Biomedical subjects

Florence Muri

Publications and source records attributed to Florence Muri.

3 recordsLinked to original sources

PubMed2006

A reversible jump Markov chain Monte Carlo algorithm for bacterial promoter motifs discovery.

Effective probabilistic modeling approaches have been developed to find motifs of biological function in DNA sequences. However, the problem of automated model choice remains largely open and becomes more essential as the number of sequences to be analyzed is constantly increasing. Here we propose a reversible jump Markov chain Monte Carlo algorithm for estimating both parameters and model dimension of a Bayesian hidden semi-Markov model dedicated to bacterial promoter motif discovery. Bacterial promoters are complex motifs composed of two boxes separated by a spacer of variable but constrained length and occurring close to the protein translation start site. The algorithm allows simultaneous estimations of the width of the boxes, of the support size of the spacer length distribution, and of the order of the Markovian model used for the "background" nucleotide composition. The application of this method on three sequence sets points out the good behavior of the algorithm and the biological relevance of the estimated promoter motifs.

Algorithms↗

PubMed2003

Short inverse complementary amino acid sequences generate protein complexity.

Inversions of short genomic sequences play a central role in the generation of protein complexity. More than half of the 1300 motifs registered in ProSite have protein inverse complementary sequences (princoms) among proteins registered in SwissProt. The observed number of princoms occurrences exceeds by far the expected number (p < 10(-10)). Princoms often endow their host proteins with a whole new range of biochemical and physiological capabilities, including the possibility of intramolecular and intermolecular disulfide bond formation. These results support the idea that, like the duplications, the inversions of small genomic fragments have been a fundamental mechanism for shaping genomes.

Amino Acid Motifs↗

PubMed2002

Mining Bacillus subtilis chromosome heterogeneities using hidden Markov models.

We present here the use of a new statistical segmentation method on the Bacillus subtilis chromosome sequence. Maximum likelihood parameter estimation of a hidden Markov model, based on the expectation-maximization algorithm, enables one to segment the DNA sequence according to its local composition. This approach is not based on sliding windows; it enables different compositional classes to be separated without prior knowledge of their content, size and localization. We compared these compositional classes, obtained from the sequence, with the annotated DNA physical map, sequence homologies and repeat regions. The first heterogeneity revealed discriminates between the two coding strands and the non-coding regions. Other main heterogeneities arise; some are related to horizontal gene transfer, some to t-enriched composition of hydrophobic protein coding strands, and others to the codon usage fitness of highly expressed genes. Concerning potential and established gene transfers, we found 9 of the 10 known prophages, plus 14 new regions of atypical composition. Some of them are surrounded by repeats, most of their genes have unknown function or possess homology to genes involved in secondary catabolism, metal and antibiotic resistance. Surprisingly, we notice that all of these detected regions are a + t-richer than the host genome, raising the question of their remote sources.

Bacillus subtilis↗