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A web tool for genome-wide annotation of human SNPs. LS-SNP/PDB provides information useful for identifying amino-acid changing SNPs (nsSNPs) that are most likely to have an impact on biological function. The system is kept up-to-date by an automated, high-throughput build pipeline that systematically maps human nsSNPs onto Protein Data Bank structures and annotates several biologically relevant features, inferred from three-dimensional experimental structures.

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  • Rachel Karchin <karchin at jhu.edu>


Department of Bioinformatics, George Mason University, Fairfax, VA, USA; Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA; Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD, USA

  • (Ryan et al., 2009) LS-SNP/PDB: annotated non-synonymous SNPs mapped to Protein Data Bank structures. Bioinformatics.
    PMID: 19369493
  • Animals
    • Homo sapiens
  • (Thusberg et al., 2011) Performance of mutation pathogenicity prediction methods on missense variants. Human mutation.
    PMID: 21412949
  • (Bendl et al., 2014) PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations. PLoS computational biology.
    PMID: 24453961
  • (Martelotto et al., 2014) Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations. Genome biology.
    PMID: 25348012
  • (Gonzalez-Perez et al., 2013) Computational approaches to identify functional genetic variants in cancer genomes. Nature methods.
    PMID: 23900255
  • (Gnad et al., 2013) Assessment of computational methods for predicting the effects of missense mutations in human cancers. BMC genomics.
    PMID: 23819521

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