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{ "count": 30414, "next": "?page=2", "previous": null, "list": [ { "name": "Breast Dense Tissue Segmentation", "description": "The tool takes a digital mammogram and performs an automatic segmentation of the breast area and the dense tissue. After the mammogram segmentation, the tool returns a DICOM-SEG image with both the dense tissue and the breast tissue mask combined.", "homepage": "https://pubmed.ncbi.nlm.nih.gov/36010173/", "biotoolsID": "breast_dense_tissue_segmentation", "biotoolsCURIE": "biotools:breast_dense_tissue_segmentation", "version": [ "v1.0.6" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3382", "term": "Imaging" }, { "uri": "http://edamontology.org/topic_3384", "term": "Medical imaging" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.3390/diagnostics12081822", "pmid": "36010173", "pmcid": "PMC9406546", "type": [], "version": null, "note": null, "metadata": { "title": "Breast Dense Tissue Segmentation with Noisy Labels: A Hybrid Threshold-Based and Mask-Based Approach", "abstract": "Breast density assessed from digital mammograms is a known biomarker related to a higher risk of developing breast cancer. Supervised learning algorithms have been implemented to determine this. However, the performance of these algorithms depends on the quality of the ground-truth information, which expert readers usually provide. These expert labels are noisy approximations to the ground truth, as there is both intra- and inter-observer variability among them. Thus, it is crucial to provide a reliable method to measure breast density from mammograms. This paper presents a fully automated method based on deep learning to estimate breast density, including breast detection, pectoral muscle exclusion, and dense tissue segmentation. We propose a novel confusion matrix (CM)—YNet model for the segmentation step. This architecture includes networks to model each radiologist’s noisy label and gives the estimated ground-truth segmentation as well as two parameters that allow interaction with a threshold-based labeling tool. A multi-center study involving 1785 women whose “for presentation” mammograms were obtained from 11 different medical facilities was performed. A total of 2496 mammograms were used as the training corpus, and 844 formed the testing corpus. Additionally, we included a totally independent dataset from a different center, composed of 381 women with one image per patient. Each mammogram was labeled independently by two expert radiologists using a threshold-based tool. The implemented CM-Ynet model achieved the highest DICE score averaged over both test datasets (0.82 ± 0.14) when compared to the closest dense-tissue segmentation assessment from both radiologists. The level of concordance between the two radiologists showed a DICE score of (0.76 ± 0.17). An automatic breast density estimator based on deep learning exhibited higher performance when compared with two experienced radiologists. This suggests that modeling each radiologist’s label allows for better estimation of the unknown ground-truth segmentation. The advantage of the proposed model is that it also provides the threshold parameters that enable user interaction with a threshold-based tool.", "date": "2022-08-01T00:00:00Z", "citationCount": 3, "authors": [ { "name": "Larroza A." }, { "name": "Perez-Benito F.J." }, { "name": "Perez-Cortes J.-C." }, { "name": "Roman M." }, { "name": "Pollan M." }, { "name": "Perez-Gomez B." }, { "name": "Salas-Trejo D." }, { "name": "Casals M." }, { "name": "Llobet R." } ], "journal": "Diagnostics" } } ], "credit": [], "owner": "dsilveira", "additionDate": "2025-05-22T12:06:46.866961Z", "lastUpdate": "2025-05-22T12:49:42.020647Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "NLmCED Filter", "description": "The NLmCED (Non Local mean Coherence Enhancing Diffusion) filter is a denoising tool aimed to reduce Rician Noise in 3D MR images. It is a combination between two filters such as the Non-Local mean filter and the Anisotropic Diffusion tensor method with an estimator of Rician noise. The NLmCED filter can benefit from the noise reduction capabilities of the NLM filter while maintaining the edge-preserving characteristics of anisotropic diffusion.", "homepage": "https://github.com/Feriel87/Denoising-CEST-MRI/tree/main/NLmCED_Filter", "biotoolsID": "nlmced_filter", "biotoolsCURIE": "biotools:nlmced_filter", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3443", "term": "Image analysis" }, { "uri": "http://edamontology.org/operation_3695", "term": "Data filtering" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Script" ], "topic": [ { "uri": "http://edamontology.org/topic_3382", "term": "Imaging" }, { "uri": "http://edamontology.org/topic_3444", "term": "MRI" }, { "uri": "http://edamontology.org/topic_3384", "term": "Medical imaging" } ], "operatingSystem": [], "language": [ "MATLAB" ], "license": null, "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/FerielRamdhane/Denoising-CEST-MRI/tree/main/NLmCED_Filter", "type": [ "Repository" ], "note": "Github Link" } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1504/IJCVR.2018.090012", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "A new method for three-dimensional magnetic resonance images denoising", "abstract": "Removing noise in magnetic resonance images (MRI) is a crucial issue in the field of medical image processing. These images are infected by Rician noise which is a non-Additive noise, allows to reduce the image contrast and causes random fluctuations. Our paper proposed a new method for 3D MRI denoising based on new combination between non-local means filter and the diffusion tensor with adaptative MAD estimator Rician noise. The performance of our proposed algorithm was evaluated with respect to different quantitative measures, compared to other denoising methods which illustrate that our proposed denoising algorithm efficiently removes noise and preserves more details.", "date": "2018-01-01T00:00:00Z", "citationCount": 3, "authors": [ { "name": "Romdhane F." }, { "name": "Benzarti F." }, { "name": "Amiri H." } ], "journal": "International Journal of Computational Vision and Robotics" } } ], "credit": [], "owner": "Feriel", "additionDate": "2023-07-13T08:14:40.190855Z", "lastUpdate": "2025-05-22T12:21:15.198535Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Genstore", "description": "Genstore data compression tool", "homepage": "https://www.genstore.org/", "biotoolsID": "genstore", "biotoolsCURIE": "biotools:genstore", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0219", "term": "Data curation and archival" } ], "operatingSystem": [ "Mac", "Linux" ], "language": [ "C++" ], "license": "Not licensed", "collectionID": [ "Sequencing" ], "maturity": "Emerging", "cost": null, "accessibility": "Restricted access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [], "credit": [], "owner": "isachenkoa", "additionDate": "2025-05-21T15:42:42.759887Z", "lastUpdate": "2025-05-21T15:42:42.762153Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "QP-Insights Uploader", "description": "This desktop application enables users to upload DICOM data along with associated clinical information to QP-Insights—the data management platform of the UPV Reference Node within EUCAIM.", "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/qp_insights_uploader/artifacts-tab", "biotoolsID": "qp-insights_uploader", "biotoolsCURIE": "biotools:qp-insights_uploader", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_3384", "term": "Medical imaging" }, { "uri": "http://edamontology.org/topic_3366", "term": "Data integration and warehousing" } ], "operatingSystem": [ "Mac" ], "language": [ "Python" ], "license": "CC-BY-NC-ND-4.0", "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [ { "url": "https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/qp_insights_uploader/artifacts-tab", "type": "Downloads page", "note": null, "version": null } ], "documentation": [], "publication": [], "credit": [ { "name": "Quibim", "email": null, "url": "https://quibim.com/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [], "note": null } ], "owner": "celia_martin", "additionDate": "2025-05-21T12:27:35.032026Z", "lastUpdate": "2025-05-21T13:48:01.042280Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "SPEX", "description": "Spatial Expresion Explorer (SPEX) is a web-based analysis platform offering end to end analytics for spatial omics datasets.", "homepage": "https://github.com/Genentech/SPEX", "biotoolsID": "spex", "biotoolsCURIE": "biotools:spex", "version": [ "1" ], "otherID": [], "relation": [], "function": [], "toolType": [], "topic": [], "operatingSystem": [], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [], "credit": [], "owner": "jesudasr", "additionDate": "2025-05-20T21:24:50.911022Z", "lastUpdate": "2025-05-20T21:25:21.717408Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "sylph", "description": "fast and precise species-level metagenomic profiling with ANIs", "homepage": "https://github.com/bluenote-1577/sylph", "biotoolsID": "sylph", "biotoolsCURIE": "biotools:sylph", "version": [ "v0.4.1" ], "otherID": [], "relation": [ { "biotoolsID": "gtdb", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3460", "term": "Taxonomic classification" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3494", "term": "DNA sequence" }, "format": [ { "uri": "http://edamontology.org/format_1930", "term": "FASTQ" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3028", "term": "Taxonomy" }, "format": [ { "uri": "http://edamontology.org/format_3751", "term": "DSV" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" } ], "operatingSystem": [ "Linux" ], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [], "credit": [], "owner": "pauffret", "additionDate": "2023-12-06T15:45:20.587393Z", "lastUpdate": "2025-05-20T14:59:07.767609Z", "editPermission": { "type": "group", "authors": [ "vashokan" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Chevreul", "description": "An R package and shiny app for processing, plotting, and interactive exploratory data analysis of full-length scRNA-seq as SingleCellExperiments", "homepage": "https://cobriniklab.github.io/chevreul/", "biotoolsID": "chevreul", "biotoolsCURIE": "biotools:chevreul", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2945", "term": "Data analysis" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3391", "term": "Omics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "R" ], "license": "MIT", "collectionID": [], "maturity": "Emerging", "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [ { "url": "https://github.com/cobriniklab/chevreul", "type": "Software package", "note": null, "version": "v1.0.0" } ], "documentation": [ { "url": "https://cobriniklab.github.io/chevreul/", "type": [ "Quick start guide" ], "note": null } ], "publication": [], "credit": [ { "name": "Kevin Stachelek", "email": "kevin.stachelek@gmail.com", "url": null, "orcidid": "https://orcid.org/0000-0003-2085-695X", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "stachele", "additionDate": "2025-05-20T05:54:37.118800Z", "lastUpdate": "2025-05-20T05:56:27.709815Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "nf-core airrflow", "description": "nf-core/airrflow is a bioinformatics best-practice pipeline to analyze B-cell or T-cell repertoire sequencing data. The input data can be targeted amplicon bulk sequencing data of the V, D, J and C regions of the B/T-cell receptor with multiplex PCR or 5' RACE protocol, single-cell VDJ sequencing using the 10xGenomics libraries, or assembled reads (bulk or single-cell). It can also extract BCR and TCR sequences from bulk or single-cell untargeted RNAseq data. It makes use of the Immcantation toolset as well as other AIRRseq analysis tools. The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible.", "homepage": "https://nf-co.re/airrflow/4.3.0/", "biotoolsID": "nf-core_airrflow", "biotoolsCURIE": "biotools:nf-core_airrflow", "version": [], "otherID": [], "relation": [ { "biotoolsID": "Immcantation", "type": "uses" }, { "biotoolsID": "dowser", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0495", "term": "Local alignment" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Workflow" ], "topic": [ { "uri": "http://edamontology.org/topic_3948", "term": "Immunoinformatics" }, { "uri": "http://edamontology.org/topic_0804", "term": "Immunology" }, { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" }, { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" }, { "uri": "http://edamontology.org/topic_4028", "term": "Single-cell sequencing" }, { "uri": "http://edamontology.org/topic_3293", "term": "Phylogenetics" }, { "uri": "http://edamontology.org/topic_3400", "term": "Allergy, clinical immunology and immunotherapeutics" }, { "uri": "http://edamontology.org/topic_2830", "term": "Immunoproteins and antigens" } ], "operatingSystem": [ "Linux", "Mac" ], "language": [ "Other" ], "license": "MIT", "collectionID": [ "nf-core" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/nf-core/airrflow", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "https://nf-co.re/airrflow", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1371/journal.pcbi.1012265", "pmid": "39058741", "pmcid": "PMC11305553", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "nf-core/airrflow: An adaptive immune receptor repertoire analysis workflow employing the Immcantation framework", "abstract": "Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets.", "date": "2024-07-01T00:00:00Z", "citationCount": 4, "authors": [ { "name": "Gabernet G." }, { "name": "Marquez S." }, { "name": "Bjornson R." }, { "name": "Peltzer A." }, { "name": "Meng H." }, { "name": "Aron E." }, { "name": "Lee N.Y." }, { "name": "Jensen C." }, { "name": "Ladd D." }, { "name": "Polster M." }, { "name": "Hanssen F." }, { "name": "Heumos S." }, { "name": "Yaari G." }, { "name": "Kowarik M.C." }, { "name": "Nahnsen S." }, { "name": "Kleinstein S.H." } ], "journal": "PLoS Computational Biology" } } ], "credit": [ { "name": "Gisela Gabernet", "email": "gisela.gabernet@gmail.com", "url": null, "orcidid": "https://orcid.org/0000-0001-7049-9474", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Alexander Peltzer", "email": "alex.peltzer@gmail.com", "url": null, "orcidid": "https://orcid.org/0000-0002-6503-2180", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Susanna Marquez", "email": "susanna.marquez@yale.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Steven H. Kleinstein", "email": "steven.kleinstein@yale.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Sven Nahnsen", "email": "sven.nahnsen@qbic.uni-tuebingen.de", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "ggabernet", "additionDate": "2025-05-19T15:20:08.961227Z", "lastUpdate": "2025-05-19T15:20:08.964291Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "plantiSMASH", "description": "PlantiSMASH is a specialized extension of antiSMASH for the identification and analysis of biosynthetic gene clusters (BGCs) in plant genomes. It supports advanced plant-specific detection rules and features for comparative genomics, visualization, and more.", "homepage": "https://plantismash.bioinformatics.nl/", "biotoolsID": "plantismash", "biotoolsCURIE": "biotools:plantismash", "version": [ "2.0-beta4" ], "otherID": [], "relation": [], "function": [], "toolType": [], "topic": [ { "uri": "http://edamontology.org/topic_0749", "term": "Transcription factors and regulatory sites" }, { "uri": "http://edamontology.org/topic_3473", "term": "Data mining" }, { "uri": "http://edamontology.org/topic_0085", "term": "Functional genomics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [], "license": "AGPL-3.0", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [ { "url": "https://github.com/plantismash/plantismash/releases", "type": "Software package", "note": null, "version": "2.0-beta4" } ], "documentation": [ { "url": "https://plantismash.github.io/documentation/", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/nar/gkx305", "pmid": null, "pmcid": "PMC5570173", "type": [], "version": null, "note": null, "metadata": { "title": "PlantiSMASH: Automated identification, annotation and expression analysis of plant biosynthetic gene clusters", "abstract": "Plant specialized metabolites are chemically highly diverse, play key roles in host-microbe interactions, have important nutritional value in crops and are frequently applied as medicines. It has recently become clear that plant biosynthetic pathway-encoding genes are sometimes densely clustered in specific genomic loci: Biosynthetic gene clusters (BGCs). Here, we introduce plantiSMASH, a versatile online analysis platform that automates the identification of candidate plant BGCs. Moreover, it allows integration of transcriptomic data to prioritize candidate BGCs based on the coexpression patterns of predicted biosynthetic enzyme-coding genes, and facilitates comparative genomic analysis to study the evolutionary conservation of each cluster. Applied on 48 high-quality plant genomes, plantiSMASH identifies a rich diversity of candidate plant BGCs. These results will guide further experimental exploration of the nature and dynamics of gene clustering in plant metabolism. Moreover, spurred by the continuing decrease in costs of plant genome sequencing, they will allow genome mining technologies to be applied to plant natural product discovery.", "date": "2017-07-03T00:00:00Z", "citationCount": 214, "authors": [ { "name": "Kautsar S.A." }, { "name": "Suarez Duran H.G." }, { "name": "Blin K." }, { "name": "Osbourn A." }, { "name": "Medema M.H." } ], "journal": "Nucleic Acids Research" } } ], "credit": [], "owner": "elenadelpup", "additionDate": "2025-05-15T11:35:23.428773Z", "lastUpdate": "2025-05-19T08:49:45.958175Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "WaveSeekerNet", "description": "Accurate Prediction of Influenza A Virus Subtypes and Host Source Using Attention-Based Deep Learning", "homepage": "https://github.com/nhhaidee/WaveSeekerNet", "biotoolsID": "waveseekernet", "biotoolsCURIE": "biotools:waveseekernet", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Bioinformatics portal" ], "topic": [], "operatingSystem": [ "Linux", "Windows" ], "language": [ "Python" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [], "credit": [], "owner": "nhhaidee", "additionDate": "2025-05-16T17:20:29.908599Z", "lastUpdate": "2025-05-16T17:20:29.911396Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Data Integration Quality Check Tool (DIQCT)", "description": "The Tool performs a quality assessment of a dataset based on a series of requirements that are set specifically for each use case. The tool informs the user of corrective actions that need to be taken prior to the data provision to ensure that the data provided is of high quality and reports a series of quality metrics characterizing the specific dataset.", "homepage": "https://www.mdpi.com/2939712", "biotoolsID": "data_integration_quality_check_tool_diqct", "biotoolsCURIE": "biotools:data_integration_quality_check_tool_diqct", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Desktop application", "Suite" ], "topic": [ { "uri": "http://edamontology.org/topic_3572", "term": "Data quality management" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python", "R" ], "license": "Proprietary", "collectionID": [ "eucaim" ], "maturity": "Mature", "cost": "Commercial", "accessibility": "Restricted access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://docs.google.com/document/d/1EnkkmFOGAo-WhPwx3OQ8vxSb3FKFn4b-/edit?usp=sharing&ouid=106267739460923945711&rtpof=true&sd=true", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.3390/info15090533", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Toward Ensuring Data Quality in Multi-Site Cancer Imaging Repositories", "abstract": "Cancer remains a major global health challenge, affecting diverse populations across various demographics. Integrating Artificial Intelligence (AI) into clinical settings to enhance disease outcome prediction presents notable challenges. This study addresses the limitations of AI-driven cancer care due to low-quality datasets by proposing a comprehensive three-step methodology to ensure high data quality in large-scale cancer-imaging repositories. Our methodology encompasses (i) developing a Data Quality Conceptual Model with specific metrics for assessment, (ii) creating a detailed data-collection protocol and a rule set to ensure data homogeneity and proper integration of multi-source data, and (iii) implementing a Data Integration Quality Check Tool (DIQCT) to verify adherence to quality requirements and suggest corrective actions. These steps are designed to mitigate biases, enhance data integrity, and ensure that integrated data meets high-quality standards. We applied this methodology within the INCISIVE project, an EU-funded initiative aimed at a pan-European cancer-imaging repository. The use-case demonstrated the effectiveness of our approach in defining quality rules and assessing compliance, resulting in improved data integration and higher data quality. The proposed methodology can assist the deployment of big data centralized or distributed repositories with data from diverse data sources, thus facilitating the development of AI tools.", "date": "2024-09-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Kosvyra A." }, { "name": "Filos D.T." }, { "name": "Fotopoulos D.T." }, { "name": "Tsave O." }, { "name": "Chouvarda I." } ], "journal": "Information (Switzerland)" } }, { "doi": "10.1109/EMBC48229.2022.9871018", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Data Quality Check in Cancer Imaging Research: Deploying and Evaluating the DIQCT Tool", "abstract": "Data harmonization is one of the greatest challenges in cancer imaging studies, especially when it comes to multi-source data provision. Properly integrated data deriving from various sources can ensure data fairness on one side and can lead to a trusted dataset that will enhance AI engine development on the other side. Towards this direction, we are presenting a data integration quality check tool that ensures that all data uploaded to the repository are homogenized and share the same principles. The tool's aim is to report any human-induced errors and propose corrective actions. It focuses on checking the data prior to their upload to the repository in five levels: (i) clinical metadata integrity, (ii) template-imaging consistency, (iii) anonymization protocol applied, (iv) imaging analysis requirements, (v) case completeness. The tool produces reports with the corrective actions that must be followed by the user. This way the tool ensures that the data that will become available to the developers of the AI engine are homogenized, properly structured and contain all the necessary information needed for the analysis. The tool was validated in two rounds, internal and external, and at the user experience level. Clinical Relevance - Supporting the harmonized preparation and provision of medical imaging data and related clinical data will ensure data fairness and enhance the AI development.", "date": "2022-01-01T00:00:00Z", "citationCount": 3, "authors": [ { "name": "Kosvyra A." }, { "name": "Filos D." }, { "name": "Fotopoulos D." }, { "name": "Tsave O." }, { "name": "Chouvarda I." } ], "journal": "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS" } } ], "credit": [ { "name": "Ioanna Chouvarda", "email": "ioannach@auth.gr", "url": null, "orcidid": "https://orcid.org/0000-0001-8915-6658", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "owner": "authEucaim", "additionDate": "2025-05-16T09:56:39.880752Z", "lastUpdate": "2025-05-16T12:48:52.350815Z", "editPermission": { "type": "group", "authors": [ "dimfilos" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "VIPER", "description": "VIPER is a framework and tool for the automated design of inhibitory decoy peptides based on structural binding data of two proteins.", "homepage": "https://github.com/A-Klingenberg/VIPER/tree/main", "biotoolsID": "viper-decoypep", "biotoolsCURIE": "biotools:viper-decoypep", "version": [ "1.0.1" ], "otherID": [ { "value": "doi:10.5281/zenodo.10897888", "type": "doi", "version": "1.0.1" } ], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_4008", "term": "Protein design" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1461", "term": "Protein-ligand complex" }, "format": [ { "uri": "http://edamontology.org/format_1476", "term": "PDB" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1460", "term": "Protein structure" }, "format": [ { "uri": "http://edamontology.org/format_1476", "term": "PDB" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_2814", "term": "Protein structure analysis" }, { "uri": "http://edamontology.org/topic_0781", "term": "Virology" }, { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" } ], "operatingSystem": [ "Linux" ], "language": [ "Python" ], "license": "MIT", "collectionID": [], "maturity": "Emerging", "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/A-Klingenberg/VIPER/tree/main", "type": [ "Repository" ], "note": "Source code" } ], "download": [ { "url": "https://github.com/A-Klingenberg/VIPER/releases/tag/1.0.1", "type": "Downloads page", "note": "Source code", "version": "1.0.1" } ], "documentation": [ { "url": "https://github.com/A-Klingenberg/VIPER/blob/main/VIPER_Manual.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1089/cmb.2024.0866", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "VIPER: Virus Inhibition Via Peptide Engineering and Receptor Mimicry", "abstract": "A key step in most viral infections is the binding of a viral protein to a host receptor, leading to the virus entering the host cell. Disrupting this protein-protein interaction is an effective strategy for preventing infection and subsequent disease. Building on recent advances in computational tools for structural biology, we introduce Virus Inhibition via Peptide Engineering and Receptor Mimicry (VIPER), a novel approach for the automatic derivation and optimization of biomimetic decoy peptides that mimic binding sites of human proteins. VIPER leverages structural data from human-pathogen protein complexes, yielding peptides that can competitively inhibit viral entry by mimicking the natural receptor. We computationally validated VIPER using molecular dynamics simulations and showcased its applicability on three clinically relevant viruses, highlighting its potential to accelerate therapeutic development. With a focus on reproducibility and extensibility, VIPER can facilitate the rapid development of antiviral inhibitors by automating the design and optimization of biomimetic compounds.", "date": "2025-04-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Klingenberg A.S." }, { "name": "Ghersi D." } ], "journal": "Journal of computational biology : a journal of computational molecular cell biology" } }, { "doi": "10.5281/zenodo.10897858", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Anna Sophie Klingenberg", "email": "aklingenberg@unomaha.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer" ], "note": null }, { "name": "Dario Ghersi", "email": "dghersi@unomaha.edu", "url": null, "orcidid": "https://orcid.org/0000-0002-0630-0843", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "owner": "aklingenberg", "additionDate": "2024-03-30T14:51:09.814337Z", "lastUpdate": "2025-05-16T12:04:59.507285Z", "editPermission": { "type": "group", "authors": [ "aklingenberg" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "metaGEM", "description": "Reconstruction of genome scale metabolic models directly from metagenomes.\n\nA Snakemake-based workflow to generate high quality metagenome assembled genomes from short read paired-end data, reconstruct genome scale metabolic models, and perform community metabolic interaction simulations on high performance computing clusters.\n\nmetaGEM integrates an array of existing bioinformatics and metabolic modeling tools using Snakemake, for the purpose of interrogating social interactions in bacterial communities of the human gut microbiome. From WMGS datasets, metagenome assembled genomes (MAGs) are reconstructed, which are then converted into genome-scale metabolic models (GEMs) for in silico simulations of cross feeding interactions within sample based communities. Additional outputs include abundance estimates, taxonomic assignment, growth rate estimation, pangenome analysis, and eukaryotic MAG identification.", "homepage": "https://github.com/franciscozorrilla/metaGEM", "biotoolsID": "metagem", "biotoolsCURIE": "biotools:metagem", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0310", "term": "Sequence assembly" }, { "uri": "http://edamontology.org/operation_3660", "term": "Metabolic network modelling" }, { "uri": "http://edamontology.org/operation_2426", "term": "Modelling and simulation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" }, { "uri": "http://edamontology.org/topic_3697", "term": "Microbial ecology" }, { "uri": "http://edamontology.org/topic_3407", "term": "Endocrinology and metabolism" }, { "uri": "http://edamontology.org/topic_0196", "term": "Sequence assembly" }, { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" } ], "operatingSystem": [], "language": [ "Python", "R", "Shell" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1101/2020.12.31.424982", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Aleksej Zelezniak", "email": "azelnet@gmail.com", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "owner": "Niclaskn", "additionDate": "2021-03-19T08:29:11Z", "lastUpdate": "2025-05-16T09:58:56.886153Z", "editPermission": { "type": "group", "authors": [ "metagenomez" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "Bracken", "description": "Statistical method that computes the abundance of species in DNA sequences from a metagenomics sample.", "homepage": "https://ccb.jhu.edu/software/bracken/", "biotoolsID": "bracken", "biotoolsCURIE": "biotools:bracken", "version": [], "otherID": [], "relation": [ { "biotoolsID": "kraken2", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2238", "term": "Statistical calculation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" }, { "uri": "http://edamontology.org/topic_3697", "term": "Microbial ecology" } ], "operatingSystem": [ "Linux" ], "language": [ "Perl", "Python" ], "license": "GPL-3.0", "collectionID": [ "Animal and Crop Genomics" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/jenniferlu717/Bracken", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/jenniferlu717/Bracken/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [], "documentation": [ { "url": "https://ccb.jhu.edu/software/bracken/index.shtml?t=manual", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.7717/peerj-cs.104", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Bracken: Estimating species abundance in metagenomics data", "abstract": "Metagenomic experiments attempt to characterize microbial communities using high-throughput DNA sequencing. Identification of the microorganisms in a sample provides information about the genetic profile, population structure, and role of microorganisms within an environment. Until recently, most metagenomics studies focused on high-level characterization at the level of phyla, or alternatively sequenced the 16S ribosomalRNAgene that is present in bacterial species. As the cost of sequencing has fallen, though, metagenomics experiments have increasingly used unbiased shotgun sequencing to capture all the organisms in a sample. This approach requires a method for estimating abundance directly from the raw read data. Here we describe a fast, accurate new method that computes the abundance at the species level using the reads collected in a metagenomics experiment. Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN) uses the taxonomic assignments made by Kraken, a very fast read-level classifier, along with information about the genomes themselves to estimate abundance at the species level, the genus level, or above. We demonstrate that Bracken can produce accurate species- and genus-level abundance estimates even when a sample contains multiple near-identical species.", "date": "2017-01-01T00:00:00Z", "citationCount": 1054, "authors": [ { "name": "Lu J." }, { "name": "Breitwieser F.P." }, { "name": "Thielen P." }, { "name": "Salzberg S.L." } ], "journal": "PeerJ Computer Science" } } ], "credit": [ { "name": null, "email": "jlu26@jhmi.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "admin", "additionDate": "2017-08-20T15:17:20Z", "lastUpdate": "2025-05-15T14:35:55.934316Z", "editPermission": { "type": "group", "authors": [ "animalandcropgenomics", "ELIXIR-CZ", "bebatut", "vashokan" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "kraken2", "description": "Kraken 2 is the newest version of Kraken, a taxonomic classification system using exact k-mer matches to achieve high accuracy and fast classification speeds. This classifier matches each k-mer within a query sequence to the lowest common ancestor (LCA) of all genomes containing the given k-mer. The k-mer assignments inform the classification algorithm.\nAny assumption that Kraken’s raw read assignments can be directly translated into species or strain-level abundance estimates is flawed. Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN), estimates species abundances in metagenomics samples by probabilistically re-distributing reads in the taxonomic tree. (Lu, Jennifer et al. “Bracken: estimating species abundance in metagenomics data.”)", "homepage": "https://ccb.jhu.edu/software/kraken2/", "biotoolsID": "kraken2", "biotoolsCURIE": "biotools:kraken2", "version": [ "2.0.8-beta" ], "otherID": [], "relation": [ { "biotoolsID": "kraken", "type": "isNewVersionOf" }, { "biotoolsID": "bracken", "type": "usedBy" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3460", "term": "Taxonomic classification" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3494", "term": "DNA sequence" }, "format": [ { "uri": "http://edamontology.org/format_1930", "term": "FASTQ" }, { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3028", "term": "Taxonomy" }, "format": [ { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] } ], "note": null, "cmd": "`kraken2 --db <kraken2_database> <input.fastq>`" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0637", "term": "Taxonomy" }, { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" } ], "operatingSystem": [], "language": [ "C++", "Perl" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/DerrickWood/kraken2", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/DerrickWood/kraken2/archive/v2.0.8-beta.tar.gz", "type": "Source code", "note": null, "version": "2.0.8-beta" } ], "documentation": [ { "url": "https://github.com/DerrickWood/kraken2/wiki/Manual", "type": [ "User manual" ], "note": null }, { "url": "https://benlangmead.github.io/aws-indexes/k2", "type": [ "User manual" ], "note": "Links to multiple Kraken 2 and bracken databases and indexes" } ], "publication": [ { "doi": "10.1101/762302", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Derrick E. Wood", "email": null, "url": null, "orcidid": "http://orcid.org/0000-0002-7429-1854", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Jennifer Lu", "email": null, "url": null, "orcidid": "http://orcid.org/0000-0001-9167-2002", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Ben Langmead", "email": "langmea@cs.jhu.edu", "url": null, "orcidid": "http://orcid.org/0000-0003-2437-1976", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "owner": "pvanheus", "additionDate": "2019-09-13T12:51:16Z", "lastUpdate": "2025-05-15T14:35:24.829481Z", "editPermission": { "type": "group", "authors": [ "vashokan" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "pytximport", "description": "pytximport is a Python package for efficient (gene-)count estimation from transcript quantification files produced by pseudoalignment/quasi-mapping tools such as salmon, kallisto, rsem and others.", "homepage": "https://github.com/complextissue/pytximport", "biotoolsID": "pytximport", "biotoolsCURIE": "biotools:pytximport", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3800", "term": "RNA-Seq quantification" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [], "topic": [], "operatingSystem": [], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://pytximport.complextissue.com/en/stable/", "type": [ "API documentation" ], "note": "Includes vignette" } ], "publication": [ { "doi": "10.1093/bioinformatics/btae700", "pmid": "39565903", "pmcid": null, "type": [ "Method" ], "version": null, "note": null, "metadata": { "title": "Gene count estimation with pytximport enables reproducible analysis of bulk RNA sequencing data in Python", "abstract": "Transcript quantification tools efficiently map bulk RNA sequencing (RNA-seq) reads to reference transcriptomes. However, their output consists of transcript count estimates that are subject to multiple biases and cannot be readily used with existing differential gene expression analysis tools in Python. Here we present pytximport, a Python implementation of the tximport R package that supports a variety of input formats, different modes of bias correction, inferential replicates, gene-level summarization of transcript counts, transcript-level exports, transcript-to-gene mapping generation, and optional filtering of transcripts by biotype. pytximport is part of the scverse ecosystem of open-source Python software packages for omics analyses and includes both a Python as well as a command-line interface. With pytximport, we propose a bulk RNA-seq analysis workflow based on Bioconda and scverse ecosystem packages, ensuring reproducible analyses through Snakemake rules. We apply this pipeline to a publicly available RNA-seq dataset, demonstrating how pytximport enables the creation of Python-centric workflows capable of providing insights into transcriptomic alterations.", "date": "2024-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Kuehl M." }, { "name": "Wong M.N." }, { "name": "Wanner N." }, { "name": "Bonn S." }, { "name": "Puelles V.G." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Malte Kuehl", "email": "malte.kuehl@clin.au.dk", "url": null, "orcidid": "https://orcid.org/0000-0003-4167-2498", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "maltekuehl", "additionDate": "2025-05-15T09:02:59.172438Z", "lastUpdate": "2025-05-15T09:02:59.174828Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "KVFinder-web", "description": "KVFinder-web is an open-source web-based application of an updated version of parKVFinder software (v1.2.0) for cavity detection and characterization of any type of biomolecular structure, including but not limited to proteins and nucleic acids. \n\nKVFinder-web employs a geometric grid-and-sphere-based method with a dual-probe system for efficient cavity detection. The web-based application provides comprehensive characterizations, including shape, volume, area, depth, constitutional, and hydropathy information.", "homepage": "https://kvfinder-web.cnpem.br/", "biotoolsID": "kvfinder-web", "biotoolsCURIE": "biotools:kvfinder-web", "version": [ "v1.1.1" ], "otherID": [], "relation": [ { "biotoolsID": "parKVFinder", "type": "uses" }, { "biotoolsID": "KVFinder", "type": "isNewVersionOf" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2575", "term": "Binding site prediction" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0883", "term": "Structure" }, "format": [ { "uri": "http://edamontology.org/format_1476", "term": "PDB" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2878", "term": "Protein structural motif" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Web service", "Web API" ], "topic": [ { "uri": "http://edamontology.org/topic_1317", "term": "Structural biology" }, { "uri": "http://edamontology.org/topic_3307", "term": "Computational biology" }, { "uri": "http://edamontology.org/topic_0166", "term": "Protein structural motifs and surfaces" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "R", "C", "Other" ], "license": "Apache-2.0", "collectionID": [ "COVID-19", "Rare Disease", "KVFinder suite" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/LBC-LNBio/KVFinder-web", "type": [ "Other" ], "note": null }, { "url": "https://github.com/LBC-LNBio/KVFinder-web-service", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/LBC-LNBio/KVFinder-web-portal", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/LBC-LNBio/PyMOL-KVFinder-web-Tools", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "https://lbc-lnbio.github.io/KVFinder-web/", "type": [ "API documentation" ], "note": null } ], "publication": [ { "doi": "10.1093/nar/gkad324", "pmid": "37140050", "pmcid": "PMC10320092", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "KVFinder-web: A web-based application for detecting and characterizing biomolecular cavities", "abstract": "Molecular interactions that modulate catalytic processes occur mainly in cavities throughout the molecular surface. Such interactions occur with specific small molecules due to geometric and physicochemical complementarity with the receptor. In this scenario, we present KVFinder-web, an open-source web-based application of parKVFinder software for cavity detection and characterization of biomolecular structures. The KVFinder-web has two independent components: A RESTful web service and a web graphical portal. Our web service, KVFinder-web service, handles client requests, manages accepted jobs, and performs cavity detection and characterization on accepted jobs. Our graphical web portal, KVFinder-web portal, provides a simple and straightforward page for cavity analysis, which customizes detection parameters, submits jobs to the web service component, and displays cavities and characterizations. We provide a publicly available KVFinder-web at https://kvfinder-web.cnpem.br, running in a cloud environment as docker containers. Further, this deployment type allows KVFinder-web components to be configured locally and customized according to user demand. Hence, users may run jobs on a locally configured service or our public KVFinder-web.", "date": "2023-07-05T00:00:00Z", "citationCount": 13, "authors": [ { "name": "Guerra J.V.S." }, { "name": "Ribeiro-Filho H.V." }, { "name": "Pereira J.G.C." }, { "name": "Lopes-De-Oliveira P.S." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1021/acs.jcim.3c00328", "pmid": "37129917", "pmcid": null, "type": [ "Benchmarking study" ], "version": null, "note": null, "metadata": { "title": "Cavity Characterization in Supramolecular Cages", "abstract": "Confining molecular guests within artificial hosts has provided a major driving force in the rational design of supramolecular cages with tailored properties. Over the last 30 years, a set of design strategies have been developed that enabled the controlled synthesis of a myriad of cages. Recently, there has been a growing interest in involving in silico methods in this toolbox. Cavity shape and size are important parameters that can be easily accessed by inexpensive geometric algorithms. Although these algorithms are well developed for the detection of nonartificial cavities (e.g., enzymes), they are not routinely used for the rational design of supramolecular cages. In order to test the capabilities of this tool, we have evaluated the performance and characteristics of seven different cavity characterization software in the context of 22 analogues of well-known supramolecular cages. Among the tested software, KVFinder project and Fpocket proved to be the most software to characterize supramolecular cavities. With the results of this work, we aim to popularize this underused technique within the supramolecular community.", "date": "2023-06-26T00:00:00Z", "citationCount": 3, "authors": [ { "name": "Guerra J.V.S." }, { "name": "Alves L.F.G." }, { "name": "Bourissou D." }, { "name": "Lopes-De-Oliveira P.S." }, { "name": "Szaloki G." } ], "journal": "Journal of Chemical Information and Modeling" } } ], "credit": [ { "name": "João V. S. Guerra", "email": "joao.guerra@lnbio.cnpem.br", "url": "https://github.com/jvsguerra", "orcidid": "https://orcid.org/0000-0002-6800-4425", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Developer", "Primary contact" ], "note": null }, { "name": "Helder V. Ribeiro-Filho", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "José G. C. Pereira", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Paulo S. 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"editPermission": { "type": "group", "authors": [ "Bharathi" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "NetGene2", "description": "Neural network predictions of splice sites in human, C. elegans and A. thaliana DNA.", "homepage": "http://cbs.dtu.dk/services/NetGene2/", "biotoolsID": "netgene2", "biotoolsCURIE": "biotools:netgene2", "version": [ "1.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0433", "term": "Splice site prediction" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2044", "term": "Sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1277", "term": "Protein features" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" } ] }, { "data": { "uri": "http://edamontology.org/data_2955", "term": 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"pmid": "8811101", "pmcid": "PMC146109", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "Splice site prediction in Arabidopsis thaliana pre-mRNA by combining local and global sequence information", "abstract": "Artificial neural networks have been combined with a rule based system to predict intron splice sites in the dicot plant Arabidopsis thaliana. A two step prediction scheme, where a global prediction of the coding potential regulates a cutoff level for a local prediction of splice sites, is refined by rules based on splice site confidence values, prediction scores, coding context and distances between potential splice sites. In this approach, the prediction of splice sites mutually affect each other in a non-local manner. The combined approach drastically reduces the large amount of false positive splice sites normally haunting splice site prediction. An analysis of the errors made by the networks in the first step of the method revealed a previously unknown feature, a frequent T-tract prolongation containing cryptic acceptor sites in the 5' end of exons. The method presented here has been compared with three other approaches, GeneFinder, GeneMark and Grail. Overall the method presented here is an order of magnitude better. We show that the new method is able to find a donor site in the coding sequence for the jelly fish Green Fluorescent Protein, exactly at the position that was experimentally observed in A.thaliana transformants. Predictions for alternatively spliced genes are also presented, together with examples of genes from other dicots, monocots and algae. The method has been made available through electronic mail.", "date": "1996-01-01T00:00:00Z", "citationCount": 690, "authors": [ { "name": "Hebsgaard S.M." }, { "name": "Korning P.G." }, { "name": "Tolstrup N." }, { "name": "Engelbrecht J." }, { "name": "Rouze P." }, { "name": "Brunak S." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1093/nar/24.17.3439", "pmid": "8811101", "pmcid": "PMC146109", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "Splice site prediction in Arabidopsis thaliana pre-mRNA by combining local and global sequence information", "abstract": "Artificial neural networks have been combined with a rule based system to predict intron splice sites in the dicot plant Arabidopsis thaliana. A two step prediction scheme, where a global prediction of the coding potential regulates a cutoff level for a local prediction of splice sites, is refined by rules based on splice site confidence values, prediction scores, coding context and distances between potential splice sites. In this approach, the prediction of splice sites mutually affect each other in a non-local manner. The combined approach drastically reduces the large amount of false positive splice sites normally haunting splice site prediction. An analysis of the errors made by the networks in the first step of the method revealed a previously unknown feature, a frequent T-tract prolongation containing cryptic acceptor sites in the 5' end of exons. The method presented here has been compared with three other approaches, GeneFinder, GeneMark and Grail. Overall the method presented here is an order of magnitude better. We show that the new method is able to find a donor site in the coding sequence for the jelly fish Green Fluorescent Protein, exactly at the position that was experimentally observed in A.thaliana transformants. Predictions for alternatively spliced genes are also presented, together with examples of genes from other dicots, monocots and algae. 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The main focus of PIA lays on the integrated inference algorithms, i.e. concluding the proteins from a set of identified spectra. But it also allows you to inspect your peptide spectrum matches, calculate FDR values across different search engine results and visualize the correspondence between PSMs, peptides and proteins. Search engine results in several formats peptide spectrum matches (PSMs) and peptides Inferred Proteins", "cmd": null } ], "toolType": [ "Command-line tool", "Library", "Desktop application", "Workflow" ], "topic": [ { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_3120", "term": "Protein variants" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "Java" ], "license": "BSD-3-Clause", "collectionID": [ "KNIME", "de.NBI", "Proteomics", "BioInfra.Prot", "CUBiMed.RUB" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [ "Germany" ], "elixirCommunity": [ "Proteomics" ], "link": [ { "url": "https://github.com/medbioinf/pia", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/mpc-bioinformatics/pia", "type": "Source code", "note": null, "version": null }, { "url": "http://bioconda.github.io/recipes/pia/README.html", "type": "Software package", "note": null, "version": null }, { "url": "https://github.com/mpc-bioinformatics/pia/releases", "type": "Binaries", "note": null, "version": null }, { "url": "https://hub.docker.com/r/julianusz/pia", "type": "Container file", "note": null, "version": null } ], "documentation": [ { "url": "https://github.com/medbioinf/pia/wiki", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1021/acs.jproteome.5b00121", "pmid": "25938255", "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "PIA: An Intuitive Protein Inference Engine with a Web-Based User Interface", "abstract": "Protein inference connects the peptide spectrum matches (PSMs) obtained from database search engines back to proteins, which are typically at the heart of most proteomics studies. Different search engines yield different PSMs and thus different protein lists. Analysis of results from one or multiple search engines is often hampered by different data exchange formats and lack of convenient and intuitive user interfaces. We present PIA, a flexible software suite for combining PSMs from different search engine runs and turning these into consistent results. PIA can be integrated into proteomics data analysis workflows in several ways. A user-friendly graphical user interface can be run either locally or (e.g., for larger core facilities) from a central server. For automated data processing, stand-alone tools are available. PIA implements several established protein inference algorithms and can combine results from different search engines seamlessly. On several benchmark data sets, we show that PIA can identify a larger number of proteins at the same protein FDR when compared to that using inference based on a single search engine. PIA supports the majority of established search engines and data in the mzIdentML standard format. It is implemented in Java and freely available at https://github.com/mpc-bioinformatics/pia.", "date": "2015-07-02T00:00:00Z", "citationCount": 57, "authors": [ { "name": "Uszkoreit J." }, { "name": "Maerkens A." }, { "name": "Perez-Riverol Y." }, { "name": "Meyer H.E." }, { "name": "Marcus K." }, { "name": "Stephan C." }, { "name": "Kohlbacher O." }, { "name": "Eisenacher M." } ], "journal": "Journal of Proteome Research" } }, { "doi": "10.1021/acs.jproteome.8b00723", "pmid": "30474983", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Protein Inference Using PIA Workflows and PSI Standard File Formats", "abstract": "Proteomics using LC-MS/MS has become one of the main methods to analyze the proteins in biological samples in high-throughput. But the existing mass-spectrometry instruments are still limited with respect to resolution and measurable mass ranges, which is one of the main reasons why shotgun proteomics is the major approach. Here proteins are digested, which leads to the identification and quantification of peptides instead. While often neglected, the important step of protein inference needs to be conducted to infer from the identified peptides to the actual proteins in the original sample. In this work, we highlight some of the previously published and newly added features of the tool PIA - Protein Inference Algorithms, which helps the user with the protein inference of measured samples. We also highlight the importance of the usage of PSI standard file formats, as PIA is the only current software supporting all available standards used for spectrum identification and protein inference. Additionally, we briefly describe the benefits of working with workflow environments for proteomics analyses and show the new features of the PIA nodes for the KNIME Analytics Platform. Finally, we benchmark PIA against a recently published data set for isoform detection. PIA is open source and available for download on GitHub (https://github.com/mpc-bioinformatics/pia) or directly via the community extensions inside the KNIME analytics platform.", "date": "2019-02-01T00:00:00Z", "citationCount": 30, "authors": [ { "name": "Uszkoreit J." }, { "name": "Perez-Riverol Y." }, { "name": "Eggers B." }, { "name": "Marcus K." }, { "name": "Eisenacher M." } ], "journal": "Journal of Proteome Research" } } ], "credit": [ { "name": "Julian Uszkoreit", "email": "julian.uszkoreit@rub.de", "url": null, "orcidid": "http://orcid.org/0000-0001-7522-4007", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Maintainer" ], "note": null }, { "name": "CUBiMed.RUB", "email": "cubimed@rub.de", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null } ], "owner": "julianu", "additionDate": "2016-07-12T10:54:05Z", "lastUpdate": "2025-05-02T13:15:26.717477Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "StripePy", "description": "StripePy recognizes architectural stripes in 3C and Hi-C contact maps using geometric reasoning", "homepage": "https://github.com/paulsengroup/StripePy", "biotoolsID": "stripepy", "biotoolsCURIE": "biotools:stripepy", "version": [ "0.0.1", "0.0.2", "1.0.0", "1.1.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" } ], "operatingSystem": [ "Linux", "Mac", "Windows" ], "language": [ "Python" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/paulsengroup/StripePy/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "https://github.com/paulsengroup/StripePy/discussions", "type": [ "Discussion forum" ], "note": null }, { "url": "https://github.com/paulsengroup/StripePy.git", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/paulsengroup/StripePy/releases", "type": "Source code", "note": null, "version": null }, { "url": "https://hub.docker.com/r/paulsengroup/stripepy", "type": "Container file", "note": null, "version": null }, { "url": "https://doi.org/10.5281/zenodo.14283921", "type": "Test data", "note": null, "version": null }, { "url": "https://pypi.org/project/stripepy-hic/", "type": "Binaries", "note": null, "version": null }, { "url": "https://anaconda.org/bioconda/stripepy-hic", "type": "Binaries", "note": null, "version": null } ], "documentation": [ { "url": "https://github.com/paulsengroup/StripePy/blob/main/README.md", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1101/2024.12.20.629789", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [], "owner": "robomics", "additionDate": "2024-12-21T15:24:55.662209Z", "lastUpdate": "2025-05-02T11:01:00.332596Z", "editPermission": { "type": "group", "authors": [ "rea1991" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "epimutacions", "description": "The package includes some statistical outlier detection methods for epimutations detection in DNA methylation data. The methods included in the package are MANOVA, Multivariate linear models, isolation forest, robust mahalanobis distance, quantile and beta. The methods compare a case sample with a suspected disease against a reference panel (composed of healthy individuals) to identify epimutations in the given case sample. It also contains functions to annotate and visualize the identified epimutations.", "homepage": "https://www.bioconductor.org/packages/release/bioc/html/epimutacions.html", "biotoolsID": "epimutacions", "biotoolsCURIE": "biotools:epimutacions", "version": [ "1.2.0" ], "otherID": [], "relation": [], "function": [], "toolType": [], "topic": [ { "uri": "http://edamontology.org/topic_3295", "term": "Epigenetics" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://www.bioconductor.org/packages/epimutacions", "type": [ "Mirror" ], "note": null } ], "download": [ { "url": "https://www.bioconductor.org/packages/release/bioc/src/contrib/epimutacions_1.2.0.tar.gz", "type": "Source code", "note": null, "version": "1.2.0" } ], "documentation": [ { "url": "https://www.bioconductor.org/packages/release/bioc/manuals/epimutacions/man/epimutacions.pdf", "type": [ "API documentation" ], "note": null }, { "url": "https://www.bioconductor.org/packages/release/bioc/vignettes/epimutacions/inst/doc/epimutacions.html", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1080/15592294.2023.2230670", "pmid": "37409354", "pmcid": "PMC10327521", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Epimutation detection in the clinical context: guidelines and a use case from a new Bioconductor package", "abstract": "Epimutations are rare alterations of the normal DNA methylation pattern at specific loci, which can lead to rare diseases. Methylation microarrays enable genome-wide epimutation detection, but technical limitations prevent their use in clinical settings: methods applied to rare diseases’ data cannot be easily incorporated to standard analyses pipelines, while epimutation methods implemented in R packages (ramr) have not been validated for rare diseases. We have developed epimutacions, a Bioconductor package (https://bioconductor.org/packages/release/bioc/html/epimutacions.html). epimutacions implements two previously reported methods and four new statistical approaches to detect epimutations, along with functions to annotate and visualize epimutations. Additionally, we have developed an user-friendly Shiny app to facilitate epimutations detection (https://github.com/isglobal-brge/epimutacionsShiny) to non-bioinformatician users. We first compared the performance of epimutacions and ramr packages using three public datasets with experimentally validated epimutations. Methods in epimutacions had a high performance at low sample sizes and outperformed methods in ramr. Second, we used two general population children cohorts (INMA and HELIX) to determine the technical and biological factors that affect epimutations detection, providing guidelines on how designing the experiments or preprocessing the data. In these cohorts, most epimutations did not correlate with detectable regional gene expression changes. Finally, we exemplified how epimutacions can be used in a clinical context. We run epimutacions in a cohort of children with autism disorder and identified novel recurrent epimutations in candidate genes for autism. Overall, we present epimutacions a new Bioconductor package for incorporating epimutations detection to rare disease diagnosis and provide guidelines for the design and data analyses.", "date": "2023-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Ruiz-Arenas C." }, { "name": "Abarrategui L." }, { "name": "Hernandez-Ferrer C." }, { "name": "Escriba-Montagut X." }, { "name": "Pelegri-Siso D." }, { "name": "Ryser-Welch P." }, { "name": "Vrijheid M." }, { "name": "Bustamante M." }, { "name": "Grazuleviciene R." }, { "name": "Lepeule J." }, { "name": "Mathai M." }, { "name": "Vafeiadi M." }, { "name": "Beltran S." }, { "name": "Perez-Jurado L.A." }, { "name": "Gonzalez J.R." } ], "journal": "Epigenetics" } } ], "credit": [ { "name": "Carlos Ruiz-Arenas", "email": "carlos.ruiz@isglobal.org", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Leire Abarrategui", "email": "leire.abarrategui@isglobal.org", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Carles Hernandez-Ferrer", "email": "carles.hernandez@isglobal.org", "url": "http://www.carleshf.com", "orcidid": "https://orcid.org/0000-0002-8029-7160", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Dolors Pelegri-Siso", "email": "dolors.pelegri@isglobal.org", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Maintainer" ], "note": null }, { "name": "Juan R. 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All rights reserved.High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomolecular structural information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of MS data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease.", "date": "2016-08-30T00:00:00Z", "citationCount": 246, "authors": [ { "name": "Rost H.L." }, { "name": "Sachsenberg T." }, { "name": "Aiche S." }, { "name": "Bielow C." }, { "name": "Weisser H." }, { "name": "Aicheler F." }, { "name": "Andreotti S." }, { "name": "Ehrlich H.-C." }, { "name": "Gutenbrunner P." }, { "name": "Kenar E." }, { "name": "Liang X." }, { "name": "Nahnsen S." }, { "name": "Nilse L." }, { "name": "Pfeuffer J." }, { "name": "Rosenberger G." }, { "name": "Rurik M." }, { "name": "Schmitt U." }, { "name": "Veit J." }, { "name": "Walzer M." }, { "name": "Wojnar D." }, { "name": "Wolski W.E." }, { "name": "Schilling O." }, { "name": "Choudhary J.S." }, { "name": "Malmstrom L." }, { "name": "Aebersold R." }, { "name": "Reinert K." }, { "name": "Kohlbacher O." } ], "journal": "Nature Methods" } }, { "doi": "10.1007/978-1-60761-987-1_23", "pmid": null, "pmcid": null, "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "OpenMS and TOPP: open source software for LC-MS data analysis.", "abstract": "Proteomics experiments based on state-of-the-art mass spectrometry produce vast amounts of data, which cannot be analyzed manually. Hence, software is needed which is able to analyze the data in an automated fashion. The need for robust and reusable software tools triggered the development of libraries implementing different algorithms for the various analysis steps. OpenMS is such a software library and provides a wealth of data structures and algorithms for the analysis of mass spectrometric data. For users unfamiliar with programming, TOPP (\"The OpenMS Proteomics Pipeline\") offers a wide range of already implemented tools sharing the same interface and designed for a specific analysis task each. TOPP thus makes the sophisticated algorithms of OpenMS accessible to nonprogrammers. The individual TOPP tools can be strung together into pipelines for analyzing mass spectrometry-based experiments starting from the raw output of the mass spectrometer. These analysis pipelines can be constructed using a graphical editor. Even complex analytical workflows can thus be analyzed with ease.", "date": "2011-01-01T00:00:00Z", "citationCount": 65, "authors": [ { "name": "Bertsch A." }, { "name": "Gropl C." }, { "name": "Reinert K." }, { "name": "Kohlbacher O." } ], "journal": "Methods in molecular biology (Clifton, N.J.)" } } ], "credit": [ { "name": "ETH Zürich", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Eberhard-Karls-Universität Tübingen", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Freie Universität Berlin", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Center for Integrative Bioinformatics (CiBi)", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Consortium", "typeRole": [], "note": null }, { "name": "cibi", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "General Mailinglist", "email": "open-ms-general@lists.sourceforge.net", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Support" ], "note": null }, { "name": "General Mailinglist", "email": "open-ms-general@lists.sourceforge.net", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Hannes Röst", "email": null, "url": null, "orcidid": "http://orcid.org/0000-0003-0990-7488", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Contributor" ], "note": null } ], "owner": "samwein", "additionDate": "2016-01-19T16:39:56Z", "lastUpdate": "2025-04-30T11:39:30.251890Z", "editPermission": { "type": "group", "authors": [ "proteomics.bio.tools" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "pyOpenMS", "description": "Python library for mass spectrometry, specifically for the analysis of proteomics and metabolomics data.", "homepage": "https://github.com/OpenMS/pyopenms-extra/blob/master/docs/source/index.rst", "biotoolsID": "pyOpenMS", "biotoolsCURIE": "biotools:pyOpenMS", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_0601", "term": "Protein modifications" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "Python" ], "license": "BSD-3-Clause", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/OpenMS/pyopenms-extra", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/OpenMS/pyopenms-extra/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [], "documentation": [ { "url": "https://pyopenms.readthedocs.io", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.7287/PEERJ.PREPRINTS.27736", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Hannes L. Rost", "email": "hannes.rost@utoronto.ca", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "samwein", "additionDate": "2019-08-09T13:16:33Z", "lastUpdate": "2025-04-30T11:39:29.549782Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "OpenSWATH", "description": "OpenSWATH is a proteomics software that allows analysis of LC-MS/MS DIA (data independent acquisition) and implemented as part of OpenMS.", "homepage": "http://www.openswath.org", "biotoolsID": "OpenSWATH", "biotoolsCURIE": "biotools:OpenSWATH", "version": [ "OpenMS 2.4.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0335", "term": "Formatting" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3108", "term": "Experimental 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Rosenberger", "email": "gr2578@cumc.columbia.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "owner": "samwein", "additionDate": "2019-06-18T11:06:01Z", "lastUpdate": "2025-04-30T11:39:29.102190Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "OpenPepXL", "description": "An Open-Source Tool for Sensitive Identification of Cross-Linked Peptides in XL-MS.\n\nProtein-Protein Cross-Linking (OpenPepXL) – OpenMS.\n\nOpenPepXL is a protein-protein cross-link identification tool implemented in C++ as part of OpenMS. It works with all uncleavable labeled and label-free cross-linkers but not (yet) with cleavable ones.", "homepage": "https://openms.org/openpepxl", "biotoolsID": "openpepxl", "biotoolsCURIE": "biotools:openpepxl", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3631", "term": "Peptide identification" }, { "uri": "http://edamontology.org/operation_3629", "term": "Deisotoping" }, { "uri": "http://edamontology.org/operation_3767", "term": "Protein identification" }, { "uri": "http://edamontology.org/operation_3431", "term": "Deposition" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" } ], "operatingSystem": [], "language": [], "license": "BSD-3-Clause", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://www.openms.de/openpepxl", "type": [ "Other" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1074/MCP.TIR120.002186", "pmid": "33067342", "pmcid": "PMC7710140", "type": [], "version": null, "note": null, "metadata": { "title": "OpenPepXL: An Open-Source Tool for Sensitive Identification of Cross-Linked Peptides in XL-MS", "abstract": "© 2020 Netz et al. Published under exclusive license by The American Society for Biochemistry and Molecular Biology, Inc.Cross-linking MS (XL-MS) has been recognized as an effective source of information about protein structures and interactions. In contrast to regular peptide identification, XL-MS has to deal with a quadratic search space, where peptides from every protein could potentially be cross-linked to any other protein. To cope with this search space, most tools apply different heuristics for search space reduction. We introduce a new open-source XL-MS database search algorithm, OpenPepXL, which offers increased sensitivity compared with other tools. OpenPepXL searches the full search space of an XL-MS experiment without using heuristics to reduce it. Because of efficient data structures and built-in parallelization OpenPepXL achieves excellent runtimes and can also be deployed on large compute clusters and cloud services while maintaining a slim memory footprint. We compared OpenPepXL to several other commonly used tools for identification of noncleavable labeled and label-free cross-linkers on a diverse set of XL-MS experiments. In our first comparison, we used a data set from a fraction of a cell lysate with a protein database of 128 targets and 128 decoys. At 5% FDR, OpenPepXL finds from 7% to over 50% more unique residue pairs (URPs) than other tools. On data sets with available high-resolution structures for cross-link validation OpenPepXL reports from 7% to over 40% more structurally validated URPs than other tools. Additionally, we used a synthetic peptide data set that allows objective validation of cross-links without relying on structural information and found that OpenPepXL reports at least 12% more validated URPs than other tools. It has been built as part of the OpenMS suite of tools and supports Windows, macOS, and Linux operating systems. OpenPepXL also supports the MzIdentML 1.2 format for XL-MS identification results. It is freely available under a three-clause BSD license at https://openms.org/openpepxl.", "date": "2020-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Netz E." }, { "name": "Dijkstra T.M.H." }, { "name": "Sachsenberg T." }, { "name": "Zimmermann L." }, { "name": "Walzer M." }, { "name": "Monecke T." }, { "name": "Ficner R." }, { "name": "Dybkov O." }, { "name": "Urlaub H." }, { "name": "Kohlbacher O." } ], "journal": "Molecular and Cellular Proteomics" } } ], "credit": [ { "name": "Eugen Netz", "email": "eugen.netz@tuebingen.mpg.de", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Oliver Kohlbacher", "email": "oliver.kohlbacher@uni-tuebingen.de", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "owner": "samwein", "additionDate": "2021-01-18T11:10:26Z", "lastUpdate": "2025-04-30T11:39:28.349250Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "FLASHIda", "description": "FLASHIda is an intelligent online data acquisition algorithm for top-down proteomics (TDP) that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast decharging algorithms in FLASHDeconv and machine learning-based quality assessment to identify optimal precursors for fragmentation. Currently the C# source code and instruction of FLASHIda is available in here under a BSD three-clause license. 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FLASHIda combines fast decharging algorithms in FLASHDeconv and machine learning-based quality assessment to identify optimal precursors for fragmentation. Currently the C# source code and instruction of FLASHIda is available in here under a BSD three-clause license. We are working on merging FLASHIda into OpenMS.", "homepage": "https://www.openms.org/applications/flashida/", "biotoolsID": "flashida2", "biotoolsCURIE": "biotools:flashida2", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [], "topic": [], "operatingSystem": [], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [], "credit": [], "owner": "samwein", "additionDate": "2023-09-30T19:58:13.012473Z", "lastUpdate": "2025-04-30T11:39:17.046987Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "BioSurfDB", "description": "BioSurfDB is a online database for biodegradation and biosurfactants.", "homepage": "https://www.biosurfdb.org", "biotoolsID": "biosurfdb", "biotoolsCURIE": "biotools:biosurfdb", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0495", "term": "Local alignment" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3494", "term": "DNA sequence" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_2886", "term": "Protein sequence record" }, "format": [] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1383", "term": "Nucleic acid sequence alignment" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_1385", "term": "Hybrid sequence alignment" }, "format": [] } ], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_0349", "term": "Sequence database search (by property)" } ], "input": [], "output": [ { "data": { "uri": "http://edamontology.org/data_1026", "term": "Gene symbol" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_1027", "term": "Gene ID (NCBI)" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_2299", "term": "Gene name" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_3494", "term": "DNA sequence" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Database portal" ], "topic": [ { "uri": "http://edamontology.org/topic_3297", "term": "Biotechnology" }, { "uri": "http://edamontology.org/topic_3301", "term": "Microbiology" }, { "uri": "http://edamontology.org/topic_0821", "term": "Enzymes" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/database/bav033", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "BioSurfDB: Knowledge and algorithms to support biosurfactants and biodegradation studies", "abstract": "Crude oil extraction, transportation and use provoke the contamination of countless ecosystems. Therefore, bioremediation through surfactants mobilization or biodegradation is an important subject, both economically and environmentally. Bioremediation research had a great boost with the recent advances in Metagenomics, as it enabled the sequencing of uncultured microorganisms providing new insights on surfactant-producing and/or oil-degrading bacteria. Many research studies are making available genomic data from unknown organisms obtained from metagenomics analysis of oil-contaminated environmental samples. These new datasets are presently demanding the development of new tools and data repositories tailored for the biological analysis in a context of bioremediation data analysis. This work presents BioSurfDB, www.biosurfdb.org, a curated relational information system integrating data from: (i) metagenomes; (ii) organisms; (iii) biodegradation relevant genes; proteins and their metabolic pathways; (iv) bioremediation experiments results, with specific pollutants treatment efficiencies by surfactant producing organisms; and (v) a biosurfactant-curated list, grouped by producing organism, surfactant name, class and reference. 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null }, { "name": "MetExplore", "description": "Metabolic network curation, visualisation and omics data analysis. 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This metabolic network provides a rich knowledge to contextualize omics data and to elaborate hypotheses on metabolic modulations. Nevertheless, performing this kind of integrative analysis is challenging for end users with not sufficiently advanced computer skills since it requires the use of various tools and web servers. MetExplore offers an all-in-one online solution composed of interactive tools for metabolic network curation, network exploration and omics data analysis. In particular, it is possible to curate and annotate metabolic networks in a collaborative environment. The network exploration is also facilitated in MetExplore by a system of interactive tables connected to a powerful network visualization module. Finally, the contextualization of metabolic elements in the network and the calculation of over-representation statistics make it possible to interpret any kind of omics data.", "date": "2018-07-02T00:00:00Z", "citationCount": 92, "authors": [ { "name": "Cottret L." }, { "name": "Frainay C." }, { "name": "Chazalviel M." }, { "name": "Cabanettes F." }, { "name": "Gloaguen Y." }, { "name": "Camenen E." }, { "name": "Merlet B." }, { "name": "Heux S." }, { "name": "Portais J.-C." }, { "name": "Poupin N." }, { "name": "Vinson F." }, { "name": "Jourdan F." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1093/nar/gkq312", "pmid": "20444866", "pmcid": "PMC2896158", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "MetExplore: A web server to link metabolomic experiments and genome-scale metabolic networks", "abstract": "High-throughput metabolomic experiments aim at identifying and ultimately quantifying all metabolites present in biological systems. The metabolites are interconnected through metabolic reactions, generally grouped into metabolic pathways. Classical metabolic maps provide a relational context to help interpret metabolomics experiments and a wide range of tools have been developed to help place metabolites within metabolic pathways. However, the representation of metabolites within separate disconnected pathways overlooks most of the connectivity of the metabolome. By definition, reference pathways cannot integrate novel pathways nor show relationships between metabolites that may be linked by common neighbours without being considered as joint members of a classical biochemical pathway. MetExplore is a web server that offers the possibility to link metabolites identified in untargeted metabolomics experiments within the context of genome-scale reconstructed metabolic networks. The analysis pipeline comprises mapping metabolomics data onto the specific metabolic network of an organism, then applying graph-based methods and advanced visualization tools to enhance data analysis. The MetExplore web server is freely accessible at http://metexplore.toulouse.inra.fr. © The Author(s) 2010. Published by Oxford University Press.", "date": "2010-05-05T00:00:00Z", "citationCount": 144, "authors": [ { "name": "Cottret L." }, { "name": "Wildridge D." }, { "name": "Vinson F." }, { "name": "Barrett M.P." }, { "name": "Charles H." }, { "name": "Sagot M.-F." }, { "name": "Jourdan F." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1093/bioinformatics/btx588", "pmid": "28968733", "pmcid": "PMC5860210", "type": [ "Method" ], "version": null, "note": null, "metadata": { "title": "MetExploreViz: Web component for interactive metabolic network visualization", "abstract": "Summary MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyse omics data in a biochemical context.", "date": "2018-01-15T00:00:00Z", "citationCount": 36, "authors": [ { "name": "Chazalviel M." }, { "name": "Frainay C." }, { "name": "Poupin N." }, { "name": "Vinson F." }, { "name": "Merlet B." }, { "name": "Gloaguen Y." }, { "name": "Cottret L." }, { "name": "Jourdan F." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Fabien JOURDAN", "email": "fabien.jourdan@inra.fr", "url": "https://sites.google.com/site/fabienjourdan/", "orcidid": "https://orcid.org/0000-0001-9401-2894", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": null, "email": "ludovic.cottret@inra.fr", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": null, "email": "metexplore@oulouse.inra.fr", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Support" ], "note": null }, { "name": "MetaboHub", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Consortium", "typeRole": [], "note": null } ], "owner": "fabienjourdan", "additionDate": "2018-07-09T23:06:59Z", "lastUpdate": "2025-04-28T07:17:11.601858Z", "editPermission": { "type": "group", "authors": [ "lcottret", "Jennifer" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "EUCAIM DICOM Anonymizer", "description": "The EUCAIM DICOM Anonymizer anonymizes a set of DICOM files. 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This cloud-based computational environment enables researchers to construct customized analytical platforms suited to their specific research requirements. 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We re-analyze raw data from public sources (primarily NCBI GEO), annotate experimental conditions, conduct quality control and compute differential expression using standardized procedures. 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Unfortunately, the description, conditions and parameters of the experiments are less commonly formalized and often occur as natural language text. This hinders searching, high-throughput analysis, organization and integration of the datasets. Results: We use the lexical resources and software tools from the Unified Medical Language System (UMLS) to extract concepts from text. We then link the UMLS concepts to classes in open biomedical ontologies. The result is accessible and clear semantic annotations of gene expression experiments. We applied the method to 595 expression experiments from Gemma, a resource for re-use and meta-analysis of gene expression profiling data. We evaluated and corrected all stages of the annotation process. The majority of missed annotations were due to a lack of cross-references. The most error-prone stage was the extraction of concepts from phrases. Final review of the annotations in context of the experiments revealed 89% precision. A naive system, lacking the phrase to concept corrections is 68% precise. We have integrated this annotation pipeline into Gemma. © 2009 The Author(s).", "date": "2009-06-09T00:00:00Z", "citationCount": 9, "authors": [ { "name": "French L." }, { "name": "Lane S." }, { "name": "Law T." }, { "name": "Xu L." }, { "name": "Pavlidis P." } ], "journal": "Bioinformatics" } }, { "doi": "10.1093/database/baab006", "pmid": "33599246", "pmcid": "PMC7904053", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Curation of over 10 000 transcriptomic studies to enable data reuse", "abstract": "Vast amounts of transcriptomic data reside in public repositories, but effective reuse remains challenging. Issues include unstructured dataset metadata, inconsistent data processing and quality control, and inconsistent probe-gene mappings across microarray technologies. Thus, extensive curation and data reprocessing are necessary prior to any reuse. The Gemma bioinformatics system was created to help address these issues. Gemma consists of a database of curated transcriptomic datasets, analytical software, a web interface and web services. Here we present an update on Gemma's holdings, data processing and analysis pipelines, our curation guidelines, and software features. As of June 2020, Gemma contains 10 811 manually curated datasets (primarily human, mouse and rat), over 395 000 samples and hundreds of curated transcriptomic platforms (both microarray and RNA sequencing). Dataset topics were represented with 10 215 distinct terms from 12 ontologies, for a total of 54 316 topic annotations (mean topics/dataset = 5.2). While Gemma has broad coverage of conditions and tissues, it captures a large majority of available brain-related datasets, accounting for 34% of its holdings. Users can access the curated data and differential expression analyses through the Gemma website, RESTful service and an R package. Database URL: https://gemma.msl.ubc.ca/home.html", "date": "2021-01-01T00:00:00Z", "citationCount": 21, "authors": [ { "name": "Lim N." }, { "name": "Tesar S." }, { "name": "Belmadani M." }, { "name": "Poirier-Morency G." }, { "name": "Mancarci B.O." }, { "name": "Sicherman J." }, { "name": "Jacobson M." }, { "name": "Leong J." }, { "name": "Tan P." }, { "name": "Pavlidis P." } ], "journal": "Database" } } ], "credit": [ { "name": "Pavlidis Lab Support", "email": "pavlab-support@msl.ubc.ca", "url": "https://pavlab.msl.ubc.ca/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Support" ], "note": null } ], "owner": "artrymix", "additionDate": "2017-04-22T17:35:18Z", "lastUpdate": "2025-04-22T22:09:03.811075Z", "editPermission": { "type": "group", "authors": [ "artrymix", "paulpavlidis" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null } ] }