LeARN: a platform for detecting, clustering and annotating non-coding RNAs

LeARN is a flexible software package which handles the complete process of ncRNA annotation by integrating the layers of automatic detection and human curation.This software provides the infrastructure to deal properly with ncRNAs in the framework of any annotation project. It fills the gap between existing prediction software, that detect independent ncRNA occurrences, and public ncRNA repositories, that do not offer the flexibility and interactivity required for annotation projects. The software is freely available from the download section of the website http://bioinfo.genopole-toulouse.prd.fr/LeARN webciteOur knowledge of small non-protein-coding RNAs (ncRNAs) has considerably evolved during the last decade. In 2002, Science magazine selected the discovery of small RNA with a regulatory function as a scientific breakthrough of the year [1]. Since, it has been discovered that various forms of ncRNA molecules play an important function in regulating gene expression. First examples include small temporal RNA, or microRNA, that regulates development in C. elegans [2]. It is now confirmed that genomes in all kingdoms encode for ncRNA playing regulatory roles [3-5]. Because it is believed that only a small fraction, corresponding to the tip of the iceberg, has been discovered, different approaches including experimental and computational ones have recently been developed in order to identify more ncRNAs.Detecting novel ncRNAs by experimental RNomics is not an easy task [6]. This has led both to the proposition of alternative computational methods that aim to detect and analyze ncRNAs in genomic sequences and, simultaneously, to an increasing development of generalist and specific RNA databases. These tools cover a broad spectrum of the needs in the RNA field [7]. Currently, Rfam [8] can be considered as the most comprehensive repository of validated ncRNA families and the Infernal[9]/Rfam pair is used in the framework of the majority of genome annotation projects. In