Investigating DNA-, RNA-, and protein-based features as a means to discriminate pathogenic synonymous variants

Mark Livingstone, Lukas Folkman, Yuedong Yang, Ping Zhang, Matthew Mort, David N. Cooper, Yunlong Liu, Bela Stantic, Yaoqi Zhou

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Synonymous single-nucleotide variants (SNVs), although they do not alter the encoded protein sequences, have been implicated in many genetic diseases. Experimental studies indicate that synonymous SNVs can lead to changes in the secondary and tertiary structures of DNA and RNA, thereby affecting translational efficiency, cotranslational protein folding as well as the binding of DNA-/RNA-binding proteins. However, the importance of these various features in disease phenotypes is not clearly understood. Here, we have built a support vector machine (SVM) model (termed DDIG-SN) as a means to discriminate disease-causing synonymous variants. The model was trained and evaluated on nearly 900 disease-causing variants. The method achieves robust performance with the area under the receiver operating characteristic curve of 0.84 and 0.85 for protein-stratified 10-fold cross-validation and independent testing, respectively. We were able to show that the disease-causing effects in the immediate proximity to exon–intron junctions (1–3 bp) are driven by the loss of splicing motif strength, whereas the gain of splicing motif strength is the primary cause in regions further away from the splice site (4–69 bp). The method is available as a part of the DDIG server at

Original languageEnglish (US)
Pages (from-to)1336-1347
Number of pages12
JournalHuman Mutation
Issue number10
StatePublished - Oct 2017


  • bioinformatics
  • machine learning
  • same-sense variant
  • silent mutation
  • synonymous SNV

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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