Dataset Information

Navigating the unexplored seascape of pre-miRNA candidates in single-genome approaches.

ABSTRACT:

Motivation

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.

Availability and implementation

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

Contact

backofen@informatik.uni-freiburg.de

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Mendes ND

PROVIDER: S-EPMC3516144 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Publications

Navigating the unexplored seascape of pre-miRNA candidates in single-genome approaches.

Mendes Nuno D ND Heyne Steffen S Freitas Ana T AT Sagot Marie-France MF Backofen Rolf R

Bioinformatics (Oxford, England) 20121010 23

<h4>Motivation</h4>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 precurs ...[more]

PMID: 23052038

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Dataset Information

Navigating the unexplored seascape of pre-miRNA candidates in single-genome approaches.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

Navigating the unexplored seascape of pre-miRNA candidates in single-genome approaches.

Similar Datasets

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