Inferring Noncoding RNA Families and Classes by Means of Genome-Scale Structure-Based Clustering

The RFAM database defines families of ncRNAs by means of sequence similarities that are sufficient to establish homology. In some cases, such as microRNAs and box H/ACA snoRNAs, functional commonalities define classes of RNAs that are characterized by structural similarities, and typically consist o...

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Main Authors:	Will, Sebastian, Reiche, Kristin, Hofacker, Ivo L, Stadler, Peter F, Backofen, Rolf
Format:	Online
Language:	English
Published:	Public Library of Science 2007
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1851984/

id	pubmed-1851984
recordtype	oai_dc
spelling	pubmed-18519842007-04-13 Inferring Noncoding RNA Families and Classes by Means of Genome-Scale Structure-Based Clustering Will, Sebastian Reiche, Kristin Hofacker, Ivo L Stadler, Peter F Backofen, Rolf Research Article The RFAM database defines families of ncRNAs by means of sequence similarities that are sufficient to establish homology. In some cases, such as microRNAs and box H/ACA snoRNAs, functional commonalities define classes of RNAs that are characterized by structural similarities, and typically consist of multiple RNA families. Recent advances in high-throughput transcriptomics and comparative genomics have produced very large sets of putative noncoding RNAs and regulatory RNA signals. For many of them, evidence for stabilizing selection acting on their secondary structures has been derived, and at least approximate models of their structures have been computed. The overwhelming majority of these hypothetical RNAs cannot be assigned to established families or classes. We present here a structure-based clustering approach that is capable of extracting putative RNA classes from genome-wide surveys for structured RNAs. The LocARNA (local alignment of RNA) tool implements a novel variant of the Sankoff algorithm that is sufficiently fast to deal with several thousand candidate sequences. The method is also robust against false positive predictions, i.e., a contamination of the input data with unstructured or nonconserved sequences. We have successfully tested the LocARNA-based clustering approach on the sequences of the RFAM-seed alignments. Furthermore, we have applied it to a previously published set of 3,332 predicted structured elements in the Ciona intestinalis genome (Missal K, Rose D, Stadler PF (2005) Noncoding RNAs in Ciona intestinalis. Bioinformatics 21 (Supplement 2): i77–i78). In addition to recovering, e.g., tRNAs as a structure-based class, the method identifies several RNA families, including microRNA and snoRNA candidates, and suggests several novel classes of ncRNAs for which to date no representative has been experimentally characterized. Public Library of Science 2007-04 2007-04-13 /pmc/articles/PMC1851984/ /pubmed/17432929 http://dx.doi.org/10.1371/journal.pcbi.0030065 Text en © 2007 Will et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Will, Sebastian Reiche, Kristin Hofacker, Ivo L Stadler, Peter F Backofen, Rolf
spellingShingle	Will, Sebastian Reiche, Kristin Hofacker, Ivo L Stadler, Peter F Backofen, Rolf Inferring Noncoding RNA Families and Classes by Means of Genome-Scale Structure-Based Clustering
author_facet	Will, Sebastian Reiche, Kristin Hofacker, Ivo L Stadler, Peter F Backofen, Rolf
author_sort	Will, Sebastian
title	Inferring Noncoding RNA Families and Classes by Means of Genome-Scale Structure-Based Clustering
title_short	Inferring Noncoding RNA Families and Classes by Means of Genome-Scale Structure-Based Clustering
title_full	Inferring Noncoding RNA Families and Classes by Means of Genome-Scale Structure-Based Clustering
title_fullStr	Inferring Noncoding RNA Families and Classes by Means of Genome-Scale Structure-Based Clustering
title_full_unstemmed	Inferring Noncoding RNA Families and Classes by Means of Genome-Scale Structure-Based Clustering
title_sort	inferring noncoding rna families and classes by means of genome-scale structure-based clustering
description	The RFAM database defines families of ncRNAs by means of sequence similarities that are sufficient to establish homology. In some cases, such as microRNAs and box H/ACA snoRNAs, functional commonalities define classes of RNAs that are characterized by structural similarities, and typically consist of multiple RNA families. Recent advances in high-throughput transcriptomics and comparative genomics have produced very large sets of putative noncoding RNAs and regulatory RNA signals. For many of them, evidence for stabilizing selection acting on their secondary structures has been derived, and at least approximate models of their structures have been computed. The overwhelming majority of these hypothetical RNAs cannot be assigned to established families or classes. We present here a structure-based clustering approach that is capable of extracting putative RNA classes from genome-wide surveys for structured RNAs. The LocARNA (local alignment of RNA) tool implements a novel variant of the Sankoff algorithm that is sufficiently fast to deal with several thousand candidate sequences. The method is also robust against false positive predictions, i.e., a contamination of the input data with unstructured or nonconserved sequences. We have successfully tested the LocARNA-based clustering approach on the sequences of the RFAM-seed alignments. Furthermore, we have applied it to a previously published set of 3,332 predicted structured elements in the Ciona intestinalis genome (Missal K, Rose D, Stadler PF (2005) Noncoding RNAs in Ciona intestinalis. Bioinformatics 21 (Supplement 2): i77–i78). In addition to recovering, e.g., tRNAs as a structure-based class, the method identifies several RNA families, including microRNA and snoRNA candidates, and suggests several novel classes of ncRNAs for which to date no representative has been experimentally characterized.
publisher	Public Library of Science
publishDate	2007
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1851984/
_version_	1611395670947659776

Inferring Noncoding RNA Families and Classes by Means of Genome-Scale Structure-Based Clustering

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