Abstract:
BACKGROUND:MicroRNAs (miRNAs) are a class of endogenous regulatory small RNAs which play an important role in posttranscriptional regulations by targeting mRNAs for cleavage or translational repression. The base-pairing between the 5'-end of miRNA and the target mRNA 3'-UTRs is essential for the miRNA:mRNA recognition. Recent studies show that many seed matches in 3'-UTRs, which are fully complementary to miRNA 5'-ends, are highly conserved. Based on these features, a two-stage strategy can be implemented to achieve the de novo identification of miRNAs by requiring the complete base-pairing between the 5'-end of miRNA candidates and the potential seed matches in 3'-UTRs. RESULTS:We presented a new method, which combined multiple pairwise conservation information, to identify the frequently-occurred and conserved 7-mers in 3'-UTRs. A pairwise conservation score (PCS) was introduced to describe the conservation of all 7-mers in 3'-UTRs between any two Drosophila species. Using PCSs computed from 6 pairs of flies, we developed a support vector machine (SVM) classifier ensemble, named Cons-SVM and identified 689 conserved 7-mers including 63 seed matches covering 32 out of 38 known miRNA families in the reference dataset. In the second stage, we searched for 90 nt conserved stem-loop regions containing the complementary sequences to the identified 7-mers and used the previously published miRNA prediction software to analyze these stem-loops. We predicted 47 miRNA candidates in the genome-wide screen. CONCLUSION:Cons-SVM takes advantage of the independent evolutionary information from the 6 pairs of flies and shows high sensitivity in identifying seed matches in 3'-UTRs. Combining the multiple pairwise conservation information by the machine learning approach, we finally identified 47 miRNA candidates in D. melanogaster.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Gu J,Fu H,Zhang X,Li Ydoi
10.1186/1471-2105-8-432subject
Has Abstractpub_date
2007-11-08 00:00:00pages
432issn
1471-2105pii
1471-2105-8-432journal_volume
8pub_type
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