A Bayesian Approach for Identifying miRNA Targets by Combining Sequence Prediction and Gene Expression Profiling

Hui Liu1 , Dong Yue2 , Lin Zhang1 , Yidong Chen 4,5, Shou-Jiang Gao3,4and Yufei Huang2,5

 

1SIEE, China University of Mining and Technology, Xuzhou, China
2Department of Electrical and Computer Engineering, University of Texas at San Antonio
3Department of Pediatrics, University of Texas Health Science Center at San Antonio
4Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio
5Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio

Email addresses:
YH: yufei.huang@utsa.edu
HL: lhcumt@hotmail.com
DY:danieldongyue@gmail.com
YC:cheny8@uthscsa.edu
SJG:gaos@uthscsa.edu

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Abstract

 

MicroRNAs (miRNAs) are single-stranded non-coding RNAs shown to plays important regulatory
roles in a wide range of biological processes and diseases. The functions and regulatory mechanisms of most of
miRNAs are still poorly understood in part because of the diffculty in identifying the miRNA regulatory targets.
To this end, computational methods have evolved as important tools for genome-wide target screening.
Although considerable work in the past few years has produced many target prediction algorithms, most of them
are solely based on sequence, and the accuracy is still poor. In contrast, gene expression proling from miRNA
transfection experiments can provide additional information about miRNA targets. However, most of existing
research assumes down-regulated mRNAs as targets. Given the fact that the primary function of miRNA is
protein inhibition, this assumption is neither suffcient nor necessary.

Results: A novel Bayesian approach is proposed in this paper that integrates sequence level prediction with
expression proling of miRNA transfection. This approach does not restrict the target to be down-expressed and
thus improve the performance of existing target prediction algorithm. The proposed algorithm was tested on
simulated data, proteomics data, and IP pull-down data and shown to achieve better performance than existing
approaches for target prediction.

Conclusions

The proposed Bayesian algorithm integrates properly the sequence paring data and mRNA
expression proles for miRNA target prediction. This algorithm is shown to have better prediction performance
than existing algorithms.


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