MOTIVATION: Recent advances in transcriptomics have enabled unprecedented insight into gene expression analysis at a single-cell resolution. While it is anticipated that the number of publications based on such technologies will increase in the next decade, there is currently no public resource to centralize and enable scientists to explore single-cell datasets published in the field of reproductive biology. RESULTS: Here, we present a major update of the ReproGenomics Viewer, a cross-species and cross-technology web-based resource of manually-curated sequencing datasets related to reproduction. The redesign of the ReproGenomics Viewer's architecture is accompanied by significant growth of the database content including several landmark single-cell RNA-sequencing datasets. The implementation of additional tools enables users to visualize and browse the complex, high-dimensional data now being generated in the reproductive field. AVAILABILITY AND IMPLEMENTATION: The ReproGenomics Viewer resource is freely accessible at http://rgv.genouest.org. The website is implemented in Python, JavaScript and MongoDB, and is compatible with all major browsers. Source codes can be downloaded from https://github.com/fchalmel/RGV. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: At the same time that toxicologists express increasing concern about reproducibility in this field, the development of dedicated databases has already smoothed the path toward improving the storage and exchange of raw toxicogenomic data. Nevertheless, none provides access to analyzed and interpreted data as originally reported in scientific publications. Given the increasing demand for access to this information, we developed TOXsIgN, a repository for TOXicogenomic sIgNatures. Results: The TOXsIgN repository provides a flexible environment that facilitates online submission, storage and retrieval of toxicogenomic signatures by the scientific community. It currently hosts 754 projects that describe more than 450 distinct chemicals and their 8491 associated signatures. It also provides users with a working environment containing a powerful search engine as well as bioinformatics/biostatistics modules that enable signature comparisons or enrichment analyses. Availability and implementation: The TOXsIgN repository is freely accessible at http://toxsign.genouest.org. Website implemented in Python, JavaScript and MongoDB, with all major browsers supported. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: DNA sequence integrity, mRNA concentrations and protein-DNA interactions have been subject to genome-wide analyses based on microarrays with ever increasing efficiency and reliability over the past fifteen years. However, very recently novel technologies for Ultra High-Throughput DNA Sequencing (UHTS) have been harnessed to study these phenomena with unprecedented precision. As a consequence, the extensive bioinformatics environment available for array data management, analysis, interpretation and publication must be extended to include these novel sequencing data types. DESCRIPTION: MIMAS was originally conceived as a simple, convenient and local Microarray Information Management and Annotation System focused on GeneChips for expression profiling studies. MIMAS 3.0 enables users to manage data from high-density oligonucleotide SNP Chips, expression arrays (both 3'UTR and tiling) and promoter arrays, BeadArrays as well as UHTS data using MIAME-compliant standardized vocabulary. Importantly, researchers can export data in MAGE-TAB format and upload them to the EBI's ArrayExpress certified data repository using a one-step procedure. CONCLUSION: We have vastly extended the capability of the system such that it processes the data output of six types of GeneChips (Affymetrix), two different BeadArrays for mRNA and miRNA (Illumina) and the Genome Analyzer (a popular Ultra-High Throughput DNA Sequencer, Illumina), without compromising on its flexibility and user-friendliness. MIMAS, appropriately renamed into Multiomics Information Management and Annotation System, is currently used by scientists working in approximately 50 academic laboratories and genomics platforms in Switzerland and France. MIMAS 3.0 is freely available via http://multiomics.sourceforge.net/.

UNLABELLED: Large- and medium-scale computational molecular biology projects require accurate bioinformatics software and numerous heterogeneous biological databanks, which are distributed around the world. BioMAJ provides a flexible, robust, fully automated environment for managing such massive amounts of data. The JAVA application enables automation of the data update cycle process and supervision of the locally mirrored data repository. We have developed workflows that handle some of the most commonly used bioinformatics databases. A set of scripts is also available for post-synchronization data treatment consisting of indexation or format conversion (for NCBI blast, SRS, EMBOSS, GCG, etc.). BioMAJ can be easily extended by personal homemade processing scripts. Source history can be kept via html reports containing statements of locally managed databanks. AVAILABILITY: http://biomaj.genouest.org. BioMAJ is free open software. It is freely available under the CECILL version 2 license.

Classical adaptation approaches generally allow a reliably trained model to match a particular condition. In this paper, we define an incremental version of the segmental-EM algorithm. This method permits one to incrementally enrich a model first trained with a limited amount of data. Resource memory constraints allow only the initial data statistics to be stored. The proposed method uses these statistics by fixing, within the segmental-EM algorithm applied on both initial and new data, the initial optimal paths in the model for the initial data. We proved theoretically that this is equivalent to the segmental MAP adaptation with specific choice of priors. Experiments on two speaker dependent telephone databases, showed that the approach permitted one to incrementally integrate new conditions of use. The performance was slightly less than that obtained with classical training over the whole data. As expected with the MAP interpretation of the algorithm, initial data characteristics influence largely the model evolution.