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For more information please contact Prof. Christophe Béroud or Dr. David Salgado
UMD-Predictor: A mutation pathogenicity prediction system
With the development of Next Generation Sequencing technologies, the amount of data generated has reached an unprecedent level.
Approximately half of gene lesions responsible for human inherited diseases are due to an amino acid substitution. Distinguishing neutral sequence variations from those responsible for the phenotype is of major interest in human genetics.
To further differentiate neutral variants from pathogenic nucleotide substitutions, we developed a new tool, UMD-Predictor. This tool provides a combinatorial approach, to identify potential pathogenic variations, that associates the following data: localization within the protein, conservation, biochemical properties of the mutant and wild-type residues, and the potential impact of the variation on mRNA.
Privacy GuaranteedAs underlined by Pabinger et al, legal issues might arise when annotating lists of variants through on-line systems as they do not guarantee data confidentiality. To solve this issue, once batch analyses have been performed, corresponding files are automatically deleted from the UMD-Predictor system and no data are stored.
Evaluate the pathogenicity of variations.
Medical Genetics and Functional Genomics - UMR_S910
Director: Nicolas LEVY
Genetics and Bioinformatics Team
Director: Christophe BEROUD
Other Prediction Tools
- February 2016 - UMD-Predictor has been accepted for publication in the Human Mutation journal.
Salgado, D., Desvignes, J.-P., Rai, G., Blanchard, A., Miltgen, M., Pinard, A., Lévy, N., Collod-Béroud, G. and Béroud, C. (2016),
UMD-Predictor: a High Throughput Sequencing Compliant System for Pathogenicity Prediction of any Human cDNA Substitution. Human Mutation. Accepted Author Manuscript. doi:10.1002/humu.22965 More detail here