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Vincent Lefort

Publications and source records attributed to Vincent Lefort.

2 recordsLinked to original sources

Characterization of 43 non-protein-coding mRNA genes in Arabidopsis, including the MIR162a-derived transcripts.

Messenger RNAs that do not contain a long open reading frame (ORF) or non-protein-coding RNAs (npcRNAs) are an emerging novel class of transcripts. Their functions may involve the RNA molecule itself and/or short ORF-encoded peptides. npcRNA genes are difficult to identify using standard gene prediction programs that rely on the presence of relatively long ORFs. Here, we used detailed bioinformatic analyses of expressed sequence tag/cDNA databases to detect a restricted set of npcRNAs in the Arabidopsis (Arabidopsis thaliana) genome and further characterized these transcripts using a combination of bioinformatic and molecular approaches. Compositional analyses revealed strong nucleotide strand asymmetries in the npcRNAs, as well as a biased GC content, suggesting the existence of functional constraints on these RNAs. Thirteen of these transcripts display tissue-specific expression patterns, and three are regulated in conditions affecting root architecture. The npcRNA 78 gene contains the miR162 sequence in an alternative intron and corresponds to the MIR162a locus. Although DICER-LIKE 1 (DCL1) mRNA is known to be regulated by miR162-guided cleavage, its level does not change in a mir162a mutant. Alternative splicing of npcRNA 78 leads to several transcript isoforms, which all accumulate in a dcl1 mutant. This suggests that npcRNA 78 is a genuine substrate of DCL1 and that splicing of this microRNA primary transcript and miR162 processing are competitive nuclear events. Our results provide new insights into Arabidopsis npcRNA biology and the potential roles of these genes.

Alternative Splicing↗

GPS@ bioinformatics portal: from network to EGEE grid.

Bioinformatics analysis of data produced by complete genome sequencing projects is one of the major challenges of the current years. Integrating up-to-date databanks and relevant algorithms is a clear requirement of such analysis. Grid computing would be a viable solution to distribute data, algorithms, computing and storage resources for Genomics. Providing bioinformaticians with a good interface to grid infrastructure, such as the one provided by the EGEE European project, is also a challenge to take up. The GPS@ web portal, "Grid Protein Sequence Analysis", aims to provide such a user-friendly interface for these grid genomic resources on the EGEE grid.

Computational Biology↗