PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations
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{"title"=>"PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations", "type"=>"journal", "authors"=>[{"first_name"=>"Jaroslav", "last_name"=>"Bendl", "scopus_author_id"=>"56079853300"}, {"first_name"=>"Jan", "last_name"=>"Stourac", "scopus_author_id"=>"56079976100"}, {"first_name"=>"Ondrej", "last_name"=>"Salanda", "scopus_author_id"=>"56079723000"}, {"first_name"=>"Antonin", "last_name"=>"Pavelka", "scopus_author_id"=>"35070349000"}, {"first_name"=>"Eric D.", "last_name"=>"Wieben", "scopus_author_id"=>"7003825667"}, {"first_name"=>"Jaroslav", "last_name"=>"Zendulka", "scopus_author_id"=>"22735800300"}, {"first_name"=>"Jan", "last_name"=>"Brezovsky", "scopus_author_id"=>"26421634100"}, {"first_name"=>"Jiri", "last_name"=>"Damborsky", "scopus_author_id"=>"56243349300"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"372348787", "sgr"=>"84896698938", "pmid"=>"24453961", "scopus"=>"2-s2.0-84896698938", "isbn"=>"1553-7358 (Electronic)", "doi"=>"10.1371/journal.pcbi.1003440", "issn"=>"1553734X"}, "id"=>"9a6492d1-9a91-37ea-8b4f-3398d9cc5e6d", "abstract"=>"Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.", "link"=>"http://www.mendeley.com/research/predictsnp-robust-accurate-consensus-classifier-prediction-diseaserelated-mutations", "reader_count"=>192, "reader_count_by_academic_status"=>{"Unspecified"=>9, "Professor > Associate Professor"=>12, "Researcher"=>35, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>44, "Student > Postgraduate"=>16, "Student > Master"=>36, "Other"=>3, "Student > Bachelor"=>16, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>8}, "reader_count_by_user_role"=>{"Unspecified"=>9, "Professor > Associate Professor"=>12, "Researcher"=>35, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>44, "Student > Postgraduate"=>16, "Student > Master"=>36, "Other"=>3, "Student > Bachelor"=>16, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>8}, "reader_count_by_subject_area"=>{"Engineering"=>9, "Unspecified"=>14, "Biochemistry, Genetics and Molecular Biology"=>35, "Mathematics"=>1, "Agricultural and Biological Sciences"=>98, "Medicine and Dentistry"=>10, "Neuroscience"=>3, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>2, "Chemistry"=>3, "Social Sciences"=>1, "Computer Science"=>15}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>9}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>10}, "Neuroscience"=>{"Neuroscience"=>3}, "Chemistry"=>{"Chemistry"=>3}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>98}, "Computer Science"=>{"Computer Science"=>15}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>35}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>14}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"Greece"=>1, "Canada"=>2, "Sweden"=>2, "Czech Republic"=>1, "Belgium"=>1, "United States"=>5, "Sri Lanka"=>1, "Brazil"=>1, "United Kingdom"=>2, "Switzerland"=>1, "Germany"=>2, "Spain"=>3}, "group_count"=>12}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/1349073"], "description"=>"<p>Upon submission of the input sequence and specification of investigated mutations, integrated predictors of pathogenicity are employed for evaluation of the mutation and the consensus prediction is calculated. In the meantime, UniProt and PMD databases are queried to gather the relevant annotations.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "population genetics", "mutation", "genetics", "diagram"], "article_id"=>902391, "categories"=>["Biological Sciences"], "users"=>["Jaroslav Bendl", "Jan Stourac", "Ondrej Salanda", "Antonin Pavelka", "Eric D. Wieben", "Jaroslav Zendulka", "Jan Brezovsky", "Jiří Damborský"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003440.g004", "stats"=>{"downloads"=>1, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Workflow_diagram_of_PredictSNP_/902391", "title"=>"Workflow diagram of PredictSNP.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-16 03:01:51"}
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  • {"files"=>["https://ndownloader.figshare.com/files/1349075"], "description"=>"<p>Principles and training datasets of eight evaluated tools.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "population genetics", "mutation", "genetics", "datasets", "evaluated"], "article_id"=>902393, "categories"=>["Biological Sciences"], "users"=>["Jaroslav Bendl", "Jan Stourac", "Ondrej Salanda", "Antonin Pavelka", "Eric D. Wieben", "Jaroslav Zendulka", "Jan Brezovsky", "Jiří Damborský"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003440.t001", "stats"=>{"downloads"=>1, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Principles_and_training_datasets_of_eight_evaluated_tools_/902393", "title"=>"Principles and training datasets of eight evaluated tools.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-16 03:01:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1349076"], "description"=>"<p>PPH-1 – PolyPhen-1; PPH-2 – PolyPhen-2; PMD dataset – dataset from Protein Mutant Database; MMP – dataset of massively mutated proteins;</p><p><sup>a</sup>– detailed evaluation is available in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003440#pcbi.1003440.s011\" target=\"_blank\">Tables S5</a>, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003440#pcbi.1003440.s012\" target=\"_blank\">S6</a>, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003440#pcbi.1003440.s013\" target=\"_blank\">S7</a>;</p>b<p>– these metrics were calculated with normalized numbers.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "population genetics", "mutation", "genetics", "predictsnp", "tools"], "article_id"=>902394, "categories"=>["Biological Sciences"], "users"=>["Jaroslav Bendl", "Jan Stourac", "Ondrej Salanda", "Antonin Pavelka", "Eric D. Wieben", "Jaroslav Zendulka", "Jan Brezovsky", "Jiří Damborský"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003440.t002", "stats"=>{"downloads"=>2, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_individual_and_PredictSNP_prediction_tools_with_three_independent_datasets_/902394", "title"=>"Performance of individual and PredictSNP prediction tools with three independent datasets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-16 03:01:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1349077"], "description"=>"a<p>– detailed evaluation is available in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003440#pcbi.1003440.s018\" target=\"_blank\">Table S12</a>;</p>b<p>– these metrics were calculated with normalized numbers.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "population genetics", "mutation", "genetics", "classifiers", "pmd-uniprot", "mmp"], "article_id"=>902395, "categories"=>["Biological Sciences"], "users"=>["Jaroslav Bendl", "Jan Stourac", "Ondrej Salanda", "Antonin Pavelka", "Eric D. Wieben", "Jaroslav Zendulka", "Jan Brezovsky", "Jiří Damborský"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003440.t003", "stats"=>{"downloads"=>1, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_consensus_classifiers_with_PMD_UNIPROT_and_MMP_datasets_/902395", "title"=>"Performance of consensus classifiers with PMD-UNIPROT and MMP datasets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-16 03:01:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1349078", "https://ndownloader.figshare.com/files/1349079", "https://ndownloader.figshare.com/files/1349080", "https://ndownloader.figshare.com/files/1349081", "https://ndownloader.figshare.com/files/1349082", "https://ndownloader.figshare.com/files/1349083", "https://ndownloader.figshare.com/files/1349084", "https://ndownloader.figshare.com/files/1349085", "https://ndownloader.figshare.com/files/1349086", "https://ndownloader.figshare.com/files/1349087", "https://ndownloader.figshare.com/files/1349088", "https://ndownloader.figshare.com/files/1349089", "https://ndownloader.figshare.com/files/1349090", "https://ndownloader.figshare.com/files/1349091", "https://ndownloader.figshare.com/files/1349092", "https://ndownloader.figshare.com/files/1349093", "https://ndownloader.figshare.com/files/1349094", "https://ndownloader.figshare.com/files/1349095", "https://ndownloader.figshare.com/files/1349096", "https://ndownloader.figshare.com/files/1349097", "https://ndownloader.figshare.com/files/1349098"], "description"=>"<div><p>Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at <a href=\"http://loschmidt.chemi.muni.cz/predictsnp\" target=\"_blank\">http://loschmidt.chemi.muni.cz/predictsnp</a>.</p></div>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "population genetics", "mutation", "genetics", "robust", "classifier", "disease-related"], "article_id"=>902396, "categories"=>["Biological Sciences"], "users"=>["Jaroslav Bendl", "Jan Stourac", "Ondrej Salanda", "Antonin Pavelka", "Eric D. Wieben", "Jaroslav Zendulka", "Jan Brezovsky", "Jiří Damborský"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003440.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s002", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s003", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s004", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s005", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s006", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s007", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s008", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s009", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s010", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s011", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s012", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s013", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s014", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s015", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s016", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s017", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s018", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s019", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s020", "https://dx.doi.org/10.1371/journal.pcbi.1003440.s021"], "stats"=>{"downloads"=>182, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PredictSNP_Robust_and_Accurate_Consensus_Classifier_for_Prediction_of_Disease_Related_Mutations_/902396", "title"=>"PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-01-16 03:01:51"}

PMC Usage Stats | Further Information

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Relative Metric

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