Computational Prediction of RNA Structural Motifs Involved in Post-Transcriptional Regulatory Processes
Methods in Molecular Biology, ISSN: 1940-6029, Vol: 714, Page: 467-479
2011
- 10Citations
- 128Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations10
- Citation Indexes10
- 10
- CrossRef7
- Captures128
- Readers128
- 128
Book Chapter Description
mRNA molecules are tightly regulated, mostly through interactions with proteins and other RNAs, but the mechanisms that confer the specificity of such interactions are poorly understood. It is clear, however, that this specificity is determined by both the nucleotide sequence and secondary structure of the mRNA. We developed RNApromo, an efficient computational tool for identifying structural elements within mRNAs that are involved in specifying post-transcriptional regulations. Using RNApromo, we predicted putative motifs in sets of mRNAs with substantial experimental evidence for common post-transcriptional regulation, including mRNAs with similar decay rates, mRNAs that are bound by the same RNA binding protein, and mRNAs with a common cellular localization. Our new RNA motif discovery tool reveals unexplored layers of post-transcriptional regulations in groups of RNAs, and is therefore an important step toward a better understanding of the regulatory information conveyed within RNA molecules.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=79960068471&origin=inward; http://dx.doi.org/10.1007/978-1-61779-005-8_28; http://www.ncbi.nlm.nih.gov/pubmed/21431758; http://link.springer.com/10.1007/978-1-61779-005-8_28; https://dx.doi.org/10.1007/978-1-61779-005-8_28; https://link.springer.com/protocol/10.1007/978-1-61779-005-8_28
Springer Science and Business Media LLC
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