UMD-Predictor: A High-Throughput Sequencing Compliant System for Pathogenicity Prediction of any Human cDNA Substitution

Abstract : Whole-exome sequencing (WES) is increasingly applied to research and clinical diagnosis of human diseases. It typically results in large amounts of genetic variations. Depending on the mode of inheritance, only one or two correspond to pathogenic mutations responsible for the disease and present in affected individuals. Therefore , it is crucial to filter out nonpathogenic variants and limit downstream analysis to a handful of candidate mutations. We have developed a new computational combi-natorial system UMD-Predictor ( to efficiently annotate cDNA substitutions of all human transcripts for their potential pathogenicity. It combines biochemical properties, impact on splicing signals, localiza-tion in protein domains, variation frequency in the global population, and conservation through the BLOSUM62 global substitution matrix and a protein-specific conservation among 100 species. We compared its accuracy with the seven most used and reliable prediction tools, using the largest reference variation datasets including more than 140,000 annotated variations. This system consistently demonstrated a better accuracy, specificity, Matthews correlation coefficient, diagnostic odds ratio, speed, and provided the shortest list of candidate mutations for WES. Webservices allow its implementation in any bioinfor-matics pipeline for next-generation sequencing analysis. It could benefit to a wide range of users and applications varying from gene discovery to clinical diagnosis.
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Contributeur : Gwenaëlle Collod-Beroud <>
Soumis le : jeudi 21 décembre 2017 - 10:39:45
Dernière modification le : mercredi 7 mars 2018 - 11:58:02


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David Salgado, Jean-Pierre Desvignes, Ghadi Rai, Arnaud Blanchard, Morgane Miltgen, et al.. UMD-Predictor: A High-Throughput Sequencing Compliant System for Pathogenicity Prediction of any Human cDNA Substitution. Human Mutation, Wiley, 2016, 37 (5), pp.439 - 446. 〈10.1002/humu.22965〉. 〈hal-01670164〉



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