BACKGROUND: Most known eukaryotic genomes contain mobile copied elements called transposable elements. In some species, these elements account for the majority of the genome sequence. They have been subject to many mutations and other genomic events (copies, deletions, captures) during transposition. The identification of these transformations remains a difficult issue. The study of families of transposable elements is generally founded on a multiple alignment of their sequences, a critical step that is adapted to transposons containing mostly localized nucleotide mutations. Many transposons that have lost their protein-coding capacity have undergone more complex rearrangements, needing the development of more complex methods in order to characterize the architecture of sequence variations. RESULTS: In this study, we introduce the concept of a transposable element module, a flexible motif present in at least two sequences of a family of transposable elements and built on a succession of maximal repeats. The paper proposes an assembly method working on a set of exact maximal repeats of a set of sequences to create such modules. It results in a graphical view of sequences segmented into modules, a representation that allows a flexible analysis of the transformations that have occurred between them. We have chosen as a demonstration data set in depth analysis of the transposable element Foldback in Drosophila melanogaster. Comparison with multiple alignment methods shows that our method is more sensitive for highly variable sequences. The study of this family and the two other families AtREP21 and SIDER2 reveals new copies of very different sizes and various combinations of modules which show the potential of our method. CONCLUSIONS: ModuleOrganizer is available on the Genouest bioinformatics center at http://moduleorganizer.genouest.org.

SUMMARY: The CRISPR genomic structures (Clustered Regularly Interspaced Short Palindromic Repeats) form a family of repeats that is largely present in archaea and frequent in bacteria. On the basis of a formal model of CRISPR using very few parameters, a systematic study of all their occurrences in all available genomes of Archaea and Bacteria has been carried out. This has resulted in a relational database, CRISPI, which also includes a complete repertory of associated CRISPR-associated genes (CAS). A user-friendly web interface with many graphical tools and functions allows users to extract results, find CRISPR in personal sequences or calculate sequence similarity with spacers. AVAILABILITY: CRISPI free access at http://crispi.genouest.org CONTACT: [email protected]; [email protected]

MOTIVATION: The analysis of repeated elements in genomes is a fascinating domain of research that is lacking relevant tools for transposable elements (TEs), the most complex ones. The dynamics of TEs, which provides the main mechanism of mutation in some genomes, is an essential component of genome evolution. In this study we introduce a new concept of domain, a segmentation unit useful for describing the architecture of different copies of TEs. Our method extracts occurrences of a terminus-defined family of TEs, aligns the sequences, finds the domains in the alignment and searches the distribution of each domain in sequences. After a classification step relative to the presence or the absence of domains, the method results in a graphical view of sequences segmented into domains. RESULTS: Analysis of the new non-autonomous TE AtREP21 in the model plant Arabidopsis thaliana reveals copies of very different sizes and various combinations of domains which show the potential of our method. AVAILABILITY: DomainOrganizer web page is available at www.irisa.fr/symbiose/DomainOrganizer/.

BACKGROUND: A large number of studies on genome sequences have revealed the major role played by repeated sequences in the structure, function, dynamics and evolution of genomes. In-depth repeat analysis requires specialized methods, including visualization techniques, to achieve optimum exploratory power. RESULTS: This article presents Pygram, a new visualization application for investigating the organization of repeated sequences in complete genome sequences. The application projects data from a repeat index file on the analysed sequences, and by combining this principle with a query system, is capable of locating repeated sequences with specific properties. In short, Pygram provides an efficient, graphical browser for studying repeats. Implementation of the complete configuration is illustrated in an analysis of CRISPR structures in Archaea genomes and the detection of horizontal transfer between Archaea and Viruses. CONCLUSION: By proposing a new visualization environment to analyse repeated sequences, this application aims to increase the efficiency of laboratories involved in investigating repeat organization in single genomes or across several genomes.

SUMMARY: We have developed STAN (suffix-tree analyser), a tool to search for nucleotidic and peptidic patterns within whole chromosomes. Pattern syntax uses a string variable grammar-like formalism which allows the description of complex patterns including ambiguities, insertions/deletions, gaps, repeats and palindromes. STAN is based on a reduction to multipart matching on a suffix-tree data structure and can handle large DNA sequences, whether assembled or not.

MOTIVATION: Continued development of analytical techniques based on gas chromatography and mass spectrometry now facilitates the generation of larger sets of metabolite concentration data. An important step towards the understanding of metabolite dynamics is the recognition of stable states where metabolite concentrations exhibit a simple behaviour. Such states can be characterized through the identification of significant thresholds in the concentrations. But general techniques for finding discretization thresholds in continuous data prove to be practically insufficient for detecting states due to the weak conditional dependences in concentration data. RESULTS: We introduce a method of recognizing states in the framework of decision tree induction. It is based upon a global analysis of decision forests where stability and quality are evaluated. It leads to the detection of thresholds that are both comprehensible and robust. Applied to metabolite concentration data, this method has led to the discovery of hidden states in the corresponding variables. Some of these reflect known properties of the biological experiments, and others point to putative new states. AVAILABILITY: An implementation of this approach can be obtained from the authors upon request.

Supervised classification has already been the subject of numerous studies in the fields of Statistics, Pattern Recognition and Artificial Intelligence under various appellations which include discriminant analysis, discrimination and concept learning. Many practical applications relating to this field have been developed. New methods have appeared in recent years, due to developments concerning Neural Networks and Machine Learning. These "hybrid" approaches share one common factor in that they combine symbolic and numerical aspects. The former are characterized by the representation of knowledge, the latter by the introduction of frequencies and probabilistic criteria. In the present study, we shall present a certain number of hybrid methods, conceived (or improved) by members of the SYMENU research group. These methods issue mainly from Machine Learning and from research on Classification Trees done in Statistics, and they may also be qualified as "rule-based". They shall be compared with other more classical approaches. This comparison will be based on a detailed description of each of the twelve methods envisaged, and on the results obtained concerning the "Waveform Recognition Problem" proposed by Breiman et al.,4 which is difficult for rule based approaches.