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Noncoding RNAs and their annotation using metagenomics algorithms

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This article provides an overview of noncoding RNAs (ncRNA) involving their structure, function, computational methods for structure prediction and the algorithms for analyzing ncRNAs from metagenome samples. Different techniques for ncRNA structure prediction such as dynamic programming (DP), genetic algorithm (GA), artificial neural network (ANN) and stochastic context‐free grammar (SCFG) are discussed. The basic concepts of metagenomics along with their biological basis are mentioned and the relevance of ncRNAs in metagenomics is also explored. Similarity and composition based computational methods for analyzing noncoding sequences in metagenomes are then mentioned along with their biological findings. An extensive bibliography is included. WIREs Data Mining Knowl Discov 2015, 5:1–20. doi: 10.1002/widm.1142 Conflict of interest: The authors have declared no conflicts of interest for this article. This article is categorized under: Algorithmic Development > Biological Data Mining Algorithmic Development > Structure Discovery Application Areas > Science and Technology
Different types of secondary substructures in RNA. (Reprinted with permission from Ref . Copyright 1992).
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