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A method that combines Grantham Variation (GV) and Grantham Deviation (GD) scores to predict the transactivation activity of each missense substitution. We compared our predictions against experimentally measured transactivation activity (yeast assays) to evaluate their accuracy. Finally, the prediction results were compared with those obtained by the program Sorting Intolerant from Tolerant (SIFT) and Dayhoff’s classification.

Interface:
Web user interface
Restrictions to use:
None
Input data:
List of mutations, MSA
Input format:
Text, FASTA
Programming languages:
Perl
Computer skills:
Basic
Stability:
Stable
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Maintainer

  • Sean V. Tavtigian <sean.tavtigian at hci.utah.edu>

Institution(s)

International Agency for Research on Cancer Lyon, France; Department of Bioinformatics and Computational Biology, George Mason University Manassas, VA, USA; Department of Clinical Oncology, Institute of Development Aging and Cancer, Tohoku University Sendai 980-8575, Japan

  • (Mathe et al., 2006) Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods. Nucleic acids research.
    PMID: 16522644
  • 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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