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Beyond Bioinformatics | |
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Title
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sRNATarget: a web server for prediction of bacterial sRNA targets | |||||||||||||||||||||||||||
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Authors
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Yuan Cao, Yalin Zhao, Lei Cha, Xiaomin Ying, Ligui Wang, Ningsheng Shao, Wuju Li* | ||||||||||||||||||||||||||||
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Affiliation |
Beijing Institute of Basic Medical Sciences, Taiping Road 27, Haidian district, Beijing 100850, | ||||||||||||||||||||||||||||
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liwj@nic.bmi.ac.cn | ||||||||||||||||||||||||||||
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Article Type |
Web Server | ||||||||||||||||||||||||||||
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Date
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received January 19, 2009; revised March 06, 2009; accepted March 29, 2009; published April 29, 2009
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Abstract |
In bacteria, there exist some small non-coding RNAs (sRNAs) with 40–500 nucleotides in length. Most of them function as post-transcriptional regulation of gene expression through binding to their target mRNAs, in which Hfq protein acts as RNA chaper-one. With the increase of identified sRNA genes in the bacterium, prediction of sRNA targets plays a more important role in determining sRNA functions. However, there are few available computational tools for predicting sRNA targets at present. Here we introduced a web server, sRNATarget, for genome-scale prediction of bacterial sRNA targets. The server is based on a re-cently published model which uses Naive Bayes method as the supervised method and take RNA secondary structure profile as the feature. The prediction results will be returned to the users through E-mail.
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Availability |
sRNATarget web server is freely available at http://ccb.bmi.ac.cn/srnatarget/
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Keywords
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prediction of sRNA target; model; Naive Bayes method
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Citation |
Cao et al, Bioinformation 3(8): 364-366 (2009)
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Edited by |
P. Kangueane
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ISSN |
0973-2063
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Publisher |
Biomedical Informatics Publishing Group
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Copyright |
Publisher
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Copyright Transfer Agreement
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The authors of published articles in Bioinformation automatically transfer the copyright to the publisher upon formal acceptance. However, the authors reserve right to use the information contained in the article for non commercial purposes.
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License
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This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
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