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Yang, Fan; Sun, Song; Tan, Guihong; Costanzo, Michael; Hill, David E.; Vidal, Marc; Andrews, Brenda J.; Boone, Charles; Roth, Frederick P. 2018-05-25 To better understand the health implications of personal genomes, we now face a largely unmet challenge to identify functional variants within disease-associated genes. Functional variants can be identified by trans-species complementation, e.g., by failure to rescue a yeast strain bearing a mutation in an orthologous human gene. Although orthologous complementation assays are powerful predictors of pathogenic variation, they are available for only a few percent of human disease genes. Here we systematically examine the question of whether complementation assays based on paralogy relationships can expand the number of human disease genes with functional variant detection assays. We tested over 1,000 paralogous human-yeast gene pairs for complementation, yielding 34 complementation relationships, of which 33 (97%) were novel. We found that paralog-based assays identified disease variants with success on par with that of orthology-based assays. Combining all homology-based assay results, we found that complementation can often identify pathogenic variants outside the homologous sequence region, presumably because of global effects on protein folding or stability. Within our search space, paralogy-based complementation more than doubled the number of human disease genes with a yeast-based complementation assay for disease variation.
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Banff International Research Station for Mathematical Innovation and Discovery
Yang, Fan 2016-09-23 Non UBC Unreviewed Author affiliation: University of Waterloo Faculty http://creativecommons.org/licenses/by-nc-nd/4.0/
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Wu, Zhiqiang; Han, Yelin; Liu, Bo; Li, Hongying; Zhu, Guangjian; Latinne, Alice; Dong, Jie; Sun, Lilin; Su, Haoxiang; Liu, Liguo; Du, Jiang; Zhou, Siyu; Chen, Mingxing; Kritiyakan, Anamika; Jittapalapong, Sathaporn; Chaisiri, Kittipong; Buchy, Phillipe; Duong, Veasna; Yang, Jian; Jiang, Jinyong; Xu, Xiang; Zhou, Hongning; Yang, Fan; Irwin, David M.; Morand, Serge; Daszak, Peter; Wang, Jianwei; Jin, Qi 2021 Additional file 1. Table S1. Samples of the 30 animal species used in this study and the countries (provinces) and dates of collection. Table S2. Overview of virus-associated reads. Table S3. Overview of sequence reads from mammal related viral families and unclassified RNA viruses. Table S4. Origin and accession number of viruses identified in this study. Table S5. Nucleotide sequence identity of novel rodent hantaviruses and known hantaviruses within the L, M, and S segment. Table S6. Nucleotide sequence identity of novel rodent PhleVs and known PhleVs within the L open reading frame. Table S7. Nucleotide sequence identity of novel rodent arenaviruses and known arenaviruses (AreV) in the L-, G- and N-encoding regions. Table S8. Nucleotide sequence identity of novel rodent rhabdoviruses and known rhabdoviruses (RhaV) in the L protein-encoding regions. Table S9. Nucleotide sequence identity of novel rodent paramyxoviruses and known paramyxoviruses (ParaV) in the L protein-encoding regions. Table S10. Nucleotide sequence identity of novel rodent and known hepaci-, pegi- and pestviruses. Table S11. ORF1b nucleotide sequence identity of novel rodent and known arteriviruses. Table S12. Nucleotide sequence identity of novel rodent and known coronaviruses (CoVs) in the RdRP-encoding region. Table S13. Nucleotide sequence identity of novel rodent and known hepeviruses (HEVs) in the ORF1 region. Table S14. Nucleotide sequence identity of novel rodent and known picornaviruses (PicoVs) in the RdRP-encoding region. Table S15. Nucleotide sequence identity of novel rodent and known astroviruses (AstroVs) in the RdRP-encoding region. Table S16. Nucleotide sequence identity of novel rodent unclassified RNA viruses and known viruses in the RdRP-encoding region. https://creativecommons.org/licenses/by/4.0/legalcode

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