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This article is part of the supplement: Seventh International Conference on Bioinformatics (InCoB2008)

Open Access Research

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

Wing-Sze Leung1, Marie CM Lin2, David W Cheung1 and SM Yiu1*

Author Affiliations

1 Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong

2 Department of Chemistry, Open Laboratory of Chemical Biology, The University of Hong Kong, Pokfulam Road, Hong Kong

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BMC Bioinformatics 2008, 9(Suppl 12):S3  doi:10.1186/1471-2105-9-S12-S3

Published: 12 December 2008

Abstract

Background

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.