Abstract
BackgroundMicroRNAs (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.ResultsWe 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.ConclusionThe MiRFinder can be a good alternative to current miRNA discovery software. This tool is available at .
Highlights
MicroRNAs are recognized as one of the most important families of noncoding RNAs that serve as important sequence-specific post-transcriptional regulators of gene expression
An overview of miRNA MicroRNA is a special class of endogenic RNA molecules that can down-regulate the expression of protein coding genes at the post-transcriptional level by means of incomplete complementary interactions
The biogenesis of miRNA involves several steps: 1) The majority of long primary transcripts of the miRNA genes are transcribed by RNA polymerase II [1,2]; 2) The 7-methylguanosine capped and poly(A) tailed transcripts are cleaved by the nuclear RNase III Drosha to release the precursors of miRNA in the nucleus [3]; 3) The precursors of miRNA that possess a thermodynamic stabile hairpin structure are exported into the cytoplasm by Exportin-5 or HASTY [4,5,6,7] and 4) An additional cleavage in the cytoplasm yields 18–23 nt mature miRNA [8,9,10]
Summary
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 established correlations between different types of mutations and the secondary structures of pre-miRNAs. We have established correlations between different types of mutations and the secondary structures of pre-miRNAs Utilizing these features and combining some improvements of the current premiRNA 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 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
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