We are analyzing https://link.springer.com/article/10.1186/s13059-014-0484-1.

Title:
Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations | Genome Biology
Description:
Background Massively parallel sequencing studies have led to the identification of a large number of mutations present in a minority of cancers of a given site. Hence, methods to identify the likely pathogenic mutations that are worth exploring experimentally and clinically are required. We sought to compare the performance of 15 mutation effect prediction algorithms and their agreement. As a hypothesis-generating aim, we sought to define whether combinations of prediction algorithms would improve the functional effect predictions of specific mutations. Results Literature and database mining of single nucleotide variants (SNVs) affecting 15 cancer genes was performed to identify mutations supported by functional evidence or hereditary disease association to be classified either as non-neutral (n = 849) or neutral (n = 140) with respect to their impact on protein function. These SNVs were employed to test the performance of 15 mutation effect prediction algorithms. The accuracy of the prediction algorithms varies considerably. Although all algorithms perform consistently well in terms of positive predictive value, their negative predictive value varies substantially. Cancer-specific mutation effect predictors display no-to-almost perfect agreement in their predictions of these SNVs, whereas the non-cancer-specific predictors showed no-to-moderate agreement. Combinations of predictors modestly improve accuracy and significantly improve negative predictive values. Conclusions The information provided by mutation effect predictors is not equivalent. No algorithm is able to predict sufficiently accurately SNVs that should be taken forward for experimental or clinical testing. Combining algorithms aggregates orthogonal information and may result in improvements in the negative predictive value of mutation effect predictions.
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Keywords {🔍}

snvs, mutation, mutations, prediction, effect, neutral, algorithms, predictors, additional, single, cancer, file, pubmed, nonneutral, candra, variants, predictor, combinations, functional, article, breast, confidence, nucleotide, accuracy, chasm, cosmic, present, database, agreement, google, scholar, included, performance, dataset, figure, low, predictions, genes, study, fathmm, considered, score, composite, top, subset, npv, moesmesmpdf, based, bona, fide,

Topics {✒️}

cancer-specific high-throughput annotation article download pdf dna/rna binding affinity specific chemical/biological compounds species-independent/evolutionary units performing single/independent predictor cancer-specific prediction algorithms hormone-resistant breast cancer black boxes meta-predictors analyze locus-specific databases google scholar cancer-specific predictors showed developmental stage-specific interplay related meta-predictor condel conserved wild-type function related subjects sole cancer-specific predictor perez-fidalgo ja machine-learning system trained statistically significant reduction full size image prediction algorithm combinations single prediction algorithms privacy choices/manage cookies tcga pan-cancer dataset full access functionally/experimentally assessed mutations finding disease-causal variants mutation effect predictor mutation effect type studies investigating functionally orange bars meta-predictors cancer genome sequencing anchorage-independent growth microarray gene signatures single nucleotide variants cancer-related databases mutation effect predictions recognizing protein domains mutation effect predictors cancer-specific predictor fathmm reis-filho js tumor suppressor genes error bars represent ligand binding domain tumor growth/induction false-negative rates based specific tumor types cancer driver annotation protein-protein interactions

Schema {🗺️}

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         headline:Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations
         description:Massively parallel sequencing studies have led to the identification of a large number of mutations present in a minority of cancers of a given site. Hence, methods to identify the likely pathogenic mutations that are worth exploring experimentally and clinically are required. We sought to compare the performance of 15 mutation effect prediction algorithms and their agreement. As a hypothesis-generating aim, we sought to define whether combinations of prediction algorithms would improve the functional effect predictions of specific mutations. Literature and database mining of single nucleotide variants (SNVs) affecting 15 cancer genes was performed to identify mutations supported by functional evidence or hereditary disease association to be classified either as non-neutral (n = 849) or neutral (n = 140) with respect to their impact on protein function. These SNVs were employed to test the performance of 15 mutation effect prediction algorithms. The accuracy of the prediction algorithms varies considerably. Although all algorithms perform consistently well in terms of positive predictive value, their negative predictive value varies substantially. Cancer-specific mutation effect predictors display no-to-almost perfect agreement in their predictions of these SNVs, whereas the non-cancer-specific predictors showed no-to-moderate agreement. Combinations of predictors modestly improve accuracy and significantly improve negative predictive values. The information provided by mutation effect predictors is not equivalent. No algorithm is able to predict sufficiently accurately SNVs that should be taken forward for experimental or clinical testing. Combining algorithms aggregates orthogonal information and may result in improvements in the negative predictive value of mutation effect predictions.
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         dateModified:2014-10-28T00:00:00Z
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            Single Predictor
            Sort Intolerant From Tolerant
            Animal Genetics and Genomics
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            Plant Genetics and Genomics
            Microbial Genetics and Genomics
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      headline:Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations
      description:Massively parallel sequencing studies have led to the identification of a large number of mutations present in a minority of cancers of a given site. Hence, methods to identify the likely pathogenic mutations that are worth exploring experimentally and clinically are required. We sought to compare the performance of 15 mutation effect prediction algorithms and their agreement. As a hypothesis-generating aim, we sought to define whether combinations of prediction algorithms would improve the functional effect predictions of specific mutations. Literature and database mining of single nucleotide variants (SNVs) affecting 15 cancer genes was performed to identify mutations supported by functional evidence or hereditary disease association to be classified either as non-neutral (n = 849) or neutral (n = 140) with respect to their impact on protein function. These SNVs were employed to test the performance of 15 mutation effect prediction algorithms. The accuracy of the prediction algorithms varies considerably. Although all algorithms perform consistently well in terms of positive predictive value, their negative predictive value varies substantially. Cancer-specific mutation effect predictors display no-to-almost perfect agreement in their predictions of these SNVs, whereas the non-cancer-specific predictors showed no-to-moderate agreement. Combinations of predictors modestly improve accuracy and significantly improve negative predictive values. The information provided by mutation effect predictors is not equivalent. No algorithm is able to predict sufficiently accurately SNVs that should be taken forward for experimental or clinical testing. Combining algorithms aggregates orthogonal information and may result in improvements in the negative predictive value of mutation effect predictions.
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         Positive Predictive Value
         Negative Predictive Value
         Composite Score
         Single Predictor
         Sort Intolerant From Tolerant
         Animal Genetics and Genomics
         Human Genetics
         Plant Genetics and Genomics
         Microbial Genetics and Genomics
         Bioinformatics
         Evolutionary Biology
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               type:PostalAddress
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PostalAddress:
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      name:Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, USA
      name:Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, USA
      name:Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, USA
      name:Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, USA
      name:Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, USA
      name:Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA
      name:Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, USA
      name:Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, USA
      name:Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, USA

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