Genomicus in 2022: comparative tools for thousands of genomes and reconstructed ancestors.

Genomicus is a database and web-server dedicated to comparative genomics in eukaryotes. Its main functionality is to graphically represent the conservation of genomic blocks between multiple genomes, locally around a specific gene of interest or genome-wide through karyotype comparisons. Since 2010 and its first release, Genomicus has synchronized with 60 Ensembl releases and seen the addition of functions that have expanded the type of analyses that users can perform. Today, five public instances of Genomicus are supporting a total number of 1029 extant genomes and 621 ancestral reconstructions from all eukaryotes kingdoms available in Ensembl and Ensembl Genomes databases complemented with four additional instances specific to taxonomic groups of interest. New visualization and query tools are described in this manuscript. Genomicus is freely available at http://www.genomicus.bio.ens.psl.eu/genomicus.

RSAT 2022: regulatory sequence analysis tools.

RSAT (Regulatory Sequence Analysis Tools) enables the detection and the analysis of cis-regulatory elements in genomic sequences. This software suite performs (i) de novo motif discovery (including from genome-wide datasets like ChIP-seq/ATAC-seq) (ii) genomic sequences scanning with known motifs, (iii) motif analysis (quality assessment, comparisons and clustering), (iv) analysis of regulatory variations and (v) comparative genomics. RSAT comprises 50 tools. Six public Web servers (including a teaching server) are offered to meet the needs of different biological communities. RSAT philosophy and originality are: (i) a multi-modal access depending on the user needs, through web forms, command-line for local installation and programmatic web services, (ii) a support for virtually any genome (animals, bacteria, plants, totalizing over 10 000 genomes directly accessible). Since the 2018 NAR Web Software Issue, we have developed a large REST API, extended the support for additional genomes and external motif collections, enhanced some tools and Web forms, and developed a novel tool that builds or refine gene regulatory networks using motif scanning (network-interactions). The RSAT website provides extensive documentation, tutorials and published protocols. RSAT code is under open-source license and now hosted in GitHub. RSAT is available at http://www.rsat.eu/.

Genomicus 2018: karyotype evolutionary trees and on-the-fly synteny computing.

Since 2010, the Genomicus web server is available online at http://genomicus.biologie.ens.fr/genomicus. This graphical browser provides access to comparative genomic analyses in four different phyla (Vertebrate, Plants, Fungi, and non vertebrate Metazoans). Users can analyse genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants, in an integrated evolutionary context. New analyses and visualization tools have recently been implemented in Genomicus Vertebrate. Karyotype structures from several genomes can now be compared along an evolutionary pathway (Multi-KaryotypeView), and synteny blocks can be computed and visualized between any two genomes (PhylDiagView).

RSAT 2018: regulatory sequence analysis tools 20th anniversary.

RSAT (Regulatory Sequence Analysis Tools) is a suite of modular tools for the detection and the analysis of cis-regulatory elements in genome sequences. Its main applications are (i) motif discovery, including from genome-wide datasets like ChIP-seq/ATAC-seq, (ii) motif scanning, (iii) motif analysis (quality assessment, comparisons and clustering), (iv) analysis of regulatory variations, (v) comparative genomics. Six public servers jointly support 10 000 genomes from all kingdoms. Six novel or refactored programs have been added since the 2015 NAR Web Software Issue, including updated programs to analyse regulatory variants (retrieve-variation-seq, variation-scan, convert-variations), along with tools to extract sequences from a list of coordinates (retrieve-seq-bed), to select motifs from motif collections (retrieve-matrix), and to extract orthologs based on Ensembl Compara (get-orthologs-compara). Three use cases illustrate the integration of new and refactored tools to the suite. This Anniversary update gives a 20-year perspective on the software suite. RSAT is well-documented and available through Web sites, SOAP/WSDL (Simple Object Access Protocol/Web Services Description Language) web services, virtual machines and stand-alone programs at http://www.rsat.eu/.

RSAT 2015: Regulatory Sequence Analysis Tools.

RSAT (Regulatory Sequence Analysis Tools) is a modular software suite for the analysis of cis-regulatory elements in genome sequences. Its main applications are (i) motif discovery, appropriate to genome-wide data sets like ChIP-seq, (ii) transcription factor binding motif analysis (quality assessment, comparisons and clustering), (iii) comparative genomics and (iv) analysis of regulatory variations. Nine new programs have been added to the 43 described in the 2011 NAR Web Software Issue, including a tool to extract sequences from a list of coordinates (fetch-sequences from UCSC), novel programs dedicated to the analysis of regulatory variants from GWAS or population genomics (retrieve-variation-seq and variation-scan), a program to cluster motifs and visualize the similarities as trees (matrix-clustering). To deal with the drastic increase of sequenced genomes, RSAT public sites have been reorganized into taxon-specific servers. The suite is well-documented with tutorials and published protocols. The software suite is available through Web sites, SOAP/WSDL Web services, virtual machines and stand-alone programs at http://www.rsat.eu/.

MiCoViTo: a tool for gene-centric comparison and visualization of yeast transcriptome states.

BACKGROUND: Information obtained by DNA microarray technology gives a rough snapshot of the transcriptome state, i.e., the expression level of all the genes expressed in a cell population at any given time. One of the challenging questions raised by the tremendous amount of microarray data is to identify groups of co-regulated genes and to understand their role in cell functions. RESULTS: MiCoViTo (Microarray Comparison Visualization Tool) is a set of biologists' tools for exploring, comparing and visualizing changes in the yeast transcriptome by a gene-centric approach. A relational database includes data linked to genome expression and graphical output makes it easy to visualize clusters of co-expressed genes in the context of available biological information. To this aim, upload of personal data is possible and microarray data from fifty publications dedicated to S. cerevisiae are provided on-line. A web interface guides the biologist during the usage of this tool and is freely accessible at http://www.transcriptome.ens.fr/micovito/. CONCLUSIONS: MiCoViTo offers an easy-to-read picture of local transcriptional changes connected to current biological knowledge. This should help biologists to mine yeast microarray data and better understand the underlying biology. We plan to add functional annotations from other organisms. That would allow inter-species comparison of transcriptomes via orthology tables.

Comparing gene expression networks in a multi-dimensional space to extract similarities and differences between organisms.

MOTIVATION: Molecular evolution, which is classically assessed by comparison of individual proteins or genes between species, can now be studied by comparing co-expressed functional groups of genes. This approach, which better reflects the functional constraints on the evolution of organisms, can exploit the large amount of data generated by genome-wide expression analyses. However, it requires new methodologies to represent the data in a more accessible way for cross-species comparisons. RESULTS: In this work, we present an approach based on Multi-dimensional Scaling techniques, to compare the conformation of two gene expression networks, represented in a multi-dimensional space. The expression networks are optimally superimposed, taking into account two criteria: (1) inter-organism orthologous gene pairs have to be nearby points in the final multi-dimensional space and (2) the distortion of the gene expression networks, the organization of which reflects the similarities between the gene expression measurements, has to be circumscribed. Using this approach, we compared the transcriptional programs that drive sporulation in budding and fission yeasts, extracting some common properties and differences between the two species.

D-ASSIRC: distributed program for finding sequence similarities in genomes.

MOTIVATION: Locating the regions of similarity in a genome requires the availability of appropriate tools such as 'Accelerated Search for SImilar Regions in Chromosomes' (ASSIRC; Vincens et al., Bioinformatics, 14, 715-725, 1998). The aim of this paper is to present different strategies for improving this program by distributing the operations and data to multiple processing units and to assess the efficiency of the different implementations in terms of running time as a function of the number of processing units. RESULTS: The new version D-ASSIRCis based on three alternative strategies of task sharing: (1) a distributed search using the splitting of studied sequences into large overlapping subsequences (strategy ASS); (2) two distributed searches for repeated exact motifs of fixed size either managed by a central processor (strategy AGD) or locally managed by numerous processors (strategy ALD). The result is that the strategy ASSis suitable for a large number of processing units (the time was divided by a factor of 12 when the number of processing units was increased from 1 to 16) wheras the strategy ALDis better for a small set of processors (typically for four or six). The different proposed strategies are efficient for various applications in genomic research, particularly for locating similarities of nucleic sequences in large genomes. AVAILABILITY: D-ASSIRCis freely available by anonymous FTP at ftp://ftp.ens.fr/pub/molbio/dassirc.tar.gz. Sources and binaries for Solaris and Linux are included in the distribution.

Genome compartimentation by a hybrid chromosome model (HXM). Application to Saccharomyces cerevisae subtelomeres.

The aim of this paper is to present a new approach, called 'Hybrid Chromosome Model' (HXM), which allows both the extraction of regions of similarity between two sequences, and the compartimentation of a set of DNA sequences. The principle of the method consists in compacting a set of sequences (split into fragments of fixed length) into a 'hybrid chromosome', which results from the stacking of the whole sequence fragments. We have illustrated our approach on the 32 subtelomeres of Saccharomyces cerevisae. The compartimentation of these chromosome extremities into common regions of similarity has been carried out. The approach HXM is a fast and efficient tool for mapping entire genomes and for extracting ancient duplications within or between genomes.