Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. | Betel et al. Genome Biology 2010 11 R90 http 2010 11 8 R90 Genome Biology METHOD Open Access Comprehensive modeling of mieroRNA targets predicts functional non-eonserved and non-eanonieal sites 1 2 1 1 1 Doron Betel Anjali Koppal Phaedra Agius Chris Sander Christina Leslie Abstract mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly the method identifies a significant number of experimentally determined non-canonical and non-conserved sites. Background microRNAs are a class of small regulatory RNAs that are involved in post-transcriptional gene silencing. These small approximately 22 nucleotide single-strand RNAs guide a gene silencing complex to an mRNA by complementary base pairing mostly at the 3 untranslated region 3 UTR . The association of the RNA-induced silencing complex RISC to the conjugate mRNA results in silencing the gene either by translational repression or by degradation of the mRNA 1 . Reliable microRNA target prediction is an important and still unsolved computational challenge hampered both by insufficient knowledge of microRNA biology as well as the limited number of experimentally validated targets. Early studies of target recognition revealed that nearperfect complementarity at the 5 end of the microRNA the so-called seed region at positions 2 to 7 is a primary determinant of target specificity 2 . However a perfect seed match by itself is a poor predictor for microRNA regulation due to the large number of random occurrences of any given hexamer in 3 UTRs. Conversely a number of studies have shown that some target sites with a mismatch or a G U
