alexa Performance and Evaluation of MicroRNA Gene Identification Tools
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Research Article

Performance and Evaluation of MicroRNA Gene Identification Tools

Swati Sinha1*, T.S. Vasulu1, and Rajat K. De2*

1Biological Anthropology Unit, Indian Statistical Institute, Kolkata, India

2Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

*Corresponding Author:
Dr. Swati Sinha
Biological Anthropology Unit
Indian Statistical Institute
Kolkata-108, India
Tel: 91-33-25753215
E-mail : [email protected]

Dr. Rajat K De
Machine Intelligence Unit
Indian Statistical Institute
Kolkata-108, India
Tel: 91-33-25753105
Fax: +91-33-25753026
E-mail : [email protected]

Received Date: July 02, 2009; Accepted Date: August 11, 2009; Published Date: August 12, 2009

Citation: Sinha S, Vasulu TS, De RK (2009) Performance and Evaluation of MicroRNA Gene Identification Tools. J Proteomics Bioinform 2: 336-343. doi: 10.4172/jpb.1000093

Copyright: © 2009 Sinha S, et al. 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 credited.



MicroRNAs are small single stranded RNA molecules o f ~ 22 nt in length which play important role in po st transcriptional gene regulation either by translati onal repression of mRNA or by their cleavage. Since their discovery, continuous efforts to identify the miRNA genes led to the discovery of several miRNAs in plants as wel l as animals. Owing to the limitations of the molecular genetic t echniques of miRNA identification, computational ap proaches were introduced for better and affordable in silico- miRNA predictions. Here, we compared a few miRNA ge ne identification tools, such as ‘MiPred’, ‘Triplet-SVM ’, ‘BayesMiRNAfind’, ‘OneClassmiRNAfind’and ‘BayesSVMmiRNAfind’ to evaluate the performance of its predictability based on the real and pseudo pre cursor miRNA datasets. Of all the tools examined MiPred is more sensitive (96%) in identifying pseudo miRNAs than Triplet-SVM for real/pseudo miRNA classification, w hereas for mature miRNA prediction ‘one-class’ SVM classifier shows best specificity (96%), while BayesSVMmiRNAfi nd shows least specificity (8%).


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