Browsing by Subject "synonymous single-nucleotide variants"

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    Investigating DNA-, RNA-, and protein-based features as a means to discriminate pathogenic synonymous variants
    (Wiley, 2017) Livingstone, Mark; Folkman, Lukas; Yang, Yuedong; Zhang, Ping; Mort, Matthew; Cooper, David N.; Liu, Yunlong; Stantic, Bela; Zhou, Yaoqi; Department of Medical & Molecular Genetics, IU School of Medicine
    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 http://sparks-lab.org/ddig.
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    regSNPs-splicing: a tool for prioritizing synonymous single-nucleotide substitution
    (Springer, 2017) Zhang, Xinjun; Li, Meng; Lin, Hai; Rao, Xi; Feng, Weixing; Yang, Yuedong; Mort, Matthew; Cooper, David N.; Wang, Yue; Wang, Yadong; Wells, Clark; Zhou, Yaoqi; Liu, Yunlong; Department of Medical & Molecular Genetics, IU School of Medicine
    While synonymous single-nucleotide variants (sSNVs) have largely been unstudied, since they do not alter protein sequence, mounting evidence suggests that they may affect RNA conformation, splicing, and the stability of nascent-mRNAs to promote various diseases. Accurately prioritizing deleterious sSNVs from a pool of neutral ones can significantly improve our ability of selecting functional genetic variants identified from various genome-sequencing projects, and, therefore, advance our understanding of disease etiology. In this study, we develop a computational algorithm to prioritize sSNVs based on their impact on mRNA splicing and protein function. In addition to genomic features that potentially affect splicing regulation, our proposed algorithm also includes dozens structural features that characterize the functions of alternatively spliced exons on protein function. Our systematical evaluation on thousands of sSNVs suggests that several structural features, including intrinsic disorder protein scores, solvent accessible surface areas, protein secondary structures, and known and predicted protein family domains, show significant differences between disease-causing and neutral sSNVs. Our result suggests that the protein structure features offer an added dimension of information while distinguishing disease-causing and neutral synonymous variants. The inclusion of structural features increases the predictive accuracy for functional sSNV prioritization.