C/C++

MicroRNAs (miRNAs) are ~22 nt long integral elements responsible for post-transcriptional control of gene expressions. After the identification of thousands of miRNAs, the challenge is now to explore their specific biological functions. To this end, it will be greatly helpful to construct a reasonable organization of these miRNAs according to their homologous relationships. Given an established miRNA family system (e.g.

MicroRNA is a type of small non-coding RNAs, which usually has a stem-loop structure. As an important stage of microRNA, the pre-microRNA is transported from nuclear to cytoplasm by exportin5 and finally cleaved into mature microRNA. Structure-sequence features and minimum of free energy of secondary structure have been used for predicting pre-microRNA. Meanwhile, the double helix structure with free nucleotides and base-pairing features is used to identify pre-miRNA for the first time.

It has been proven that the accessibility of the target sites has a critical influence on RNA-RNA binding, in general and the specificity and efficiency of miRNAs and siRNAs, in particular. Recently, O(N(6)) time and O(N(4)) space dynamic programming (DP) algorithms have become available that compute the partition function of RNA-RNA interaction complexes, thereby providing detailed insights into their thermodynamic properties.

Clustering methods are a useful and common first step in gene expression studies, but the results may be hard to interpret. We bring in explicitly an indicator of which genes tie each cluster, changing the setup to biclustering. Furthermore, we make the indicators hierarchical, resulting in a hierarchy of progressively more specific biclusters. A non-parametric Bayesian formulation makes the model rigorous yet flexible and computations feasible. The model can additionally be used in information retrieval for relating relevant samples.