BACKGROUND: Estimating the phylogenetic position of bacterial and archaeal organisms by genetic sequence comparisons is considered as the gold-standard in taxonomy. This is also a way to identify the species of origin of the sequence. The quality of the reference database used in such analyses is crucial: the database must reflect the up-to-date bacterial nomenclature and accurately indicate the species of origin of its sequences. DESCRIPTION: leBIBI(QBPP) is a web tool taking as input a series of nucleotide sequences belonging to one of a set of reference markers (e.g., SSU rRNA, rpoB, groEL2) and automatically retrieving closely related sequences, aligning them, and performing phylogenetic reconstruction using an approximate maximum likelihood approach. The system returns a set of quality parameters and, if possible, a suggested taxonomic assigment for the input sequences. The reference databases are extracted from GenBank and present four degrees of stringency, from the "superstringent" degree (one type strain per species) to the loosely parsed degree ("lax" database). A set of one hundred to more than a thousand sequences may be analyzed at a time. The speed of the process has been optimized through careful hardware selection and database design. CONCLUSION: leBIBI(QBPP) is a powerful tool helping biologists to position bacterial or archaeal sequence commonly used markers in a phylogeny. It is a diagnostic tool for clinical, industrial and environmental microbiology laboratory, as well as an exploratory tool for more specialized laboratories. Its main advantages, relatively to comparable systems are: i) the use of a broad set of databases covering diverse markers with various degrees of stringency; ii) the use of an approximate Maximum Likelihood approach for phylogenetic reconstruction; iii) a speed compatible with on-line usage; and iv) providing fully documented results to help the user in decision making.

BACKGROUND: The information in large collections of phylogenetic trees is useful for many comparative genomic studies. Therefore, there is a need for flexible tools that allow exploration of such collections in order to retrieve relevant data as quickly as possible. RESULTS: In this paper, we present TPMS (Tree Pattern-Matching Suite), a set of programs for handling and retrieving gene trees according to different criteria. The programs from the suite include utilities for tree collection building, specific tree-pattern search strategies and tree rooting. Use of TPMS is illustrated through three examples: systematic search for incongruencies in a large tree collection, a short study on the Coelomata/Ecdysozoa controversy and an evaluation of the level of support for a recently published Mammal phylogeny. CONCLUSION: TPMS is a powerful suite allowing to quickly retrieve sets of trees matching complex patterns in large collection or to root trees using more rigorous approaches than the classical midpoint method. As it is made of a set of command-line programs, it can be easily integrated in any sequence analysis pipeline for an automated use.

BACKGROUND: Comparative genomics is a central step in many sequence analysis studies, from gene annotation and the identification of new functional regions in genomes, to the study of evolutionary processes at the molecular level (speciation, single gene or whole genome duplications, etc.) and phylogenetics. In that context, databases providing users high quality homologous families and sequence alignments as well as phylogenetic trees based on state of the art algorithms are becoming indispensable. METHODS: We developed an automated procedure allowing massive all-against-all similarity searches, gene clustering, multiple alignments computation, and phylogenetic trees construction and reconciliation. The application of this procedure to a very large set of sequences is possible through parallel computing on a large computer cluster. RESULTS: Three databases were developed using this procedure: HOVERGEN, HOGENOM and HOMOLENS. These databases share the same architecture but differ in their content. HOVERGEN contains sequences from vertebrates, HOGENOM is mainly devoted to completely sequenced microbial organisms, and HOMOLENS is devoted to metazoan genomes from Ensembl. Access to the databases is provided through Web query forms, a general retrieval system and a client-server graphical interface. The later can be used to perform tree-pattern based searches allowing, among other uses, to retrieve sets of orthologous genes. The three databases, as well as the software required to build and query them, can be used or downloaded from the PBIL (Pôle Bioinformatique Lyonnais) site at http://pbil.univ-lyon1.fr/.

MOTIVATION: Microarrays are widely used to measure gene expression differences between sets of biological samples. Many of these differences will be due to differences in the activities of transcription factors. In principle, these differences can be detected by associating motifs in promoters with differences in gene expression levels between the groups. In practice, this is hard to do. RESULTS: We combine correspondence analysis, between group analysis and co-inertia analysis to determine which motifs, from a database of promoter motifs, are strongly associated with differences in gene expression levels. Given a database of motifs and gene expression levels from a set of arrays, the method produces a ranked list of motifs associated with any specified split in the arrays. We give an example using the Gene Atlas compendium of gene expression levels for human tissues where we search for motifs that are associated with expression in central nervous system (CNS) or muscle tissues. Most of the motifs that we find are known from previous work to be strongly associated with expression in CNS or muscle. We give a second example using a published prostate cancer dataset where we can simply and clearly find which transcriptional pathways are associated with differences between benign and metastatic samples. AVAILABILITY: The source code is freely available upon request from the authors.

UNLABELLED: We present a web service allowing to automatically assign sequences to homologous gene families from a set of databases. After identification of the most similar gene family to the query sequence, this sequence is added to the whole alignment and the phylogenetic tree of the family is rebuilt. Thus, the phylogenetic position of the query sequence in its gene family can be easily identified. AVAILABILITY: http://pbil.univ-lyon1.fr/software/HoSeqI/.

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.

Summary: MADE4, microarray ade4, is a software package that facilitates multivariate analysis of microarray gene-expression data. MADE4 accepts a wide variety of gene-expression data formats. MADE4 takes advantage of the extensive multivariate statistical and graphical functions in the R package ade4, extending these for application to microarray data. In addition, MADE4 provides new graphical and visualization tools that aid in interpretation of multivariate analysis of microarray data. Availability: The R package MADE4 is available from Bioconductor http://bioinf.vcd.ie/software and from Bioconductor http://www.bioconductor.org Contact: [email protected] Supplementary information: MADE4 is well documented. There are tutorials, in the form of vignettes, which describe typical analyses. In addition, the MADE4 manual provides descriptions and examples for each function.

Motivation: Comparative sequence analysis is widely used to study genome function and evolution. This approach first requires the identification of homologous genes and then the interpretation of their homology relationships (orthology or paralogy). To provide help in this complex task, we developed three databases of homologous genes containing sequences, multiple alignments and phylogenetic trees: HOBACGEN, HOVERGEN and HOGENOM. In this paper, we present two new tools for automating the search for orthologs or paralogs in these databases. Results: First, we have developed and implemented an algorithm to infer speciation and duplication events by comparison of gene and species trees (tree reconciliation). Second, we have developed a general method to search in our databases the gene families for which the tree topology matches a peculiar tree pattern. This algorithm of unordered tree pattern matching has been implemented in the FamFetch graphical interface. With the help of a graphical editor, the user can specify the topology of the tree pattern, and set constraints on its nodes and leaves. Then, this pattern is compared with all the phylogenetic trees of the database, to retrieve the families in which one or several occurrences of this pattern are found. By specifying ad hoc patterns, it is therefore possible to identify orthologs in our databases. Availability: The tree reconciliation program and the FamFetch interface are available from the Pôle Bioinformatique Lyonnais Web server at the following addresses: http://pbil.univ-lyon1.fr/software/RAP/RAP.htm and http://pbil.univ-lyon1.fr/software/famfetch.html Contact: [email protected]

BACKGROUND: A recent publication described a supervised classification method for microarray data: Between Group Analysis (BGA). This method which is based on performing multivariate ordination of groups proved to be very efficient for both classification of samples into pre-defined groups and disease class prediction of new unknown samples. Classification and prediction with BGA are classically performed using the whole set of genes and no variable selection is required. We hypothesize that an optimized selection of highly discriminating genes might improve the prediction power of BGA. RESULTS: We propose an optimized between-group classification (OBC) which uses a jackknife-based gene selection procedure. OBC emphasizes classification accuracy rather than feature selection. OBC is a backward optimization procedure that maximizes the percentage of between group inertia by removing the least influential genes one by one from the analysis. This selects a subset of highly discriminative genes which optimize disease class prediction. We apply OBC to four datasets and compared it to other classification methods. CONCLUSION: OBC considerably improved the classification and predictive accuracy of BGA, when assessed using independent data sets and leave-one-out cross-validation. AVAILABILITY: The R code is freely available [see Additional file 1] as well as supplementary information [see Additional file 2].

BACKGROUND: Rapid development of DNA microarray technology has resulted in different laboratories adopting numerous different protocols and technological platforms, which has severely impacted on the comparability of array data. Current cross-platform comparison of microarray gene expression data are usually based on cross-referencing the annotation of each gene transcript represented on the arrays, extracting a list of genes common to all arrays and comparing expression data of this gene subset. Unfortunately, filtering of genes to a subset represented across all arrays often excludes many thousands of genes, because different subsets of genes from the genome are represented on different arrays. We wish to describe the application of a powerful yet simple method for cross-platform comparison of gene expression data. Co-inertia analysis (CIA) is a multivariate method that identifies trends or co-relationships in multiple datasets which contain the same samples. CIA simultaneously finds ordinations (dimension reduction diagrams) from the datasets that are most similar. It does this by finding successive axes from the two datasets with maximum covariance. CIA can be applied to datasets where the number of variables (genes) far exceeds the number of samples (arrays) such is the case with microarray analyses. RESULTS: We illustrate the power of CIA for cross-platform analysis of gene expression data by using it to identify the main common relationships in expression profiles on a panel of 60 tumour cell lines from the National Cancer Institute (NCI) which have been subjected to microarray studies using both Affymetrix and spotted cDNA array technology. The co-ordinates of the CIA projections of the cell lines from each dataset are graphed in a bi-plot and are connected by a line, the length of which indicates the divergence between the two datasets. Thus, CIA provides graphical representation of consensus and divergence between the gene expression profiles from different microarray platforms. Secondly, the genes that define the main trends in the analysis can be easily identified. CONCLUSIONS: CIA is a robust, efficient approach to coupling of gene expression datasets. CIA provides simple graphical representations of the results making it a particularly attractive method for the identification of relationships between large datasets.

Abstract Motivation: Most supervised classification methods are limited by the requirement for more cases than variables. In microarray data the number of variables (genes) far exceeds the number of cases (arrays), and thus filtering and pre-selection of genes is required. We describe the application of Between Group Analysis (BGA) to the analysis of microarray data. A feature of BGA is that it can be used when the number of variables (genes) exceeds the number of cases (arrays). BGA is based on carrying out an ordination of groups of samples, using a standard method such as Correspondence Analysis (COA), rather than an ordination of the individual microarray samples. As such, it can be viewed as a method of carrying out COA with grouped data. Results: We illustrate the power of the method using two cancer data sets. In both cases, we can quickly and accurately classify test samples from any number of specified a priori groups and identify the genes which characterize these groups. We obtained very high rates of correct classification, as determined by jack-knife or validation experiments with training and test sets. The results are comparable to those from other methods in terms of accuracy but the power and flexibility of BGA make it an especially attractive method for the analysis of microarray cancer data. Availability: The methods described are implemented in ADE-4 which runs under MacOS and Windows, and is freely available at http://pbil.univ-lyon1.fr/ADE-4/. All scripts are available on request. Contact: [email protected] Supplementary information: Supplementary figures and tables are available at http://bioinfo.ucc.ie/BGA/. * To whom correspondence should be addressed.

SUMMARY: JaDis is a Java application for computing evolutionary distances between nucleic acid sequences and G+C base frequencies. It allows specific comparison of coding sequences, of non-coding sequences or of a non-coding sequence with coding sequences. AVAILABILITY: http://pbil.univ-lyon1.fr/software/jadis.html

LALNVIEW is a graphical program for visualising local alignments between two sequences (protein or nucleic acids). Sequences are represented by coloured rectangles to give an overall picture of their similarities. LALNVIEW can display sequence features (exon, intron, active site, domain, propeptide, etc.) along with the alignment. When using LALNVIEW through our Web servers, sequence features are automatically extracted from database annotations (SWISS-PROT, GenBank, EMBL or HOVERGEN) and displayed with the alignment. LALNVIEW is a useful tool for analysing pairwise sequence alignments and for making the link between sequence homology and what is known about the structure or function of sequences. LALNVIEW executables for UNIX, Macintosh and PC computers are freely available from our server (http:// expasy.hcuge.ch/sprot/lalnview.html).

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).

We have developed a World-Wide Web server for browsing sequence collections structured under the ACNUC format and for performing multivariate analyses on sequences. General collections (like GenBank or EMBL), as well as specialized data banks (like Hovergen and NRSub) can be accessed. This system allows complex queries to be constructed, and the result of each query, represented by a list of sequences, is stored on the server. It is then possible to reuse this list to compute multivariate analyses on the sequences. Two examples of applications are shown. The first one consists in a study of codon usage with correspondence analysis on all the protein genes of Haemophilus influenzae Rd. This study allows the highly expressed genes and the integral membrane proteins of this organism to be identified. The second one consists in an ordering of 70 aligned protein sequences of growth hormone with principal coordinate analysis. With this method, we are able to re-establish the patterns of relationships between the sequences previously determined with tree building programs.