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                        "title": "A web site for the computational analysis of yeast regulatory sequences",
                        "abstract": "A series of computer programs were developed for the analysis of regulatory sequences, with a special focus on yeast. These tools are publicly available on the web (http://www.ucmb.ulb.ac.be/bioinformatics/rsa-tools/). Basically, three classical problems can be addressed: (a) search for known regulatory patterns in the upstream regions of known genes; (b) discovery of unknown regulatory patterns within a set of upstream regions known to be co-regulated; (c) search for unknown genes potentially regulated by a known transcription factor. Each of these tasks can be performed on basis of a simple (string) or more refined (matrix) description of the regulatory patterns. A feature-map progam automatically generates visual representations of the positions at which patterns were found. The site also provides a series of general utilities, such as generation of random sequence, automatic drawing of XY graphs, interconversions between sequence formats, etc. Several tools are linked together to allow their sequential utilization (piping), but each one can also be used independently by filling the web form with external data. This widens the scope of the site to the analysis of nonregulatory and/or non-yeast sequences. Copyright (C) 2000 John Wiley and Sons, Ltd.",
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                                "name": "Van Helden J."
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                        "title": "RSAT 2018: Regulatory sequence analysis tools 20th anniversary",
                        "abstract": "© 2018 The Author(s). RSAT (Regulatory Sequence Analysis Tools) is a suite ofmodular 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/ATACseq, (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, variationscan, convert-variations), along with tools to extract sequences from a list of coordinates (retrieve-seqbed), to select motifs from motif collections (retrievematrix), 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.",
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                                "name": "Nguyen N.T.T."
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                            {
                                "name": "Contreras-Moreira B."
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                                "name": "Van Helden J."
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                        "title": "Inference of pathway decomposition across multiple species through gene clustering",
                        "abstract": "© 2015 World Scientific Publishing Company.In the wake of gene-oriented data analysis in large-scale bioinformatics studies, focus in research is currently shifting towards the analysis of the functional association of genes, namely the metabolic pathways in which genes participate. The goal of this paper is to attempt to identify the core genes in a specific pathway, based on a user-defined selection of genomes. To this end, a novel algorithm has been developed that uses data from the KEGG database, and through the application of the MCL clustering algorithm, identifies clusters that correspond to different layers of genes, either on a phylogenetic or a functional level. The algorithm's complexity, evaluated experimentally, is presented and the results on three characteristic case studies are discussed.",
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                        "journal": "International Journal on Artificial Intelligence Tools"
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                        "title": "X!TandemPipeline: A Tool to Manage Sequence Redundancy for Protein Inference and Phosphosite Identification",
                        "abstract": "© 2016 American Chemical Society. X!TandemPipeline is a software designed to perform protein inference and to manage redundancy in the results of phosphosite identification by database search. It provides the minimal list of proteins or phosphosites that are present in a set of samples using grouping algorithms based on the principle of parsimony. Regarding proteins, a two-level classification is performed, where groups gather proteins sharing at least one peptide and subgroups gather proteins that are not distinguishable according to the identified peptides. Regarding phosphosites, an innovative approach based on the concept of phosphoisland is used to gather overlapping phosphopeptides. The graphical interface of X!TandemPipeline allows the users to launch X!tandem identification, to inspect spectra and to manually validate their assignment to peptides, to launch the grouping program, and to visualize elementary data as well as grouping and redundancy information. Identification results obtained from other search engines can also be processed. X!TandemPipeline results can be exported as ready-to-use tabulated files or as XML files that can be directly used by the PROTICdb database or by the MassChroQ quantification software. X!TandemPipeline runs fast, is easy to use, and can process hundreds of samples simultaneously. It is freely available under the GNU General Public License v3.0 at http://pappso.inra.fr/bioinfo/xtandempipeline/.",
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                            {
                                "name": "Langella O."
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                            {
                                "name": "Valot B."
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                                "name": "Balliau T."
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                        "title": "XINA: A Workflow for the Integration of Multiplexed Proteomics Kinetics Data with Network Analysis",
                        "abstract": "Copyright © 2018 American Chemical Society. Quantitative proteomics experiments, using for instance isobaric tandem mass tagging approaches, are conducive to measuring changes in protein abundance over multiple time points in response to one or more conditions or stimulations. The aim is often to determine which proteins exhibit similar patterns within and across experimental conditions, since proteins with coabundance patterns may have common molecular functions related to a given stimulation. In order to facilitate the identification and analyses of coabundance patterns within and across conditions, we previously developed a software inspired by the isobaric mass tagging method itself. Specifically, multiple data sets are tagged in silico and combined for subsequent subgrouping into multiple clusters within a single output depicting the variation across all conditions, converting a typical inter-data-set comparison into an intra-data-set comparison. An updated version of our software, XINA, not only extracts coabundance profiles within and across experiments but also incorporates protein-protein interaction databases and integrative resources such as KEGG to infer interactors and molecular functions, respectively, and produces intuitive graphical outputs. In this report, we compare the kinetics profiles of >5600 unique proteins derived from three macrophage cell culture experiments and demonstrate through intuitive visualizations that XINA identifies key regulators of macrophage activation via their coabundance patterns.",
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                            {
                                "name": "Lee L.H."
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                            {
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                            {
                                "name": "Iwata H."
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                            {
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