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Integrative Modeling Defines the Nova Splicing-Regulatory Network and Its Combinatorial Controls
Open accessAbstract
Making the Final Cut: RNA splicing, which involves selectively cutting and pasting messenger RNA to generate different proteins, is critical in regulating human physiology and diseases. However, our knowledge of the underlying rules governing splicing regulation remains incomplete. The recent emergence of next-generation sequencing and other high-throughput technologies has provided an opportunity to transform our understanding of RNA regulation. Zhang et al. (p. 439 , published online 17 June) combined multiple data sets to generate a robust and relatively complete picture of splicing regulation by the mammalian neuronal splicing factor Nova in the brain. About 700 splicing events were identified, including many novel target exons, some likely to be involved in neurological disease. Combining genomic studies with computational biology also yielded insight into the regulation of alternative splicing.
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“Matching the expansion of RNA regulatory mechanisms, Pleurobrachia has more RNA-binding proteins (especially RRM/ELAV, KH and NOVAs 26,27 , Supplementary Table 21) thananybasalmetazoanorchoanoflagellateexamined.”
From The ctenophore genome and the evolutionary origins of neural systems (2014) · cites this paper as [27]
In the citing paper
“We previously demonstrated that Rbfox target transcripts predicted bioinformatically based on conserved motif sites have significant overlap with genes implicated in autism, supporting the notion that disruption of either Rbfox1 itself or of its targets observed in autism patients is likely pathogenic (Zhang et al., 2010).”
From HITS-CLIP and integrative modeling define the Rbfox splicing-regulatory network linked to brain development and autism (2014) · cites this paper as Zhang et al., 2010
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“The second critical aspect of this study is the use of an integrative modeling approach to combine multiple complementary types of data, so that individually weak bits of information can be integrated to make strong predictions of Rbfox targets (Zhang et al., 2010).”
From HITS-CLIP and integrative modeling define the Rbfox splicing-regulatory network linked to brain development and autism (2014) · cites this paper as Zhang et al., 2010
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Metadata
- Authors
- Zhang, C., Frias, M. A., Mele, A., Ruggiu, M., Eom, T., Marney, C. B., Wang, H., Licatalosi, D. D., Fak, J. J., & Darnell, R. B.
- Affiliations
- Howard Hughes Medical Institute (US)
- Laboratory of Molecular Neuro-Oncology, Howard Hughes Medical Institute, The Rockefeller University, 1230 York Avenue, New York, NY 10021, USA.
- Corresponding author
- Zhang, Chaolin
- Darnell, Robert B.
- ORCID
- Venue
- Science
- Published
- 2010-07-23
- Volume
- 329
- Issue
- 5990
- Pages
- 439-443
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC3412410
- OpenAlex
- W2036583346
- Cited by
- 294
- References
- 27
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 15 · 2023: 15 · 2022: 8 · 2021: 8 · 2020: 10 · 2019: 15 · 2018: 16 · 2017: 17 · 2016: 25 · 2015: 21 · 2014: 34 · 2013: 28 · 2012: 26
- FWCI
- 11.73
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institute of General Medical Sciences (K99GM095713)
- National Institute of Neurological Disorders and Stroke (NS34389, R01NS034389, R56NS034389)
- National Center for Research Resources (UL1RR024143)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- alternative splicing, nova, RNA-binding proteins, splicing regulatory network, Bayesian networks, combinatorial regulation, mouse brain, neurological disorders
- MeSH terms
- Neuro-Oncological Ventral Antigen, Animals, Antigens, Neoplasm, Artificial Intelligence, Bayes Theorem, Binding Sites, Brain, Cell Line, Exons, Humans, Introns, Models, Genetic, Nerve Tissue Proteins, Nervous System Diseases, Phosphorylation, Protein Binding, Proteins, RNA, Models, Statistical, RNA-Binding Proteins, Alternative Splicing, Evolution, Molecular, Computational Biology, Oligonucleotide Array Sequence Analysis, Mice, Gene Regulatory Networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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