ArticleBMC genomics2015
Better prediction of functional effects for sequence variants.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 322 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
322 citing papers in PubMed.
- Whole Exome Sequencing IdentifiesInternational journal of molecular sciences · 2021Trial
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- Computational hybrid framework integrating clinical-mutation profiling and physics-based simulations to predict structural tolerance of Bruton tyrosine kinase (BTK) and sustained ARQ 531 binding.Current research in structural biology · 2026Article
- Identification of non-synonymous SNPs affecting structure and function of MLH1 and NBN proteins: a computational approach.Journal of applied genetics · 2026Article
- Molecular and physiological acclimation to low light and iron scarcity in a globally abundant oceanic pelagophyte.Nature communications · 2026Article
- The Genetic Landscape of Paediatric Cataract in Saudi Arabia: A Two-Decade Cohort with Novel Variants, Genotype-Phenotype Correlations, and Bioinformatic Analysis.Journal of clinical medicine · 2026Article
- Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network.BMC microbiology · 2026Article
- Computational analysis of the structural and functional impact of the deleterious nsSNPs in the humanBiochemistry and biophysics reports · 2026Article
- Genetic and bioinformatic analysis of RHO and PRPH2 variants in north Indian retinitis pigmentosa patients.Japanese journal of ophthalmology · 2026Article
- Predicting Pathogenicity ofInternational journal of molecular sciences · 2026Article
- pLM-SAV: a Δ-embedding approach for predicting pathogenic single amino acid variants.Bioinformatics advances · 2026Article
- Article
- Biomarker Variants of Dopamine Receptor Genes Influence the Binding Interaction Between Dopamine Receptor and Risperidone.Drug design, development and therapy · 2026Article
- Comprehensive In Silico Analysis of the CXCL12, C55Y Mutation Reveals Structural and Functional Disruption in CXCR4/CXCR7 Signaling.Evolutionary bioinformatics online · 2026Article
- Polymorphism of theGenes · 2025Article
- Xenobiotic metabolizing gene polymorphisms and their influence on ovarian cancer susceptibility: A Case-Control and an in silico approach.Molecular biology reports · 2025Article
- Association Between FABP3 and FABP4 Genes with Changes in Milk Composition and Fatty Acid Profiles in the Native Southern Yellow Cattle Breed.Veterinary sciences · 2025Article
- In Silico Analysis and Validation of A Disintegrin and Metalloprotease (ADAM) 17 Gene Missense Variants: Structural Bioinformatics Study.JMIR bioinformatics and biotechnology · 2025Article
262 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.