C.Campbell@bristol.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online. |
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| AbstractList | Motivation: Technological advances have enabled the identification of an increasingly large spectrum of single nucleotide variants within the human genome, many of which may be associated with monogenic disease or complex traits. Here, we propose an integrative approach, named FATHMM-MKL, to predict the functional consequences of both coding and non-coding sequence variants. Our method utilizes various genomic annotations, which have recently become available, and learns to weight the significance of each component annotation source.Results: We show that our method outperforms current state-of-the-art algorithms, CADD and GWAVA, when predicting the functional consequences of non-coding variants. In addition, FATHMM-MKL is comparable to the best of these algorithms when predicting the impact of coding variants. The method includes a confidence measure to rank order predictions.Availability and implementation: The FATHMM-MKL webserver is available at: http://fathmm.biocompute.org.ukContact: H.Shihabristol.ac.uk or Mark.Rogersristol.ac.uk or C.Campbellristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online. Technological advances have enabled the identification of an increasingly large spectrum of single nucleotide variants within the human genome, many of which may be associated with monogenic disease or complex traits. Here, we propose an integrative approach, named FATHMM-MKL, to predict the functional consequences of both coding and non-coding sequence variants. Our method utilizes various genomic annotations, which have recently become available, and learns to weight the significance of each component annotation source.MOTIVATIONTechnological advances have enabled the identification of an increasingly large spectrum of single nucleotide variants within the human genome, many of which may be associated with monogenic disease or complex traits. Here, we propose an integrative approach, named FATHMM-MKL, to predict the functional consequences of both coding and non-coding sequence variants. Our method utilizes various genomic annotations, which have recently become available, and learns to weight the significance of each component annotation source.We show that our method outperforms current state-of-the-art algorithms, CADD and GWAVA, when predicting the functional consequences of non-coding variants. In addition, FATHMM-MKL is comparable to the best of these algorithms when predicting the impact of coding variants. The method includes a confidence measure to rank order predictions.RESULTSWe show that our method outperforms current state-of-the-art algorithms, CADD and GWAVA, when predicting the functional consequences of non-coding variants. In addition, FATHMM-MKL is comparable to the best of these algorithms when predicting the impact of coding variants. The method includes a confidence measure to rank order predictions. Motivation: Technological advances have enabled the identification of an increasingly large spectrum of single nucleotide variants within the human genome, many of which may be associated with monogenic disease or complex traits. Here, we propose an integrative approach, named FATHMM-MKL, to predict the functional consequences of both coding and non-coding sequence variants. Our method utilizes various genomic annotations, which have recently become available, and learns to weight the significance of each component annotation source. Results: We show that our method outperforms current state-of-the-art algorithms, CADD and GWAVA, when predicting the functional consequences of non-coding variants. In addition, FATHMM-MKL is comparable to the best of these algorithms when predicting the impact of coding variants. The method includes a confidence measure to rank order predictions. Availability and implementation: The FATHMM-MKL webserver is available at: http://fathmm.biocompute.org.uk Contact: H.Shihab@bristol.ac.uk or Mark.Rogers@bristol.ac.uk or C.Campbell@bristol.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. Motivation: Technological advances have enabled the identification of an increasingly large spectrum of single nucleotide variants within the human genome, many of which may be associated with monogenic disease or complex traits. Here, we propose an integrative approach, named FATHMM-MKL, to predict the functional consequences of both coding and non-coding sequence variants. Our method utilizes various genomic annotations, which have recently become available, and learns to weight the significance of each component annotation source. Results: We show that our method outperforms current state-of-the-art algorithms, CADD and GWAVA, when predicting the functional consequences of non-coding variants. In addition, FATHMM-MKL is comparable to the best of these algorithms when predicting the impact of coding variants. The method includes a confidence measure to rank order predictions. Availability and implementation: The FATHMM-MKL webserver is available at: http://fathmm.biocompute.org.uk Contact: H.Shihab@bristol.ac.uk or Mark.Rogers@bristol.ac.uk or C.Campbell@bristol.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. Technological advances have enabled the identification of an increasingly large spectrum of single nucleotide variants within the human genome, many of which may be associated with monogenic disease or complex traits. Here, we propose an integrative approach, named FATHMM-MKL, to predict the functional consequences of both coding and non-coding sequence variants. Our method utilizes various genomic annotations, which have recently become available, and learns to weight the significance of each component annotation source. We show that our method outperforms current state-of-the-art algorithms, CADD and GWAVA, when predicting the functional consequences of non-coding variants. In addition, FATHMM-MKL is comparable to the best of these algorithms when predicting the impact of coding variants. The method includes a confidence measure to rank order predictions. |
| Author | Rogers, Mark F. Campbell, Colin Gaunt, Tom R. Shihab, Hashem A. Gough, Julian Day, Ian N. M. Mort, Matthew Cooper, David N. |
| Author_xml | – sequence: 1 givenname: Hashem A. surname: Shihab fullname: Shihab, Hashem A. – sequence: 2 givenname: Mark F. surname: Rogers fullname: Rogers, Mark F. – sequence: 3 givenname: Julian surname: Gough fullname: Gough, Julian – sequence: 4 givenname: Matthew surname: Mort fullname: Mort, Matthew – sequence: 5 givenname: David N. surname: Cooper fullname: Cooper, David N. – sequence: 6 givenname: Ian N. M. surname: Day fullname: Day, Ian N. M. – sequence: 7 givenname: Tom R. surname: Gaunt fullname: Gaunt, Tom R. – sequence: 8 givenname: Colin surname: Campbell fullname: Campbell, Colin |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25583119$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1093/bioinformatics/btt182 10.1186/1471-2105-10-267 10.1093/bioinformatics/btq671 10.1038/nmeth.2832 10.1186/1479-7364-8-11 10.1038/nature11632 10.1007/s00439-013-1358-4 10.1007/978-3-031-01552-6 10.1038/nature11247 10.1002/humu.21445 10.1038/ng.2892 10.1038/nature09534 10.1017/CBO9780511809682 10.1016/j.gpb.2013.05.001 10.1073/pnas.0903103106 10.1002/humu.22225 |
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| References | Stenson (2023020115420222200_btv009-B14) 2014; 133 Shawe-Taylor (2023020115420222200_btv009-B10) 2004 Shihab (2023020115420222200_btv009-B13) 2014; 8 Ritchie (2023020115420222200_btv009-B9) 2014; 11 Campbell (2023020115420222200_btv009-B1) 2011 Hindorff (2023020115420222200_btv009-B3) 2009; 106 Thusberg (2023020115420222200_btv009-B18) 2011; 32 Gönen (2023020115420222200_btv009-B2) 2011; 12 Ying (2023020115420222200_btv009-B19) 2009; 10 Kircher (2023020115420222200_btv009-B4) 2014; 46 Shihab (2023020115420222200_btv009-B12) 2013; 34 Platt (2023020115420222200_btv009-B7) 1999 Landrum (2023020115420222200_btv009-B5) 2013 Li (2023020115420222200_btv009-B6) 2011; 27 The ENCODE Project Consortium (2023020115420222200_btv009-B17) 2012; 489 Shihab (2023020115420222200_btv009-B11) 2013 The 1000 Genomes Project Consortium (2023020115420222200_btv009-B16) 2012; 491 The 1000 Genomes Project (2023020115420222200_btv009-B15) 2010; 467 Qu (2023020115420222200_btv009-B8) 2013; 11 |
| References_xml | – year: 2013 ident: 2023020115420222200_btv009-B11 article-title: Predicting the functional consequences of cancer-associated amino acid substitutions publication-title: Bioinformatics doi: 10.1093/bioinformatics/btt182 – volume: 10 start-page: 267 year: 2009 ident: 2023020115420222200_btv009-B19 article-title: Enhanced protein fold recognition through a novel data integration approach publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-10-267 – year: 2013 ident: 2023020115420222200_btv009-B5 article-title: Clinvar: public archive of relationships among sequence variation and human phenotype publication-title: Nucleic Acids Res – volume: 27 start-page: 718 year: 2011 ident: 2023020115420222200_btv009-B6 article-title: Tabix: fast retrieval of sequence features from generic tab-delimited files publication-title: Bioinformatics doi: 10.1093/bioinformatics/btq671 – volume: 11 start-page: 294 year: 2014 ident: 2023020115420222200_btv009-B9 article-title: Functional annotation of noncoding sequence variants publication-title: Nat. Methods doi: 10.1038/nmeth.2832 – volume: 8 start-page: 11 year: 2014 ident: 2023020115420222200_btv009-B13 article-title: Ranking non-synonymous single nucleotide polymorphisms based on disease concepts publication-title: Hum. Genomics doi: 10.1186/1479-7364-8-11 – volume: 491 start-page: 56 year: 2012 ident: 2023020115420222200_btv009-B16 article-title: An integrated map of genetic variation from 1,092 human genomes publication-title: Nature doi: 10.1038/nature11632 – volume: 133 start-page: 1 year: 2014 ident: 2023020115420222200_btv009-B14 article-title: The human gene mutation database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine publication-title: Hum. Genet. doi: 10.1007/s00439-013-1358-4 – volume-title: Learning with Support Vector Machines year: 2011 ident: 2023020115420222200_btv009-B1 doi: 10.1007/978-3-031-01552-6 – volume: 489 start-page: 57 year: 2012 ident: 2023020115420222200_btv009-B17 article-title: An integrated encyclopedia of DNA elements in the human genome publication-title: Nature doi: 10.1038/nature11247 – volume: 32 start-page: 358 year: 2011 ident: 2023020115420222200_btv009-B18 article-title: Performance of mutation pathogenicity prediction methods on missense variants publication-title: Hum. Mutat. doi: 10.1002/humu.21445 – start-page: 61 volume-title: Advances in Large Margin Classifiers year: 1999 ident: 2023020115420222200_btv009-B7 article-title: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods – volume: 12 start-page: 2211 year: 2011 ident: 2023020115420222200_btv009-B2 article-title: Multiple kernel learning algorithms publication-title: J. Mach. Learn. Res. – volume: 46 start-page: 310 year: 2014 ident: 2023020115420222200_btv009-B4 article-title: A general framework for estimating the relative pathogenicity of human genetic variants publication-title: Nat. Genet. doi: 10.1038/ng.2892 – volume: 467 start-page: 1061 year: 2010 ident: 2023020115420222200_btv009-B15 article-title: A map of human genome variation from population-scale sequencing publication-title: Nature doi: 10.1038/nature09534 – volume-title: Kernel Methods for Pattern Analysis year: 2004 ident: 2023020115420222200_btv009-B10 doi: 10.1017/CBO9780511809682 – volume: 11 start-page: 135 year: 2013 ident: 2023020115420222200_btv009-B8 article-title: A brief review on the human encyclopedia of DNA elements (encode) project publication-title: Genom. Proteom. Bioinform. doi: 10.1016/j.gpb.2013.05.001 – volume: 106 start-page: 9362 year: 2009 ident: 2023020115420222200_btv009-B3 article-title: Potential etiologic and functional implications of genome-wide association loci for human diseases and traits publication-title: Proc. Natl Acad. Sci., USA doi: 10.1073/pnas.0903103106 – volume: 34 start-page: 57 year: 2013 ident: 2023020115420222200_btv009-B12 article-title: Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden markov models publication-title: Hum. Mutat. doi: 10.1002/humu.22225 |
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| Snippet | Motivation: Technological advances have enabled the identification of an increasingly large spectrum of single nucleotide variants within the human genome,... Technological advances have enabled the identification of an increasingly large spectrum of single nucleotide variants within the human genome, many of which... Motivation: Technological advances have enabled the identification of an increasingly large spectrum of single nucleotide variants within the human genome,... |
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| SubjectTerms | Algorithms Annotations Bioinformatics Coding Confidence Genetic Variation - genetics Genome, Human Genome-Wide Association Study Genomics - methods Human Humans Molecular Sequence Annotation Nucleotides Open Reading Frames - genetics Original Papers Phenotype State of the art Untranslated Regions - genetics |
| Title | An integrative approach to predicting the functional effects of non-coding and coding sequence variation |
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