Identification of clustered microRNAs using an <it>ab initio </it>prediction method

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are endogenous 21 to 23-nucleotide RNA molecules that regulate protein-coding gene expression in plants and animals via the RNA interference pathway. Hundreds of them have been identified in the last five years and...

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Main Authors: Brownstein Michael J, Pfeffer Sébastien, Aravin Alexei, Landgraf Pablo, Paul Nicodème, Sewer Alain, Tuschl Thomas, van Nimwegen Erik, Zavolan Mihaela
Format: Article
Language:English
Published: BMC 2005-11-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/6/267
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spelling doaj-8e6e97445c8347f4a8ea6910b0d1df642020-11-24T21:44:39ZengBMCBMC Bioinformatics1471-21052005-11-016126710.1186/1471-2105-6-267Identification of clustered microRNAs using an <it>ab initio </it>prediction methodBrownstein Michael JPfeffer SébastienAravin AlexeiLandgraf PabloPaul NicodèmeSewer AlainTuschl Thomasvan Nimwegen ErikZavolan Mihaela<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are endogenous 21 to 23-nucleotide RNA molecules that regulate protein-coding gene expression in plants and animals via the RNA interference pathway. Hundreds of them have been identified in the last five years and very recent works indicate that their total number is still larger. Therefore miRNAs gene discovery remains an important aspect of understanding this new and still widely unknown regulation mechanism. Bioinformatics approaches have proved to be very useful toward this goal by guiding the experimental investigations.</p> <p>Results</p> <p>In this work we describe our computational method for miRNA prediction and the results of its application to the discovery of novel mammalian miRNAs. We focus on genomic regions around already known miRNAs, in order to exploit the property that miRNAs are occasionally found in clusters. Starting with the known human, mouse and rat miRNAs we analyze 20 kb of flanking genomic regions for the presence of putative precursor miRNAs (pre-miRNAs). Each genome is analyzed separately, allowing us to study the species-specific identity and genome organization of miRNA loci. We only use cross-species comparisons to make conservative estimates of the number of novel miRNAs. Our <it>ab initio </it>method predicts between fifty and hundred novel pre-miRNAs for each of the considered species. Around 30% of these already have experimental support in a large set of cloned mammalian small RNAs. The validation rate among predicted cases that are conserved in at least one other species is higher, about 60%, and many of them have not been detected by prediction methods that used cross-species comparisons. A large fraction of the experimentally confirmed predictions correspond to an imprinted locus residing on chromosome 14 in human, 12 in mouse and 6 in rat. Our computational tool can be accessed on the world-wide-web.</p> <p>Conclusion</p> <p>Our results show that the assumption that many miRNAs occur in clusters is fruitful for the discovery of novel miRNAs. Additionally we show that although the overall miRNA content in the observed clusters is very similar across the three considered species, the internal organization of the clusters changes in evolution.</p> http://www.biomedcentral.com/1471-2105/6/267
collection DOAJ
language English
format Article
sources DOAJ
author Brownstein Michael J
Pfeffer Sébastien
Aravin Alexei
Landgraf Pablo
Paul Nicodème
Sewer Alain
Tuschl Thomas
van Nimwegen Erik
Zavolan Mihaela
spellingShingle Brownstein Michael J
Pfeffer Sébastien
Aravin Alexei
Landgraf Pablo
Paul Nicodème
Sewer Alain
Tuschl Thomas
van Nimwegen Erik
Zavolan Mihaela
Identification of clustered microRNAs using an <it>ab initio </it>prediction method
BMC Bioinformatics
author_facet Brownstein Michael J
Pfeffer Sébastien
Aravin Alexei
Landgraf Pablo
Paul Nicodème
Sewer Alain
Tuschl Thomas
van Nimwegen Erik
Zavolan Mihaela
author_sort Brownstein Michael J
title Identification of clustered microRNAs using an <it>ab initio </it>prediction method
title_short Identification of clustered microRNAs using an <it>ab initio </it>prediction method
title_full Identification of clustered microRNAs using an <it>ab initio </it>prediction method
title_fullStr Identification of clustered microRNAs using an <it>ab initio </it>prediction method
title_full_unstemmed Identification of clustered microRNAs using an <it>ab initio </it>prediction method
title_sort identification of clustered micrornas using an <it>ab initio </it>prediction method
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2005-11-01
description <p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are endogenous 21 to 23-nucleotide RNA molecules that regulate protein-coding gene expression in plants and animals via the RNA interference pathway. Hundreds of them have been identified in the last five years and very recent works indicate that their total number is still larger. Therefore miRNAs gene discovery remains an important aspect of understanding this new and still widely unknown regulation mechanism. Bioinformatics approaches have proved to be very useful toward this goal by guiding the experimental investigations.</p> <p>Results</p> <p>In this work we describe our computational method for miRNA prediction and the results of its application to the discovery of novel mammalian miRNAs. We focus on genomic regions around already known miRNAs, in order to exploit the property that miRNAs are occasionally found in clusters. Starting with the known human, mouse and rat miRNAs we analyze 20 kb of flanking genomic regions for the presence of putative precursor miRNAs (pre-miRNAs). Each genome is analyzed separately, allowing us to study the species-specific identity and genome organization of miRNA loci. We only use cross-species comparisons to make conservative estimates of the number of novel miRNAs. Our <it>ab initio </it>method predicts between fifty and hundred novel pre-miRNAs for each of the considered species. Around 30% of these already have experimental support in a large set of cloned mammalian small RNAs. The validation rate among predicted cases that are conserved in at least one other species is higher, about 60%, and many of them have not been detected by prediction methods that used cross-species comparisons. A large fraction of the experimentally confirmed predictions correspond to an imprinted locus residing on chromosome 14 in human, 12 in mouse and 6 in rat. Our computational tool can be accessed on the world-wide-web.</p> <p>Conclusion</p> <p>Our results show that the assumption that many miRNAs occur in clusters is fruitful for the discovery of novel miRNAs. Additionally we show that although the overall miRNA content in the observed clusters is very similar across the three considered species, the internal organization of the clusters changes in evolution.</p>
url http://www.biomedcentral.com/1471-2105/6/267
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