Abstract
Massively parallel sequencing greatly facilitates the discovery of novel disease genes causing Mendelian and oligogenic disorders. However, many mutations are present in any individual genome, and identifying which ones are disease causing remains a largely open problem. We introduce eXtasy, an approach to prioritize nonsynonymous single-nucleotide variants (nSNVs) that substantially improves prediction of disease-causing variants in exome sequencing data by integrating variant impact prediction, haploinsufficiency prediction and phenotype-specific gene prioritization.
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Acknowledgements
This research is supported by Research Council KU Leuven: GOA/10/09 Manet, PFV/10/016 SymBioSys, IOF 3M120274 Immunosuppressive drugs; iMinds: SBO 2013; Hercules Stichting: Hercules III PacBio RS; the Flemish Institute for Science and Technology: IWT-SB/093289, IWT-TBM Haplotyping; EU: Cost Action BM1006: NGS Data Analysis Network, FCT Neuroclinomics.
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A.S., D.P. and Y.M. conceptually defined the project. A.S. and D.P. wrote the initial draft of the manuscript and performed the analyses. A.S. generated the data sets and developed the software tools. D.P. developed the benchmarks and trained the models. L.-C.T. and A.S. computed the Endeavour gene prioritizations. A.A. and A.S. developed the web tool. R.S. and J.A. advised on data visualization and visual analytics. P.K. advised on statistical concerns. J.R.V. advised on genetical concerns. All authors revised and proofread the paper. B.D.M. cosupervised the project. Y.M. supervised the project.
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Sifrim, A., Popovic, D., Tranchevent, LC. et al. eXtasy: variant prioritization by genomic data fusion. Nat Methods 10, 1083–1084 (2013). https://doi.org/10.1038/nmeth.2656
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DOI: https://doi.org/10.1038/nmeth.2656
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