title: Identification of deleterious synonymous variants in human genomes.
abstract: BACKGROUND
  The prioritization and identification of disease-causing mutations is one of the most significant challenges in medical genomics. Currently available methods address this problem for non-synonymous single nucleotide variants (SNVs) and variation in promoters/enhancers; however, recent research has implicated synonymous (silent) exonic mutations in a number of disorders.
  
  
  RESULTS
  We have curated 33 such variants from literature and developed the Silent Variant Analyzer (SilVA), a machine-learning approach to separate these from among a large set of rare polymorphisms. We evaluate SilVA's performance on in silico 'infection' experiments, in which we implant known disease-causing mutations into a human genome, and show that for 15 of 33 disorders, we rank the implanted mutation among the top five most deleterious ones. Furthermore, we apply the SilVA method to two additional datasets: synonymous variants associated with Meckel syndrome, and a collection of silent variants clinically observed and stratified by a molecular diagnostics laboratory, and show that SilVA is able to accurately predict the harmfulness of silent variants in these datasets.
  
  
  BACKGROUND
  SilVA is open source and is freely available from the project website: http://compbio.cs.toronto.edu/silva
  
  
  BACKGROUND
  silva-snv@cs.toronto.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada.
authors.forename: Orion J
authors.initials: OJ
authors.lastname: Buske
authors.forename: AshokKumar
authors.initials: A
authors.lastname: Manickaraj
authors.forename: Seema
authors.initials: S
authors.lastname: Mital
authors.forename: Peter N
authors.initials: PN
authors.lastname: Ray
authors.forename: Michael
authors.initials: M
authors.lastname: Brudno
mesh_terms: D001185:Artificial Intelligence; D002925:Ciliary Motility Disorders / Q000235:genetics; D003198:Computer Simulation; D004194:Disease / Q000235:genetics*; D004677:Encephalocele / Q000235:genetics; D005091:Exons; D015894:Genome, Human; D023281:Genomics / Q000379:methods; D006801:Humans; D009154:Mutation; D007690:Polycystic Kidney Diseases / Q000235:genetics; D011110:Polymorphism, Genetic; D012174:Retinitis Pigmentosa