Détection des ARNnc dans les séquences génomiques. Application au génome de Ralstonia solanacearum

Les ARN non-codant sont des régulateurs clés des divers processus cellulaires, chez les procaryotes et les eucaryotes. Malgré le grand nombre des ARN non-codant connus à ce jour il n'existe pas de méthode bioinformatique universelle permettant leur détection. Il est connu que, dans les genomes archéans A+T riches hyperthermophiles, la détection est possible à l'aide de leur composition en G+C elevée dans ces génomes. Ici nous étudions l'approche par biais de composition pour la détection des ARN non-codant dans le génome G+C riche de Ralstonia solanacearum pour lequel aucun étude de recherche des ARNnc n'a pas été menée à ce jour. Nous étudions tout d'abord l'existence d'un biais de composition dans les ARNnc du génome A+T riche de Staphylococcus aureus. D'un point de vue méthodologique, ce travail propose une procédure pour tester l'existence d'un biais en G+C dans différents éléments génomiques. La procédure est basée sur la theorie des Modeles Lineaires Generalises. Nous montrons que les ARNnc de S. aureus ensemble avec certaines séquences repetées, sont caracterisées par le G+C% plus elevée et ceci peut être utilisé pour leur détection. La même approche à été utilisée avec moins de succès sur le génome de R. somanacearum. De façon complémentaire a l'approche par biais de composition, nous avons utilise l'analyse comparative des différentes souches de R. solanacearum pour la détection des ARNnc conserves. Nous avons développe la nouvelle version de RNAsim, un outil utilisant la théorie des graphes pour identifier les régions intergeniques conservées entre plusieurs génomes. Les candidats choisis a l'aide de l'approche comparative ont été analyses par rapport a la conservation de leur structure secondaire, éléments de syntenie etc. afin d'évaluer leur pertinence biologique. Huit candidats ont été sélectionnes et ils seront testes biologiquement.Recently, noncoding RNAs (ncRNAs) have emerged as key regulators in control of diverse cellular processes both in procaryotes and eucaryotes. Despite a great number of noncoding RNA known today, no universal feature allowing their reliable prediction has been found. Nevertheless, it is known that in archean A+T rich thermophiles ncRNA detection is possible on the basis on their elevated G+C contents. On the other hand, there are no studies exploring the compositional properties of noncoding RNA in G+C rich genomes. Here we study the noncoding RNA detection in Ralstonia solanacearum G+C rich beta-proteobacterium in which no previous systematic search of noncoding RNAs had been undertaken.e first studied the existence of the compositional bias in ncRNAs in A+T rich bacterium Staphylococcus aureus. From the methodological point of view, this work resulted in proposition of a procedure for testing the G+C bias in different genomes features, and noncoding RNA in particular, based on the Generalised Linear Modelling. We show that S. aureus ncRNAs, as well as some repeat sequences, are caracterised by a significant compositional bias which can be used for their detection. The same approach was less succesiful when applied on R. solanacearum genome. Complementary to the compositional bias approach, we used the comparative genome analysis between different strains of R. solanacearum in order to detect conserved noncoding RNA. During this work, we developed a new version of RNAsim, a tool using graph theory approach in order to predict conserved intergenic regions in multiple genomes. The candidates selectionned on their conservation were analysed on the basis of their secondary structure conservation, elements of synteny and other features, in order to determine their biological relevance. Eight candidates were selected and theirtranscription will be tested biologically

miRBase: annotating high confidence microRNAs using deep sequencing data

ABSTRACT We describe an update of the miRBase databas

Sex-Biased Expression of MicroRNAs in Schistosoma mansoni

Schistosomiasis is an important neglected tropical disease caused by digenean helminth parasites of the genus Schistosoma. Schistosomes are unusual in that they are dioecious and the adult worms live in the blood system. MicroRNAs play crucial roles during gene regulation and are likely to be important in sex differentiation in dioecious species. Here we characterize 112 microRNAs from adult Schistosoma mansoni individuals, including 84 novel microRNA families, and investigate the expression pattern in different sexes. By deep sequencing, we measured the relative expression levels of conserved and newly identified microRNAs between male and female samples. We observed that 13 microRNAs exhibited sex-biased expression, 10 of which are more abundant in females than in males. Sex chromosomes showed a paucity of female-biased genes, as predicted by theoretical evolutionary models. We propose that the recent emergence of separate sexes in Schistosoma had an effect on the chromosomal distribution and evolution of microRNAs, and that microRNAs are likely to participate in the sex differentiation/maintenance process

RNAcentral: A vision for an international database of RNA sequences

During the last decade there has been a great increase in the number of noncoding RNA genes identified, including new classes such as microRNAs and piRNAs. There is also a large growth in the amount of experimental characterization of these RNA components. Despite this growth in information, it is still difficult for researchers to access RNA data, because key data resources for noncoding RNAs have not yet been created. The most pressing omission is the lack of a comprehensive RNA sequence database, much like UniProt, which provides a comprehensive set of protein knowledge. In this article we propose the creation of a new open public resource that we term RNAcentral, which will contain a comprehensive collection of RNA sequences and fill an important gap in the provision of biomedical databases. We envision RNA researchers from all over the world joining a federated RNAcentral network, contributing specialized knowledge and databases. RNAcentral would centralize key data that are currently held across a variety of databases, allowing researchers instant access to a single, unified resource. This resource would facilitate the next generation of RNA research and help drive further discoveries, including those that improve food production and human and animal health. We encourage additional RNA database resources and research groups to join this effort. We aim to obtain international network funding to further this endeavor

miRBase: integrating microRNA annotation and deep-sequencing data

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MiRBase: Annotating high confidence microRNAs using deep sequencing data

We describe an update of the miRBase database (http://www.mirbase.org/), the primary microRNA sequence repository. The latest miRBase release (v20, June 2013) contains 24 521 microRNA loci from 206 species, processed to produce 30 424 mature microRNA products. The rate of deposition of novel microRNAs and the number of researchers involved in their discovery continue to increase, driven largely by small RNA deep sequencing experiments. In the face of these increases, and a range of microRNA annotation methods and criteria, maintaining the quality of the microRNA sequence data set is a significant challenge. Here, we describe recent developments of the miRBase database to address this issue. In particular, we describe the collation and use of deep sequencing data sets to assign levels of confidence to miRBase entries. We now provide a high confidence subset of miRBase entries, based on the pattern of mapped reads. The high confidence microRNA data set is available alongside the complete microRNA collection at http://www.mirbase.org/. We also describe embedding microRNA-specific Wikipedia pages on the miRBase website to encourage the microRNA community to contribute and share textual and functional information

Détection des ARNnc dans les séquences génomiques (application au génome de Ralstonia solanacearum)

TOULOUSE3-BU Sciences (315552104) / SudocSudocFranceF