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Computational Prediction of miRNA Genes from Small RNA Sequencing Data
Next-generation sequencing now for the first time allows researchers to gage the depth and variation of entire transcriptomes. However, now as rare transcripts can be detected that are present in cells at single copies, more advanced computational tools are needed to accurately annotate and profile...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4306309/ https://www.ncbi.nlm.nih.gov/pubmed/25674563 http://dx.doi.org/10.3389/fbioe.2015.00007 |
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author | Kang, Wenjing Friedländer, Marc R. |
author_facet | Kang, Wenjing Friedländer, Marc R. |
author_sort | Kang, Wenjing |
collection | PubMed |
description | Next-generation sequencing now for the first time allows researchers to gage the depth and variation of entire transcriptomes. However, now as rare transcripts can be detected that are present in cells at single copies, more advanced computational tools are needed to accurately annotate and profile them. microRNAs (miRNAs) are 22 nucleotide small RNAs (sRNAs) that post-transcriptionally reduce the output of protein coding genes. They have established roles in numerous biological processes, including cancers and other diseases. During miRNA biogenesis, the sRNAs are sequentially cleaved from precursor molecules that have a characteristic hairpin RNA structure. The vast majority of new miRNA genes that are discovered are mined from small RNA sequencing (sRNA-seq), which can detect more than a billion RNAs in a single run. However, given that many of the detected RNAs are degradation products from all types of transcripts, the accurate identification of miRNAs remain a non-trivial computational problem. Here, we review the tools available to predict animal miRNAs from sRNA sequencing data. We present tools for generalist and specialist use cases, including prediction from massively pooled data or in species without reference genome. We also present wet-lab methods used to validate predicted miRNAs, and approaches to computationally benchmark prediction accuracy. For each tool, we reference validation experiments and benchmarking efforts. Last, we discuss the future of the field. |
format | Online Article Text |
id | pubmed-4306309 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-43063092015-02-11 Computational Prediction of miRNA Genes from Small RNA Sequencing Data Kang, Wenjing Friedländer, Marc R. Front Bioeng Biotechnol Bioengineering and Biotechnology Next-generation sequencing now for the first time allows researchers to gage the depth and variation of entire transcriptomes. However, now as rare transcripts can be detected that are present in cells at single copies, more advanced computational tools are needed to accurately annotate and profile them. microRNAs (miRNAs) are 22 nucleotide small RNAs (sRNAs) that post-transcriptionally reduce the output of protein coding genes. They have established roles in numerous biological processes, including cancers and other diseases. During miRNA biogenesis, the sRNAs are sequentially cleaved from precursor molecules that have a characteristic hairpin RNA structure. The vast majority of new miRNA genes that are discovered are mined from small RNA sequencing (sRNA-seq), which can detect more than a billion RNAs in a single run. However, given that many of the detected RNAs are degradation products from all types of transcripts, the accurate identification of miRNAs remain a non-trivial computational problem. Here, we review the tools available to predict animal miRNAs from sRNA sequencing data. We present tools for generalist and specialist use cases, including prediction from massively pooled data or in species without reference genome. We also present wet-lab methods used to validate predicted miRNAs, and approaches to computationally benchmark prediction accuracy. For each tool, we reference validation experiments and benchmarking efforts. Last, we discuss the future of the field. Frontiers Media S.A. 2015-01-26 /pmc/articles/PMC4306309/ /pubmed/25674563 http://dx.doi.org/10.3389/fbioe.2015.00007 Text en Copyright © 2015 Kang and Friedländer. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Kang, Wenjing Friedländer, Marc R. Computational Prediction of miRNA Genes from Small RNA Sequencing Data |
title | Computational Prediction of miRNA Genes from Small RNA Sequencing Data |
title_full | Computational Prediction of miRNA Genes from Small RNA Sequencing Data |
title_fullStr | Computational Prediction of miRNA Genes from Small RNA Sequencing Data |
title_full_unstemmed | Computational Prediction of miRNA Genes from Small RNA Sequencing Data |
title_short | Computational Prediction of miRNA Genes from Small RNA Sequencing Data |
title_sort | computational prediction of mirna genes from small rna sequencing data |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4306309/ https://www.ncbi.nlm.nih.gov/pubmed/25674563 http://dx.doi.org/10.3389/fbioe.2015.00007 |
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