This track collection shows MutScore scores for predicting the deleteriousness of each nucleotide change in the genome.
Large-scale sequencing efforts of genomes from patients often lead to the identification of DNA variants of unknown significance (VUS), i.e. of genomic variations that cannot be immediately recognized as pathogenic mutations or benign DNA changes. Despite many in silico predictors exist, none of them takes advantage from the wealth of information contained in the positional clustering of mutations already detected in disease-associated genes. We have therefore developed MutScore, a new pathogenicity predictor that integrates unsupervised features of single DNA variants with information derived from such clusters. The predictive model for MutScore was trained with a random forest approach on medically-relevant mutations and subsequently tested against various genomic databases for both hereditary conditions and cancer (ClinVar, HGMD, and DoCM), achieving a very high performance. The use of MutScore on 840 genes from the ClinVar database also allowed the detection of significant clusters of disease-associated and of benign variants in 505 and 345 of them, respectively, revealing protein domains with diverging functional importance. In addition, an open-access web-based application, MutLand, was developed to provide a comprehensive graphical landscape of all known medically-relevant and clearly benign DNA variants for individual genes, as a help in appraising new VUS identified in clinical testing. Altogether, our work reveals the presence of widespread clustering of missense variants associated with normal and clinical phenotypes and that this information can be systematically used to improve and to understand pathogenicity at the molecular level.
There are four subtracks for this track, one for every nucleotide, showing scores for mutation from the reference to that nucleotide. All subtracks show MutScore. Across the exome, there are three values per position, one for every possible nucleotide mutation. The fourth value, "no mutation", representing the reference allele, e.g. A to A, is always set to zero, "0.0". MutScore only takes into account amino acid changes (missense), so a nucleotide change that results in no amino acid change (synonymous) also receives the score "0.0".
MutScore scores are available at the MutScore website. The site provides precomputed MutScore scores for all possible human missense variants to facilitate the identification of pathogenic variants among the large number of rare variants discovered in sequencing studies. As well as MutLand representation of genes and variants.
The MutScore data on the UCSC Genome Browser can be explored interactively with the
Table Browser or the
Data Integrator.
For automated download and analysis, the genome annotation is stored at UCSC in bigWig
files that can be downloaded from
our download server.
The files for this track are called MutScore.hg38.A.bw, MutScore.hg38.C.bw, MutScore.hg38.G.bw, MutScore.hg38.T.bw, MutScore.hg19.A.bw, MutScore.hg19.C.bw, MutScore.hg19.G.bw, MutScore.hg19.T.bw. Individual
regions or the whole genome annotation can be obtained using our tool bigWigToWig
which can be compiled from the source code or downloaded as a precompiled
binary for your system. Instructions for downloading source code and binaries can be found
here.
The tools can also be used to obtain features confined to given range, e.g.
bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 MutScore.hg38.A.bw stdout
Data were converted from the files provided on the MutScore website.
The list of authors can be found in the MutScore publication. MutScore and MutLand are freely available for non-commercial use. For other uses, please contact IOB. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Mathieu Quinodoz
Institute of Molecular and Clinical Ophthalmology Basel (IOB) Mittlere Strasse 91, 4031 Basel, Switzerland
mathieu.quinodoz[at]iob.ch
Quinodoz, M., Peter, V. G., Cisarova, K., Royer-Bertrand, B., Stenson, P. D., Cooper, D. N., ... & Rivolta, C. Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity Am J Hum Genet. 2012. PMID: 35120630; PMC: PMC8948164