Section: New Results

MicroRNA predictor

We developed a microRNA predictor using structural and target information. The method shows 97% sensitivity and 90% specificity for the Acyrthosiphon pisum genome. Comparing to the results of the previous method we developed in 2010 (available in the software Cravela ) we obtained a better performance (sensitivity 90% and specificity 88%). However, as we are working on a genome wide scale, it is important to obtain even better specificity (obviously, maintaining a reasonable sensitivity). This work is currently in development.

On the other hand, the computational search for novel miRNA precursors often involves also some sort of structural analysis with the aim of identifying which type of structures are recognised and processed by the cellular miRNA-maturation machinery. A natural way to tackle this problem is to perform clustering over the candidate structures along with known miRNA precursor structures. Mixed clusters allows then the identification of candidates that are similar to known precursors. Given the large number of pre-miRNA candidates that can be identified in single-genome approaches, even after applying several filters for precursor robustness and stability, a conventional structural clustering approach is unfeasible. We proposed a method to represent candidate structures in a feature space which summarises key sequence/structure characteristics of each candidate [21] . We showed that proximity in this feature space is related to sequence/structure similarity, and we selected candidates which have a high similarity to known precursors. Additional filtering steps were then applied to further reduce the number of candidates to those with greater transcriptional potential.