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{ "count": 12957, "previous": null, "list": [ { "collectionID": [], "accessibility": [ "Open access" ], "topic": [ { "term": "Microbiology", "uri": "http://edamontology.org/topic_3301" }, { "term": "Machine learning", "uri": "http://edamontology.org/topic_3474" }, { "term": "Data visualisation", "uri": "http://edamontology.org/topic_0092" }, { "term": "Statistics and probability", "uri": "http://edamontology.org/topic_2269" }, { "term": "Bioinformatics", "uri": "http://edamontology.org/topic_0091" } ], "owner": "markw", "cost": "Free of charge", "relation": [ { "type": "uses", "biotoolsID": "phyloseq" } ], "download": [ { "url": "https://github.com/beatrizgj/MDPbiome.git", "note": null, "version": null, "type": "Source code" } ], "validated": 0, "publication": [ { "doi": "10.1093/bioinformatics/bty562", "version": null, "pmid": null, "type": "Primary", "pmcid": null, "metadata": { "title": "MDPbiome: Microbiome engineering through prescriptive perturbations", "abstract": "© The Author(s) 2018. Published by Oxford University Press.Motivation Recent microbiome dynamics studies highlight the current inability to predict the effects of external perturbations on complex microbial populations. To do so would be particularly advantageous in fields such as medicine, bioremediation or industrial scenarios. Results MDPbiome statistically models longitudinal metagenomics samples undergoing perturbations as a Markov Decision Process (MDP). Given a starting microbial composition, our MDPbiome system suggests the sequence of external perturbation(s) that will engineer that microbiome to a goal state, for example, a healthier or more performant composition. It also estimates intermediate microbiome states along the path, thus making it possible to avoid particularly undesirable/unhealthy states. We demonstrate MDPbiome performance over three real and distinct datasets, proving its flexibility, and the reliability and universality of its output 'optimal perturbation policy'. For example, an MDP created using a vaginal microbiome time series, with a goal of recovering from bacterial vaginosis, suggested avoidance of perturbations such as lubricants or sex toys; while another MDP provided a quantitative explanation for why salmonella vaccine accelerates gut microbiome maturation in chicks. This novel analytical approach has clear applications in medicine, where it could suggest low-impact clinical interventions that will lead to achievement or maintenance of a healthy microbial population, or alternately, the sequence of interventions necessary to avoid strongly negative microbiome states. Availability and implementation Code (https://github.com/beatrizgj/MDPbiome) and result files (https://tomdelarosa.shinyapps.io/MDPbiome/) are available online. Supplementary information Supplementary data are available at Bioinformatics online.", "citationCount": 2, "authors": [ { "name": "Garcia-Jimenez B." }, { "name": "De La Rosa T." }, { "name": "Wilkinson M.D." } ], "date": "2018-09-01T00:00:00Z", "journal": "Bioinformatics" } } ], "homepage_status": 0, "credit": [ { "typeRole": [ "Primary contact" ], "name": "Beatriz García-Jiménez", "url": null, "note": "Researcher in machine learning and artificial intelligence applied to biological data and systems", "orcidid": "https://orcid.org/0000-0002-8129-6506", "typeEntity": "Person", "email": "beatrizgj@gmail.com" }, { "typeRole": [ "Developer" ], "name": "Tomas Euardo de la Rosa", "url": null, "note": null, "orcidid": "https://orcid.org/0000-0002-1977-9777", "typeEntity": "Person", "email": "tomdelarosa@gmail.com" }, { "typeRole": [ "Contributor" ], "name": "Mark D Wilkinson", "url": "http://wilkinsonlab.info", "note": null, "orcidid": "https://orcid.org/0000-0001-6960-357X", "typeEntity": "Person", "email": "markw@illuminae.com" } ], "biotoolsCURIE": "biotools:MDPBiome", "elixirPlatform": [], "elixirNode": [], "version": [], "elixir_badge": 0, "homepage": "https://github.com/beatrizgj/MDPbiome", "function": [ { "note": "microbiome engineering, microbiome response prediction", "input": [ { "data": { "term": "Count matrix", "uri": "http://edamontology.org/data_3917" }, "format": [ { "term": "BIOM format", "uri": "http://edamontology.org/format_3746" } ] } ], "operation": [ { "term": "Probabilistic data generation", "uri": "http://edamontology.org/operation_3480" }, { "term": "Operation", "uri": "http://edamontology.org/operation_0004" }, { "term": "Visualisation", "uri": "http://edamontology.org/operation_0337" }, { "term": "Clustering", "uri": "http://edamontology.org/operation_3432" }, { "term": "Filtering", "uri": "http://edamontology.org/operation_3695" }, { "term": "Sample comparison", "uri": "http://edamontology.org/operation_3731" }, { "term": "Formatting", "uri": "http://edamontology.org/operation_0335" } ], "cmd": "source(\"initMDPBiome.R\")\nmdpBiomeBase()\nmdpBiomeLoocv()", "output": [ { "data": { "term": "Histogram", "uri": "http://edamontology.org/data_3905" }, "format": [ { "term": "PDF", "uri": "http://edamontology.org/format_3508" } ] }, { "data": { "term": "Mathematical model", "uri": "http://edamontology.org/data_0950" }, "format": [ { "term": "Matrix format", "uri": "http://edamontology.org/format_3033" } ] } ] } ], "lastUpdate": "2019-10-04T13:08:36.946129Z", "otherID": [], "description": "MDPbiome is a software developed in R that uses Markov Decision Processes to create \"policy prescriptions\" for microbiome engineering. MDPbiome performs a variety of analysis describing the robustness of the prescription, as well as a variety of visualizations to assist in manual interpretation of the state transitions and biological understanding of a microbiome's dynamics.", "toolType": [ "Library" ], "editPermission": { "type": "public", "authors": [] }, "language": [ "R" ], "link": [ { "url": "https://tomdelarosa.shinyapps.io/MDPbiome/", "note": "Result files", "type": "Other" }, { "url": "https://github.com/beatrizgj/MDPbiome", "note": "Source code", "type": "Repository" } ], "biotoolsID": "MDPBiome", "additionDate": "2019-10-04T13:08:36.945458", "name": "MDPBiome", "license": "MIT", "documentation": [ { "url": "https://github.com/beatrizgj/MDPbiome/blob/master/README.md", "note": "Instructions for use", "type": "Manual" } ], "maturity": "Mature", "operatingSystem": [ "Linux", "Windows", "Mac" ] }, { "collectionID": [], "accessibility": [ "Open access" ], "topic": [ { "term": "Transcriptomics", "uri": "http://edamontology.org/topic_3308" } ], "owner": "alban.gaignard@univ-nantes.fr", "cost": "Free of charge", "relation": [], "download": [], "validated": 0, "publication": [ { "doi": "10.1038/s41467-017-02107-w", "version": null, "pmid": "29367672", "type": "Usage", "pmcid": "PMC5783949", "metadata": null }, { "doi": "10.3389/fimmu.2017.02014", "version": null, "pmid": null, "type": null, "pmcid": null, "metadata": { "title": "Effects of MicroRNA on regulatory T Cells and implications for adoptive cellular therapy to ameliorate graft-versus-host disease", "abstract": "© 2018 Hippen, Loschi, Nicholls, MacDonald and Blazar.Regulatory T cells (Tregs) are key mediators of the immune system. MicroRNAs (miRNAs) are a family of ~22 nucleotide non-coding RNAs that are processed from longer precursors by the RNases Drosha and Dicer. miRNA regulates protein expression posttranscriptionally through mRNA destabilization or translational silencing. A critical role for miRNA in Treg function was initially discovered when both Dicer and Drosha knockout (KO) mice were found to develop a fatal autoimmune disease phenotypically similar to Foxp3 KO mice.", "citationCount": 6, "authors": [ { "name": "Hippen K.L." }, { "name": "Loschi M." }, { "name": "Nicholls J." }, { "name": "MacDonald K.P.A." }, { "name": "Blazar B.R." } ], "date": "2018-01-31T00:00:00Z", "journal": "Frontiers in Immunology" } }, { "doi": "10.1016/j.jaut.2017.10.005", "version": null, "pmid": null, "type": null, "pmcid": null, "metadata": { "title": "An intermediate level of CD161 expression defines a novel activated, inflammatory, and pathogenic subset of CD8 + T cells involved in multiple sclerosis", "abstract": "© 2017 The Authors Several lines of evidence support a key role for CD8 + T cells in central nervous system tissue damage of patients with multiple sclerosis. However, the precise phenotype of the circulating CD8 + T cells that may be recruited from the peripheral blood to invade the CNS remains largely undefined to date. It has been suggested that IL-17 secreting CD8 (Tc17) T cells may be involved, and in humans these cells are characterized by the expression of CD161. We focused our study on a unique and recently described subset of CD8 T cells characterized by an intermediate expression of CD161 as its role in neuroinflammation has not been investigated to date. The frequency, phenotype, and function of CD8 + T cells with an intermediate CD161 expression level were characterized ex-vivo, in vitro, and in situ using RNAseq, RT-PCR, flow cytometry, TCR sequencing, and immunohistofluorescence of cells derived from healthy volunteers (n = 61), MS subjects (n = 90), as well as inflammatory (n = 15) and non-inflammatory controls (n = 6). We report here that CD8 + CD161 int T cells present characteristics of effector cells, up-regulate cell-adhesion molecules and have an increased ability to cross the blood-brain barrier and to secrete IL-17, IFNγ GM-CSF, and IL-22. We further demonstrate that these cells are recruited and enriched in the CNS of MS subjects where they produce IL-17. In the peripheral blood, RNAseq, RT-PCR, high-throughput TCR repertoire analyses, and flow cytometry confirmed an increased effector and transmigration pattern of these cells in MS patients, with the presence of supernumerary clones compared to healthy controls. Our data demonstrate that intermediate levels of CD161 expression identifies activated and effector CD8 + T cells with pathogenic properties that are recruited to MS lesions. This suggests that CD161 may represent a biomarker and a valid target for the treatment of neuroinflammation.", "citationCount": 3, "authors": [ { "name": "Nicol B." }, { "name": "Salou M." }, { "name": "Vogel I." }, { "name": "Garcia A." }, { "name": "Dugast E." }, { "name": "Morille J." }, { "name": "Kilens S." }, { "name": "Charpentier E." }, { "name": "Donnart A." }, { "name": "Nedellec S." }, { "name": "Jacq-Foucher M." }, { "name": "Le Frere F." }, { "name": "Wiertlewski S." }, { "name": "Bourreille A." }, { "name": "Brouard S." }, { "name": "Michel L." }, { "name": "David L." }, { "name": "Gourraud P.-A." }, { "name": "Degauque N." }, { "name": "Nicot A.B." }, { "name": "Berthelot L." }, { "name": "Laplaud D.-A." } ], "date": "2018-03-01T00:00:00Z", "journal": "Journal of Autoimmunity" } }, { "doi": "10.1172/jci.insight.90088", "version": null, "pmid": null, "type": null, "pmcid": null, "metadata": null } ], "homepage_status": 0, "credit": [ { "typeRole": [ "Primary contact", "Developer" ], "name": "Eric Charpentier", "url": "https://pf-bird.univ-nantes.fr/qui-sommes-nous-/membres/", "note": null, "orcidid": null, "typeEntity": "Person", "email": "eric.charpentier@univ-nantes.fr" }, { "typeRole": [ "Provider", "Support" ], "name": "BiRD", "url": "https://pf-bird.univ-nantes.fr", "note": null, "orcidid": null, "typeEntity": "Division", "email": "pf-bird@univ-nantes.fr" }, { "typeRole": [ "Developer" ], "name": "Solenne Dumont", "url": "https://pf-bird.univ-nantes.fr/qui-sommes-nous-/membres/", "note": null, "orcidid": "https://orcid.org/0000-0003-3237-7382", "typeEntity": "Person", "email": "solenne.dumont@univ-nantes.fr" } ], "biotoolsCURIE": "biotools:3SRP", "elixirPlatform": [], "elixirNode": [ "France" ], "version": [ "1.0" ], "elixir_badge": 0, "homepage": "https://gitlab.univ-nantes.fr/bird_pipeline_registry/DGE-pipeline", "function": [ { "note": "First generate a configuration file and then run the pipeline with the Snakemake engine.", "input": [ { "data": { "term": "RNA sequence", "uri": "http://edamontology.org/data_3495" }, "format": [ { "term": "FASTQ-illumina", "uri": "http://edamontology.org/format_1931" } ] } ], "operation": [ { "term": "Clustering profile plotting", "uri": "http://edamontology.org/operation_2935" }, { "term": "RNA-seq read count analysis", "uri": "http://edamontology.org/operation_3563" }, { "term": "Differential gene expression analysis", "uri": "http://edamontology.org/operation_3223" }, { "term": "Heat map generation", "uri": "http://edamontology.org/operation_0531" } ], "cmd": "python SCRIPTS/make_dge_config.py -s TESTDATA/samplesheet.tsv -r TESTDATA/REFERENCES/ -w RESULTS -f TESTDATA/fastqFiles.txt -c TESTDATA/conditions.tsv --minGenes 0 --minReads 0 > config.json ; 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Cloud-based proteomic data analysis that helps users go from raw MS data to results efficiently. \nModern app + open source algorithms + powerful analysis tools", "toolType": [ "Web application" ], "editPermission": { "type": "group", "authors": [ "proteomics.bio.tools" ] }, "language": [], "link": [ { "url": "https://www.help.proteoworker.com/?utm_source=elixir", "note": "This is a link to our knowledgebase", "type": "Helpdesk" }, { "url": "https://www.proteoworker.com/?utm_source=elixir", "note": "Just enter your email for updates and you'll be added to our mailing list", "type": "Mailing list" } ], "biotoolsID": "ProteoWorker", "additionDate": "2019-02-06T23:22:10Z", "name": "ProteoWorker", "license": "Proprietary", "documentation": [ { "url": "https://www.help.proteoworker.com", "note": "This is a link to our knowledgebase which is constantly being updated.", "type": "General" } ], "maturity": "Emerging", "operatingSystem": [ "Linux", "Windows", "Mac" ] }, { "collectionID": [], 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"name": "salmobase2", "license": null, "documentation": [], "maturity": null, "operatingSystem": [] }, { "collectionID": [], "accessibility": [ "Freeware" ], "topic": [ { "term": "Machine learning", "uri": "http://edamontology.org/topic_3474" }, { "term": "Biomedical science", "uri": "http://edamontology.org/topic_3344" } ], "owner": "ELIXIR-EE", "cost": "Free of charge", "relation": [], "download": [ { "url": "https://www.ilastik.org/download.html", "note": null, "version": null, "type": "Binaries" } ], "validated": 0, "publication": [ { "doi": "10.1038/s41592-019-0582-9", "version": null, "pmid": null, "type": "Primary", "pmcid": null, "metadata": null } ], "homepage_status": 0, "credit": [], "biotoolsCURIE": "biotools:ilastik", "elixirPlatform": [], "elixirNode": [], "version": [], "elixir_badge": 0, "homepage": "https://www.ilastik.org/index.html", "function": [ { "note": null, "input": [ { "data": { "term": "Image", "uri": "http://edamontology.org/data_2968" }, "format": [ { 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"DNA binding sites", "uri": "http://edamontology.org/topic_3125" }, { "term": "Epigenomics", "uri": "http://edamontology.org/topic_3173" } ], "owner": "mpaya", "cost": null, "relation": [], "download": [ { "url": "https://hub.docker.com/r/mpaya/epigenomics_galaxy", "note": "Workflows to run major steps of RNA-Seq and ChIP-Seq data, on provided or custom data.", "version": null, "type": "Container file" }, { "url": "https://hub.docker.com/r/mpaya/epigenomics_jupyter", "note": "Steps to finalize data analysis and integrate results.", "version": null, "type": "Container file" } ], "validated": 0, "publication": [ { "doi": "10.5281/zenodo.3298029", "version": null, "pmid": null, "type": "Method", "pmcid": null, "metadata": null } ], "homepage_status": 0, "credit": [ { "typeRole": [], "name": "Agencia Estatal de Investigación", "url": null, "note": "This work has been financial supported by the \"Severo Ochoa Programme\nfor Centres of Excellence in R&D” from the Agencia Estatal 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Despite the development of integrative databases, building a consensus map of genes, QTL and other loci gathered from multiple maps remains a manual and tedious task. Nevertheless, this is a critical step to reveal co-locations between genes and QTL. Another important matter is to determine whether QTL linked to same traits or related ones is detected in independent experiments and located in the same region, and represents a single locus or not. Statistical tools such as meta-analysis can be used to answer this question. 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Stem Cells published by Wiley Periodicals, Inc. on behalf of AlphaMed Press 2019The general population is chronically exposed to multiple environmental contaminants such as pesticides. We have previously demonstrated that human mesenchymal stem cells (MSCs) exposed in vitro to low doses of a mixture of seven common pesticides showed a permanent phenotype modification with a specific induction of an oxidative stress-related senescence. Pesticide mixture also induced a shift in MSC differentiation toward adipogenesis. Thus, we hypothesized that common combination of pesticides may induce a premature cellular aging of adult MSCs. Our goal was to evaluate if the prolonged exposure to pesticide mixture could accelerate aging-related markers and in particular deteriorate the immunosuppressive properties of MSCs. MSCs exposed to pesticide mixture, under long-term culture and obtained from aging donor, were compared by bulk RNA sequencing analysis. Aging, senescence, and immunomodulatory markers were compared. The protein expression of cellular aging-associated metabolic markers and immune function of MSCs were analyzed. Functional analysis of the secretome impacts on immunomodulatory properties of MSCs was realized after 21 days' exposure to pesticide mixture. The RNA sequencing analysis of MSCs exposed to pesticide showed some similarities with cells from prolonged culture, but also with the MSCs of an aged donor. Changes in the metabolic markers MDH1, GOT and SIRT3, as well as an alteration in the modulation of active T cells and modifications in cytokine production are all associated with cellular aging. A modified functional profile was found with similarities to aging process. 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