This web site ives access to the material produced by the ULB team (Jacques van Helden and Didier Croes) in the context of the EU-funded MICROME project.
The contribution of the ULB group to WP4 of the MICROME project is to combine bioinformatics approaches for the prediciton of operons, regulons and phyogenetic profiles (implemented in the Regulatory Sequence Analysis Tools) with pathway projection and pathway discovery methods (implemented in Network Analysis Tools) in order to predict metabolic pathways for each bacterial genome.
An important part of our group activities has been to develop, assess and apply bioinformatics approaches to analyze regulatory sequences, biomolecular networks and metabolic pathways. The table below describes the tools developed for or adapted to the needs of the MICROME project..
|Tool name||Availability||Description||Usage for the MICROME project||Related publications|
||Generic tool to compare sets of elements with other sets of elements. Compute various statistics (intersection, union, hypergeometric significance, ...).||
||Predict operons in bacterial genomes on the basis of intergenic distances. The tool can also be used to predict directons (maximal sets of contiguous genes by simply setting the distance threshold to a very large value (e.g. 1.000.000).||Prediction of operons and directons for the bacterial genomes selected for the MICROME project.|
||Predict cis-regulatory elements by discovering phylogenetic footprints, i.e. conserved motifs in promoters (upstream non-coding sequences) of groups of orthologous genes. As a second step, the program can also regroup genes having similar phylogenetic footprints, in order to predict a co-regulation network and regulons..||Predict regulons in the bacterial genomes selected for the MICROME project.|
||Using as input a metabolic network (exhaustive collection of reactions and compounds) and a set of seed elements (genes, reactions, compounds), extract from the network a sub-graph that connects as well as possible these seed elements. Various algorithms are supported for subgraph extraction, and different criteria can be used to define the optimality of the subgraph extraction.||Infer metabolic pathways from predicted operons, directons, and regulons.|
Beyond the resources developed by the individual partners (databases, annotation and analysis tools, annotations, models), an important goal of the MICROME project is to develop a collective framework for annotatin bacterial metabolism.
One componentn to achieve this goal is to enable a programmatic inter-connection between the bioinformatics resources developed by the respective partners.
Several of the partners made their tools and databases accessible via Web services (through a SOAP/WSDL programmatic interface).
The first MICROME training workshop was organized in Marseille, from March 28 to March 30, 2012. This workshop was primarily conceived for cross-training of the patrners: each contributing partner prepared presentations and practicals to learn the other partners how they could use their resources (databases, annotation and analysis tools).
The workshop regrouped 20 persons, including 17 MICROME partners and 3 collaborators from Aix-Marseille Université (2 Master students who developed Web services clients for MICROME, and their professor of computer sciences).
The teaching material contributed by all partners was organized on a web site, which will remain available after the training, and may serve as basis to be extended for future training workshops, oriented towards external audience.
The Web site of the training workshop is available at:
Goal: Starting from set of reference pathways (e.g. all pathways annotated in EcoCyc, or in MetaCyc), compute the coverage of each reference pathway in an organism of interest (fraction of enzymes for which a gene has been identified in the genome).
Approach: we used the RSAT/NeAT tool compare-classes to cpompare the set of enzymes of each reference pathway with the set of enzymes found in each bacterial genome.
Datasets: Reference pathways: lists of EC numbers and reactions of all the EcoCyc and MetaCyc pathways, provided by CEA partner. Lists of reactions/EC for each bacterial genome annotated in MetaCyc, provided by the CEA partner.
The prediction of operons is based on a very simplistic distance-based method, inspired from the Salgao-Moreno method (Proc Natl Acad Sci U S A. 2000;97:6652-7). The Salgado-Moreno method classifies intergenic distances as TUB (transcription unit border) or OP (inside operon), and infers operons by iteratively collecting genes until a TUB is found. In the original method, the TUB or OP assignation relies on a log-likelihood score calculated from a training set.
The prediction of regulons relies on a two-step procedure: