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Combines classical keyword search with AI-assisted exploration, including semantic keyword grouping, knowledge graph visualization, and AI-driven summarization. All AI-generated insights are traceable to source articles. Designed to support transparent, user-steered literature exploration for biology and biomedical research. Currently in early access.https://www.bionoculars.com/bionocularsbiotools:bionocularsRRID:SCR_028217rridWeb applicationWeb servicehttp://edamontology.org/topic_3344Biomedical sciencehttp://edamontology.org/topic_3303Medicinehttp://edamontology.org/topic_3070Biologyhttp://edamontology.org/topic_0218Natural language processinghttp://edamontology.org/topic_0089Ontology and terminologyhttp://edamontology.org/topic_3068Literature and languageMacLinuxWindowsProprietaryEmergingCommercialOpen access (with restrictions)http://edamontology.org/operation_0305Literature searchhttp://edamontology.org/operation_0224Query and retrievalhttps://www.bionoculars.com/tutorialQuick start guidehttps://www.bionoculars.com/blog/early-accessRelease notesLethe DICOM AnonymizerA DICOM Anonymization pipeline in a Docker container. This pipeline is designed to anonymize DICOM files according to the EUCAIM standard and includes the following steps:
Step 1 (Optional): Perform OCR on DICOM pixel data to remove sensitive information (burned-in information).
Step 2: Deidentify DICOM metadata using the RSNA CTP Anonymizer and the EUCAIM anonymization script.
Step 3 (Optional): Deidentify clinical data provided in CSV files so that the referenced patient id is anonymized the same way CTP does in Step 2.https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/lethe-dicom-anonymizer/info-tablethe_dicom_anonymizerbiotools:lethe_dicom_anonymizer0.9.120.10.1Desktop applicationCommand-line toolhttp://edamontology.org/topic_3384Medical imaginghttp://edamontology.org/topic_4044Data protectionhttp://edamontology.org/topic_4012FAIR dataMacLinuxWindowsPythonEUPL-1.2EUCAIMhttp://edamontology.org/operation_3283Anonymisationhttp://edamontology.org/data_3424Raw imagehttp://edamontology.org/format_3548DICOM formathttps://github.com/cbml-forth/lethe_anon_pipelineRepositoryhttps://github.com/cbml-forth/lethe_anon_pipeline/blob/main/README.mdUser manual10.5281/zenodo.19097245Computational BioMedicine Laboratory, Foundation for Research and Technology Hellas (FORTH)https://www.ics.forth.gr/cbml/?lang=enInstituteProviderpathotyprA fast, offline, pathogen-agnostic toolkit for lineage classification and marker-driven genotyping from whole-genome sequencing data. Bring your own SNP marker panel for any microbial pathogen — pathotypr handles the rest. Ships with curated panels for Mycobacterium tuberculosis complex (MTBC): 3,707 lineage-defining SNPs and over 102,000 resistance-associated mutations. Works with assembled genomes (FASTA) and raw reads (FASTQ). Available as command-line interface and cross-platform desktop GUI (Tauri).https://github.com/PathoGenOmics-Lab/pathotyprpathotyprbiotools:pathotypr1.0.0Command-line toolDesktop applicationhttp://edamontology.org/topic_0622Genomicshttp://edamontology.org/topic_3301Microbiologyhttp://edamontology.org/topic_0625Genotype and phenotypehttp://edamontology.org/topic_3168Sequencinghttp://edamontology.org/topic_3305Public health and epidemiologyhttp://edamontology.org/topic_3474Machine learningRustAGPL-3.0MatureFree of chargeOpen accesshttp://edamontology.org/operation_3196Genotypinghttp://edamontology.org/data_2044Sequencehttp://edamontology.org/format_1929FASTAtrain — Build a Random Forest classifier from labeled genomes.http://edamontology.org/operation_3225Variant classificationhttp://edamontology.org/data_2044Sequencehttp://edamontology.org/format_1929FASTApredict — Assign lineages to genomes using a pre-trained Random Forest model.http://edamontology.org/operation_3196Genotypinghttp://edamontology.org/operation_3225Variant classificationhttp://edamontology.org/data_2044Sequencehttp://edamontology.org/format_1929FASTAclassify — Call known SNP markers in assembled genomes against a user-defined marker panel.http://edamontology.org/operation_3196Genotypinghttp://edamontology.org/data_2044Sequencehttp://edamontology.org/format_1930FASTQsplit-fastq — Alignment-free genotyping directly from raw reads using k-mer matching.http://edamontology.org/operation_0346Sequence similarity searchhttp://edamontology.org/data_2044Sequencehttp://edamontology.org/format_1930FASTQmatch — Find the closest reference genome from a set of references for raw reads.https://github.com/PathoGenOmics-Lab/pathotypr/releasesBinarieshttps://github.com/PathoGenOmics-Lab/pathotyprSource codehttps://github.com/PathoGenOmics-Lab/pathotypr/tree/main/docsUser manual10.64898/2026.03.24.714002PrimaryPaula Ruiz-Rodriguezhttps://orcid.org/0000-0003-0727-5974PersonDeveloperMaintainerMireia Coscollahttps://orcid.org/0000-0003-0752-0538PersonPrimary contactI2SysBio (CSIC - Universitat de València)InstituteProviderPathoGenOmics LabDivisionProviderCOVID19-datasetCOVID-19 sequencing data obtained from hundreds of exomes.https://portfolio-covid19-dataset.imib.es/covid19-datasetbiotools:covid19-datasetDatabase portalhttp://edamontology.org/topic_0199Genetic variationhttp://edamontology.org/topic_3391Omicshttp://edamontology.org/topic_3071Data managementIMPaCT-DataIMPaCT-ISCIIIEmergingFree of charge (with restrictions)Restricted accessÁngel Estebanangel.esteban@ffis.esPersonPrimary contactCOLPORTALPlatform for the combined analysis of clinical data and various omics data applied to a cohort of colorectal cancer patients.https://colportal.imib.es/colportalbiotools:colportalDatabase portalWeb applicationhttp://edamontology.org/topic_2640Oncologyhttp://edamontology.org/topic_3391Omicshttp://edamontology.org/topic_3063Medical informaticsWindowsLinuxMacIMPaCT-DataIMPaCT-ISCIIIEmergingFree of charge (with restrictions)Open accessÁngel Estebanangel.esteban@ffis.esPersonPrimary contacteddymotionEddymotion is an open-source framework for volume-to-volume artifact estimation in neuroimaging, inspired by FSL’s eddy. 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Containerized, currently supports input datasets in CSV, JSON, XLSX.https://github.com/EUCAIM/etl-toolset/eetl_toolsetbiotools:eetl_toolset0.1.10.1.20.2.1LibraryWorkflowhttp://edamontology.org/topic_3345Data identity and mappinghttp://edamontology.org/topic_0769WorkflowsLinuxWindowsJavaPythonApache-2.0EUCAIMEmergingFree of chargeOpen access (with restrictions)ToolsSpainhttp://edamontology.org/operation_0335Data formattinghttp://edamontology.org/operation_3431Data depositionhttp://edamontology.org/operation_0336Format validationhttps://github.com/EUCAIM/etl-toolset/Repositoryhttps://docs.google.com/document/d/1QNiqAvVVVsg_3-YuUp1YGNWwakL8yap-GeneralTool documentation from EUCAIM WP5 Task Force 2 efforttabular_data_curatorusesLipid Molecular Dynamics SimulationLipid molecular dynamics simulation is a powerful tool for investigating the structure and dynamics of biological membranes and their molecular interactions. Leveraging advanced computational biology and extensive experience, CD ComputaBio offers comprehensive lipid simulation services, spanning atomic to mesoscopic scales, to support drug development, materials science, and basic biology research. The simulation bridges the gap between theory and experiment, providing unparalleled insights into lipid membranes and their interactions.https://www.computabio.com/lipid-molecular-dynamics-simulation.htmllipid-molecular-dynamics-simulationbiotools:lipid-molecular-dynamics-simulationProtein Molecular Dynamics SimulationProtein molecular dynamics simulation employs physical principles to computationally track the dynamic behavior of proteins at atomic resolution, revealing crucial insights into their conformational changes, interaction mechanisms, and functional regulation. CD ComputaBio integrates state-of-the-art algorithms, powerful computing resources, and artificial intelligence to provide clients with high-precision simulation services, driving advancements in drug design, enzyme engineering, and basic biological research.https://www.computabio.com/protein-molecular-dynamics-simulation.htmlprotein-molecular-dynamics-simulationbiotools:protein-molecular-dynamics-simulationMolecular Dynamics Simulation ServiceMolecular dynamics (MD) is a computer simulation method for analyzing the physical movements of atoms and molecules. CD ComputaBio can simulate thousands or even millions of atoms and molecules, which are often used to simulate particles in liquids, ensembles of solids, and vapors, and can also use different force fields and boundary conditions to simulate all atoms, polymers, biological, metallic, granular and coarsening systems.https://www.computabio.com/molecular-dynamics-simulation-service.htmlmolecular-dynamics-simulation-servicebiotools:molecular-dynamics-simulation-serviceCarbohydrate Molecule Modeling ServiceCarbohydrates, often referred to as glycans, are one of the four fundamental biomolecules essential for life, alongside proteins, lipids, and nucleic acids. Despite their critical roles in biological systems, carbohydrates remain understudied compared to their molecular counterparts. Carbohydrate modeling is crucial for understanding structure and function. It employs various methods, including molecular dynamics simulations, quantum chemical calculations, geometry optimization, and experimental data-driven structure prediction. CD ComputaBio provides its clients with high-quality computational modeling services, encompassing a wide range of scientific areas, including carbohydrate modeling.https://www.computabio.com/carbohydrate-molecule-modeling-service.htmlcarbohydrate-molecule-modeling-servicebiotools:carbohydrate-molecule-modeling-serviceInhibitor Modeling ServiceInhibitors are central to modern drug discovery, providing therapeutic opportunities through the selective blockade of disease-related proteins, enzymes, and signaling pathways. The global demand for targeted therapies has grown substantially, particularly in oncology, neurodegenerative disorders, and infectious diseases. 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Our protein-DNA docking service enables researchers to explore the intricate interactions between proteins and DNA. Leveraging our expertise and sophisticated computational tools, we provide a comprehensive, customized approach to meet the unique needs of our clients.https://www.computabio.com/protein-dna-docking-service.htmlprotein-dna_docking_servicebiotools:protein-dna_docking_serviceProtein-Peptide Docking ServiceCD ComputaBio offers specialized protein-peptide docking services that are designed to facilitate your research and development projects. 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CD ComputaBio's Protein-Protein Docking Service provides a robust and scientifically rigorous computational solution to predict biologically relevant protein complex structures with high confidence. By integrating advanced conformational sampling, multi-criteria scoring, structural refinement, and interface hotspot analysis, we deliver ranked docking models that are not only structurally plausible but also experimentally actionable.https://www.computabio.com/protein-protein-docking-service.htmlprotein-protein_docking_servicebiotools:protein-protein_docking_serviceMolecular Docking ServicesCD ComputaBio provides a comprehensive molecular docking and structure-based drug design (SBDD) platform, offering high-accuracy simulations across small molecules, proteins, peptides, antibodies, RNA, and nucleic acids. Powered by proprietary AI engines—including BioAI-Dock, TurboAI-Screen, FlexAI-Fit, and FragAI-Grow—the service supports virtual screening, binding mode prediction, covalent docking, PPI modeling, peptide flexibility handling, and fragment-based drug design. Applications span small-molecule discovery, enzyme engineering, antibody design, RNA therapeutics, protein engineering, and macrocycle optimization. Through a streamlined workflow from consultation to data delivery, the platform enables researchers and biotech teams to accelerate hit identification, lead optimization, and complex biological interface analysis with speed, precision, and scientific rigor.https://www.computabio.com/molecular-docking-service.htmlmolecular_docking_servicesbiotools:molecular_docking_servicesSearch OntologiesSearch Ontologies is a tool for the automated extraction of ontology ID codes from web-based ontology repositories using metadata values. It supports the mapping of metadata fields from a structured Excel template to standardized ontology terms by using the BioPortal Search API.https://github.com/cim-unito/SearchOntologiessearch_ontologiesbiotools:search_ontologiesAutoencoder_RF_TabPFN for Cross-Cancer MetabolomicsAccurate and rapid disease diagnosis, particularly in prostate cancer (PC) and breast cancer (BC), is critical for early intervention and improved patient outcomes. Metabolomic signatures represent a robust molecular framework for elucidating cancer-associated biochemical reprogramming. The use of Artificial Intelligence (AI) in biology in recent years has become widespread and promising. This study introduces a novel predictive method that integrates an Autoencoder, random forest based feature selection and Tabular Prior-data Fitted Network (TabPFN) to achieve high diagnostic accuracy from metabolomics data of prostate and breast cancer patients.https://github.com/SvenHauns/metabolomic_classifier.gitautoencoder_rf_tabpfn_for_cross-cancer_metabolomicsbiotools:autoencoder_rf_tabpfn_for_cross-cancer_metabolomicsCommand-line toolhttp://edamontology.org/topic_2815Human biologyhttp://edamontology.org/topic_0091Bioinformaticshttp://edamontology.org/topic_3474Machine learningPythonMITEmergingFree of chargeOpen accesshttp://edamontology.org/operation_2990Classificationpython main.py --dataset {dataset_to_be_used} --setting {ae or rf} --size {size}
python main.py --test_dataset {./data/toy_predict.csv} --dataset {./data/toy_train.csv} --predict True --setting {ae or rf} --size {size}
see Readme for detailed instructionshttps://github.com/SvenHauns/metabolomic_classifier.gitRepositoryhttps://github.com/SvenHauns/metabolomic_classifier.gitSource codehttps://github.com/SvenHauns/metabolomic_classifier.gitUser manualSven Haunshaunss@informatik.uni-freiburg.dePersonContributorquacQuaC is a snakemake-based pipeline that runs several QC tools for WGS/WES samples and then summarizes their results using pre-defined, configurable QC thresholds.https://github.com/uab-cgds-worthey/quacquacbiotools:quac2.0Command-line toolhttp://edamontology.org/topic_0080Sequence analysisPythonGPL-3.0SequencingMatureFree of chargeOpen accesshttp://edamontology.org/operation_3218Sequencing quality controlhttps://github.com/uab-cgds-worthey/quacRepositoryhttps://github.com/uab-cgds-worthey/quac/releases/tag/2.0Source code2.0https://quac.readthedocs.io/General10.21105/joss.05313Manavalan Gajapathyhttps://github.com/ManavalanGhttps://orcid.org/0000-0002-8606-0113PersonPrimary contactGenxfloGenXflo is a no-code visual Nextflow pipeline builder for bioinformatics. Users design workflows on a drag-and-drop canvas and export clean, production-ready Nextflow DSL2 code. Supports Docker and Singularity containerisation, local, HPC, and cloud deployment on AWS, Azure, and Google Cloud, and includes AI-assisted workflow validation.https://www.genxflo.comgenxflobiotools:genxfloWorkbenchhttp://edamontology.org/topic_0769Workflowshttp://edamontology.org/topic_0091BioinformaticsJavaScriptPythonTypeScriptCWLEmergingFree of charge (with restrictions)Open accesshttp://edamontology.org/operation_2428Validationhttp://edamontology.org/operation_2430Designhttps://genxflo.com/app/resourcesSoftware cataloguedocumentation on how to use the toolhttps://genxflo.com/product/overviewOtherProduct Overviewhttps://genxflo.com/app/resourcesQuick start guideOmics StudioOmics Studio is a bioinformatics platform designed to support the exploration, contextualization, interpretation, and presentation of omics data. It provides guided workflows, visualization, annotation, database search, and analysis tools that help researchers work more efficiently with complex biological datasets.
Omics Studio is intended for use in applications such as biomarker discovery, systems biology, and precision medicine.https://omicsstudio.comomics_studiobiotools:omics_studio1.0Web applicationhttp://edamontology.org/topic_0091Bioinformaticshttp://edamontology.org/topic_0121Proteomicshttp://edamontology.org/topic_3172Metabolomicshttp://edamontology.org/topic_3308Transcriptomicshttp://edamontology.org/topic_4021MultiomicsMacLinuxWindowsOtherProprietaryProteomicsTranscriptomicsmetabolomic platformMatureCommercialhttp://edamontology.org/operation_2945Data analysishttp://edamontology.org/operation_0226Annotationhttp://edamontology.org/operation_0337Visualisationhttp://edamontology.org/operation_3501Enrichment analysishttp://edamontology.org/operation_3928Pathway analysishttps://platform.omicsstudio.comServiceIf you already have an account, or would like to sign up, please use this link. If you do not yet have access, contact support@omicsstudio.com.https://help.omicsstudio.com/Quick start guidehicapIdentify cap locus serotype and structure in your Haemophilus influenzae assemblies.https://github.com/scwatts/hicaphicapbiotools:hicap1.0.4Command-line tool10.1128/jcm.00190-1930944197PMC6535587RosalutionRosalution assists researchers study genetic variation 🧬 in patients 🧑🏾🤝🧑🏼 by helping select candidate animal models 🐀🐁🐠🪱 to replicate the variation to further research to derive, diagnose, and provide therapies for ultra-rare diseases. Developed by the UAB Center for Computational Genomics and Data Science (CGDS).https://github.com/uab-cgds-worthey/rosalutionrosalutionbiotools:rosalution0.8.8-erWeb applicationhttp://edamontology.org/topic_0199Genetic variationJavaScriptPythonGPL-3.0Rare DiseaseMatureFree of chargehttps://github.com/uab-cgds-worthey/rosalutionRepositoryhttps://github.com/uab-cgds-worthey/rosalution/issuesIssue trackerhttps://github.com/uab-cgds-worthey/rosalution/releases/tag/0.8.8-erSource code0.8.8-er10.21105/joss.05443Angelina Uno-Antonisonhttps://github.com/SeriousHorncatLab Integrated Data (LabID)Lab Integrated Data (LabID) is an open-source web-based platform for research data management in life science institutes, featuring sample and dataset management, an inventory management system and an electronic lab notebook. LabID allows recording extensive experimental information about the provenance of data (samples, reagents, instrument, protocols, assay parameters) and is designed to help individual scientists, research groups and core facilities better manage, annotate and share their research according to FAIR principles.https://grp-gbcs.embl-community.io/labid-user-docs/labidbiotools:labidWeb applicationCommand-line toolhttp://edamontology.org/topic_3070Biologyhttp://edamontology.org/topic_0219Data curation and archivalhttp://edamontology.org/topic_4012FAIR datahttp://edamontology.org/topic_0769Workflowshttp://edamontology.org/topic_3071Data managementMITMatureFree of chargeOpen accesshttps://gitlab.com/lab-integrated-dataRepositoryOpen source repositories for the LabID subprojects (UI, Server, Command Line Interface, Library)https://join.slack.com/t/labintegrateddata/shared_invite/zt-2eb4ivxyc-1C4RZP_For0uiWcHeiTw0QDiscussion forumChannel for questions from externalshttps://www.embl.org/groups/modis/OtherWebsite of the MODIS team behind LabIDhttps://grp-gbcs.embl-community.io/labid-user-docs/GeneralMain documentation including installation instructions and traininghttps://labid-demo.embl.de/Training materialTest server for e.g LabID and trainingsCharles Girardothttps://orcid.org/0000-0003-4301-3920PersonMaintainerPrimary contactDeveloperHead of MODIS team at EMBL HeidelbergJelle Scholtalbershttps://orcid.org/0000-0002-6090-2482PersonDeveloperSupportFormer MODIS team member, now working as a freelance (LabRise solutions)Matthias MonfortPersonMaintainerDeveloperSupportNayeem Rezahttps://orcid.org/0000-0003-2068-5812PersonDeveloperMaintainerSupportLaurent Thomaslaurent.thomas@embl.dehttps://orcid.org/0000-0001-7686-3249PersonDeveloperEMBL Heidelberg03mstc592InstituteCPSM: Cancer patient survival modelCPSM is an R package that provides a comprehensive computational pipeline for predicting survival probabilities and risk groups in cancer patients. It includes dedicated modules to perform key steps such as data preprocessing, training/test splitting, and normalization. CPSM enables feature selection through univariate cox-regression survival analysis, feature selection though LASSO method, and calculates a LASSO-based Prognostic Index (PI) score. It supports the development of predictive models using different feature sets and offers a suite of visualization tools, including survival curves based on predicted probabilities, barplots of predicted mean and median survival times, Kaplan-Meier (KM) plots overlaid with individual survival predictions, and nomograms for estimating 1-, 3-, 5-, and 10-year survival probabilities. Together, these functionalities make CPSM a powerful and versatile tool for survival analysis in cancer research.https://www.bioconductor.org/packages/devel/bioc/html/CPSM.htmlcpsm_cancer_patient_survival_modelbiotools:cpsm_cancer_patient_survival_model1.3.1