The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Maximum Entropy Modeling of Short Sequence Motifs with Applications to RNA Splicing Signals
Open accessAbstract
We propose a framework for modeling sequence motifs based on the maximum entropy principle (MEP). We recommend approximating short sequence motif distributions with the maximum entropy distribution (MED) consistent with low-order marginal constraints estimated from available data, which may include dependencies between nonadjacent as well as adjacent positions. Many maximum entropy models (MEMs) are specified by simply changing the set of constraints. Such models can be utilized to discriminate between signals and decoys. Classification performance using different MEMs gives insight into the relative importance of dependencies between different positions. We apply our framework to large datasets of RNA splicing signals. Our best models out-perform previous probabilistic models in the discrimination of human 5' (donor) and 3' (acceptor) splice sites from decoys. Finally, we discuss mechanistically motivated ways of comparing models.
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Cited by
Alzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946
Predicting splicing from primary sequence with deep learning
Jaganathan, K., Kyriazopoulou Panagiotopoulou, S., McRae, J. F., Darbandi, S. F., Knowles, D., Li, Y. I., Kosmicki, J. A., Arbelaez, J., Cui, W., Schwartz, G. B., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.015 · Cited by 2,853
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Neural circular RNAs are derived from synaptic genes and regulated by development and plasticity
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Metadata
- Authors
- Yeo, G., & Burge, C. B.
- Venue
- Journal of Computational Biology
- Publisher
- SAGE Publications
- Published
- 2004-03
- Volume
- 11
- Issue
- 2-3
- Pages
- 377-394
- ISSN
- 1066-5277 (ISSN-L)
- 1557-8666
- OpenAlex
- W2034691490
- Cited by
- 2,033
- References
- 1
- FWCI
- 8.31
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.liebertpub.com/nv/resources-tools/text-and-data-mining-policy/121/ (tdm)
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- Yeo, G., & Burge, C. B. (2004). Maximum Entropy Modeling of Short Sequence Motifs with Applications to RNA Splicing Signals. Journal of Computational Biology. https://doi.org/10.1089/1066527041410418
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
