Abstract
Biological background: Plant microRNAs (miRNAs) are short RNA sequences that bind to target genes (mRNAs) and change their expression levels by redirecting their stabilities and marking them for cleavage. In Arabidopsis thaliana, microRNAs have been shown to regulate development and are believed to impact expression both under many conditions, such as stress and stimuli, as well as in various tissue types.
Methods: mirXdeNovo is a novel prototype tool for the de-novo prediction of microRNAs associated with a given cell condition. The work of mirXdeNovo is composed of two off-line preprocessing stages, which are executed only once per genome in the database, and a dynamic online main stage, which is executed again and again for each newly obtained expression profile. During the preprocessing stages, a set of candidate microRNAs is computed for the genome of interest and then each microRNA is associated with a set of mRNAs which are its predicted targets.
Then, during the main stage, given a newly obtained cell condition represented by a vector describing the expression level of each of the genes under this condition, the tool will efficiently compute the subset of microRNA candidates which are predicted to be active under this condition. The efficiency of the main stage is based in a novel branch-and-bound search of a tree constructed over the microRNA candidates and annotated with the corresponding predicted targets. This search exploits the monotonicity of the target prediction decision with respect to microRNA prefix size in order to apply an efficient yet admissible pruning. Our testing indicates that this pruning results in a substantial speed up over the naive search.
Biological Results: We employed mirXdeNovo to conduct a study, using the plant Arabidopsis thaliana as our model organism and the subject of our ”hunt for microRNAs”. During the preprocessing stage, 2000 microRNA precursor candidates were extracted from the genome. Our study included the 3’UTRs of 5800 mRNAs. 380 different conditions were analyzed including various tissues and hormonal treatments. This led to the discovery of some interesting and statistically significant newly predicted microRNAs, annotated with their potential condition of activity.
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Zilberstein, C.BZ., Ziv-Ukelson, M. (2005). Dynamic De-Novo Prediction of microRNAs Associated with Cell Conditions: A Search Pruned by Expression. In: Casadio, R., Myers, G. (eds) Algorithms in Bioinformatics. WABI 2005. Lecture Notes in Computer Science(), vol 3692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11557067_2
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DOI: https://doi.org/10.1007/11557067_2
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