Navigating the unexplored seascape of pre-miRNA candidates in single-genome approaches.
BACKGROUND
The computational search for novel microRNA (miRNA) precursors often involves some sort of structural analysis with the aim of identifying which type of structures are prone to being recognized 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 allow 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.


RESULTS
We propose a method to represent candidate structures in a feature space, which summarizes key sequence/structure characteristics of each candidate. We demonstrate that proximity in this feature space is related to sequence/structure similarity, and we select candidates that have a high similarity to known precursors. Additional filtering steps are then applied to further reduce the number of candidates to those with greater transcriptional potential. Our method is compared with another single-genome method (TripletSVM) in two datasets, showing better performance in one and comparable performance in the other, for larger training sets. Additionally, we show that our approach allows for a better interpretation of the results.


METHODS
The MinDist method is implemented using Perl scripts and is freely available at http://www.cravela.org/?mindist=1.


BACKGROUND
backofen@informatik.uni-freiburg.de


BACKGROUND
Supplementary data are available at Bioinformatics online.
Équipe BAOBAB, Laboratoire de Biométrie et Biologie Évolutive (UMR 5558), CNRS, University of Lyon 1, Lyon 69622, Villeurbanne Cedex, France.
Nuno D
ND
Mendes
Steffen
S
Heyne
Ana T
AT
Freitas
Marie-France
MF
Sagot
Rolf
R
Backofen
D000465:Algorithms; D000818:Animals; D000852:Anopheles / Q000235:genetics; D001483:Base Sequence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D004331:Drosophila melanogaster / Q000235:genetics; D016678:Genome; D035683:MicroRNAs / Q000737:chemistry* / Q000235:genetics; D009690:Nucleic Acid Conformation; D025341:Principal Component Analysis; D012372:ROC Curve; D012984:Software
D035683:MicroRNAs