We are analyzing https://link.springer.com/article/10.1186/1471-2164-16-s8-s1.

Title:
Better prediction of functional effects for sequence variants | BMC Genomics
Description:
Elucidating the effects of naturally occurring genetic variation is one of the major challenges for personalized health and personalized medicine. Here, we introduce SNAP2, a novel neural network based classifier that improves over the state-of-the-art in distinguishing between effect and neutral variants. Our method
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Keywords {🔍}

variants, pubmed, snap, data, effect, protein, article, google, scholar, cas, methods, neutral, performance, sequence, set, proteins, prediction, variant, predictions, method, human, central, function, polyphen, features, feature, effects, amino, acid, functional, information, rost, predicted, reliability, structure, disease, training, sift, sets, eqn, accuracy, database, figure, difficult, predicting, cases, nucleic, acids, res, significantly,

Topics {✒️}

𝑖 𝑛 𝑡 2 𝑛 𝑛 - 1 = 𝑆 𝐷 reduces t-cell proliferation t-cell-stimulating activities article download pdf homology-derived protein structures sickle-cell anemia variants find unclear/contradicting signals predicting disease-causing variation article number s1 central variant position enables cross-genome comparisons brown triangle/arrow marks /bmcgenomics/supplements/16/s8 open access license org/services/snap2web definitions full size image synonymous single-nucleotide polymorphisms full cross-validation testing human cancer-causing mutations receiver operating characteristic optimize predictive behavior sunyaev sr technische universität münchen discerning disease-causing variants seemingly clear-cut cases user-defined decision threshold tenfold cross-validation article hecht annotate disease-variant relationships privacy choices/manage cookies amino acid substitutions disease-related mutations predicted protein-protein binding affinity deep sequencing data amino acid properties human disease-related mutations silico mutagenesis studies actual disease-causing variants amino acid indices amino acid composition b-cell activation expresses b-cell amino acid sequence german research foundation free network parameters native amino acid variant amino acid

Questions {❓}

More and better data needed to advance further?
One question is: how many variants will be identified as being neutral with respect to protein function?
Still, are we close to a saturation of performance, or can we expect another leap?
Tenfold cross-validation implies training ten networks: which one to use for future applications?
What are the true data that we want to assess our method upon?
What to expect from variant prediction?
Have we reached the limits for a method using only sequence information as input?

Schema {🗺️}

WebPage:
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         headline:Better prediction of functional effects for sequence variants
         description:Elucidating the effects of naturally occurring genetic variation is one of the major challenges for personalized health and personalized medicine. Here, we introduce SNAP2, a novel neural network based classifier that improves over the state-of-the-art in distinguishing between effect and neutral variants. Our method's improved performance results from screening many potentially relevant protein features and from refining our development data sets. Cross-validated on >100k experimentally annotated variants, SNAP2 significantly outperformed other methods, attaining a two-state accuracy (effect/neutral) of 83%. SNAP2 also outperformed combinations of other methods. Performance increased for human variants but much more so for other organisms. Our method's carefully calibrated reliability index informs selection of variants for experimental follow up, with the most strongly predicted half of all effect variants predicted at over 96% accuracy. As expected, the evolutionary information from automatically generated multiple sequence alignments gave the strongest signal for the prediction. However, we also optimized our new method to perform surprisingly well even without alignments. This feature reduces prediction runtime by over two orders of magnitude, enables cross-genome comparisons, and renders our new method as the best solution for the 10-20% of sequence orphans. SNAP2 is available at: https://rostlab.org/services/snap2web Delta, input feature that results from computing the difference feature scores for native amino acid and feature scores for variant amino acid; nsSNP, non-synoymous SNP; PMD, Protein Mutant Database; SNAP, Screening for non-acceptable polymorphisms; SNP, single nucleotide polymorphism; variant, any amino acid changing sequence variant.
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            variant effect
            neural network
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            general
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            Proteomics
            Animal Genetics and Genomics
            Microbial Genetics and Genomics
            Plant Genetics and Genomics
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      headline:Better prediction of functional effects for sequence variants
      description:Elucidating the effects of naturally occurring genetic variation is one of the major challenges for personalized health and personalized medicine. Here, we introduce SNAP2, a novel neural network based classifier that improves over the state-of-the-art in distinguishing between effect and neutral variants. Our method's improved performance results from screening many potentially relevant protein features and from refining our development data sets. Cross-validated on >100k experimentally annotated variants, SNAP2 significantly outperformed other methods, attaining a two-state accuracy (effect/neutral) of 83%. SNAP2 also outperformed combinations of other methods. Performance increased for human variants but much more so for other organisms. Our method's carefully calibrated reliability index informs selection of variants for experimental follow up, with the most strongly predicted half of all effect variants predicted at over 96% accuracy. As expected, the evolutionary information from automatically generated multiple sequence alignments gave the strongest signal for the prediction. However, we also optimized our new method to perform surprisingly well even without alignments. This feature reduces prediction runtime by over two orders of magnitude, enables cross-genome comparisons, and renders our new method as the best solution for the 10-20% of sequence orphans. SNAP2 is available at: https://rostlab.org/services/snap2web Delta, input feature that results from computing the difference feature scores for native amino acid and feature scores for variant amino acid; nsSNP, non-synoymous SNP; PMD, Protein Mutant Database; SNAP, Screening for non-acceptable polymorphisms; SNP, single nucleotide polymorphism; variant, any amino acid changing sequence variant.
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      dateModified:2015-06-18T00:00:00Z
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         functional effect prediction
         variant effect
         neural network
         from sequence
         SNP effect
         Life Sciences
         general
         Microarrays
         Proteomics
         Animal Genetics and Genomics
         Microbial Genetics and Genomics
         Plant Genetics and Genomics
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         type:PostalAddress
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         name:Department of Genetics, Rutgers University, Piscataway, USA
         type:PostalAddress
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         name:Institute of Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany & WZW - Weihenstephan, Freising, Germany
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               type:PostalAddress
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      email:[email protected]
      name:Yana Bromberg
      affiliation:
            name:Rutgers University
            address:
               name:Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, USA
               type:PostalAddress
            type:Organization
            name:Rutgers University
            address:
               name:Department of Genetics, Rutgers University, Piscataway, USA
               type:PostalAddress
            type:Organization
            name:Lichtenbergstr. 2a, 85748 Garching/Munich, Germany & WZW - Weihenstephan
            address:
               name:Institute of Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany & WZW - Weihenstephan, Freising, Germany
               type:PostalAddress
            type:Organization
      name:Burkhard Rost
      affiliation:
            name:Technische Universität München
            address:
               name:Department of Bioinformatics & Computational Biology, Technische Universität München, Garching/Munich, Germany
               type:PostalAddress
            type:Organization
            name:Lichtenbergstr. 2a, 85748 Garching/Munich, Germany & WZW - Weihenstephan
            address:
               name:Institute of Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany & WZW - Weihenstephan, Freising, Germany
               type:PostalAddress
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      name:Department of Bioinformatics & Computational Biology, Technische Universität München, Garching/Munich, Germany
      name:Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, USA
      name:Department of Genetics, Rutgers University, Piscataway, USA
      name:Institute of Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany & WZW - Weihenstephan, Freising, Germany
      name:Department of Bioinformatics & Computational Biology, Technische Universität München, Garching/Munich, Germany
      name:Institute of Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany & WZW - Weihenstephan, Freising, Germany

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