מיכל זיו-יוקלסון

Plant microRNAs (miRNAs) are short RNA sequences that bind to target mRNAs and change their expression levels by influencing their stabilities and marking them for cleavage. We present a high throughput approach for associating between microRNAs and conditions in which they act, using novel statistical and algorithmic measures. Our new prototype tool, miRNAXpress, computes a (binary) matrix T denoting the potential targets of microRNAs. Then, using T and an additional predefined matrix X indicating expression of genes under various conditions, it produces a new matrix that predicts associations between microRNAs and the conditions in which they act. The computational intensive part of miRNAXpress is the calculation of T. We provide a hybridization search algorithm which given a query microRNA, a text mRNA, and a predefined energy cutoff threshold, finds and reports all targets (putative binding sites) of the query in the text with binding energy below the predefined threshold. In order to speed it up, we utilize the sparsity of the search space without sacrificing the optimality of the results. Consequently, the time complexity of the search algorithm is almost linear in the size of a sparse set of locations where base-pairs are stacked at a height of three or more. We employed our tool to conduct a study, using the plant Arabidopsis thaliana as our model organism. By applying miRNAXpress to 98 microRNAs and 380 conditions, some biologically interesting and statistically strong relations were discovered.