Integrating many co-splicing networks to reconstruct splicing regulatory modules
Open accessAbstract
Background: Alternative splicing is a ubiquitous gene regulatory mechanism that dramatically increases the complexity of the proteome. However, the mechanism for regulating alternative splicing is poorly understood, and study of coordinated splicing regulation has been limited to individual cases. To study genome-wide splicing regulation, we integrate many human RNA-seq datasets to identify splicing module, which we define as a set of cassette exons co-regulated by the same splicing factors. Results: We have designed a tensor-based approach to identify co-splicing clusters that appear frequently across multiple conditions, thus very likely to represent splicing modules - a unit in the splicing regulatory network. In particular, we model each RNA-seq dataset as a co-splicing network, where the nodes represent exons and the edges are weighted by the correlations between exon inclusion rate profiles. We apply our tensor-based method to the 38 co-splicing networks derived from human RNA-seq datasets and indentify an atlas of frequent co-splicing clusters. We demonstrate that these identified clusters represent potential splicing modules by validating against four biological knowledge databases. The likelihood that a frequent co-splicing cluster is biologically meaningful increases with its recurrence across multiple datasets, highlighting the importance of the integrative approach. Conclusions: Co-splicing clusters reveal novel functional groups which cannot be identified by co-expression clusters, particularly they can grant new insights into functions associated with post-transcriptional regulation, and the same exons can dynamically participate in different pathways depending on different conditions and different other exons that are co-spliced. We propose that by identifying splicing module, a unit in the splicing regulatory network can serve as an important step to decipher the splicing code.
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The co-splicing network approach has identified novel functional modules that would not be detected using traditional co-expression networks.
“This approach has identified novel functional modules, which would not be detected using traditional co-expression networks [51].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [51]
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A NOVEL COMPUTATIONAL FRAMEWORK FOR TRANSCRIPTOME ANALYSIS WITH RNA-SEQ DATA
Hu, Y. (2013) · UKnowledge (University of Kentucky)
- Authors
- Dai, C., Li, W., Liu, J., & Zhou, X. J.
- Affiliations
- University of Southern California (US)
- Wuhan University (CN)
- Corresponding author
- Zhou, Xianghong Jasmine
- Venue
- BMC Systems Biology
- Published
- 2012-07
- Volume
- 6
- Issue
- S1
- Pages
- S17-S17
- ISSN
- 1752-0509 (ISSN-L)
- PMC
- PMC3403501
- OpenAlex
- W2100544620
- Cited by
- 20
- References
- 49
- Citations by year
- 2025: 1 · 2024: 1 · 2021: 1 · 2019: 2 · 2018: 3 · 2017: 2 · 2016: 3 · 2015: 2 · 2014: 3 · 2013: 2
- FWCI
- 0.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Systems Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Science Foundation (0747475)
- National Natural Science Foundation of China (NCET-10, 60970063)
- Ministry of Education of the People's Republic of China
- National Institutes of Health
- Program for New Century Excellent Talents in University (NCET-10)
- Fundamental Research Funds for the Central Universities
- National Institute of General Medical Sciences (R01GM074163)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- alternative splicing, cassette exons, splicing factors, RNA-seq data integration, post-transcriptional regulation, human transcriptome
- MeSH terms
- Exons, Gene Expression Regulation, Humans, Transcription, Genetic, Cluster Analysis, Alternative Splicing, Sequence Analysis, RNA, Computational Biology, Databases, Protein, Epigenomics
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