UNLABELLED: Correspondence analysis of codon usage data is a widely used method in sequence analysis, but the variability in amino acid composition between proteins is a confounding factor when one wants to analyse synonymous codon usage variability. A simple and natural way to cope with this problem is to use within-group correspondence analysis. There is, however, no user-friendly implementation of this method available for genomic studies. Our motivation was to provide to the community a Web facility to easily study synonymous codon usage on a subset of data available in public genomic databases. AVAILABILITY: Availability through the Pole Bioinformatique Lyonnais (PBIL) Web server at http://pbil.univ-lyon1.fr/datasets/charif04/ with a demo allowing us to reproduce the figure in the present application note. All underlying software is distributed under a GPL licence. CONTACT: http://pbil.univ-lyon1.fr/members/lobry.

Abstract Summary: A program called Oriloc has been developed for the prediction of bacterial replication origins. The method builds on the fact that there are compositional asymmetries between the leading and the lagging strand for replication. The program works with unannotated sequences in fasta format and therefore uses glimmer 2.0 outputs to discriminate between codon positions so as to increase the signal/noise ratio. Availability: The ANSI C source code is freely available for academic use at ftp://pbil.univ-lyon1.fr/pub/logiciel/oriloc/oriloc.c. Contact: [email protected] Supplementary information: ftp://pbil.univ-lyon1.fr/pub/logiciel/oriloc/oriloc.ps * To whom correspondence should be addressed.

This report describes two applications of a multivariate method for studying classes of nucleotide or protein sequences: correspondence discriminant analysis (CDA). The first example is the discrimination between Escherichia coli proteins according to their subcellular location (membrane, cytoplasm and periplasm). The high resolution of the method made it possible to predict the subcellular location of E.coli proteins for whom this information is not known. The second example is discrimination between the coding sequences of leading and lagging strands in four bacteria: Mycoplasma genitalium, Haemophilus influenzae, E.coli and Bacillus subtilis. The programs used for computing the analysis are integrated in a publicly available package that runs on MacOS 7.x or Windows 95 operating systems (http:/(/)biomserv.univ-lyonl.fr/ADE-4.html). These programs are also accessible through our World Wide Web server (http:/(/)biomserv.univ-lyonl.fr/Net Mul.html).

A multivariate analysis method called co-inertia analysis was used to determine the main relationships between two data tables having identical rows. This method is available in the ADE multivariate analysis package for Macintosh micro-computers. It was applied to two data sets, one containing the amino-acid composition of 999 E. coli proteins, and the other the values of 402 physico-chemical properties for the 20 natural amino-acids. There were strong relationships between amino-acid physico-chemical properties and the composition of proteins. The first common factor was hydrophobicity; it is linked to the biological environment of proteins, either in the cytoplasm (or outside the cell), or in the nonpolar environment of the phospholipid bilayer of biological membranes. The second factor linked the expressivity of protein genes and the propensity of amino-acids to form alpha helix/beta sheets. The third factor showed that heavy, aromatic amino-acids tend to be avoided, except when they are needed for structural or functional reasons. These results are discussed in terms of selective pressure acting on amino-acid composition of proteins.