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Michaël Bekaert

Publications and source records attributed to Michaël Bekaert.

5 recordsLinked to original sources
PubMed2006

ARFA: a program for annotating bacterial release factor genes, including prediction of programmed ribosomal frameshifting.

UNLABELLED: Correct annotation of genes encoding release factors in bacterial genomes is often complicated by utilization of +1 programmed ribosomal frameshifting during synthesis of release factor 2, RF2. In the absence of robust computational approaches for predicting ribosomal frameshifting, the success of proper annotation depends on annotators' familiarity with this phenomenon. Here we describe a novel computer tool that allows automatic discrimination of genes encoding class-I bacterial release factors, RF1, RF2 and RFH. Most usefully, this program identifies and automatically annotates +1 frameshifting in RF2 encoding genes. Comparison of ARFA performance with existing annotations of bacterial genomes revealed that only 20% of RF2 genes utilizing ribosomal frameshifting during their expression are annotated correctly. AVAILABILITY: The PHP based web interface of ARFA and the source code are located at http://recode.genetics.utah.edu/arfa

Base Sequence↗
PubMed2005

An extended signal involved in eukaryotic -1 frameshifting operates through modification of the E site tRNA.

By using a sensitive search program based on hidden Markov models (HMM), we identified 74 viruses carrying frameshift sites among 1500 fully sequenced virus genomes. These viruses are clustered in specific families or genera. Sequence analysis of the frameshift sites identified here, along with previously characterized sites, identified a strong bias toward the two nucleotides 5' of the shifty heptamer signal. Functional analysis in the yeast Saccharomyces cerevisiae demonstrated that high frameshifting efficiency is correlated with the presence of a Psi39 modification in the tRNA present in the E site of the ribosome at the time of frameshifting. These results demonstrate that an extended signal is involved in eukaryotic frameshifting and suggest additional interactions between tRNAs and the ribosome during decoding.

Base Sequence↗
PubMed2005

Identification of programmed translational -1 frameshifting sites in the genome of Saccharomyces cerevisiae.

Frameshifting is a recoding event that allows the expression of two polypeptides from the same mRNA molecule. Most recoding events described so far are used by viruses and transposons to express their replicase protein. The very few number of cellular proteins known to be expressed by a -1 ribosomal frameshifting has been identified by chance. The goal of the present work was to set up a systematic strategy, based on complementary bioinformatics, molecular biology, and functional approaches, without a priori knowledge of the mechanism involved. Two independent methods were devised. The first looks for genomic regions in which two ORFs, each carrying a protein pattern, are in a frameshifted arrangement. The second uses Hidden Markov Models and likelihood in a two-step approach. When this strategy was applied to the Saccharomyces cerevisiae genome, 189 candidate regions were found, of which 58 were further functionally investigated. Twenty-eight of them expressed a full-length mRNA covering the two ORFs, and 11 showed a -1 frameshift efficiency varying from 5% to 13% (50-fold higher than background), some of which corresponds to genes with known functions. From other ascomycetes, four frameshifted ORFs are found fully conserved. Strikingly, most of the candidates do not display a classical viral-like frameshift signal and would have escaped a search based on current models of frameshifting. These results strongly suggest that -1 frameshifting might be more widely distributed than previously thought.

Ascomycota↗
PubMed2003

Towards a computational model for -1 eukaryotic frameshifting sites.

MOTIVATION: Unconventional decoding events are now well acknowledged, but not yet well formalized. In this study, we present a bioinformatics analysis of eukaryotic -1 frameshifting, in order to model this event. RESULTS: A consensus model has already been established for -1 frameshifting sites. Our purpose here is to provide new constraints which make the model more precise. We show how a machine learning approach can be used to refine the current model. We identify new properties that may be involved in frameshifting. Each of the properties found was experimentally validated. Initially, we identify features of the overall model that are to be simultaneously satisfied. We then focus on the following two components: the spacer and the slippery sequence. As a main result, we point out that the identity of the primary structure of the so-called spacer is of great importance. AVAILABILITY: Sequences of the oligonucleotides in the functional tests are available at http://www.igmors.u-psud.fr/rousset/bioinformatics/.

Algorithms↗