Statistical Inference on Distinct RNA Stem-Loops in Genomic Sequences

摘要

Functional RNA elements in post-transcriptional regulation of gene expression are often correlated with distinct RNA stem-loop structures that are both thermodynamically stable and highly well-ordered. Recent Discoveries of microRNA (miRNA) and small regulatory RN As indicate that there are a large class of small non-coding RNAs having the potential to form a distinct, well-ordered and/or stable stem-loop in numbers of genomes. The distinct RNA structure can be well evaluated by a quantitative measure, the energy difference (E_(diff)) between the optimal structure folded from the segment and its corresponding optimal restrained structure where all base pairings formed in the original optimal structure are forbidden. In this study, we present an efficient algorithm to compute E_(diff) of local segment by scanning a window along a genomic sequence. The complexity of computational time is O(L × n~2), where L is the length of the genomic sequence and n is the size of the sliding window. Our results indicate that the known stem-loops folded by miRNA precursors have high normalized E_(diff) scores with highly statistical significance. The distinct well-ordered structures related to the known miRNA can be predicted in a genomic sequence by a robust statistical inference. Our computational method StemED can be used as a general approach for the discovery of distinct stem-loops in genomic sequences.