MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans.
BACKGROUND
MicroRNAs (miRNAs) are recognized as one of the most important families of non-coding RNAs that serve as important sequence-specific post-transcriptional regulators of gene expression. Identification of miRNAs is an important requirement for understanding the mechanisms of post-transcriptional regulation. Hundreds of miRNAs have been identified by direct cloning and computational approaches in several species. However, there are still many miRNAs that remain to be identified due to lack of either sequence features or robust algorithms to efficiently identify them.


RESULTS
We have evaluated features valuable for pre-miRNA prediction, such as the local secondary structure differences of the stem region of miRNA and non-miRNA hairpins. We have also established correlations between different types of mutations and the secondary structures of pre-miRNAs. Utilizing these features and combining some improvements of the current pre-miRNA prediction methods, we implemented a computational learning method SVM (support vector machine) to build a high throughput and good performance computational pre-miRNA prediction tool called MiRFinder. The tool was designed for genome-wise, pair-wise sequences from two related species. The method built into the tool consisted of two major steps: 1) genome wide search for hairpin candidates and 2) exclusion of the non-robust structures based on analysis of 18 parameters by the SVM method. Results from applying the tool for chicken/human and D. melanogaster/D. pseudoobscura pair-wise genome alignments showed that the tool can be used for genome wide pre-miRNA predictions.


CONCLUSIONS
The MiRFinder can be a good alternative to current miRNA discovery software. This tool is available at http://www.bioinformatics.org/mirfinder/.
Huang
Ting-Hua
TH
Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education & Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, P R China. huangtinghua@webmail.hzau.edu.cn
Fan
Bin
B
Rothschild
Max F
MF
Hu
Zhi-Liang
ZL
Li
Kui
K
Zhao
Shu-Hong
SH
D000818:Animals; D001185:Artificial Intelligence; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D016678:Genome; D006801:Humans; D035683:MicroRNAs / Q000032:analysis* / Q000096:biosynthesis / Q000235:genetics*; D008957:Models, Genetic; D009690:Nucleic Acid Conformation; D012322:RNA Precursors / Q000032:analysis / Q000235:genetics; D016415:Sequence Alignment; D017423:Sequence Analysis, RNA; D012985:Software Design; D013045:Species Specificity
D035683:MicroRNAs; D012322:RNA Precursors