qiRNApredictor: A Novel Computational Program for the Prediction of qiRNAs in Neurospora crassa
Recently, a new type of small interfering RNAs (qiRNAs) of typically 20~21 nucleotides was found in Neurospora crassa and rice and has been shown to regulate gene silencing in the DNA damage response. Identification of qiRNAs is fundamental for dissecting regulatory functions and molecular mechanism...
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description | Recently, a new type of small interfering RNAs (qiRNAs) of typically 20~21 nucleotides was found in Neurospora crassa and rice and has been shown to regulate gene silencing in the DNA damage response. Identification of qiRNAs is fundamental for dissecting regulatory functions and molecular mechanisms. In contrast to other expensive and time-consuming experimental methods, the computational prediction of qiRNAs is a conveniently rapid method for gaining valuable information for a subsequent experimental verification. However, no tool existed to date for the prediction of qiRNAs. To this purpose, we developed the novel qiRNA prediction software package qiRNApredictor. This software demonstrates a promising sensitivity of 93.55% and a specificity of 71.61% from the leave-one-out validation. These studies might be beneficial for further experimental investigation. Furthermore, the local package of qiRNApredictor was implemented and made freely available to the academic community at Supplementary material. |
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Identification of qiRNAs is fundamental for dissecting regulatory functions and molecular mechanisms. In contrast to other expensive and time-consuming experimental methods, the computational prediction of qiRNAs is a conveniently rapid method for gaining valuable information for a subsequent experimental verification. However, no tool existed to date for the prediction of qiRNAs. To this purpose, we developed the novel qiRNA prediction software package qiRNApredictor. This software demonstrates a promising sensitivity of 93.55% and a specificity of 71.61% from the leave-one-out validation. These studies might be beneficial for further experimental investigation. Furthermore, the local package of qiRNApredictor was implemented and made freely available to the academic community at Supplementary material.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0159487</identifier><identifier>PMID: 27428111</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Base Sequence ; Binding sites ; Biology ; Biology and Life Sciences ; Biosynthesis ; Computation ; Computational biology ; Computational neuroscience ; Computer programs ; Damage detection ; Datasets ; Deoxyribonucleic acid ; DNA ; DNA damage ; Experimental methods ; Fungi ; Gene Silencing ; Genetic aspects ; Genomes ; Genomics ; Identification ; Internet ; MicroRNAs ; Molecular modelling ; Neurospora ; Neurospora crassa ; Neurospora crassa - genetics ; Nucleotides ; Oryza ; Penicillium ; Physics ; Research and Analysis Methods ; RNA ; RNA, Small Interfering - analysis ; RNA, Small Interfering - genetics ; Science ; Sensitivity and Specificity ; Software</subject><ispartof>PloS one, 2016-07, Vol.11 (7), p.e0159487-e0159487</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Deng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2016 Deng et al 2016 Deng et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c725t-8c88c662388ab31aa2689cb477ccf83364fc6328b24b0f64edd4e6f0eb70505e3</citedby><cites>FETCH-LOGICAL-c725t-8c88c662388ab31aa2689cb477ccf83364fc6328b24b0f64edd4e6f0eb70505e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4948831/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4948831/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2095,2914,23846,27903,27904,53769,53771,79346,79347</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27428111$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Scaria, Vinod</contributor><creatorcontrib>Deng, Haiyou</creatorcontrib><creatorcontrib>Liu, Quan</creatorcontrib><creatorcontrib>Cao, Wei</creatorcontrib><creatorcontrib>Gui, Rong</creatorcontrib><creatorcontrib>Ma, Chengzhang</creatorcontrib><creatorcontrib>Yi, Ming</creatorcontrib><creatorcontrib>Yao, Yuangen</creatorcontrib><title>qiRNApredictor: A Novel Computational Program for the Prediction of qiRNAs in Neurospora crassa</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Recently, a new type of small interfering RNAs (qiRNAs) of typically 20~21 nucleotides was found in Neurospora crassa and rice and has been shown to regulate gene silencing in the DNA damage response. Identification of qiRNAs is fundamental for dissecting regulatory functions and molecular mechanisms. In contrast to other expensive and time-consuming experimental methods, the computational prediction of qiRNAs is a conveniently rapid method for gaining valuable information for a subsequent experimental verification. However, no tool existed to date for the prediction of qiRNAs. To this purpose, we developed the novel qiRNA prediction software package qiRNApredictor. This software demonstrates a promising sensitivity of 93.55% and a specificity of 71.61% from the leave-one-out validation. These studies might be beneficial for further experimental investigation. 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Identification of qiRNAs is fundamental for dissecting regulatory functions and molecular mechanisms. In contrast to other expensive and time-consuming experimental methods, the computational prediction of qiRNAs is a conveniently rapid method for gaining valuable information for a subsequent experimental verification. However, no tool existed to date for the prediction of qiRNAs. To this purpose, we developed the novel qiRNA prediction software package qiRNApredictor. This software demonstrates a promising sensitivity of 93.55% and a specificity of 71.61% from the leave-one-out validation. These studies might be beneficial for further experimental investigation. Furthermore, the local package of qiRNApredictor was implemented and made freely available to the academic community at Supplementary material.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27428111</pmid><doi>10.1371/journal.pone.0159487</doi><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Base Sequence Binding sites Biology Biology and Life Sciences Biosynthesis Computation Computational biology Computational neuroscience Computer programs Damage detection Datasets Deoxyribonucleic acid DNA DNA damage Experimental methods Fungi Gene Silencing Genetic aspects Genomes Genomics Identification Internet MicroRNAs Molecular modelling Neurospora Neurospora crassa Neurospora crassa - genetics Nucleotides Oryza Penicillium Physics Research and Analysis Methods RNA RNA, Small Interfering - analysis RNA, Small Interfering - genetics Science Sensitivity and Specificity Software |
title | qiRNApredictor: A Novel Computational Program for the Prediction of qiRNAs in Neurospora crassa |
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