Motivation: More than 20 years ago, our laboratory published an original statistical test [referred to as the Audic-Claverie (AC) test in the literature] to identify differentially expressed genes from the pairwise comparison of counts of 'expressed sequence tags' determined in different conditions. Despite its antiquity and the publications of more sophisticated packages, this original publication continued to gather more than 200 citations per year, indicating the persistent usefulness of the simple AC test for the community. This prompted us to propose a fully revamped version of the AC test with a user interface adapted to the diverse and much larger datasets produced by contemporary omics techniques. Results: ACDtool is a freely accessible web-service proposing three types of analyses: (i) the pairwise comparison of individual counts, (ii) pairwise comparisons of arbitrary large lists of counts and (iii) the all-at-once pairwise comparisons of multiple datasets. Statistical computations are implemented using standard R functions and can accommodate all practical ranges of counts as generated by modern omic experiments. ACDtool is well suited for large datasets without replicates. Availability and implementation: http://www.igs.cnrs-mrs.fr/acdtool/. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: As numerous diseases involve errors in signal transduction, modern therapeutics often target proteins involved in cellular signaling. Interpretation of the activity of signaling pathways during disease development or therapeutic intervention would assist in drug development, design of therapy, and target identification. Microarrays provide a global measure of cellular response, however linking these responses to signaling pathways requires an analytic approach tuned to the underlying biology. An ongoing issue in pattern recognition in microarrays has been how to determine the number of patterns (or clusters) to use for data interpretation, and this is a critical issue as measures of statistical significance in gene ontology or pathways rely on proper separation of genes into groups. RESULTS: Here we introduce a method relying on gene annotation coupled to decompositional analysis of global gene expression data that allows us to estimate specific activity on strongly coupled signaling pathways and, in some cases, activity of specific signaling proteins. We demonstrate the technique using the Rosetta yeast deletion mutant data set, decompositional analysis by Bayesian Decomposition, and annotation analysis using ClutrFree. We determined from measurements of gene persistence in patterns across multiple potential dimensionalities that 15 basis vectors provides the correct dimensionality for interpreting the data. Using gene ontology and data on gene regulation in the Saccharomyces Genome Database, we identified the transcriptional signatures of several cellular processes in yeast, including cell wall creation, ribosomal disruption, chemical blocking of protein synthesis, and, critically, individual signatures of the strongly coupled mating and filamentation pathways. CONCLUSION: This works demonstrates that microarray data can provide downstream indicators of pathway activity either through use of gene ontology or transcription factor databases. This can be used to investigate the specificity and success of targeted therapeutics as well as to elucidate signaling activity in normal and disease processes.

BACKGROUND: The large amount of completely sequenced genomes allows genomic context analysis to predict reliable functional associations between prokaryotic proteins. Major methods rely on the fact that genes encoding physically interacting partners or members of shared metabolic pathways tend to be proximate on the genome, to evolve in a correlated manner and to be fused as a single sequence in another organism. RESULTS: The new "Gene Function Predictor", linked to the web server Phydbac proposes putative associations between Escherichia coli K-12 proteins derived from a combination of these methods. We show that associations made by this tool are more accurate than linkages found in the other established databases. Predicted assignments to GO categories, based on pre-existing functional annotations of associated proteins are also available. This new database currently holds 9,379 pairwise links at an expected success rate of at least 80%, the 6,466 functional predictions to GO terms derived from these links having a level of accuracy higher than 70%. CONCLUSION: The "Gene Function Predictor" is an automatic tool that aims to help biologists by providing them hypothetical functional predictions out of genomic context characteristics. The "Gene Function predictor" is available at http://www.igs.cnrs-mrs.fr/phydbac/indexPS.html.

A novel approach to design robust nonlinear control laws for dynamic systems that are not in canonical form or unknown is introduced. A competitive coevolutionary algorithm is used to design a sliding mode controller based on an approximate model of both the system and uncertainties. The power of evolutionary computation leads to a systematic convergence on accurate nominal model based controllers and the use of competitive coevolution offers a new method to handle model uncertainties with a sliding control structure.

To improve the detection of nucleotide sequence signals (e.g. promoter elements) by position-weight matrices (PWM) using the concept of statistically significant matches. The Mksite program was originally developed for analyzing protein sequences. We report NMksite, a new version adapted to the processing of nucleotide sequences. NMksite creates PWM from nucleotide sequence block alignments or occurrence tables using three weight computation schemes. An original feature of NMksite is the numerical computation of the statistical significance of PWM matches. The utility of this concept is demonstrated in the context of the prediction of splice sites and promoter regions. Mksite and other components of the MODEST (Motif DEsign and Search Tool) package (written in C/Unix) are available at http://igs-server.cnrs-mrs.fr E-mail: [email protected]

Jean-Michel Claverie, Christophe Daulmerie; Smoothing profiles with sliding windows: better to wear a hat!, Bioinformatics, Volume 7, Issue 1, 1 January 19

We describe FORTRAN 77 software allowing for convenient searching of any segmented and ambiguous pattern in the currently available protein or nucleotide sequence databanks. For proteins, this software can be instrumental in defining conserved functional domains among non-homologous overall primary structures. For nucleic acids, it is used in detecting complex and/or low consensus structural or regulatory patterns. As first applications we have studied the distribution of short consensus sequences believed to characterize heat-shock and glucocorticoid regulated promoters. This analysis allowed an evaluation of the specificity, probable role and thus biological significance of various regions of these consensus. In addition, the expression of several known genes are predicted to be heat-shock or glucocorticoid sensitive.