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Open Access Technical Note

Mini-clusters with mean probabilities for identifying effective siRNAs

Jia Xingang12*, Zuhong Lu2* and Qiuhong Han3

Author Affiliations

1 Department of Mathematics, Southeast University, Nanjing 210096, PR China

2 State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, PR China

3 Department of Mathematics, Nanjing Forestry University, Nanjing 210037, PR China

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BMC Research Notes 2012, 5:512  doi:10.1186/1756-0500-5-512

Published: 18 September 2012

Abstract

Background

The distinction between the effective siRNAs and the ineffective ones is in high demand for gene knockout technology. To design effective siRNAs, many approaches have been proposed. Those approaches attempt to classify the siRNAs into effective and ineffective classes but they are difficult to decide the boundary between these two classes.

Findings

Here, we try to split effective and ineffective siRNAs into many smaller subclasses by RMP-MiC(the relative mean probabilities of siRNAs with the mini-clusters algorithm). The relative mean probabilities of siRNAs are the modified arithmetic mean value of three probabilities, which come from three Markov chain of effective siRNAs. The mini-clusters algorithm is a modified version of micro-cluster algorithm.

Conclusions

When the RMP-MiC was applied to the experimental siRNAs, the result shows that all effective siRNAs can be identified correctly, and no more than 9% ineffective siRNAs are misidentified as effective ones. We observed that the efficiency of those misidentified ineffective siRNAs exceed 70%, which is very closed to the used efficiency threshold. From the analysis of the siRNAs data, we suggest that the mini-clusters algorithm with relative mean probabilities can provide new insights to the applications for distinguishing effective siRNAs from ineffective ones.