The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations
By Nouf S. Al-Numair, Andrew C. R. Martin. 2013
51 citations recorded by the source corpus.
Understanding and predicting the effects of mutations on protein structure and phenotype is an increasingly important area. Genes for many genetically linked diseases are now routinely sequenced in the clinic. Previously we focused on understanding the structural effects of mutations, creating the SAAPdb resource. We have updated SAAPdb to include 41% more SNPs and 36% more PDs. Introducing a hydrophobic residue on the surface, or a hydrophilic residue in the core, no longer shows significant differences between SNPs and PDs. We have improved some of the analyses significantly enhancing the analysis of clashes and of mutations to-proline and from-glycine. A new web interface has been developed allowing users to analyze their own mutations. Finally we have developed a machine learning method which gives a cross-validated accuracy of 0.846, considerably out-performing well known methods including SIFT and PolyPhen2 which give accuracies between 0.690 and 0.785. We have updated SAAPdb and improved its analyses, but with the increasing rate with which mutation data are generated, we have created a new analysis pipeline and web interface. Results of machine learning using the structural analysis results to predict pathogenicity considerably outperform other methods.
Subfields: Genetics, Molecular Biology
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In Molecular Biology
Citations in the corpus
- The druggable genome and support for target identification and validation in drug development (2016, methodology): “Integrating information from feature annotation databases such as ENCODE (54), National Institutes of Health Roadmap (55), and the SingleAminoAcid Polymorphismdatabase (56) could help reducemisclassification.”