BACKGROUND: Deciphering gene regulatory networks by in silico approaches is a crucial step in the study of the molecular perturbations that occur in diseases. The development of regulatory maps is a tedious process requiring the comprehensive integration of various evidences scattered over biological databases. Thus, the research community would greatly benefit from having a unified database storing known and predicted molecular interactions. Furthermore, given the intrinsic complexity of the data, the development of new tools offering integrated and meaningful visualizations of molecular interactions is necessary to help users drawing new hypotheses without being overwhelmed by the density of the subsequent graph. RESULTS: We extend the previously developed TranscriptomeBrowser database with a set of tables containing 1,594,978 human and mouse molecular interactions. The database includes: (i) predicted regulatory interactions (computed by scanning vertebrate alignments with a set of 1,213 position weight matrices), (ii) potential regulatory interactions inferred from systematic analysis of ChIP-seq experiments, (iii) regulatory interactions curated from the literature, (iv) predicted post-transcriptional regulation by micro-RNA, (v) protein kinase-substrate interactions and (vi) physical protein-protein interactions. In order to easily retrieve and efficiently analyze these interactions, we developed In-teractomeBrowser, a graph-based knowledge browser that comes as a plug-in for Transcriptome-Browser. The first objective of InteractomeBrowser is to provide a user-friendly tool to get new insight into any gene list by providing a context-specific display of putative regulatory and physical interactions. To achieve this, InteractomeBrowser relies on a "cell compartments-based layout" that makes use of a subset of the Gene Ontology to map gene products onto relevant cell compartments. This layout is particularly powerful for visual integration of heterogeneous biological information and is a productive avenue in generating new hypotheses. The second objective of InteractomeBrowser is to fill the gap between interaction databases and dynamic modeling. It is thus compatible with the network analysis software Cytoscape and with the Gene Interaction Network simulation software (GINsim). We provide examples underlying the benefits of this visualization tool for large gene set analysis related to thymocyte differentiation. CONCLUSIONS: The InteractomeBrowser plugin is a powerful tool to get quick access to a knowledge database that includes both predicted and validated molecular interactions. InteractomeBrowser is available through the TranscriptomeBrowser framework and can be found at: http://tagc.univ-mrs.fr/tbrowser/. Our database is updated on a regular basis.

UNLABELLED: Many different programs are available to analyze microarray images. Most programs are commercial packages, some are free. In the latter group only few propose automatic grid alignment and batch mode. More often than not a program implements only one quantification algorithm. AGScan is an open source program that works on all major platforms. It is based on the ImageJ library [Rasband (1997-2006)] and offers a plug-in extension system to add new functions to manipulate images, align grid and quantify spots. It is appropriate for daily laboratory use and also as a framework for new algorithms. AVAILABILITY: The program is freely distributed under X11 Licence. The install instructions can be found in the user manual. The software can be downloaded from http://mulcyber.toulouse.inra.fr/projects/agscan/. The questions and plug-ins can be sent to the contact listed below.

BACKGROUND: The three major mechanisms that regulate transcript formation involve the selection of alternative sites for transcription start (TS), splicing, and polyadenylation. Currently there are efforts that collect data & annotation individually for each of these variants. It is important to take an integrated view of these data sets and to derive a data set of alternate transcripts along with consolidated annotation. We have been developing in the past computational pipelines that generate value-added data at genome-scale on individual variant types; these include AltSplice on splicing and AltPAS on polyadenylation. We now extend these pipelines and integrate the resultant data sets to facilitate an integrated view of the contributions from splicing and polyadenylation in the formation of transcript variants. DESCRIPTION: The AltSplice pipeline examines gene-transcript alignments and delineates alternative splice events and splice patterns; this pipeline is extended as AltTrans to delineate isoform transcript patterns for each of which both introns/exons and 'terminating' polyA site are delineated; EST/mRNA sequences that qualify the transcript pattern confirm both the underlying splicing and polyadenylation. The AltPAS pipeline examines gene-transcript alignments and delineates all potential polyA sites irrespective of underlying splicing patterns. Resultant polyA sites from both AltTrans and AltPAS are merged. The generated database reports data on alternative splicing, alternative polyadenylation and the resultant alternate transcript patterns; the basal data is annotated for various biological features. The data (named as integrated AltTrans data) generated for both the organisms of human and mouse is made available through the Alternate Transcript Diversity web site at http://www.ebi.ac.uk/atd/. CONCLUSION: The reported data set presents alternate transcript patterns that are annotated for both alternative splicing and alternative polyadenylation. Results based on current transcriptome data indicate that the contribution of alternative splicing is larger than that of alternative polyadenylation.

Alternative polyadenylation sites produce transcript isoforms with 3' untranslated regions (UTRs) of different lengths. If a microRNA (miRNA) target is present in the UTR, then only those target-containing isoforms should be sensitive to control by a cognate miRNA. We carried out a systematic examination of 3' UTRs containing multiple poly(A) sites and putative miRNA targets. Based on expressed sequence tag (EST) counts and EST library information, we observed that levels of isoforms containing targets for miR-1 or miR-124, two miRNAs causing downregulation of transcript levels, were reduced in tissues expressing the corresponding miRNA. This analysis was repeated for all conserved 7-mers in 3' UTRs, resulting in a selection of 312 motifs. We show that this set is significantly enriched in known miRNA targets and mRNA-destabilizing elements, which validates our initial hypothesis. We scanned the human genome for possible cognate miRNAs and identified phylogenetically conserved precursors matching our motifs. This analysis can help identify target-miRNA couples that went undetected in previous screens, but it may also reveal targets for other types of regulatory factors.