ExonImpact: Prioritizing Pathogenic Alternative Splicing Events

Overview

Journal Hum Mutat

Specialty Genetics

Date 2016 Sep 9

PMID 27604408

Citations 10

Authors

Meng Li

Weixing Feng

Xinjun Zhang

Yuedong Yang

Kejun Wang

Matthew Mort

David N Cooper

Yue Wang

Yaoqi Zhou

Yunlong Liu

Affiliations

Soon will be listed here.

Abstract

Alternative splicing (AS) is a closely regulated process that allows a single gene to encode multiple protein isoforms, thereby contributing to the diversity of the proteome. Dysregulation of the splicing process has been found to be associated with many inherited diseases. However, among the pathogenic AS events, there are numerous "passenger" events whose inclusion or exclusion does not lead to significant changes with respect to protein function. In this study, we evaluate the secondary and tertiary structural features of proteins associated with disease-causing and neutral AS events, and show that several structural features are strongly associated with the pathological impact of exon inclusion. We further develop a machine-learning-based computational model, ExonImpact, for prioritizing and evaluating the functional consequences of hitherto uncharacterized AS events. We evaluated our model using several strategies including cross-validation, and data from the Gene-Tissue Expression (GTEx) and ClinVar databases. ExonImpact is freely available at http://watson.compbio.iupui.edu/ExonImpact.

Citing Articles

Homozygous variants in WDR83OS lead to a neurodevelopmental disorder with hypercholanemia.

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PMID: 39471804 PMC: 11568760. DOI: 10.1016/j.ajhg.2024.10.002.

Splicing defects in rare diseases: transcriptomics and machine learning strategies towards genetic diagnosis.

Wang R, Helbig I, Edmondson A, Lin L, Xing Y Brief Bioinform. 2023; 24(5).

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The Impact of Pro-Inflammatory Cytokines on Alternative Splicing Patterns in Human Islets.

Wu W, Syed F, Simpson E, Lee C, Liu J, Chang G Diabetes. 2021; .

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RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants.

Lin H, Hargreaves K, Li R, Reiter J, Wang Y, Mort M Genome Biol. 2019; 20(1):254.

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When one becomes many-Alternative splicing in β-cell function and failure.

Alvelos M, Juan-Mateu J, Colli M, Turatsinze J, Eizirik D Diabetes Obes Metab. 2018; 20 Suppl 2:77-87.

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References

Ivanova N, Lofgren A, Tournev I, Rousev R, Andreeva A, Jordanova A . Spastin gene mutations in Bulgarian patients with hereditary spastic paraplegia. Clin Genet. 2006; 70(6):490-5. DOI: 10.1111/j.1399-0004.2006.00705.x. View

Chen L, Bush S, Tovar-Corona J, Castillo-Morales A, Urrutia A . Correcting for differential transcript coverage reveals a strong relationship between alternative splicing and organism complexity. Mol Biol Evol. 2014; 31(6):1402-13. PMC: 4032128. DOI: 10.1093/molbev/msu083. View

Lu H, Lin L, Sato S, Xing Y, Lee C . Predicting functional alternative splicing by measuring RNA selection pressure from multigenome alignments. PLoS Comput Biol. 2009; 5(12):e1000608. PMC: 2784930. DOI: 10.1371/journal.pcbi.1000608. View

Ghoorah A, Devignes M, Alborzi S, Smail-Tabbone M, Ritchie D . A structure-based classification and analysis of protein domain family binding sites and their interactions. Biology (Basel). 2015; 4(2):327-43. PMC: 4498303. DOI: 10.3390/biology4020327. View

Xiong H, Alipanahi B, Lee L, Bretschneider H, Merico D, Yuen R . RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease. Science. 2014; 347(6218):1254806. PMC: 4362528. DOI: 10.1126/science.1254806. View

Auton A, Brooks L, Durbin R, Garrison E, Kang H, Korbel J . A global reference for human genetic variation. Nature. 2015; 526(7571):68-74. PMC: 4750478. DOI: 10.1038/nature15393. View

Finn R, Coggill P, Eberhardt R, Eddy S, Mistry J, Mitchell A . The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 2015; 44(D1):D279-85. PMC: 4702930. DOI: 10.1093/nar/gkv1344. View

Shen S, Park J, Lu Z, Lin L, Henry M, Wu Y . rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc Natl Acad Sci U S A. 2014; 111(51):E5593-601. PMC: 4280593. DOI: 10.1073/pnas.1419161111. View

Stenson P, Mort M, Ball E, Shaw K, Phillips A, Cooper D . The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum Genet. 2013; 133(1):1-9. PMC: 3898141. DOI: 10.1007/s00439-013-1358-4. View

10.

Wu J, Akerman M, Sun S, McCombie W, Krainer A, Zhang M . SpliceTrap: a method to quantify alternative splicing under single cellular conditions. Bioinformatics. 2011; 27(21):3010-6. PMC: 3198574. DOI: 10.1093/bioinformatics/btr508. View

11.

Zhao H, Yang Y, Lin H, Zhang X, Mort M, Cooper D . DDIG-in: discriminating between disease-associated and neutral non-frameshifting micro-indels. Genome Biol. 2013; 14(3):R23. PMC: 4053752. DOI: 10.1186/gb-2013-14-3-r23. View

12.

Folkman L, Yang Y, Li Z, Stantic B, Sattar A, Mort M . DDIG-in: detecting disease-causing genetic variations due to frameshifting indels and nonsense mutations employing sequence and structural properties at nucleotide and protein levels. Bioinformatics. 2015; 31(10):1599-606. DOI: 10.1093/bioinformatics/btu862. View

13.

Wang H, You C, Chang F, Wang Y, Wang L, Qi J . Alternative splicing during Arabidopsis flower development results in constitutive and stage-regulated isoforms. Front Genet. 2014; 5:25. PMC: 3921568. DOI: 10.3389/fgene.2014.00025. View

14.

Ellis J, Barrios-Rodiles M, Colak R, Irimia M, Kim T, Calarco J . Tissue-specific alternative splicing remodels protein-protein interaction networks. Mol Cell. 2012; 46(6):884-92. DOI: 10.1016/j.molcel.2012.05.037. View

15.

Zhang T, Faraggi E, Xue B, Dunker A, Uversky V, Zhou Y . SPINE-D: accurate prediction of short and long disordered regions by a single neural-network based method. J Biomol Struct Dyn. 2012; 29(4):799-813. PMC: 3297974. DOI: 10.1080/073911012010525022. View

16.

Katz Y, Wang E, Airoldi E, Burge C . Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat Methods. 2010; 7(12):1009-15. PMC: 3037023. DOI: 10.1038/nmeth.1528. View

17.

Huang K, Su M, Kao H, Hsieh Y, Jhong J, Cheng K . dbPTM 2016: 10-year anniversary of a resource for post-translational modification of proteins. Nucleic Acids Res. 2015; 44(D1):D435-46. PMC: 4702878. DOI: 10.1093/nar/gkv1240. View

18.

Florea L, Song L, Salzberg S . Thousands of exon skipping events differentiate among splicing patterns in sixteen human tissues. F1000Res. 2014; 2:188. PMC: 3892928. DOI: 10.12688/f1000research.2-188.v2. View

19.

. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013; 45(6):580-5. PMC: 4010069. DOI: 10.1038/ng.2653. View

20.

Gray K, Yates B, Seal R, Wright M, Bruford E . Genenames.org: the HGNC resources in 2015. Nucleic Acids Res. 2014; 43(Database issue):D1079-85. PMC: 4383909. DOI: 10.1093/nar/gku1071. View