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                {
                    "term": "Gene regulation",
                    "uri": "http://edamontology.org/topic_0204"
                }
            ],
            "owner": "alban.gaignard@univ-nantes.fr",
            "cost": "Free of charge",
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                    "url": "http://download.rsat.eu",
                    "note": "Several versions available.",
                    "version": null,
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                    "pmid": "10641039",
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                    "metadata": {
                        "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.",
                        "citationCount": 145,
                        "authors": [
                            {
                                "name": "Van Helden J."
                            },
                            {
                                "name": "Andre B."
                            },
                            {
                                "name": "Collado-Vides J."
                            }
                        ],
                        "date": "2000-01-30T00:00:00Z",
                        "journal": "Yeast"
                    }
                },
                {
                    "doi": "10.1093/nar/gky317",
                    "version": null,
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                    "type": "Other",
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                    "metadata": {
                        "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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                        "authors": [
                            {
                                "name": "Nguyen N.T.T."
                            },
                            {
                                "name": "Contreras-Moreira B."
                            },
                            {
                                "name": "Castro-Mondragon J.A."
                            },
                            {
                                "name": "Santana-Garcia W."
                            },
                            {
                                "name": "Ossio R."
                            },
                            {
                                "name": "Robles-Espinoza C.D."
                            },
                            {
                                "name": "Bahin M."
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                            {
                                "name": "Collombet S."
                            },
                            {
                                "name": "Vincens P."
                            },
                            {
                                "name": "Thieffry D."
                            },
                            {
                                "name": "Van Helden J."
                            },
                            {
                                "name": "Medina-Rivera A."
                            },
                            {
                                "name": "Thomas-Chollier M."
                            }
                        ],
                        "date": "2018-07-02T00:00:00Z",
                        "journal": "Nucleic Acids Research"
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                    "note": "Retrieve upstream non-coding sequences for a given set of gene ids, and a given organism.",
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                                    "term": "Textual format",
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                    ],
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                            "term": "Data retrieval",
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                    ],
                    "cmd": "retrieve-seq  -org Saccharomyces_cerevisiae  -type upstream -from -800 -to -1 -i geneIDs.txt",
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                            "data": {
                                "term": "DNA sequence",
                                "uri": "http://edamontology.org/data_3494"
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                                    "term": "raw",
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                                },
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                                    "term": "ig",
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                                    "term": "FASTA",
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            "description": "Starting from a list of genes, returns upstream or downstream sequences.",
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                    "term": "Functional genomics",
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                    "term": "Molecular interactions, pathways and networks",
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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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                            {
                                "name": "Vitsios D.M."
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                            {
                                "name": "Psomopoulos F.E."
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                            {
                                "name": "Mitkas P.A."
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                            {
                                "name": "Ouzounis C.A."
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                        "date": "2015-01-01T00:00:00Z",
                        "journal": "International Journal on Artificial Intelligence Tools"
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                    "term": "Compound libraries and screening",
                    "uri": "http://edamontology.org/topic_3343"
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                    "term": "Cheminformatics",
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                    "metadata": {
                        "title": "Sachem: a chemical cartridge for high-performance substructure search",
                        "abstract": "© 2018, The Author(s).Background: Structure search is one of the valuable capabilities of small-molecule databases. Fingerprint-based screening methods are usually employed to enhance the search performance by reducing the number of calls to the verification procedure. In substructure search, fingerprints are designed to capture important structural aspects of the molecule to aid the decision about whether the molecule contains a given substructure. Currently available cartridges typically provide acceptable search performance for processing user queries, but do not scale satisfactorily with dataset size. Results: We present Sachem, a new open-source chemical cartridge that implements two substructure search methods: The first is a performance-oriented reimplementation of substructure indexing based on the OrChem fingerprint, and the second is a novel method that employs newly designed fingerprints stored in inverted indices. We assessed the performance of both methods on small, medium, and large datasets containing 1, 10, and 94 million compounds, respectively. Comparison of Sachem with other freely available cartridges revealed improvements in overall performance, scaling potential and screen-out efficiency. Conclusions: The Sachem cartridge allows efficient substructure searches in databases of all sizes. The sublinear performance scaling of the second method and the ability to efficiently query large amounts of pre-extracted information may together open the door to new applications for substructure searches.",
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                        "authors": [
                            {
                                "name": "Kratochvil M."
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                            {
                                "name": "Vondrasek J."
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                            {
                                "name": "Galgonek J."
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                        "date": "2018-12-01T00:00:00Z",
                        "journal": "Journal of Cheminformatics"
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                                "term": "Small molecule structure",
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                                    "term": "Chemical formula format",
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                    "term": "Gene transcripts",
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                {
                    "term": "RNA splicing",
                    "uri": "http://edamontology.org/topic_3320"
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                    "term": "DNA structural variation",
                    "uri": "http://edamontology.org/topic_3175"
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            "owner": "suhrig",
            "cost": "Free of charge",
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                    "version": null,
                    "type": "Binary package"
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            "description": "Arriba is a command-line tool to detect gene fusions from RNA-Seq data based on the STAR aligner. In addition to fusions, it can detect exon duplications/inversions and truncations of genes (i.e., breakpoints in introns and intergenic regions). Arriba is the winner of the DREAM SMC-RNA Challenge.",
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