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Conference Paper: Filtering of false positive microRNA candidates by a clustering-based approach

TitleFiltering of false positive microRNA candidates by a clustering-based approach
Authors
Issue Date2008
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcbioinformatics/
Citation
Bmc Bioinformatics, 2008, v. 9 SUPPL. 12 How to Cite?
AbstractBackground: MicroRNAs are small non-coding RNA gene products that play diversified roles from species to species. The explosive growth of microRNA researches in recent years proves the importance of microRNAs in the biological system and it is believed that microRNAs have valuable therapeutic potentials in human diseases. Continual efforts are therefore required to locate and verify the unknown microRNAs in various genomes. As many miRNAs are found to be arranged in clusters, meaning that they are in close proximity with their neighboring miRNAs, we are interested in utilizing the concept of microRNA clustering and applying it in microRNA computational prediction. Results: We first validate the microRNA clustering phenomenon in the human, mouse and rat genomes. There are 45.45%, 51.86% and 48.67% of the total miRNAs that are clustered in the three genomes, respectively. We then conduct sequence and secondary structure similarity analyses among clustered miRNAs, non-clustered miRNAs, neighboring sequences of clustered miRNAs and random sequences, and find that clustered miRNAs are structurally more similar to one another, and the RNAdistance score can be used to assess the structural similarity between two sequences. We therefore design a clustering-based approach which utilizes this observation to filter false positives from a list of candidates generated by a selected microRNA prediction program, and successfully raise the positive predictive value by a considerable amount ranging from 15.23% to 23.19% in the human, mouse and rat genomes, while keeping a reasonably high sensitivity. Conclusion: Our clustering-based approach is able to increase the effectiveness of currently available microRNA prediction program by raising the positive predictive value while maintaining a high sensitivity, and hence can serve as a filtering step. We believe that it is worthwhile to carry out further experiments and tests with our approach using data from other genomes and other prediction software tools. Better results may be achieved with fine-tuning of parameters. © 2008 Leung et al; licensee BioMed Central Ltd.
DescriptionB M C Bioinformatics
Persistent Identifierhttp://hdl.handle.net/10722/61686
ISSN
2015 Impact Factor: 2.435
2015 SCImago Journal Rankings: 1.722
PubMed Central ID
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLeung, WSen_HK
dc.contributor.authorLin, MCMen_HK
dc.contributor.authorCheung, DWen_HK
dc.contributor.authorYiu, SMen_HK
dc.date.accessioned2010-07-13T03:45:06Z-
dc.date.available2010-07-13T03:45:06Z-
dc.date.issued2008en_HK
dc.identifier.citationBmc Bioinformatics, 2008, v. 9 SUPPL. 12en_HK
dc.identifier.issn1471-2105en_HK
dc.identifier.urihttp://hdl.handle.net/10722/61686-
dc.descriptionB M C Bioinformaticsen_HK
dc.description.abstractBackground: MicroRNAs are small non-coding RNA gene products that play diversified roles from species to species. The explosive growth of microRNA researches in recent years proves the importance of microRNAs in the biological system and it is believed that microRNAs have valuable therapeutic potentials in human diseases. Continual efforts are therefore required to locate and verify the unknown microRNAs in various genomes. As many miRNAs are found to be arranged in clusters, meaning that they are in close proximity with their neighboring miRNAs, we are interested in utilizing the concept of microRNA clustering and applying it in microRNA computational prediction. Results: We first validate the microRNA clustering phenomenon in the human, mouse and rat genomes. There are 45.45%, 51.86% and 48.67% of the total miRNAs that are clustered in the three genomes, respectively. We then conduct sequence and secondary structure similarity analyses among clustered miRNAs, non-clustered miRNAs, neighboring sequences of clustered miRNAs and random sequences, and find that clustered miRNAs are structurally more similar to one another, and the RNAdistance score can be used to assess the structural similarity between two sequences. We therefore design a clustering-based approach which utilizes this observation to filter false positives from a list of candidates generated by a selected microRNA prediction program, and successfully raise the positive predictive value by a considerable amount ranging from 15.23% to 23.19% in the human, mouse and rat genomes, while keeping a reasonably high sensitivity. Conclusion: Our clustering-based approach is able to increase the effectiveness of currently available microRNA prediction program by raising the positive predictive value while maintaining a high sensitivity, and hence can serve as a filtering step. We believe that it is worthwhile to carry out further experiments and tests with our approach using data from other genomes and other prediction software tools. Better results may be achieved with fine-tuning of parameters. © 2008 Leung et al; licensee BioMed Central Ltd.en_HK
dc.languageengen_HK
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcbioinformatics/en_HK
dc.relation.ispartofBMC Bioinformaticsen_HK
dc.rightsB M C Bioinformatics. Copyright © BioMed Central Ltd.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subject.meshAlgorithmsen_HK
dc.subject.meshAnimalsen_HK
dc.subject.meshCluster Analysisen_HK
dc.subject.meshComputational Biology - methodsen_HK
dc.subject.meshComputer Simulationen_HK
dc.subject.meshFalse Positive Reactionsen_HK
dc.subject.meshGenomeen_HK
dc.subject.meshHumansen_HK
dc.subject.meshMiceen_HK
dc.subject.meshMicroRNAs - chemistry - geneticsen_HK
dc.subject.meshPredictive Value of Testsen_HK
dc.subject.meshRatsen_HK
dc.subject.meshSoftwareen_HK
dc.titleFiltering of false positive microRNA candidates by a clustering-based approachen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1471-2105&volume=9&issue=Supp 12&spage=S3&epage=&date=2008&atitle=Filtering+of+False+Positive+MicroRNA+Candidates+by+a+Clustering-based+Approachen_HK
dc.identifier.emailLin, MCM:mcllin@hkucc.hku.hken_HK
dc.identifier.emailCheung, DW:dcheung@cs.hku.hken_HK
dc.identifier.emailYiu, SM:smyiu@cs.hku.hken_HK
dc.identifier.authorityLin, MCM=rp00746en_HK
dc.identifier.authorityCheung, DW=rp00101en_HK
dc.identifier.authorityYiu, SM=rp00207en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/1471-2105-9-S12-S3en_HK
dc.identifier.pmid19091026-
dc.identifier.pmcidPMC2638143-
dc.identifier.scopuseid_2-s2.0-57649234892en_HK
dc.identifier.hkuros154196en_HK
dc.identifier.hkuros157535-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-57649234892&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume9en_HK
dc.identifier.issueSUPPL. 12en_HK
dc.identifier.isiWOS:000262154300003-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLeung, WS=14322103600en_HK
dc.identifier.scopusauthoridLin, MCM=7404816359en_HK
dc.identifier.scopusauthoridCheung, DW=34567902600en_HK
dc.identifier.scopusauthoridYiu, SM=7003282240en_HK
dc.identifier.citeulike3874780-

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