List all resources, or create a new resource.

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            "name": "EukMetaSanity",
            "description": "The high-throughput gene prediction of more than 1,700 eukaryote genomes using the software package EukMetaSanity.",
            "homepage": "https://github.com/cjneely10/EukMetaSanity",
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            "biotoolsCURIE": "biotools:eukmetasanity",
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                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0310",
                            "term": "Sequence assembly"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0362",
                            "term": "Genome annotation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2454",
                            "term": "Gene prediction"
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                    "note": null,
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                "Suite"
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                {
                    "uri": "http://edamontology.org/topic_0621",
                    "term": "Model organisms"
                },
                {
                    "uri": "http://edamontology.org/topic_3512",
                    "term": "Gene transcripts"
                },
                {
                    "uri": "http://edamontology.org/topic_0780",
                    "term": "Plant biology"
                },
                {
                    "uri": "http://edamontology.org/topic_0637",
                    "term": "Taxonomy"
                },
                {
                    "uri": "http://edamontology.org/topic_3500",
                    "term": "Zoology"
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                "Python"
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                    "doi": "10.1101/2021.07.25.453296",
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                    "name": "Christopher J. Neely",
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            "name": "ERpred",
            "description": "Web server for the prediction of subtype-specific estrogen receptor antagonists.",
            "homepage": "http://codes.bio/erpred",
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            "biotoolsCURIE": "biotools:erpred",
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3092",
                            "term": "Protein feature detection"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0478",
                            "term": "Molecular docking"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3799",
                            "term": "Quantification"
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                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
                },
                {
                    "uri": "http://edamontology.org/topic_2640",
                    "term": "Oncology"
                },
                {
                    "uri": "http://edamontology.org/topic_3047",
                    "term": "Molecular biology"
                },
                {
                    "uri": "http://edamontology.org/topic_0154",
                    "term": "Small molecules"
                }
            ],
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                "Linux",
                "Windows"
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                "R"
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                {
                    "doi": "10.7717/PEERJ.11716",
                    "pmid": "34285834",
                    "pmcid": "PMC8274494",
                    "type": [],
                    "version": null,
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                    "metadata": {
                        "title": "ERpred: a web server for the prediction of subtype-specific estrogen receptor antagonists",
                        "abstract": "© 2021 PeerJ Inc.. All rights reserved.Estrogen receptors alpha and beta (ERa and ERβ) are responsible for breast cancer metastasis through their involvement of clinical outcomes. Estradiol and hormone replacement therapy targets both ERs, but this often leads to an increased risk of breast and endometrial cancers as well as thromboembolism. A major challenge is posed for the development of compounds possessing ER subtype specificity. Herein, we present a large-scale classification structure-activity relationship (CSAR) study of inhibitors from the ChEMBL database which consisted of an initial set of 11,618 compounds for ERa and 7,810 compounds for ERβ. The IC50 was selected as the bioactivity unit for further investigation and after the data curation process, this led to a final data set of 1,593 and 1,281 compounds for ERa and ERβ, respectively. We employed the random forest (RF) algorithm for model building and of the 12 fingerprint types, models built using the PubChem fingerprint was the most robust (Ac of 94.65% and 92.25% and Matthews correlation coefficient (MCC) of 89% and 76% for ERa and ERβ, respectively) and therefore selected for feature interpretation. Results indicated the importance of features pertaining to aromatic rings, nitrogen-containing functional groups and aliphatic hydrocarbons. Finally, the model was deployed as the publicly available web server called ERpred at http://codes.bio/erpred where users can submit SMILES notation as the input query for prediction of the bioactivity against ERa and ERβ.",
                        "date": "2021-07-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Schaduangrat N."
                            },
                            {
                                "name": "Malik A.A."
                            },
                            {
                                "name": "Nantasenamat C."
                            }
                        ],
                        "journal": "PeerJ"
                    }
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                {
                    "name": "Chanin Nantasenamat",
                    "email": "chanin.nan@mahidol.edu",
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            "description": "A Novel App for Insights into Laboratory Data of Clinical Trials.",
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                        {
                            "uri": "http://edamontology.org/operation_3435",
                            "term": "Standardisation and normalisation"
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                            "term": "Visualisation"
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                "Library"
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                    "uri": "http://edamontology.org/topic_3379",
                    "term": "Preclinical and clinical studies"
                }
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                "Mac",
                "Linux",
                "Windows"
            ],
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                "R"
            ],
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                {
                    "doi": "10.1007/S43441-021-00318-4",
                    "pmid": "34196957",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "elaborator: A Novel App for Insights into Laboratory Data of Clinical Trials",
                        "abstract": "© 2021, The Drug Information Association, Inc.In clinical studies there are huge numbers of laboratory parameters available that are measured at several visits for several treatment groups. The status quo for presenting laboratory data in clinical trials consists in generating large numbers of tables and data listings. Such tables and listings are required for submissions to health authorities. However, reviewing laboratory data presented in the form of tables and listings is a lengthy and tedious process. Thus, to enable efficient exploration of laboratory data we developed elaborator, a comprehensive and easy-to-use interactive browser-based application. The elaborator app comprises three analyses types for addressing different questions, for example about changes in laboratory values that frequently occur, treatment-related changes and changes beyond the normal ranges. In this way, the app can be used by study teams for identifying safety signals in a clinical trial as well as for generating hypotheses that are further inspected with detailed analyses and possibly data from other sources. The elaborator app is implemented in the statistical software R. The R package elaborator can be obtained from https://cran.r-project.org/package=elaborator. Patients’ laboratory data need to be extracted from the clinical database and pre-processed locally for feeding into the app. For exploring data by means of the elaborator, the user needs some familiarity with R but no programming knowledge is required.",
                        "date": "2021-11-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Janitza S."
                            },
                            {
                                "name": "Majumder M."
                            },
                            {
                                "name": "Mendolia F."
                            },
                            {
                                "name": "Jeske S."
                            },
                            {
                                "name": "Kulmann H."
                            }
                        ],
                        "journal": "Therapeutic Innovation and Regulatory Science"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Silke Janitza",
                    "email": "silke.janitza@bayer.com",
                    "url": null,
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            "owner": "Chan019",
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        },
        {
            "name": "ECO-CollecTF",
            "description": "A Corpus of Annotated Evidence-Based Assertions in Biomedical Manuscripts.",
            "homepage": "https://github.com/ErillLab/ECO-CollecTF",
            "biotoolsID": "eco-collectf",
            "biotoolsCURIE": "biotools:eco-collectf",
            "version": [],
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            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0306",
                            "term": "Text mining"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3778",
                            "term": "Text annotation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0558",
                            "term": "Phylogenetic tree annotation"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0218",
                    "term": "Natural language processing"
                },
                {
                    "uri": "http://edamontology.org/topic_0089",
                    "term": "Ontology and terminology"
                },
                {
                    "uri": "http://edamontology.org/topic_0749",
                    "term": "Transcription factors and regulatory sites"
                },
                {
                    "uri": "http://edamontology.org/topic_0219",
                    "term": "Data submission, annotation and curation"
                }
            ],
            "operatingSystem": [],
            "language": [
                "Python"
            ],
            "license": "GPL-3.0",
            "collectionID": [],
            "maturity": null,
            "cost": "Free of charge",
            "accessibility": "Open access",
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            "elixirNode": [],
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            "link": [],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.3389/FRMA.2021.674205",
                    "pmid": "34327299",
                    "pmcid": "PMC8313968",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": null
                }
            ],
            "credit": [
                {
                    "name": "Ivan Erill",
                    "email": "erill@umbc.edu",
                    "url": null,
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            "community": null,
            "owner": "Chan019",
            "additionDate": "2021-11-27T18:57:43.224464Z",
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        },
        {
            "name": "DysPIA",
            "description": "A Novel Dysregulated Pathway Identification Analysis Method.",
            "homepage": "https://github.com/lemonwang2020/DysPIA",
            "biotoolsID": "dyspia",
            "biotoolsCURIE": "biotools:dyspia",
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2436",
                            "term": "Gene-set enrichment analysis"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3928",
                            "term": "Pathway analysis"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3463",
                            "term": "Expression correlation analysis"
                        }
                    ],
                    "input": [],
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                    "note": null,
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            ],
            "toolType": [
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                {
                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
                },
                {
                    "uri": "http://edamontology.org/topic_0204",
                    "term": "Gene regulation"
                },
                {
                    "uri": "http://edamontology.org/topic_2640",
                    "term": "Oncology"
                },
                {
                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
                },
                {
                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
                }
            ],
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                "Windows"
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            "language": [
                "R"
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            "license": "GPL-3.0",
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            "cost": "Free of charge",
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            "link": [
                {
                    "url": "https://cran.r-project.org/web/packages/DysPIA/index.html",
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                {
                    "url": "https://cran.r-project.org/web/packages/DysPIAData/index.html",
                    "type": [
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            "publication": [
                {
                    "doi": "10.3389/FGENE.2021.647653",
                    "pmid": "34290733",
                    "pmcid": "PMC8287415",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "DysPIA: A Novel Dysregulated Pathway Identification Analysis Method",
                        "abstract": "© Copyright © 2021 Wang, Xie, Li, Wang, Li, Feng and Li.Differential co-expression-based pathway analysis is still limited and not widely used. In most current methods, the pathways were considered as gene sets, but the gene regulation relationships were not considered, and the computational speed was slow. In this article, we proposed a novel Dysregulated Pathway Identification Analysis (DysPIA) method to overcome these shortcomings. We adopted the idea of Correlation by Individual Level Product into analysis and performed a fast enrichment analysis. We constructed a combined gene-pair background which was much more sufficient than the background used in Edge Set Enrichment Analysis. In simulation study, DysPIA was able to identify the causal pathways with high AUC (0.9584 to 0.9896). In p53 mutation data, DysPIA obtained better performance than other methods. It obtained more potential dysregulated pathways that could be literature verified, and it ran much faster (∼1,700–8,000 times faster than other methods when 10,000 permutations). DysPIA was also applied to breast cancer relapse dataset and breast cancer subtype dataset. The results show that DysPIA is effective and has a great biological significance. R packages “DysPIA” and “DysPIAData” are constructed and freely available on R CRAN (https://cran.r-project.org/web/packages/DysPIA/index.html and https://cran.r-project.org/web/packages/DysPIAData/index.html), and on GitHub (https://github.com/lemonwang2020).",
                        "date": "2021-07-05T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Wang L."
                            },
                            {
                                "name": "Xie W."
                            },
                            {
                                "name": "Li K."
                            },
                            {
                                "name": "Wang Z."
                            },
                            {
                                "name": "Li X."
                            },
                            {
                                "name": "Feng W."
                            },
                            {
                                "name": "Li J."
                            }
                        ],
                        "journal": "Frontiers in Genetics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Xia Li",
                    "email": "lixia@hrbmu.edu.cn",
                    "url": null,
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                {
                    "name": "Weixing Feng",
                    "email": "fengweixing@hrbeu.edu.cn",
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                {
                    "name": "Jin Li",
                    "email": "lijin@hainmc.edu.cn",
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        {
            "name": "DiSCo",
            "description": "Sequence-based type-specific predictor of Dsr-dependent dissimilatory sulphur metabolism in microbial data.",
            "homepage": "https://github.com/Genome-Evolution-and-Ecology-Group-GEEG/DiSCo",
            "biotoolsID": "disco_dsr",
            "biotoolsCURIE": "biotools:disco_dsr",
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                        {
                            "uri": "http://edamontology.org/operation_1777",
                            "term": "Protein function prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0303",
                            "term": "Fold recognition"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3767",
                            "term": "Protein identification"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Script"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3407",
                    "term": "Endocrinology and metabolism"
                },
                {
                    "uri": "http://edamontology.org/topic_3301",
                    "term": "Microbiology"
                },
                {
                    "uri": "http://edamontology.org/topic_0625",
                    "term": "Genotype and phenotype"
                },
                {
                    "uri": "http://edamontology.org/topic_3174",
                    "term": "Metagenomics"
                },
                {
                    "uri": "http://edamontology.org/topic_0821",
                    "term": "Enzymes"
                }
            ],
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            "language": [
                "Perl"
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            "publication": [
                {
                    "doi": "10.1099/MGEN.0.000603",
                    "pmid": "34241589",
                    "pmcid": "PMC8477390",
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "DiSCo: A sequence-based type-specific predictor of Dsr-dependent dissimilatory sulphur metabolism in microbial data",
                        "abstract": "© 2021 The Authors.Current methods in comparative genomic analyses for metabolic potential prediction of proteins involved in, or associated with the Dsr (dissimilatory sulphite reductase)-dependent dissimilatory sulphur metabolism are both time-intensive and computationally challenging, especially when considering metagenomic data. We developed DiSCo, a Dsr-dependent dissimilatory sulphur metabolism classification tool, which automatically identifies and classifies the protein type from sequence data. It takes user-supplied protein sequences and lists the identified proteins and their classification in terms of protein family and predicted type. It can also extract the sequence data from user-input to serve as basis for additional downstream analyses. DiSCo provides the metabolic functional prediction of proteins involved in Dsr-dependent dissimilatory sulphur metabolism with high levels of accuracy in a fast manner. We ran DiSCo against a dataset composed of over 190 thousand (meta) genomic records and efficiently mapped Dsr-dependent dissimilatory sulphur proteins in 1798 lineages across both prokaryotic domains. This allowed the identification of new micro-organisms belonging to Thaumarchaeota and Spirochaetes lineages with the metabolic potential to use the Dsr-pathway for energy conservation. DiSCo is implemented in Perl 5 and freely available under the GNU GPLv3 at https://​github.​com/​Genome-​Evolution-​and-​Ecology-​Group-​GEEG/​DiSCo.",
                        "date": "2021-01-01T00:00:00Z",
                        "citationCount": 0,
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                        "title": "Deep learning for peptide identification from metaproteomics datasets",
                        "abstract": "© 2021 The AuthorsMetaproteomics is becoming widely used in microbiome research for gaining insights into the functional state of the microbial community. Current metaproteomics studies are generally based on high-throughput tandem mass spectrometry (MS/MS) coupled with liquid chromatography. In this paper, we proposed a deep-learning-based algorithm, named DeepFilter, for improving peptide identifications from a collection of tandem mass spectra. The key advantage of the DeepFilter is that it does not need ad hoc training or fine-tuning as in existing filtering tools. DeepFilter is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/DeepFilter. Significance: The identification of peptides and proteins from MS data involves the computational procedure of searching MS/MS spectra against a predefined protein sequence database and assigning top-scored peptides to spectra. Existing computational tools are still far from being able to extract all the information out of MS/MS data sets acquired from metaproteome samples. Systematical experiment results demonstrate that the DeepFilter identified up to 12% and 9% more peptide-spectrum-matches and proteins, respectively, compared with existing filtering algorithms, including Percolator, Q-ranker, PeptideProphet, and iProphet, on marine and soil microbial metaproteome samples with false discovery rate at 1%. The taxonomic analysis shows that DeepFilter found up to 7%, 10%, and 14% more species from marine, soil, and human gut samples compared with existing filtering algorithms. Therefore, DeepFilter was believed to generalize properly to new, previously unseen peptide-spectrum-matches and can be readily applied in peptide identification from metaproteomics data.",
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