title: Identifications of conserved 7-mers in 3'-UTRs and microRNAs in Drosophila.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, China. wellgoo@gmail.com
authors.forename: Jin
authors.initials: J
authors.lastname: Gu
authors.forename: Hu
authors.initials: H
authors.lastname: Fu
authors.forename: Xuegong
authors.initials: X
authors.lastname: Zhang
authors.forename: Yanda
authors.initials: Y
authors.lastname: Li
mesh_terms: D020413:3' Untranslated Regions / Q000737:chemistry* / Q000235:genetics; D000818:Animals; D001185:Artificial Intelligence; D020029:Base Pairing / Q000235:genetics; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D017124:Conserved Sequence / Q000235:genetics; D030541:Databases, Genetic; D004331:Drosophila melanogaster / Q000235:genetics*; D019143:Evolution, Molecular; D005786:Gene Expression Regulation; D017344:Genes, Insect; D049750:Genome, Insect; D035683:MicroRNAs / Q000032:analysis* / Q000235:genetics*; D005810:Multigene Family / Q000235:genetics; D010802:Phylogeny; D017423:Sequence Analysis, RNA; D013045:Species Specificity
chemical_list: D020413:3' Untranslated Regions; D035683:MicroRNAs