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Computational approaches to discovering noncoding RNA

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Abstract New developments are being brought to the field of molecular biology with the mounting evidence that RNA transcripts not translated into protein (noncoding RNAs, ncRNAs) hold a variety of biological functions. Computational discovery of ncRNAs is one of these developments, fueled not only by the urge to characterize these sequences but also by necessity to prioritize ones with the most relevant functions for experimental verification. The heterogeneity in size and mode of activity of ncRNAs is reflected in the corresponding diversity of computational methods for their study. Sequence and structural analysis, conservation across species, and relative position to other genomic elements are being used for ncRNA detection. In addition, the recent development of techniques that allow deep sequencing of cell transcripts either globally or from isolated ncRNA‐related material is leading the field toward increased use of such high‐throughput data. We expect that imminent breakthroughs will include the classification of newer types of ncRNA and new insights into miRNA and piRNA biology, eventually leading toward the completion of a catalog of all human ncRNAs. WIREs RNA 2012, 3:567–579. doi: 10.1002/wrna.1121 This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs

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Evolution of the number of publications on noncoding RNA (ncRNA) sequencing studies versus ncRNA prediction. The number of Medline articles dealing with ncRNA/miRNA sequencing has increased faster than that for ncRNA/miRNA prediction, likely due to increased adoption of high‐throughput sequencing. Articles found searching for deep sequencing or next generation sequencing are shown as a proxy for high‐throughput sequencing. Also shown are release dates of analysis software for de novo ncRNA prediction (Beige; RNAz 2.014, EvoFold15, RNAz13, AlifoldZ50, MiRscan12) and analysis of high‐throughput sequencing data sets (Green: SeqCluster49, SeqGene51, MIReNA21, SeqBuster9, miRanalyzer22, miRDeep23). Figure made with MLTrends52 (http://www.ogic.ca/mltrends/) using the 2012 Medline Baseline Distribution, released December 14, 2011.

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