的鉴定 （MicroRNA identification）.ppt

MicroRNA identification based on sequence andstructure alignment Presented by - Neeta Jain Outline Introduction Motivation Experiment Materials Methods Results Conclusion Introduction What are miRNAs and why are they important? miRNAs are ~22 nt long non-coding RNAs They are derived from their ~70 nt precursors, which typically have a hairpin structure Motivation Since miRNAs are short (~22 nt), conventional sequence alignment methods can only find relatively close homologues It has been reported that miRNA genes are more conserved in their secondary structure than in primary structure This paper exploits this secondary structure conservation and proposes a novel computational approach to detect miRNAs based on both sequence and structure alignment The authors devised a tool – miRAlign and have compared it’s performance with existing searching methods such as BLAST and ERPIN Experiment Materials Reference sets Consists of 1298 miRNAs from 12 species out of which 1054 were animal miRNAs. 1054 animal miRNAs and their precursors(1104) composed our raw training set Train_All. Train_Sub_1 : All animal miRNAs except those from C.briggsae Train_Sub_2: All animal miRNAs except those from C.briggsae and C.elegans Genomic sequences Sequences of 6 species were used. Methods Preprocessing Known precursors from training set are used to BLAST against the genome Potential regions are cut from the genome with 70 nt flanking sequences to each end Such regions are scanned using a 100nt window with 10 nt step Overlapping sequences with repeat sequences are discarded. Methods (contd) miRAlign Secondary Structure Prediction Both the candidate sequence and it’s reverse complement are analyzed by RNA fold to predict hairpins. Only hairpins with MFE lower than -20 kcal/mol are retained. Pairwise sequence alignment Sequences from previous step are aligned pairwise to all the ~22 nt known miRNA sequences from the training set Sequence similarity score between the ca