ExomeFlowExomeFlow is a Python package that provides a complete, automated Whole Exome Sequencing (WES) analysis workflow from raw FASTQ files to functionally annotated variants in a single reproducible CLI command. It aims to be the standard high-level pipeline for WES analysis in Python, combining GATK best-practice variant calling, hard filtering, and ANNOVAR annotation into one modular, maintainable package. It handles cohort-level processing (multiple samples), checkpointing for resumable runs, structured logging, and parallel execution out of the box.https://pypi.org/project/exomeflow/exomeflowbiotools:exomeflow1.0.91.0.8WorkflowCommand-line toolhttp://edamontology.org/topic_3307Computational biologyhttp://edamontology.org/topic_0080Sequence analysishttp://edamontology.org/topic_2533DNA mutationLinuxPythonBashFreewareEmergingFree of chargeOpen accesshttps://pypi.org/project/exomeflow/Repositoryhttps://pypi.org/project/exomeflow/Software package1.0.08https://pypi.org/project/exomeflow/Generalfastpusesbio-samtoolsincludesgatk_haplotype_callerincludesRobin Kumaritsrobintomar@gmail.comhttps://pypi.org/project/exomeflow/https://orcid.org/0009-0002-9084-2019PersonDeveloperFrameXploreFrameXplore is a Google Colab notebook that extends ColabFold to automatically identify and correct disulfide bonds in cysteine‑rich protein structures. After a standard ColabFold prediction, it extracts all cysteine residues, computes Cα–Cα and S–S distances, identifies true disulfide bonds using geometric cutoffs (Cα–Cα < 6.5 Å, S–S < 2.5 Å), writes a corrected PDB with proper CONECT records, and provides an interactive 3D view.https://github.com/Vidhusv/FRAMEXPLORERframexplorebiotools:framexplore1.0.0doi:10.5281/zenodo.20135079doiWeb applicationhttp://edamontology.org/topic_4019BiosciencesLinuxMacWindowsPythonMITEmergingFree of chargeOpen accesshttps://github.com/Vidhusv/FRAMEXPLORERRepositoryGitHub repository with source code, documentation, and logohttps://github.com/Vidhusv/FRAMEXPLORER/issuesIssue trackerReport bugs, suggest features, or ask questionshttps://github.com/Vidhusv/FRAMEXPLORER/archive/refs/heads/main.zipSource codeLatest version of the complete repository as a ZIP file1.0.0https://raw.githubusercontent.com/Vidhusv/FRAMEXPLORER/main/FrameXplore.ipynbBinariesDirect download of the Colab notebook (.ipynb file). Can be run locally or uploaded to Google Colab.1.0.0https://github.com/Vidhusv/FRAMEXPLORER/blob/main/README.mdGeneralQuick start guide, example test case, methodology explanation, citation, and repository contents10.1038/s41592-022-01488-135637307PMC9184281PrimaryColabFold: making protein folding accessible to all. Nature Methods, 2022.Vidhu S Vijayvidhuvijay2003@gmail.comhttps://github.com/Vidhusvhttps://orcid.org/0009-0009-1182-2089PersonDeveloperMaintainerMS Bioinformatics student at Amrita Vishwa Vidyapeetham; creator and maintainer of FrameXplore.GlyComboCLIGlyComboCLI is an open source command line interface for combinatorial glycan composition determination to identify glycans in MS acquisitions of glycan-containing samples in text or mzML formats. This application enables rapid extraction of precursor m/z values from mzML files, a vendor-neutral format that ensures cross-platform compatibility.https://github.com/Protea-Glycosciences/GlyComboCLIglycomboclibiotools:glycombocli0.1.0Command-line toolhttp://edamontology.org/topic_3292Biochemistryhttp://edamontology.org/topic_3391OmicsLinuxWindowsC#Apache-2.0EmergingFree of chargeOpen accessToolsSystems Biologyhttp://edamontology.org/operation_3214Spectral analysishttp://edamontology.org/operation_0236Sequence composition calculationhttp://edamontology.org/data_0943Mass spectrumhttp://edamontology.org/format_3244mzML

http://edamontology.org/data_2900Carbohydrate accessionhttp://edamontology.org/format_3752CSV

Requires MS2 scans with precursor m/z annotated if using the mzml approachGlyComboCLI.exe -F=".\masses.txt" -D=Native -R=Reduced -T=Da -E=0.6 -A=Neutral -hMax=9 -nMin=2 -nMax=10https://github.com/Protea-Glycosciences/GlyComboCLITechnical monitoringMonitored GitHub repositoryhttps://github.com/Protea-Glycosciences/GlyComboCLI/releases/latestDownloads pagehttps://hub.docker.com/r/proteaglycosciences/glycombocliContainer filehttps://glycombocli.readthedocs.io/en/latest/Quick start guidemsconvertusesChristopher Ashwoodchris@proteaglyco.comhttps://orcid.org/0000-0001-5944-6179PersonPrimary contactMaia Kellyhttps://orcid.org/0000-0002-9383-1107PersonDeveloperRepeatAfterMeRepeatAfterMe is a package for the extension of repetitive DNA cores. The tool automatically extends a multiple sequence alignment that may represent only a fragment of a longer repetitive sequence family.https://github.com/Dfam-consortium/RepeatAfterMerepeataftermebiotools:repeataftermev0.0.7Command-line toolhttp://edamontology.org/topic_0622GenomicsMacLinuxCCC0-1.0MatureFree of chargeOpen accesshttp://edamontology.org/operation_0495Local alignmenthttp://edamontology.org/data_3494DNA sequencehttp://edamontology.org/format_30092bithttp://edamontology.org/data_1017Sequence rangehttp://edamontology.org/format_3585bed6

http://edamontology.org/data_0863Sequence alignmenthttp://edamontology.org/format_1929FASTA

./RAMExtend -ranges test/extension-test2.tsv -twobit test/extension-test2.2bitRobert HubleyPersonDeveloperMR-based neuroblastoma tumour detection and segmentation (EUCAIM-SW-019_T-01-02-002)This tool is specifically designed and validated for automated detection and segmentation of neuroblastic tumours in T2-weighted magnetic resonance images (T2-MR) using deep learning. It processes DICOM or NIfTI input data and outputs in NIFTI or DICOM SEG. TRAINING & VALIDATION COHORTS: Initial Development (Veiga-Canuto 2022): -Training: 106 patients, 5-fold CV (median DSC 0.965 ± 0.018). -Internal validation: 26 patients (median DSC 0.918 ± 0.067). -Sources: La Fe (Spain), SIOPEN HR-NBL1/LINES, St. Anna (Austria), Pisa (Italy). -Mean age: 37.6 ± 39.3 months. -Median tumor volume: 116,518 mm³. External Validation (Veiga-Canuto 2023): -300 patients, 535 independent T2 MRI scans (486 at diagnosis, 49 post-chemotherapy). -Performance: median DSC 0.997 (0.944–1.000), 94% successful detection. -Sources: 12 European countries (HR-NBL1/SIOPEN 119, LINES/SIOPEN 107, German Registry 62, others 12). -Heterogeneous data: 1.5T (435), 3T (100); Siemens (318), Philips (109), GE (105), Canon (3).https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/mr_based_neuroblastoma_tumour_detection_and_segmentation/info-tabmr-based_neuroblastoma_tumour_detection_and_segmentationbiotools:mr-based_neuroblastoma_tumour_detection_and_segmentation2.0.0Libraryhttp://edamontology.org/topic_2640Oncologyhttp://edamontology.org/topic_3474Machine learninghttp://edamontology.org/topic_3365Data architecture, analysis and designMacWindowsLinuxPythonCC-BY-NC-ND-4.0EUCAIMMatureFree of charge (with restrictions)Open access (with restrictions)http://edamontology.org/operation_3553Image annotationhttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3549niihttp://edamontology.org/format_3548DICOM formathttp://edamontology.org/data_2048Reporthttp://edamontology.org/format_3464JSONhttp://edamontology.org/format_3752CSV

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3549niihttp://edamontology.org/format_3548DICOM format

docker run --rm \ -v "<input_path>:/input" \ -v "<output_path>:/output" \ --gpus all \ harbor.eucaim.cancerimage.eu/processing-tools/mr_based_neuroblastoma_tumour_detection_and_segmentation:2.0.0 \ --mode dicom-seg \ --series-selector /output/config/series_to_segment.csv \ --seg-series-number 2302001 \ --seg-algorithm-name "nnUNet_Neuroblastoma_Primage_training" \ --seg-coordinating-center "EUCAIM Consortium" \ --keep-nifti falsehttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/mr_based_neuroblastoma_tumour_detection_and_segmentation/info-tabSoftware cataloguehttps://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/Social mediahttps://drive.eucaim.cancerimage.eu/s/tS2W8nb38Zs2ZzKUser manualhttps://www.mdpi.com/2072-6694/14/15/3648Generalhttps://drive.eucaim.cancerimage.eu/s/44BxMSNpxwgsZiETerms of usehttps://www.youtube.com/watch?v=c7XOEGRA9aQ&list=PL3Q1XjQpjfg_GEmwPDrQeESh6nqCMnYyR&index=12Training material10.3390/cancers14153648Primary10.3390/cancers15051622PrimaryGIBI230 - HULAFEInstituteProviderLeonor Cerda-Alberichleonor_cerda@iislafe.eshttps://orcid.org/0000-0002-5567-4278PersonDeveloperDiana Veiga Canutodianaveigac@gmail.comhttps://orcid.org/0000-0002-6048-2940PersonDeveloperPedro-Miguel Martinez-Gironespedromiguel_martinez@iislafe.eshttps://orcid.org/0000-0002-9506-9451PersonPrimary contactSupportDocumentorDeveloperCarina Soler-Ponscarina_soler@iislafe.eshttps://orcid.org/0009-0000-2991-1391PersonSupportDocumentorLuis Marti-Bonmatiluis_marti@iislafe.eshttps://orcid.org/0000-0002-8234-010XPersonProviderDICOM File Integrity Checker (EUCAIM-SW-002_T-01-01-002)The tool performs a DICOM quality check in terms of correct number of files per sequence, corrupted files, precise directory hierarchy, separated dynamic series merging them, interest series filtering/selection by specific series description lists and diffusion sequence identification by b-values. It applies the desired changes to the dataset and generates a report containing information about the selected sequences, corrupted files, missing files and merged files. Status: Deployedhttps://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/dicom_file_integrity_checker/info-tabdicom_file_integrity_checker_by_gibi230biotools:dicom_file_integrity_checker_by_gibi2302.0.02.1.02.1.1Libraryhttp://edamontology.org/topic_3316Computer sciencehttp://edamontology.org/topic_3077Data acquisitionhttp://edamontology.org/topic_3071Data managementMacWindowsLinuxPythonCC-BY-NC-ND-4.0EUCAIMMatureFree of charge (with restrictions)Open access (with restrictions)http://edamontology.org/operation_0336Format validationhttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM formathttp://edamontology.org/data_2048Reporthttp://edamontology.org/format_3620xlsxhttp://edamontology.org/format_3464JSON

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM format

http://edamontology.org/data_2048Reporthttp://edamontology.org/format_2331HTMLhttp://edamontology.org/format_3620xlsxhttp://edamontology.org/format_3464JSON

For Data Holders (ingestion-tools)docker run -it --rm --name my-container \ -v "<input_path>:/input" \ -v "<output_path>:/output" \ -v "<config_path>:/config" \ harbor.eucaim.cancerimage.eu/ingestion-tools/dicom_file_integrity_checker:latesthttp://edamontology.org/operation_0336Format validationhttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM format

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM format

http://edamontology.org/data_2048Reporthttp://edamontology.org/format_2331HTMLhttp://edamontology.org/format_3620xlsxhttp://edamontology.org/format_3464JSON

For Data Users (processing-tools)docker run -it --rm --name my-container \ -v "<input_path>:/input" \ -v "<output_path>:/output" \ harbor.eucaim.cancerimage.eu/processing-tools/dicom_file_integrity_checker:latest \ /app/entrypoint.sh --config-string "{'QA': {'sequence_selection': ['ALL'], 'modality_selection': ['ALL'], 'input_directory': 'dataset_id'}}"https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/Social mediahttps://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/dicom_file_integrity_checker/artifacts-tabSoftware catalogueLink to EUCAIM's Harbor for Data Holdershttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/dicom_file_integrity_checker/artifacts-tabSoftware catalogueLink to EUCAIM's Harbor for Data Usershttps://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/dicom_file_integrity_checker/artifacts-tabContainer fileEUCAIM user needed2.1.0https://drive.eucaim.cancerimage.eu/s/LAKqSQZY4Bjqz5WUser manualhttps://drive.eucaim.cancerimage.eu/s/EEqjraK574HrwMfTerms of usehttps://www.youtube.com/watch?v=oUebkjLYeSs&list=PL3Q1XjQpjfg_GEmwPDrQeESh6nqCMnYyR&index=3Training materialGIBI230 - HULAFEgibi230@iislafe.esInstituteProviderPedro-Miguel Martinez-Gironespedromiguel_martinez@iislafe.eshttps://orcid.org/0000-0002-9506-9451PersonPrimary contactDeveloperDocumentorMaintainerAdrian Galiana-Borderaadrian_galiana@iislafe.eshttps://orcid.org/0000-0002-8324-8284PersonDeveloperCarina Soler-Ponscarina_soler@iislafe.eshttps://orcid.org/0009-0000-2991-1391PersonContributorDocumentorLuis Marti-Bonmatiluis_marti@iislafe.eshttps://orcid.org/0000-0002-8234-010XPersonProviderDenoising-Inhomogeneity Correction Tool (EUCAIM-SW-015_T-01-01-015)The tool is designed to perform a customisable image pre-processing to reduce noise and inhomogeneity field effect, thus improving image quality and reproducibility of radiomics features. This tool consists of two independent steps: one for denoising using one of the 5 integrated filters (Bilateral Filter, Anisotropic Diffusion Filter (ADF), Curvature Flow Filter (CFF), SUSAN and Non Local Means (NLM)), and another for the ANTs N4 and another for the ANT's N4 bias correction filter. The parameter configuration of this tool has been optimised for TW1, T2W, DWI and DCE sequences in neuroblastoma (NB) and paediatric brain tumours, but it can also be configured with some of their parameters using a JSON parameter configuration file.https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/denoising_inhomogeneity_correction_tool/info-tabdenoising-inhomogeneity_correction_toolbiotools:denoising-inhomogeneity_correction_tool1.1.0Libraryhttp://edamontology.org/topic_0219Data curation and archivalMacWindowsLinuxPythonCC-BY-NC-ND-4.0EUCAIMhttp://edamontology.org/operation_3695Data filteringhttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM formathttp://edamontology.org/data_2048Reporthttp://edamontology.org/format_3464JSON

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM format

docker run -it --rm --name container_name -v "<input_path>:/input" -v "<output_path>:/output" -v "<config_path>:/config" harbor.eucaim.cancerimage.eu/processing-tools/denoising_inhomogeneity_correction_tool:1.1.0 --config /config/config.jsonhttp://edamontology.org/operation_3695Data filteringhttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM format

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM format

docker run --rm -v input_path:/input -v output_path:/output harbor.eucaim.cancerimage.eu/processin-tools/denoising_inhomogeneity_correction_tool:1.1.0 --paths /input/Dataset/Patient_1/Study/T1W /input/Dataset/Patient_2/Study/T2W --output /output --series_number 2000 --series_description_suffix "_harmonized" --denoising adf --conductance 0.5 --iterations 3 --time_step 0.0625 --n4 --bspline_size 50 --n4_iterations 50 30 --shrink_factor 2https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/denoising_inhomogeneity_correction_tool/artifacts-tabSoftware cataloguehttps://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/Social mediahttps://drive.eucaim.cancerimage.eu/s/wYJ7Fttnk6Dp7gcUser manualhttps://drive.eucaim.cancerimage.eu/s/oseiKoeFZqwoRCATerms of usehttps://www.youtube.com/watch?v=HkHqFGXGEbo&list=PL3Q1XjQpjfg_GEmwPDrQeESh6nqCMnYyR&index=9Training material10.1007/s10278-021-00512-834505958PMC8554919Primary10.1186/s41747-020-00150-932246291PMC7125275GIBI230 - HULAFEInstituteProviderMatias Fernandez-Patonmatias_fernandez@iislafe.eshttps://orcid.org/0000-0001-9374-1411PersonDeveloperPedro-Miguel Martinez-Gironespedromiguel_martinez@iislafe.eshttps://orcid.org/0000-0002-9506-9451PersonPrimary contactDocumentorSupportCarina Soler-Ponscarina_soler@iislafe.eshttps://orcid.org/0009-0000-2991-1391PersonDocumentorSupportLeonor Cerda-Alberichleonor_cerda@iislafe.eshttps://orcid.org/0000-0002-5567-4278PersonProviderLuis Marti-Bonmatiluis_marti@iislafe.eshttps://orcid.org/0000-0002-8234-010XPersonProviderCluster based harmonization (EUCAIM-SW-044_T-01-03-006)The tool is designed to perform radiomics harmonization on large and heterogeneous datasets, where the risk of over-harmonization is present. Instead of directly applying harmonization based on predefined batch labels, the tool first identifies groups of batches that share similar characteristics through clustering of the radiomics data. It then performs harmonization using these cluster-derived labels. The tool allows the harmonization of radiomics variables using two methods: (1) original ComBat (Rabinovic, 2007) method, where each original batch group is considered for the harmonization process and (2) cluster-based ComBat method, where batch groups with similar radiomics characteristics form clusters and the latter are being considered for the harmonization process.https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/cluster_based_harmonization/info-tabcluster_based_harmonizationbiotools:cluster_based_harmonization1.1.0Libraryhttp://edamontology.org/topic_0219Data curation and archivalhttp://edamontology.org/topic_3382ImagingWindowsMacLinuxPythonCC-BY-NC-ND-4.0EUCAIMEmergingFree of charge (with restrictions)Open access (with restrictions)http://edamontology.org/operation_3432Clusteringhttp://edamontology.org/data_2526Text datahttp://edamontology.org/format_3620xlsxhttp://edamontology.org/format_3752CSVhttp://edamontology.org/data_2526Text datahttp://edamontology.org/format_3464JSON

http://edamontology.org/data_2526Text datahttp://edamontology.org/format_3620xlsxhttp://edamontology.org/format_3752CSV

http://edamontology.org/data_2526Text datahttp://edamontology.org/format_3620xlsxhttp://edamontology.org/format_3752CSV

http://edamontology.org/data_2526Text datahttp://edamontology.org/format_3835TIDE TXT

docker run --rm --cpus CPUS -v input_path:/input -v output_path:/output -v config_path:/config harbor.eucaim.cancerimage.eu/processing-tools/cluster_based_harmonization:1.1.0 --config /config/config.jsonhttp://edamontology.org/operation_3432Clusteringhttp://edamontology.org/data_2526Text datahttp://edamontology.org/format_3620xlsxhttp://edamontology.org/format_3752CSV

http://edamontology.org/data_2526Text datahttp://edamontology.org/format_3620xlsxhttp://edamontology.org/format_3752CSV

http://edamontology.org/data_2526Text datahttp://edamontology.org/format_3620xlsxhttp://edamontology.org/format_3752CSV

http://edamontology.org/data_2526Text datahttp://edamontology.org/format_3835TIDE TXT

docker run --rm -v input_path:/input -v output_path:/output harbor.eucaim.cancerimage.eu/processing-tools/cluster_based_harmonization:1.1.0 --file_path /input/data.xlsx --identifier patient_id --start_col original_shape_Elongation --end_col lbp-3D-k_ngtdm_Strength --batch_col software_versions --output_dir /output/results --min_clusters 2 --max_clusters 100 --results full --approach soft --small_groups mergehttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/cluster_based_harmonization/artifacts-tabSoftware cataloguehttps://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/Social mediahttps://drive.eucaim.cancerimage.eu/s/Kz3jfQYQZjezpxKUser manualhttps://drive.eucaim.cancerimage.eu/s/5bMA2jsZTfS8eoZTerms of usehttps://www.youtube.com/watch?v=0GJqNJv-Qf8&list=PL3Q1XjQpjfg_GEmwPDrQeESh6nqCMnYyR&index=31Training materialhttps://drive.eucaim.cancerimage.eu/s/LkczHCMZH5nd8QRGeneralGIBI230 - HULAFEInstituteProviderAikaterini Vrakaaikaterini_vraka@iislafe.eshttps://orcid.org/0000-0001-5984-904XPersonDeveloperPedro-Miguel Martínez-Gironéspedromiguel_martinez@iislafe.eshttps://orcid.org/0000-0002-9506-9451PersonPrimary contactSupport2D Digital Mammography Harmonization (EUCAIM-SW-046_T-01-03-008)This preprocessing tool is design for 2D digital mammograms in DICOM format. It standardizes and harmonizes images through a configurable pipeline that includes spatial reorientation, pseudo-3D stacking, isotropic resampling, intensity normalization, optional denoising, contrast enhancement, and mask processing (if available).https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/2d_digital_mammography_harmonization/info-tab2d_digital_mammography_harmonizationbiotools:2d_digital_mammography_harmonization1.1.0Libraryhttp://edamontology.org/topic_0219Data curation and archivalhttp://edamontology.org/topic_3316Computer sciencehttp://edamontology.org/topic_3382Imaginghttp://edamontology.org/topic_3071Data managementWindowsMacLinuxPythonCC-BY-NC-ND-4.0EUCAIMEmergingFree of charge (with restrictions)Open access (with restrictions)http://edamontology.org/operation_3695Data filteringhttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM formathttp://edamontology.org/data_2048Reporthttp://edamontology.org/format_3464JSON

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM format

docker run -it --rm --name container_name -v "<input_path>:/input" -v "<output_path>:/output" -v "<config_path>:/config" harbor.eucaim.cancerimage.eu/processing-tools/2d_digital_mammography_harmonization:1.1.0 /app/entrypoint.sh --config /config/config.jsonhttp://edamontology.org/operation_3695Data filteringhttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM format

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM format

Command Argumentsdocker run --rm -v input_path:/input -v output_path:/output harbor.eucaim.cancerimage.eu/processing-tools/2d_digital_mammography_harmonization:1.1.0 --input_directory dataset_id --output_directory gaussian_output --num_workers 4 --series_number 2301101 --series_description_suffix _harmonized --zscore_enabled true --zscore_p_low 1.0 --zscore_p_high 99.0 --denoise_method gaussian --gaussian_ksize 3 --gaussian_sigma 0.8 --clahe_enabled true --clahe_clip_limit 0.01https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/2d_digital_mammography_harmonization/artifacts-tabSoftware cataloguehttps://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/Social mediahttps://drive.eucaim.cancerimage.eu/s/CC3yJTjPz9N7GggUser manualhttps://drive.eucaim.cancerimage.eu/s/rdHWqMkNf7frQRYTerms of usehttps://www.youtube.com/watch?v=IrZhMp2lB7g&list=PL3Q1XjQpjfg_GEmwPDrQeESh6nqCMnYyR&index=13Training materialGIBI230 - HULAFEManuel Marfil-Trujillomanuel_marfil@iislafe.esPersonDeveloperPedro-Miguel Martinez-Gironespedromiguel_martinez@iislafe.eshttps://orcid.org/0000-0002-9506-9451PersonPrimary contactSupportCarina Soler-Ponscarina_soler@iislafe.eshttps://orcid.org/0009-0000-2991-1391PersonSupportML model for MR series categorisation (EUCAIM-SW-011_T-01-01-011)A tool based on artificial intelligence that is able to perform a categorisation of MRI series by using standardized DICOM tags. The categorisation includes the type of sequence (e.g. spin echo, gradient echo), the weighting (e.g. T1W, T2W, DCE, ...), the presence of fat suppression and the detection of non-relevant / junk series (e.g. localizers, calibrations, screenshots...).https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/ml_model_for_mr_series_categorisation/info-tabml_model_for_mr_series_categorisationbiotools:ml_model_for_mr_series_categorisation1.1.0Libraryhttp://edamontology.org/topic_3316Computer sciencehttp://edamontology.org/topic_3077Data acquisitionhttp://edamontology.org/topic_3071Data managementMacWindowsLinuxPythonCC-BY-NC-ND-4.0EUCAIMMatureFree of charge (with restrictions)Open access (with restrictions)http://edamontology.org/operation_2990Classificationhttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM format

http://edamontology.org/data_2048Reporthttp://edamontology.org/format_3464JSON

docker run -it --rm --name my-container \ -v "<input_path>:/input" \ -v "<output_path>:/output" \ harbor.eucaim.cancerimage.eu/processing-tools/ml_model_for_mr_series_categorisation:<version> \ --config-string "{'output_name': 'classification_results.json'}"https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/ml_model_for_mr_series_categorisation/artifacts-tabSoftware cataloguehttps://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/Social mediahttps://drive.eucaim.cancerimage.eu/s/rpm56rD5FfXAHybUser manualhttps://drive.eucaim.cancerimage.eu/s/8r3CzyQXrd7ERFpTerms of usehttps://www.youtube.com/watch?v=gudDCiuJIf8&list=PL3Q1XjQpjfg_GEmwPDrQeESh6nqCMnYyR&index=11Training material10.1186/s40537-025-01086-wPrimaryGIBI230 - HULAFEgibi230@iislafe.esInstituteProviderArmando Gomis-Mayaarmago@alumni.uv.eshttps://orcid.org/0000-0002-9527-8093PersonDeveloperPedro-Miguel Martinez-Gironespedromiguel_martinez@iislafe.eshttps://orcid.org/0000-0002-9506-9451PersonSupportCarina Soler-Ponscarina_soler@iislafe.eshttps://orcid.org/0009-0000-2991-1391PersonSupportLeonor Cerda-Alberichleonor_cerda@iislafe.eshttps://orcid.org/0000-0002-5567-4278PersonContributorLuis Marti-Bonmatiluis_marti@iislafe.eshttps://orcid.org/0000-0002-8234-010XPersonContributorTime Coherence Tool (EUCAIM-SW-001_T-01-01-001)Tool that aims to validate visually the chronological order and logical consistency of dates associated with a patient's medical history. It generates a timeline visualization for each patient from an Excel file and highlights rule violations. Status : Containerizedhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/time_coherence_tool/info-tabtime_coherence_toolbiotools:time_coherence_tool1.1.0Libraryhttp://edamontology.org/topic_0092Data visualisationhttp://edamontology.org/topic_3316Computer scienceMacWindowsLinuxPythonCC-BY-NC-ND-4.0EUCAIMEmergingFree of charge (with restrictions)Open access (with restrictions)http://edamontology.org/operation_2945Data analysishttp://edamontology.org/data_2048Reporthttp://edamontology.org/format_3620xlsxhttp://edamontology.org/format_3752CSV

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3603PNGhttp://edamontology.org/format_3604SVGhttp://edamontology.org/format_3508PDF

docker run -it --rm --name my-container -v "<input_path>:/input" -v "<output_path>:/output" harbor.eucaim.cancerimage.eu/processing-tools/time_coherence_tool:2.1.0 --config-string "{'data_file': 'my_data.xlsx', 'generate_pdf': 'true'}"https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/time_coherence_tool/artifacts-tabSoftware cataloguehttps://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/Social mediahttps://drive.eucaim.cancerimage.eu/s/M23wDa4RWXenzxXUser manualhttps://drive.eucaim.cancerimage.eu/s/9QbdWjnLBeMYSWRTerms of usehttps://www.youtube.com/watch?v=j4J_UPQqYR4&list=PL3Q1XjQpjfg_GEmwPDrQeESh6nqCMnYyR&index=10Training materialGIBI230 - HULAFEInstituteProviderAdrian Galiana-Borderaadrian_galiana@iislafe.eshttps://orcid.org/0000-0002-8324-8284PersonDeveloperPedro-Miguel Martinez-Gironespedromiguel_martinez@iislafe.eshttps://orcid.org/0000-0002-9506-9451PersonDeveloperSupportCarina Soler-Ponscarina_soler@iislafe.eshttps://orcid.org/0009-0000-2991-1391PersonSupportTabular Data CuratorA fully automated service which can be applied on any kind of tabular data (e.g. clinical) to automatically identify duplicated fields (lexically similar and/or highly correlated features), outliers, data inconsistencies. It can also deal with missing values through the application of data imputers.https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/tdc-app/info-tabtabular_data_curatorbiotools:tabular_data_curatorWeb APIhttp://edamontology.org/topic_3572Data quality managementPythonEUPL-1.2EUCAIMOpen accesshttps://github.com/vpz4/TDCRepositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/tdc-app/info-tabSoftware cataloguehttps://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/tdc-app/artifacts-tabContainer fileEUCAIM baseline versionv1.0.0https://drive.eucaim.cancerimage.eu/s/PfBxCXNsKdEccj3User manualhttps://www.youtube.com/watch?v=eowjz6c8wf4Training materialhttps://github.com/vpz4/TDCAPI documentation10.1016/j.compbiomed.2019.03.00130878889PrimaryUnit of Medical Technology and Intelligent Information Systems (MEDLAB) and FORTHhttps://medlab.cc.uoi.gr/InstituteDr. Vasileios Pezoulasbpezoulas@gmail.comhttps://orcid.org/0000-0002-1872-693XPersonPrimary contactDeveloperMaintainerSupportDr. Nikolaos Tachosntachos@gmail.comhttps://orcid.org/0000-0002-8627-6352PersonContributorSupportProf. Dimitrios Fotiadisfotiadis@uoi.grhttps://orcid.org/0000-0002-7362-5082PersonContributorProstate Anatomies and Lesion Automatic SegmentorWhole-gland, zonal & lesion prostate segmentation from multi-parametric MRI. Cascaded nnU-Net v2 (anatomies) + ProLesA-Net (lesions). A three-stage pipeline that segments the whole gland (WG), peripheral zone (PZ), transition zone (TZ) and (when ADC + DWI are provided) the prostate lesions from prostate MRI. The anatomies are produced by two cascaded nnU-Net v2 models on T2; the lesion mask is produced by ProLesA-Net (Keras / TensorFlow 2.11) operating on T2 + ADC + DWI inside the WG maskhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/prostate-anatomies-and-lesion-segmentor/info-tabprostate_anatomies_and_lesion_automatic_segmentorbiotools:prostate_anatomies_and_lesion_automatic_segmentor1.0.0Workflowhttp://edamontology.org/topic_3474Machine learninghttp://edamontology.org/topic_3063Medical informaticsWindowsMacLinuxPythonEUPL-1.2EUCAIMEmergingFree of chargeOpen accesshttp://edamontology.org/operation_3553Image annotationhttp://edamontology.org/data_3442MRI image

http://edamontology.org/data_3442MRI image

docker run --rm --gpus all \ -v /path/to/dicom:/input:ro -v /path/to/out:/output \ harbor.eucaim.cancerimage.eu/processing-tools/prostate-anatomies-and-lesion-segmentor:1.0.0 \ --input /input --output /outputhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/prostate-anatomies-and-lesion-segmentor/info-tabSoftware cataloguehttps://github.com/dzaridis/Prostate-Anatomies-and-Lesion-SegmentorRepositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/prostate-anatomies-and-lesion-segmentor/info-tabContainer file1.0.0https://github.com/dzaridis/Prostate-Anatomies-and-Lesion-SegmentorSource code1.0.0https://github.com/dzaridis/Prostate-Anatomies-and-Lesion-SegmentorQuick start guideQuick Start Commands for the Software, including pulling from Harbor Container Registry, input folders structurehttps://drive.eucaim.cancerimage.eu/s/S4qW2cRD6T5NYBQUser manualhttps://drive.eucaim.cancerimage.eu/s/HEKfYpL9DrCqJ84GeneralVideo with input folders structure, docker image execution & visualisation10.1016/j.patter.2024.10099239081575PMC11284496PrimaryFoundation for Research and Technology Hellas (FORTH)https://www.forth.gr/en/home/InstituteProviderDimitrios I. Zaridisdimzaridis@gmail.comhttps://orcid.org/0000-0003-2549-3887PersonDeveloperNikolaos Tachosntachos@gmail.comhttps://orcid.org/0000-0002-8627-6352PersonContributorDimitrios I. Fotiadisfotiadis@uoi.grhttps://orcid.org/0000-0002-7362-5082PersonContributorprotein-mosaic-qPython library for computing the mosaic Q and Q_alt structural descriptors, which quantify the degree of spatial clustering of amino acids by chemical type (acidic, basic, polar, hydrophobic, and also special for Q_alt) in protein three-dimensional structures. Operates on PDB and mmCIF files via Biopython. Associated with the Proteins Mosaic Q Project, a citizen-science initiative gathering evidence for a conserved structural clustering pattern across proteins.https://proteins-mosaic-q.org/protein-mosaic-qbiotools:protein-mosaic-q0.3.3Command-line toolLibraryhttp://edamontology.org/topic_0081Structure analysisWindowsMacLinuxPythonMITEmergingFree of chargeOpen accesshttp://edamontology.org/operation_0249Protein geometry calculationComputes mosaic Q and Q_alt parameters quantifying spatial clustering of amino acids by chemical type in protein 3D structures.mosaicq protein.pdb [--metric Q_alt]https://pypi.org/project/protein-mosaic-q/Software cataloguehttps://github.com/UPO-Sevilla-Fco-Javier-Lobo-Cabrera/clustering_trait_proteinsRepositoryhttps://citizenscience.eu/project/686Otherhttps://scistarter.org/proteins-mosaic-q-projectOtherhttps://proteins-mosaic-q.org/Otherhttps://ciencia-ciudadana.es/project/392Otherhttps://osf.io/42kjn/overviewOtherhttps://huggingface.co/ProteinsMosaicQOtherhttps://pypi.org/project/protein-mosaic-q/Software package0.3.3https://pypi.org/project/protein-mosaic-q/Generalhttps://osf.io/42kjn/wiki?wiki=zmh9dGeneralbiopythonuses10.1101/500025PreprintEarlier version of the manuscript. Updated version available in the project repository.Francisco Javier Lobo-Cabrerafjlobcab@upo.eshttps://scholar.google.com/citations?user=D4NAEvEAAAAJ&hl=enhttps://orcid.org/0000-0002-9592-6863PersonPrimary contactDeveloperPhD in Biotechnology, with publications in computational biology.José A. Prado-Bassasbassas@us.eshttps://orcid.org/0000-0002-5670-5400PersonContributorSupportProfessor at Universidad de Sevilla, Faculty of Mathematics.Prostate zone segmentation toolThis tool automatically segments the the prostate into two zones: the central and transition zones (TZ+CZ) and the peripheral zone (PZ). It takes as input a T2-weighted image and produces a segmentation in the same input format. Three datasets were used to train this model: ProstateX (n=152), Prostate158 (n=119) and ProstateNet (the ProCAncer-I dataset; n=532). We used the T2-weighted images available in each dataset and trained a standard three-class nnU-Net model with three classes: background, peripheral zone and the combination of the transition and central zones as annotated by radiologists. Using 149 cases from the three previously noted datasets, this model achieved DSC=0.81 (95% CI=[0.59, 0.92]) for PZ and 0.87 (95% CI=[0.60, 0.96]) for CZ+TZ. In a clinical validation using ProCAncer-I data, this model achieved, for PZ and TZ+CZ, DSC=0.65 and DSC=0.77, respectively, comparable to the observed between-radiologist DSC for a subset of cases (DSC=0.61 and DSC=0.73, respectively).https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/champ-prostate-zone-segmentationprostate_zone_segmentation_toolbiotools:prostate_zone_segmentation_toolCommand-line toolhttp://edamontology.org/topic_3063Medical informaticsLinuxPythonCC-BY-NC-4.0EUCAIMEmergingFree of charge (with restrictions)Open access (with restrictions)http://edamontology.org/operation_3553Image annotationhttp://edamontology.org/data_3442MRI imagehttp://edamontology.org/format_3548DICOM formathttp://edamontology.org/format_3549nii

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM formathttp://edamontology.org/format_3549nii

Image annotation for individual DICOM and Nifti images: Please note that it is easier to mount the input and output paths path and keep `--study_path` always as `/data/input` and `--output_dir` always as `/data/output`. This way only `--series_folders` requires any change. `--series_folders` can refer both to a Nifti file (if the input is Nifti) or the a DICOM series folder. If the input is DICOM, `--is_dicom` should be used.docker run --shm-size=1gb -v $(pwd)/example:/data/input:ro -v $(pwd)/test_output:/data/output:rw --gpus="all" test-eucaim:latest nnunet-predict --study_path /data/input --series_folders <series-folder> --output_dir /data/outputhttp://edamontology.org/operation_3553Image annotationhttp://edamontology.org/data_3442MRI imagehttp://edamontology.org/format_3548DICOM formathttp://edamontology.org/format_3549nii

http://edamontology.org/data_3442MRI imagehttp://edamontology.org/format_3548DICOM formathttp://edamontology.org/format_3549nii

Batch image annotation for DICOM and Nifti images using dataset JSON and data directory supports two methods: using a dataset JSON and using a data directory. The dataset JSON-based method relies on having a dataset JSON which has a list of dictionaries that follow this structure: `{"study_path": str, "series_folders": [[str]], "output_dir": str}`. These are analogous to the previous modes. Be mindful that paths should be specified accordingly for the Docker file-system. The dataset JSON should be specified using `--data_json`. The data directory-based method relies on having a directory structured as patient/study/series, where each series follows an nnU-Net-like convention (i.e. `<series_id>_0000`, `<series_id>_0001`, etc.). In this case, it should be `<series-id>_0000`. The data directory should be specified using `--data_dir` and requires the specification of an `--output_dir`. If the input is DICOM, `--is_dicom` should be specified.docker run --shm-size=1gb -v $(pwd)/example_dataset:/data/input:ro -v $(pwd)/test_output:/data/output:rw --gpus="all" test-eucaim:latest nnunet-predict-batch {{--data_json /data/input/<dataset_json>.json|--data_dir /data/input --output_dir /data/output}}https://github.com/josegcpa/nnunet_serveRepositoryThis is the nnunet_serve repository, containing the generic nnU-Net building and serving functionalities used by the Computational Clinical Imaging Group at Champalimaud Foundation. This should also be used as documentation.https://kdrive.infomaniak.com/app/share/1928123/f7bf2733-0454-47b0-a29c-0beacb51fe68OtherContains the EUCAIM model card for this model.https://github.com/josegcpa/nnunet_serveAPI documentationThis is the repository containing all of the code together with a comprehensive README for deployment and examplar files.nnunetuses10.1016/j.compbiomed.2024.10821638442555PrimaryJosé Guilherme de Almeidajose.almeida@research.fchampalimaud.orghttps://josegcpa.nethttps://orcid.org/0000-0002-1887-0157PersonPrimary contactDeveloperDocumentorMaintainerNickolas Papanikolaouhttps://orcid.org/0000-0003-3298-2072PersonSupportNuno RodriguesPersonDeveloperSub-Saharan Africa Brain Glioma SegmenterThe Sub-Saharan Africa Brain Glioma Segmenter is a software tool developed by researchers from Universidad Politécnica de Madrid and Children’s National Hospital for the segmentation and analysis of intercranial meningiomas in magnetic resonance imaging (MRI). Built in Python, it enables precise quantitative analysis of gliomas in brain MRI scans to support clinical decision-making in both diagnosis and prognosis. This tool was trained on a cohort of patients from Sub-Saharan Africa.https://github.com/BIT-UPM/EUCAIM/tree/main/sub_saharan_africa_brain_glioma_segmentersub-saharan_africa_brain_glioma_segmenterbiotools:sub-saharan_africa_brain_glioma_segmenterv1.0Web applicationCommand-line toolWorkflowhttp://edamontology.org/topic_3444MRIhttp://edamontology.org/topic_3474Machine learningWindowsLinuxMacPythonCC-BY-NC-ND-4.0EUCAIMMatureFree of chargeOpen accesshttp://edamontology.org/operation_3553Image annotationhttps://segmenter.hope4kids.io/OtherMain app webpagehttps://github.com/Pediatric-Accelerated-Intelligence-Lab/HOPE-Segmenter-KidsRepositoryGitHub repositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/ssa-brain-glioma-segmenter/info-tabRepositoryEUCAIM harbor linkhttps://docs.hope4kids.io/HOPE-Segmenter-Kids/API documentationCitation instructionsTerms of useCode of conductRelease notesUser manualGeneralQuick start guide10.1007/978-3-031-76163-8_2010.1109/ISBI56570.2024.1063546910.48550/arXiv.2412.0409410.48550/arXiv.2412.04111Daniel Capellán-Martíndaniel.capellan@upm.eshttps://orcid.org/0000-0002-9743-0845Primary contactDeveloperContributorSupportAbhijeet Paridapabhijeet@childrensnational.orghttps://orcid.org/0000-0002-4978-0576DeveloperContributorSupportMaintainerZhifan Jiangzjiang@childrensnational.orgContributorDeveloperSupportMaría Jesus Ledesma-Carbayomj.ledesma@upm.eshttps://orcid.org/0000-0001-6846-3923Primary contactContributorMarius George Lingurarumlingura@childrensnational.orgPrimary contactContributorIntercranial Meningioma SegmenterThe Intercranial Meningioma Segmenter is a software tool developed by researchers from Universidad Politécnica de Madrid and Children’s National Hospital for the segmentation and analysis of intercranial meningiomas in magnetic resonance imaging (MRI). Built in Python, it enables precise quantitative analysis of intercranial meningiomas in brain MRI scans to support clinical decision-making in both diagnosis and prognosis.https://github.com/BIT-UPM/EUCAIM/tree/main/intercranial_meningioma_segmenterintercranial_meningioma_segmenterbiotools:intercranial_meningioma_segmenterv1.0Web applicationCommand-line toolWorkflowhttp://edamontology.org/topic_3444MRIhttp://edamontology.org/topic_3474Machine learningWindowsLinuxMacPythonCC-BY-NC-ND-4.0EUCAIMMatureFree of chargeOpen accesshttp://edamontology.org/operation_3553Image annotationhttps://segmenter.hope4kids.io/OtherMain app webpagehttps://github.com/Pediatric-Accelerated-Intelligence-Lab/HOPE-Segmenter-KidsRepositoryGitHub repositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/intracranial-meningioma-segmenter/info-tabRepositoryEUCAIM harbor linkhttps://docs.hope4kids.io/HOPE-Segmenter-Kids/API documentationCitation instructionsTerms of useCode of conductRelease notesUser manualGeneralQuick start guide10.1007/978-3-031-76163-8_2010.1109/ISBI56570.2024.1063546910.48550/arXiv.2412.0409410.48550/arXiv.2412.04111Daniel Capellán-Martíndaniel.capellan@upm.eshttps://orcid.org/0000-0002-9743-0845Primary contactDeveloperContributorSupportAbhijeet Paridapabhijeet@childrensnational.orghttps://orcid.org/0000-0002-4978-0576DeveloperContributorSupportMaintainerZhifan Jiangzjiang@childrensnational.orgContributorDeveloperSupportMaría Jesus Ledesma-Carbayomj.ledesma@upm.eshttps://orcid.org/0000-0001-6846-3923Primary contactContributorMarius George Lingurarumlingura@childrensnational.orgPrimary contactContributorBrain Metastasis SegmenterThe Brain Metastasis Segmenter is a software tool developed by researchers from Universidad Politécnica de Madrid and Children’s National Hospital for the segmentation and analysis of brain metastases in magnetic resonance imaging (MRI). Built in Python, it enables precise quantitative analysis of brain metastases in MRI scans to support clinical decision-making in both diagnosis and prognosis.https://github.com/BIT-UPM/EUCAIM/tree/main/brain_metastasis_segmenterbrain_metastasis_segmenterbiotools:brain_metastasis_segmenterv1.0Web applicationCommand-line toolWorkflowhttp://edamontology.org/topic_3444MRIhttp://edamontology.org/topic_3474Machine learningWindowsLinuxMacPythonCC-BY-NC-ND-4.0EUCAIMMatureFree of chargeOpen accesshttp://edamontology.org/operation_3553Image annotationhttps://segmenter.hope4kids.io/OtherMain app webpagehttps://github.com/Pediatric-Accelerated-Intelligence-Lab/HOPE-Segmenter-KidsRepositoryGitHub repositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/brain-metastasis-segmenter/info-tabRepositoryEUCAIM harbor linkhttps://docs.hope4kids.io/HOPE-Segmenter-Kids/API documentationCitation instructionsTerms of useCode of conductRelease notesUser manualGeneralQuick start guide10.1007/978-3-031-76163-8_2010.1109/ISBI56570.2024.1063546910.48550/arXiv.2412.0409410.48550/arXiv.2412.04111Daniel Capellán-Martíndaniel.capellan@upm.eshttps://orcid.org/0000-0002-9743-0845Primary contactDeveloperContributorSupportAbhijeet Paridapabhijeet@childrensnational.orghttps://orcid.org/0000-0002-4978-0576DeveloperContributorSupportMaintainerZhifan Jiangzjiang@childrensnational.orgContributorDeveloperSupportMaría Jesus Ledesma-Carbayomj.ledesma@upm.eshttps://orcid.org/0000-0001-6846-3923Primary contactContributorMarius George Lingurarumlingura@childrensnational.orgPrimary contactContributorBrain Glioma SegmenterThe Brain Glioma Segmenter is a software tool developed by researchers from Universidad Politécnica de Madrid and Children’s National Hospital for the segmentation and analysis of brain gliomas in magnetic resonance imaging (MRI). Built in Python, it enables precise quantitative analysis of brain gliomas in MRI scans to support clinical decision-making in both diagnosis and prognosis.https://github.com/BIT-UPM/EUCAIM/tree/main/brain_glioma_segmenterbrain_glioma_segmenterbiotools:brain_glioma_segmenterv1.0Command-line toolWeb applicationWorkflowhttp://edamontology.org/topic_3444MRIhttp://edamontology.org/topic_3474Machine learningMacLinuxWindowsPythonCC-BY-NC-ND-4.0EUCAIMMatureFree of chargeOpen accesshttp://edamontology.org/operation_3553Image annotationhttps://segmenter.hope4kids.io/OtherMain app webpagehttps://github.com/Pediatric-Accelerated-Intelligence-Lab/HOPE-Segmenter-KidsRepositoryGitHub repositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/brain-glioma-segmenter/info-tabRepositoryEUCAIM harbor linkhttps://docs.hope4kids.io/HOPE-Segmenter-Kids/API documentationCitation instructionsTerms of useCode of conductRelease notesUser manualGeneralQuick start guide10.1007/978-3-031-76163-8_2010.1109/ISBI56570.2024.1063546910.48550/arXiv.2412.0409410.48550/arXiv.2412.04111Daniel Capellán-Martíndaniel.capellan@upm.eshttps://orcid.org/0000-0002-9743-0845Primary contactContributorDeveloperSupportAbhijeet Paridapabhijeet@childrensnational.orghttps://orcid.org/0000-0002-4978-0576ContributorDeveloperSupportMaintainerZhifan Jiangzjiang@childrensnational.orgContributorDeveloperSupportMaría Jesus Ledesma-Carbayomj.ledesma@upm.eshttps://orcid.org/0000-0001-6846-3923Primary contactContributorMarius George Lingurarumlingura@childrensnational.orgPrimary contactContributorPediatric Brain Tumor SegmenterThe Pediatric Brain Tumor Segmenter is a software tool developed by researchers from Universidad Politécnica de Madrid and Children’s National Hospital for the segmentation and analysis of pediatric brain tumors in magnetic resonance imaging (MRI). Built in Python, it enables precise quantitative analysis of pediatric brain tumors in MRI scans to support clinical decision-making in both diagnosis and prognosis.https://github.com/BIT-UPM/EUCAIM/tree/main/pediatric_brain_tumor_segmenterpediatric_brain_tumor_segmenterbiotools:pediatric_brain_tumor_segmenterv1.0Web applicationCommand-line toolWorkflowhttp://edamontology.org/topic_3444MRIhttp://edamontology.org/topic_3474Machine learningWindowsLinuxMacPythonCC-BY-NC-SA-4.0EUCAIMMatureFree of chargeOpen accesshttp://edamontology.org/operation_3553Image annotationhttps://segmenter.hope4kids.io/OtherMain app webpagehttps://github.com/Pediatric-Accelerated-Intelligence-Lab/HOPE-Segmenter-KidsRepositoryGitHub repositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/pediatric-brain-tumor-segmenter/info-tabRepositoryEUCAIM harbor linkhttps://docs.hope4kids.io/HOPE-Segmenter-Kids/API documentationCitation instructionsTerms of useCode of conductRelease notesUser manualGeneralQuick start guide10.1007/978-3-031-76163-8_2010.1109/ISBI56570.2024.1063546910.48550/arXiv.2412.0409410.48550/arXiv.2412.04111Daniel Capellán-Martíndaniel.capellan@upm.eshttps://orcid.org/0000-0002-9743-0845PersonPrimary contactDeveloperContributorSupportAbhijeet Paridapabhijeet@childrensnational.orghttps://orcid.org/0000-0002-4978-0576DeveloperContributorSupportMaintainerZhifan Jiangzjiang@childrensnational.orgContributorDeveloperSupportMaría Jesus Ledesma-Carbayomj.ledesma@upm.eshttps://orcid.org/0000-0001-6846-3923Primary contactContributorMarius George Lingurarumlingura@childrensnational.orgPrimary contactContributorRACLAHERACLAHE (Region-Adaptive Contrast Limited Adaptive Histogram Equalization) is an image enhancement method specifically designed for improving CNN-based segmentation of the prostate and prostatic zones in T2-Weighted MR images.https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/raclahe/info-tabraclahebiotools:raclahe3.0Workflowhttp://edamontology.org/topic_0092Data visualisationhttp://edamontology.org/topic_3474Machine learningWindowsLinuxMacPythonEUPL-1.2eucaimhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/raclahe/info-tabSoftware cataloguehttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/raclahe/info-tabhttps://github.com/dzaridis/RACLAHE_Image_Enhancement_for_CNN_model_segmentation/tree/mainRepositoryOfficial Git Repository of RACLAHEhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/raclaheContainer fileEUCAIM Harbor RACLAHE Image, Can be pulled via the following command after successfull login to Harbor docker pull harbor.eucaim.cancerimage.eu/processing-tools/raclahe@sha256:b3e9d383dfed95b7a6f1cca9dcbd271379946c97188e162d66f0d96b923410473.0https://drive.eucaim.cancerimage.eu/f/2359User manualUser Manual in the EUCAIM Drivehttps://github.com/dzaridis/RACLAHE_Image_Enhancement_for_CNN_model_segmentation/tree/mainQuick start guide10.1038/s41598-023-27671-8MethodFoundation for Research and Technology Hellas (FORTH)https://www.forth.gr/en/home/InstituteProviderDimitrios I. Zaridisdimzaridis@gmail.comhttps://orcid.org/0000-0003-2549-3887PersonDeveloperSenior Researcher@FORTHNikolaos Tachosntachos@gmail.comhttps://orcid.org/0000-0002-8627-6352ContributorSenior Researcher@FORTHDimitrios I. Fotiadisfotiadis@uoi.grhttps://orcid.org/0000-0002-7362-5082PersonContributorProfessor, University of IoanninaDeep Learning Noise Reduction (DLNR)A fully convolutional (with no pooling layers) model was trained on a set of noisy images with the ground truth being the original image without the (synthetic) noise. Different levels of noise and types were incorporated into the training set. The experiments showed reduction in noise levels, but it can impact image quality when T2ws without noise is provided to the model.https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/deep-learning-noise-reduction/info-tabdeep_learning_noise_reduction_dlnrbiotools:deep_learning_noise_reduction_dlnrCommand-line toolhttp://edamontology.org/topic_3572Data quality managementMacWindowsLinuxEUPL-1.2EUCAIMhttps://github.com/trivizakis/DLNRRepositoryhttps://drive.eucaim.cancerimage.eu/f/2363User manual10.1109/SACI66288.2025.11030174Eleftherios Trivizakistrivizakis@ics.forth.grDeveloperLethe 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_anonymizer0.9.120.10.10.11.2Command-line toolDesktop applicationhttp://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 format

http://edamontology.org/data_3424Raw imagehttp://edamontology.org/format_3548DICOM format

https://github.com/cbml-forth/lethe_anon_pipelineRepositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/lethe-dicom-anonymizer/info-tabSoftware cataloguehttps://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=enInstituteProviderStelios Sfakianakishttps://orcid.org/0000-0001-8424-4157PersonPrimary contactDeveloperMaintainerSupportValia Kalokyrihttps://orcid.org/0000-0002-5245-8238PersonContributorDeveloperCT-based neuroblastoma tumour detection and segmentation (EUCAIM-SW-022_T-01-02-005)The tool performs by deep learning an automatic segmentation of the possible neuroblastoma tumours on Contrast Enhanced CT images (CE-CTs). Model architecture is Unet-based with residual operations, atrous dilation convolution and specific batch generator. It applies preprocessing steps as RAS conversion, resizing, z-score normalization, patching; and postprocessing operations. It takes DICOM images as input and generates tumoral masks in DICOM SEG or NIFTI formats.https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/ct-based_neuroblastoma_tumour_detection_and_segmentation/info-tabct-based_neuroblastoma_tumour_detection_and_segmentationbiotools:ct-based_neuroblastoma_tumour_detection_and_segmentation2.0.0Libraryhttp://edamontology.org/topic_3365Data architecture, analysis and designhttp://edamontology.org/topic_2640Oncologyhttp://edamontology.org/topic_3474Machine learningWindowsMacLinuxPythonCC-BY-NC-ND-4.0EUCAIMMatureFree of charge (with restrictions)Open access (with restrictions)http://edamontology.org/operation_3553Image annotationhttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3549niihttp://edamontology.org/format_3548DICOM formathttp://edamontology.org/data_2048Reporthttp://edamontology.org/format_3752CSVhttp://edamontology.org/format_3464JSON

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3549niihttp://edamontology.org/format_3548DICOM format

docker run --rm --gpus all \ -v /path/to/input:/input \ -v /path/to/output:/output \ harbor.eucaim.cancerimage.eu/processing-tools/ct-based_neuroblastoma_tumour_detection_and_segmentation:2.0.0 \ --series_csv /output/config/series_to_segment.csv \ --output_dir /outputhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/ct-based_neuroblastoma_tumour_detection_and_segmentation/info-tabSoftware cataloguehttps://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/Social mediahttps://drive.eucaim.cancerimage.eu/s/GxJrJPrYBnzecqTTerms of usehttps://www.youtube.com/watch?v=JQ0dtAE_6uc&list=PL3Q1XjQpjfg_GEmwPDrQeESh6nqCMnYyR&index=37Training material10.3390/app11083508UsageGIBI230 - HULAFEInstituteProviderPedro-Miguel Martinez-Gironespedromiguel_martinez@iislafe.eshttps://orcid.org/0000-0002-9506-9451PersonPrimary contactDeveloperSupportAdrian Galiana-Borderaadrian_galiana@iislafe.eshttps://orcid.org/0000-0002-8324-8284PersonDeveloperCarina Soler-Ponscarina_soler@iislafe.eshttps://orcid.org/0009-0000-2991-1391PersonSupportLuis Marti-Bonmatiluis_marti@iislafe.eshttps://orcid.org/0000-0002-8234-010XPersonProviderMR-based DIPG tumour detection and segmentation (EUCAIM-SW-020_T-01-02-003)The tool performs an automatic segmentation of the possible DIPG tumours on MR images. DIPG (Diffuse Intrinsic Pontine Glioma), or more recently, DMG (Diffuse Midline Glioma) is a H3 K27M–mutant pediatric brainstem cancer detected in T1W and Flair/T2-weighted magnetic resonance images. The tool includes a complete workflow from DICOM images to DICOM seg tumoral masks.https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/mr-based_dipg_tumour_detection_and_segmentation/info-tabmr-based_dipg_tumour_detection_and_segmentationbiotools:mr-based_dipg_tumour_detection_and_segmentation2.0.0Libraryhttp://edamontology.org/topic_2640Oncologyhttp://edamontology.org/topic_3474Machine learninghttp://edamontology.org/topic_3365Data architecture, analysis and designMacWindowsLinuxPythonCC-BY-NC-ND-4.0EUCAIMMatureFree of charge (with restrictions)Open access (with restrictions)http://edamontology.org/operation_3553Image annotationhttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3549niihttp://edamontology.org/format_3548DICOM formathttp://edamontology.org/data_2048Reporthttp://edamontology.org/format_3464JSONhttp://edamontology.org/format_3752CSV

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3549niihttp://edamontology.org/format_3548DICOM format

docker run --rm --gpus all \ -v /path/to/input:/input \ -v /path/to/output:/output \ harbor.eucaim.cancerimage.eu/processing-tools/mr-based_glioblastoma_tumour_detection_and_segmentation:latest \ --series-selector /input/config/series.csvhttp://edamontology.org/operation_3553Image annotationhttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3549niihttp://edamontology.org/format_3548DICOM format

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3549niihttp://edamontology.org/format_3548DICOM format

docker run --rm --gpus all \ -v /path/to/input:/input -v /path/to/output:/output harbor.eucaim.cancerimage.eu/processing-tools/mr-based_dipg_tumour_detection_and_segmentation:latest \ --json-args '{"dataset_id":"DS1","patient_id":"P1","study_id":"S1","sequences":{"T1w":"/input/DICOM/DS1/P1/S1/T1","FLAIR":"/input/DICOM/DS1/P1/S1/FLAIR"}}'https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/mr-based_dipg_tumour_detection_and_segmentation/info-tabSoftware cataloguehttps://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/Social mediahttps://link.springer.com/article/10.1007/s10278-025-01557-9Generalhttps://drive.eucaim.cancerimage.eu/s/5CL2H8tyMDzFHexTerms of usehttps://www.youtube.com/watch?v=f3DPS56z6oI&list=PL3Q1XjQpjfg_GEmwPDrQeESh6nqCMnYyR&index=36Training material10.1007/s10278-025-01557-9PrimaryGIBI230 - HULAFEInstituteProviderMatias Fernandez-Patonmatias_fernandez@iislafe.eshttps://orcid.org/0000-0001-9374-1411PersonDeveloperPedro-Miguel Martinez-Gironespedromiguel_martinez@iislafe.eshttps://orcid.org/0000-0002-9506-9451PersonPrimary contactDeveloperDocumentorSupportAdrian Galiana-Borderaadrian_galiana@iislafe.eshttps://orcid.org/0000-0002-8324-8284PersonDeveloperCarina Soler-Ponscarina_soler@iislafe.eshttps://orcid.org/0009-0000-2991-1391PersonDocumentorSupportLeonor Cerda-Alberichleonor_cerda@iislafe.eshttps://orcid.org/0000-0002-5567-4278PersonProviderLuis Marti-Bonmatiluis_marti@iislafe.eshttps://orcid.org/0000-0002-8234-010XPersonProviderMR-based glioblastoma tumour detection and segmentation (EUCAIM-SW-021_T-01-02-004)The tool performs an automatic segmentation of the possible glioblastoma tumours on MRI images and its subregions: necrosis (Intratumoral necrotic core), edema (Peritumoral vasogenic edema), enhancing (Contrast-enhancing tumor region), total (Total tumor including edema and necrosis by a single model) and total-fused (Total tumor fusioning of necrosis+edema+enhancing). It applies preprocessing steps as skull stripping, intra-patient registration, z-score normalization, patching, among others. It takes DICOM images as input and generates tumoral masks in DICOM SEG or NIFTI formats.https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/mr-based_glioblastoma_tumour_detection_and_segmentation/info-tabmr-based_glioblastoma_tumour_detection_and_segmentationbiotools:mr-based_glioblastoma_tumour_detection_and_segmentation2.1.1Libraryhttp://edamontology.org/topic_3365Data architecture, analysis and designhttp://edamontology.org/topic_2640Oncologyhttp://edamontology.org/topic_3474Machine learningMacLinuxWindowsPythonCC-BY-NC-ND-4.0EUCAIMMatureFree of charge (with restrictions)Open access (with restrictions)http://edamontology.org/operation_3553Image annotationhttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3549niihttp://edamontology.org/format_3548DICOM formathttp://edamontology.org/data_2048Reporthttp://edamontology.org/format_3752CSVhttp://edamontology.org/format_3464JSON

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3549niihttp://edamontology.org/format_3548DICOM format

docker run --rm --gpus all \ -v /path/to/input:/input \ -v /path/to/output:/output \ harbor.eucaim.cancerimage.eu/processing-tools/mr-based_glioblastoma_tumour_detection_and_segmentation:latest \ --series-selector /input/config/series.csv \ --target total \ --emit-config truehttp://edamontology.org/operation_3553Image annotationhttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3549niihttp://edamontology.org/format_3548DICOM formathttp://edamontology.org/data_2048Reporthttp://edamontology.org/format_3752CSVhttp://edamontology.org/format_3464JSON

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3549niihttp://edamontology.org/format_3548DICOM format

docker run --rm --gpus all \ -v /path/to/input:/input \ -v /path/to/output:/output \ harbor.eucaim.cancerimage.eu/processing-tools/mr-based_glioblastoma_tumour_detection_and_segmentation:latest \ --series-list '[{"dataset_id":"DS001","patient_id":"PAT001","study_id":"ST001","series_path":"/input/DICOM/DS001/PAT001/ST001/T1_POST"}]' \ --target total-fused \ --emit-config truehttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/mr-based_glioblastoma_tumour_detection_and_segmentation/info-tabSoftware cataloguehttps://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/Social mediahttps://pubmed.ncbi.nlm.nih.gov/38849632/Generalhttps://drive.eucaim.cancerimage.eu/s/otrYTickHjDxnkPTerms of usehttps://www.youtube.com/watch?v=8_OJPTQUKAw&list=PL3Q1XjQpjfg_GEmwPDrQeESh6nqCMnYyR&index=22Training material10.1007/s11548-024-03205-zPrimaryGIBI230 - HULAFEInstituteProviderMaria Beser-Roblesmaria_beser@iislafe.eshttps://orcid.org/0000-0002-0072-5525PersonDeveloperPedro-Miguel Martinez-Gironespedromiguel_martinez@iislafe.eshttps://orcid.org/0000-0002-9506-9451PersonPrimary contactDeveloperSupportAdrian Galiana-Borderaadrian_galiana@iislafe.eshttps://orcid.org/0000-0002-8324-8284PersonDeveloperCarina Soler-Ponscarina_soler@iislafe.eshttps://orcid.org/0009-0000-2991-1391PersonSupportLeonor Cerda-Alberichleonor_cerda@iislafe.eshttps://orcid.org/0000-0002-5567-4278PersonProviderLuis Marti-Bonmatiluis_marti@iislafe.eshttps://orcid.org/0000-0002-8234-010XPersonProviderGeneForgeA Nextflow Pipeline for Eukaryotic Gene Prediction and Functional Annotation GeneForge is a high-throughput Nextflow pipeline designed for the comprehensive structural and functional annotation of eukaryotic genomes. It orchestrates the parallel execution of BRAKER3 and FunAnnotate, evaluates their performance using BUSCO, and provides a unified functional annotation suite.https://github.com/SequAna-Ukon/GeneForgegeneforgebiotools:geneforge2.0Workflowhttp://edamontology.org/topic_0622GenomicsLinuxiOSOtherMITSequAnaFree of chargeOpen accessGermanyhttps://github.com/SequAna-Ukon/GeneForgeRepositoryhttps://github.com/SequAna-Ukon/GeneForgeSource code2.0https://github.com/SequAna-Ukon/GeneForgeUser manual10.5281/zenodo.18592773Abdoallah Sharafabdoallah.sharaf@uni-konstanz.dehttps://orcid.org/0000-0002-3436-9290PersonDeveloperChristian R. VoolstraChristian.voolstra@uni-konstanz.dehttps://orcid.org/0000-0003-4555-3795PersonSupportKO2PathwayKO2Pathway is a command-line tool designed to map KEGG Orthology (KO) identifiers to KEGG pathways efficiently. The tool retrieves pathway information from KEGG and generates a summary of pathway descriptions with the associated KO counts. Additionally, KO2Pathway allows for excluding specific pathways based on custom-defined terms and supports caching of KO-to-pathway mappings to avoid repetitive queries to the KEGG API.https://github.com/SequAna-Ukon/KO2Pathwayko2pathwaybiotools:ko2pathwayCommand-line toolhttp://edamontology.org/topic_0085Functional genomicsLinuxiOSPythonMITSequAnaFree of chargeOpen accessGermanyhttps://github.com/SequAna-Ukon/KO2PathwayRepositoryhttps://github.com/SequAna-Ukon/KO2PathwaySource codehttps://github.com/SequAna-Ukon/KO2PathwayUser manual10.5281/zenodo.19731236Abdoallah Sharafabdoallah.sharaf@uni-konstanz.dehttps://orcid.org/0000-0002-3436-9290PersonDeveloperChristian R. VoolstraChristian.voolstra@uni-konstanz.dehttps://orcid.org/0000-0003-4555-3795PersonSupportTrueOrthoTrueOrtho is a comprehensive Nextflow pipeline for automated ortholog identification across multiple species. It integrates homology search, ortholog assignment, and domain conservation analysis into a single streamlined workflow.https://github.com/SequAna-Ukon/TrueOrthotrueorthobiotools:trueorthoWorkflowhttp://edamontology.org/topic_0797Comparative genomicsLinuxOtherMITSequAnaFree of chargeOpen accessGermanyhttps://github.com/SequAna-Ukon/TrueOrthoRepositoryhttps://github.com/SequAna-Ukon/TrueOrthoSource code1.0.0https://github.com/SequAna-Ukon/TrueOrthoUser manual10.5281/zenodo.178674421.0.0Abdoallah Sharafabdoallah.sharaf@uni-konstanz.dehttps://orcid.org/0000-0002-3436-9290PersonDeveloperChristian R. VoolstraChristian.voolstra@uni-konstanz.dehttps://orcid.org/0000-0003-4555-3795PersonProviderProteoPyProteoPy is a lightweight Python library for protein- and peptide-level quantitative proteomics analysis, built around the AnnData class as its core data structure. It streamlines data import, preprocessing, and differential analysis while preserving all metadata within a single object.https://proteopy.readthedocs.io/en/latest/proteopybiotools:proteopy0.1.1Libraryhttp://edamontology.org/topic_0091Bioinformaticshttp://edamontology.org/topic_3520Proteomics experimentWindowsMacLinuxPythonApache-2.0mass spectrometryproteomicsEmergingFree of chargeOpen accesshttps://github.com/UKHD-NP/proteopyRepositoryhttps://github.com/UKHD-NP/proteopy/discussionsDiscussion forumhttps://github.com/UKHD-NP/proteopy/issuesIssue trackerhttps://proteopy.readthedocs.io/en/latest/OtherDocumentationhttps://github.com/UKHD-NP/proteopy/archive/refs/heads/main.zipSource codehttps://proteopy.readthedocs.io/en/latest/Generalanndatauses10.64898/2026.03.31.715273PreprintIsabell Bludauisabell.bludau@med.uni-heidelberg.dehttps://orcid.org/0000-0002-2601-238XPersonPrimary contactJunior Group Leader at the Department of Neuropathology, University Hospital HeidelbergIan Dirk FichtnerIanDirk.Fichtner@med.uni-heidelberg.dehttps://orcid.org/0009-0002-3000-2907PersonDeveloperPhD student at the Department of Neuropathology, University Hospital Heidelberg and Division of Artificial Intelligence in Oncology, German Cancer Research Center (DKFZ)3D-DARTA web server for generation of 3D-structural DNA models with a defined conformation by providing control over both ‘global’ and ‘local’ conformational features.https://milou.science.uu.nl/services/3DDART/3d-dartbiotools:3d-dart1Web applicationhttp://edamontology.org/topic_1317Structural biologyhttp://edamontology.org/topic_0593NMRhttp://edamontology.org/topic_0092Data visualisationhttp://edamontology.org/topic_0097Nucleic acid structure analysisLinuxWindowsMacWeNMREOSC-HubEGIINSTRUCTMatureFree of chargeNetherlandshttp://edamontology.org/operation_0570Structure visualisationhttp://edamontology.org/operation_2476Molecular dynamicshttp://edamontology.org/operation_2481Nucleic acid structure analysis3DNA driven - DNA Analysis and Rebuilding Tools. Provides a means of analyzing nucleic acid structures and generating custom models.https://milou.science.uu.nl/services/3DDART/DARTusage.htmlUser manual10.1093/nar/gkp28719417072PMC2703913PrimaryBonvin Laba.m.j.j.bonvin@uu.nlhttp://www.bonvinlab.orghttp://orcid.org/0000-0001-7369-1322DivisionDeveloperMaintainerProviderPrimary contactUtrecht Universityhttp://www.uu.nlcovsnapCoverage inspector for targeted sequencing QC (hg38)https://github.com/enes-ak/covsnapcovsnapbiotools:covsnap0.3.00.4.0Desktop applicationCommand-line toolhttp://edamontology.org/topic_0080Sequence analysishttp://edamontology.org/topic_3572Data quality managementWindowsMacLinuxPythonOtherEmergingFree of chargeOpen accesshttp://edamontology.org/operation_3180Sequence assembly validationhttp://edamontology.org/data_1383Nucleic acid sequence alignmenthttp://edamontology.org/format_2572BAM

http://edamontology.org/data_3914Quality control reporthttp://edamontology.org/format_2331HTML

covsnap sample.bam BRCA1,TP53,ETFDH --exonshttps://pypi.org/project/covsnap/RepositoryInstall with: pip install covsnaphttps://github.com/enes-ak/covsnapRepositorygithub repositoryhttps://anaconda.org/channels/bioconda/packages/covsnap/overviewRepositoryInstall with: conda install -c bioconda covsnaphttps://pypi.org/project/covsnap/Downloads pagepip install covsnap0.3.0https://anaconda.org/channels/bioconda/packages/covsnap/overviewDownloads pagehttps://anaconda.org/channels/bioconda/packages/covsnap/overviewhttps://github.com/enes-ak/covsnap/blob/master/README.mdGeneralEnes Akakenes96@gmail.comhttps://orcid.org/0000-0001-5931-730XDeveloperGlyComboGlyCombo is an open source software for combinatorial glycan composition determination to identify glycans in MS acquisitions of glycan-containing samples in text or mzML formats. This application enables rapid extraction of precursor m/z values from mzML files, a vendor-neutral format that ensures cross-platform compatibility.https://github.com/Protea-Glycosciences/GlyComboglycombobiotools:glycombo1.3Desktop applicationhttp://edamontology.org/topic_3391OmicsWindowsC#Apache-2.0MatureFree of chargeOpen accessToolsSystems Biologyhttp://edamontology.org/operation_0226Annotationhttp://edamontology.org/data_0943Mass spectrumhttp://edamontology.org/format_3244mzML

http://edamontology.org/data_1462Carbohydrate structure

No command line interfacehttps://github.com/Protea-Glycosciences/GlyComboTechnical monitoringMonitored GitHub repositoryhttps://github.com/Protea-Glycosciences/GlyCombo/releases/latestDownloads pagehttps://www.youtube.com/watch?v=mIGxMTSfPOIQuick start guideYoutube video on getting startedmsconvertuses10.1021/jasms.4c0018839271475PrimaryChristopher Ashwoodchris@proteaglyco.comhttps://orcid.org/0000-0001-5944-6179PersonPrimary contactMaia Kellyhttps://orcid.org/0000-0002-9383-1107PersonDeveloperpathotyprA 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_1929FASTA

http://edamontology.org/data_0920Genotype/phenotype reporthttp://edamontology.org/format_3475TSV

train — Build a Random Forest classifier from labeled genomes.http://edamontology.org/operation_3225Variant classificationhttp://edamontology.org/data_2044Sequencehttp://edamontology.org/format_1929FASTA

http://edamontology.org/data_0920Genotype/phenotype reporthttp://edamontology.org/format_3475TSV

predict — 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_1929FASTA

http://edamontology.org/data_0920Genotype/phenotype reporthttp://edamontology.org/format_3475TSV

classify — 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_1930FASTQ

http://edamontology.org/data_0920Genotype/phenotype reporthttp://edamontology.org/format_3475TSV

split-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_1930FASTQ

http://edamontology.org/data_0920Genotype/phenotype reporthttp://edamontology.org/format_3475TSV

match — 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 LabDivisionProviderLab 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 Heidelberg03mstc592InstituteThe MINERVA PlatformThe MINERVA (Molecular Interaction NEtwoRk VisuAlization) platform is a standalone webserver for visualization, exploration and management of molecular networks encoded in SBGN-compliant format, including files produced using CellDesigner or SBGN editors. Visualization of uploaded networks generated by the platform is accessible via a web browser to all viewers with the weblink to the resource. The MINERVA Platform is a webservice using the Java Server Faces 2 technology. The server side, including data parsing, integration, annotation and verification, is implemented in Java. The platform uses the Postgres SQL database for data storage and the Hibernate framework as a middle layer between web server and database. The user web-interface is generated using React.js. The displayed content is visualized by OpenLayers API, dedicated JavaScript and CSS.https://minerva.uni.luMINERVA_Platformbiotools:MINERVA_Platform13.1.313.2.014.0.1315.0.316.4.017.1.318.1.119.1.019.1.320.0.3http://edamontology.org/topic_0602Molecular interactions, pathways and networkshttp://edamontology.org/topic_3391Omicshttp://edamontology.org/topic_3342Translational medicinehttp://edamontology.org/topic_2259Systems biologyAGPL-3.0LCSBELIXIR-LUMatureFree of chargeOpen accessToolsLuxembourghttp://edamontology.org/operation_3926Pathway visualisationVisualise systems biology diagrams online, on a standalone web serverhttp://edamontology.org/operation_0571Expression data visualisationVisualise omics data from multiple datasets on top of the diagramshttp://edamontology.org/operation_3434ConversionConvert between main systems biology layout formats: CellDeslgners SBML, SBML layout+render, SBGN-ML, GPMLhttp://edamontology.org/operation_2426Modelling and simulationUse MINERVA API to access systems biology formats for modellinghttps://gitlab.lcsb.uni.lu/minerva/core/RepositoryGiLab repository for core functionalities (data and format handling, service stability, API access)https://gitlab.lcsb.uni.lu/minerva/core/-/issuesIssue trackerIssue tracker for core functionalities (data and format handling, service stability, API access)https://gitlab.lcsb.uni.lu/minerva/frontendRepositoryGiLab repository for frontend functionalitieshttps://gitlab.lcsb.uni.lu/minerva/frontend/-/issuesIssue trackerIssue tracker for frontend functionalitieshttps://minerva.pages.uni.lu/doc/install/OtherInstallation instructions, including debian package, virtual machine images and docker containers.13.1.3 - 20.0.3https://minerva.uni.luQuick start guideRelease notesUser manualAPI documentationCitation instructionsTerms of usepathvisiousessbgnuseslibsbmluses10.1038/npjsba.2016.2028725475PMC5516855Primary10.010.1093/bioinformatics/btz28631074494PMC6821317Primary12.2.310.1093/bib/bbz06731273380PMC7373180Primary13.1.110.1089/big.2015.005727441714PMC4932659Usage10.010.1016/j.envpol.2019.04.0053099127913.1.1Marek Ostaszewskihttps://orcid.org/0000-0003-1473-370XPersonPrimary contactPiotr Gawronhttps://orcid.org/0000-0002-9328-8052PersonDeveloperReinhard Schneiderhttps://orcid.org/0000-0002-8278-1618PersonProviderVenkata Satagopamhttps://orcid.org/0000-0002-6532-5880PersonSupportRudi Ballinghttps://orcid.org/0000-0003-2902-5650PersonProviderDavid Hokszahttps://orcid.org/0000-0003-4679-0557PersonContributorEwa Smulahttps://orcid.org/0000-0001-7118-3164PersonMaintainerGRNsightWeb application and service for visualizing small- to medium-scale models of gene regulatory networks. It automatically lays out either an unweighted or weighted network graph based on an Excel input spreadsheet containing an adjacency matrix where regulators are named in the columns and target genes in the rows. It is best-suited for visualizing networks of fewer than 35 nodes and 70 edges and has general applicability.http://dondi.github.io/GRNsight/grnsightbiotools:grnsight7.6.0Web applicationhttp://edamontology.org/topic_0602Molecular interactions, pathways and networkshttp://edamontology.org/topic_0204Gene regulationWindowsMacLinuxJavaScriptBSD-3-ClauseMatureFree of chargehttp://edamontology.org/operation_3083Pathway or network visualisationhttp://edamontology.org/operation_1781Gene regulatory network analysisGRNsight automatically lays out a network graph based on an Excel input spreadsheet containing an adjacency matrix where regulators are named in the columns and target genes in the rows. When a user uploads a spreadsheet with an unweighted adjacency matrix, GRNsight automatically lays out the graph using black lines and pointed arrowheads. When a user uploads a spreadsheet with a weighted adjacency matrix, GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on the sign (positive for activation or negative for repression) and magnitude of the weight parameter. Nodes are rectangular and support gene labels of up to 12 characters. The edges are arcs, which become straight lines when the nodes are close together. Self-regulatory edges are indicated by a loop on the lower-right side of a node. Visualizations can be modified by sliders that adjust D3.js's force graph layout parameters and through manual node dragging.https://github.com/dondi/GRNsightRepositoryhttps://github.com/dondi/GRNsight/archive/refs/heads/main.zipSource codehttp://dondi.github.io/GRNsight/documentation.htmlGeneral10.7717/peerj-cs.85Other10.5281/zenodo.7411630OtherAlex MillerPersonDeveloperJia Celyne GarciaPersonDeveloperCindy TongPersonDeveloperAmelie DinhPersonDeveloperMilka ZekariasPersonDeveloperNgoc Kim Ngan TranPersonDeveloperCecilia ZaragozaPersonDeveloperA’Kaia PhelpsPersonDeveloperOna O. IgbinedionPersonDeveloperAhmad R. MersaghianPersonDeveloperIan M. GreenPersonDeveloperLauren L. AmparoPersonDeveloperAlexia M. FillerPersonDeveloperKevin B. PattersonPersonDeveloperJohn L. LopezPersonDeveloperJustin Kyle T. TorresPersonDeveloperEileen ChoePersonDeveloperJen ShinPersonDeveloperMihir SamdarshiPersonDeveloperAnindita VarshneyaPersonDeveloperNicole A. AnguianoPersonDeveloperBritain J. SouthwickPersonDeveloperBen G. FitzpatrickPersonDeveloperLoyola Marymount UniversityInstituteProviderNSF award 0921038; LMU Rains Research Assistant Program; LMU Summer Undergraduate Research ProgramFunding agencyContributorlmu.eduInstituteProviderKam D. Dahlquistkdahlquist@lmu.eduPersonPrimary contactJohn David N. Dionisiodondi@lmu.eduhttps://orcid.org/0000-0001-8655-4693PersonPrimary contactPangyPlotPangyPlot is a tool for visualizing graph-based pangenome data, designed to simplify the exploration of complex genetic variation. It currently visualizes data from the HPRC pangenome graph and is demonstrated in use with cystic fibrosis data.https://github.com/ScottMastro/pangyplotpangyplotbiotools:pangyplotv.0.1.0Web applicationhttp://edamontology.org/topic_0622Genomicshttp://edamontology.org/topic_3056Population geneticsJavaScriptPythonMITEmergingFree of chargehttp://edamontology.org/operation_3744Multiple sample visualisationhttp://edamontology.org/operation_3208Genome visualisationhttps://pangyplot.research.sickkids.ca/MirrorLive Instancehttps://zenodo.org/records/17174109RepositoryExample Datahttps://pangyplot.readthedocs.io/en/latest/User manualbubblegunincludes10.1101/2025.10.31.684064Preprintv.0.1.0Scott Mastromatteohttps://orcid.org/0000-0002-3551-2247PersonDeveloperWEBnmaWEBnm@ provides quick, automated computation and analysis of low-frequency normal modes for protein structures.http://apps.cbu.uib.no/webnmawebnmabiotools:webnma3.5Web APISuitehttp://edamontology.org/topic_2814Protein structure analysishttp://edamontology.org/topic_0736Protein folds and structural domainsLinuxWindowsMacPythonGPL-3.0BiB toolsCBU toolsUiB toolsELIXIR-NOELIXIR-NorwayMatureFree of chargeOpen access3D-BioInfoNorwayhttp://edamontology.org/operation_0570Structure visualisationhttp://edamontology.org/operation_0244Protein flexibility and motion analysishttp://edamontology.org/data_1460Protein structurehttp://edamontology.org/format_1476PDB

http://edamontology.org/data_2884Plothttp://edamontology.org/format_3508PDF

http://edamontology.org/data_1354Sequence profilehttp://edamontology.org/format_2330Textual format

Constructs elastic network model from alpha carbon coordinates of the protein, and computes properties to describe large scale conformations. Computes normal modes, fluctuation profiles, inter-residue correlations, conformational overlap analysis and vector field representations. Structural amino acid profiles, and normal mode characteristics describing protein motion, visualized in plots and decorated structure visualizations. White space delimited tabular data for normal modes and the provided plotshttp://edamontology.org/operation_2487Protein structure comparisonhttp://edamontology.org/operation_0337Visualisationhttp://edamontology.org/data_0886Structure alignmenthttp://edamontology.org/format_2200FASTA-like (text)http://edamontology.org/data_1460Protein structurehttp://edamontology.org/format_1476PDB

http://edamontology.org/data_2884Plothttp://edamontology.org/format_3508PDF

http://edamontology.org/data_0889Structural profilehttp://edamontology.org/format_2330Textual format

Performs comparative analysis of the normal modes of protein structures. Computes the Bhattacharyya Coefficient (BC) and the Root Mean Squared Inner Product (RMSIP) of aligned parts of the proteins. Alignment of sets of proteins to be compared. Multiple protein structures Heatmaps, dendrograms and structural amino acid profiles for visual comparison of structural similarity. White space delimited tabular data for the provided plotshttps://github.com/reuter-group/webnma3RepositoryIssue trackerhttps://elixir.no/helpdeskHelpdeskHelpdesk and support for ELIXIR Norway services.http://apps.cbu.uib.no/webnma3/howto/singleQuick start guidehttp://apps.cbu.uib.no/webnma3/qandaFAQhttp://apps.cbu.uib.no/webnma3/aboutGeneralCitation instructionsnumpyusesbiopythonusesmatplotlibusesmustangusesdsspuses10.1186/s12859-014-0427-625547242PMC4339738Primary2.010.1186/1471-2105-6-5215762993PMC1274249Primary1.0Nathalie ReuterNathalie.Reuter@uib.nohttp://www.cbu.uib.no/reuter/https://orcid.org/0000-0002-3649-7675PersonPrimary contactDeveloperSandhya P Tiwarihttps://orcid.org/0000-0002-0747-3826PersonDeveloperKidane M TeklePersonDeveloperTristan CragnoliniPersonDeveloperSvenn H GrindhaugPersonDeveloperLars SkjærvenPersonDeveloperGisle SalensmindePersonDeveloperEdvin FuglebakkPersonDeveloperSiv M HollupPersonDeveloperDepartment of Molecular Biology, University of Bergen, NorwayInstituteProviderComputational Biology Unit, Department of Informatics, University of Bergen, NorwayInstituteProviderUiBInstituteProviderQMRIToolsQMRITools is a collection of tools for quantitative muscle MRI analysis, enabling the assessment of neuromuscular disorders and conditions. The software utilizes various MRI sequences and processing pipelines to generate metrics for comparative evaluation.https://github.com/mfroeling/QMRIToolsqmribiotools:qmrihttp://edamontology.org/topic_3444MRIhttp://edamontology.org/topic_3407Endocrinology and metabolismhttp://edamontology.org/topic_0634Pathologyhttp://edamontology.org/topic_3379Preclinical and clinical studieshttp://edamontology.org/operation_3443Image analysishttp://edamontology.org/operation_3799Quantificationhttp://edamontology.org/operation_3435Standardisation and normalisationhttps://www.qmritools.com/assets/htmldoc/html/guide/qmritoolsUser manualhttps://www.qmritools.com/doc/instal/Installation instructions10.21105/joss.01204Method10.1002/jmri.2360822334539Primary10.1002/mrm.2247720725932Method10.1002/nbm.411931313867UsageMartijn Froelingm.froeling@gmail.comhttps://www.qmritools.com/https://orcid.org/0000-0003-3841-0497PersonDeveloperMaintainerRandom Number LabAn interactive set of tools including randomization of data (even stratified random sampling) and random assignments for studies, generation of random numbers following specific probability distributions, generation of random and quasi-random point distributions and sampling patterns, noise in one or two dimensions with adjustable spectral properties, random walks and Lévy flights. Own data tables can be provided for random sampling in a privacy-friendly way, as data processing takes place directly in the browser and no data is transferred to anywhere else. The Random Number Lab also offers some educational material and worksheets and can be used for scientific and educational purposes.https://randomnumberlab.orgrandom_number_labbiotools:random_number_labRRID:SCR_028010rridWeb applicationhttp://edamontology.org/topic_2269Statistics and probabilityhttp://edamontology.org/topic_3678Experimental design and studiesLinuxiOSMacAndroidWindowsJavaScriptMatureFree of chargehttp://edamontology.org/operation_0364Random sequence generationGeneration of sequences of random numbers or elements, spatial point patterns, random walkshttp://edamontology.org/operation_0337VisualisationVisualization of random distributions and random processeshttp://edamontology.org/operation_2409Data handlingRandomization and permutation of data in a privacy-preserving wayThomas Amthorhello@randomnumberlab.orghttps://orcid.org/0000-0002-1456-6131PersonDeveloperMaintainerPLASTPLAST is a heuristical method to search for highest scoring local alignments between a DNA sequence query and a graphical pangenome. It takes as input a plain DNA sequence and a pangenome which may either be a set of (multiple) FASTA or FASTQ files or a sequence graph constructed by the tool Bifrost. It then outputs statistically meaningful (gapped) alignments in the style of the NCBI BLAST standard output format. Alignments are calculated based on a "seed-and-extend approach" while traversing the sequence graph. Biologically meaningful alignments are filtered by using an alignment statistic explicitly developed for sequence-to-graph alignments involving graphical pangenomes.https://github.com/tischulz1/plastpangenome-blastbiotools:pangenome-blast0.0.1-0.2.0Command-line toolhttp://edamontology.org/topic_0797Comparative genomicshttp://edamontology.org/topic_0080Sequence analysisLinuxMacWindowsC++GPL-3.0MatureFree of chargeOpen accesshttp://edamontology.org/operation_0495Local alignmenthttp://edamontology.org/data_3494DNA sequencehttp://edamontology.org/format_1211unambiguous pure nucleotidehttp://edamontology.org/data_1234Sequence set (nucleic acid)http://edamontology.org/format_3975GFA 1http://edamontology.org/format_2333Binary format

http://edamontology.org/data_1383Nucleic acid sequence alignmenthttp://edamontology.org/format_1333BLAST results

In order to search for alignments within the pangenome graph, A pangenome graph used to search for alignments consists of (1) a file in GFA format containing all sequences of the graph, (2) a binary file produced by the tool itself or the software "Bifrost" and (3) a program-specific index data structure in binary format.PLAST Search -i pangenomeGraphCommonFilePrefix -q fileContainingOneQueryPerLinehttp://edamontology.org/operation_0227Indexinghttp://edamontology.org/data_0850Sequence sethttp://edamontology.org/format_1929FASTAhttp://edamontology.org/format_1930FASTQ

http://edamontology.org/data_0850Sequence sethttp://edamontology.org/format_3975GFA 1http://edamontology.org/format_2333Binary format

If a pangenome graph already exists and only an index has to be built, FASTA/FASTQ files are not needed.PLAST Build -i pangenomeGraphCommonFilePrefix -R *.fastahttps://gitlab.ub.uni-bielefeld.de/gi/plastRepositoryhttps://github.com/tischulz1/plastMirrorhttps://gitlab.ub.uni-bielefeld.de/gi/plast/-/blob/master/README.mdGeneral10.1093/bioinformatics/btab07733532821PMC8388040PrimaryMethodBenchmarking studyBielefeld Universityhttps://www.uni-bielefeld.de/InstituteProviderGenome Informaticshttps://gi.cebitec.uni-bielefeld.de/InstituteProviderTizian Schulzplast-service@cebitec.uni-bielefeld.dehttps://orcid.org/0000-0003-0744-7078PersonDeveloperHelix InsightAI-powered variant interpretation platform for clinical genetics laboratories. Automates ACMG/AMP 2015 classification using Bayesian point-based framework (Tavtigian et al. 2018) with BayesDel ClinGen SVI-calibrated thresholds (Pejaver et al. 2022). Integrates 8 reference databases (gnomAD v4.1, ClinVar, dbNSFP 4.9c, SpliceAI, gnomAD Constraint, HPO, ClinGen, Ensembl VEP) for comprehensive variant annotation. Supports HPO-based phenotype matching with semantic similarity scoring, automated biomedical literature mining across 2M+ PubMed publications, and structured clinical report generation. Delivers full clinical interpretation of whole genome sequencing (WGS) in under 15 minutes. EU-hosted on dedicated infrastructure in Helsinki, Finland (GDPR-compliant).https://helixinsight.bioHelixInsightbiotools:HelixInsight3.5Web applicationhttp://edamontology.org/topic_3574Human geneticshttp://edamontology.org/topic_0625Genotype and phenotypehttp://edamontology.org/topic_3325Rare diseaseshttp://edamontology.org/topic_0199Genetic variationhttp://edamontology.org/topic_3063Medical informaticsLinuxPythonProprietaryEmergingCommercialRestricted accesshttp://edamontology.org/operation_3225Variant classificationhttp://edamontology.org/data_3498Sequence variationshttp://edamontology.org/format_3016VCF

http://edamontology.org/data_2955Sequence report

http://edamontology.org/data_0920Genotype/phenotype report

http://edamontology.org/data_1622Disease report

http://edamontology.org/operation_3197Genetic variation analysishttp://edamontology.org/operation_0305Literature searchhttp://edamontology.org/operation_0362Genome annotationhttps://helixinsight.bio/docsGeneralhttps://helixinsight.bio/docs/getting-startedAPI documentationhttps://helixinsight.bio/how-it-worksInstallation instructionshttps://helixinsight.bio/termsTerms of usehttps://helixinsight.bio/privacyOtherPrivacy policyHelena Bioinformaticscontact@helena.biohttps://helena.bioInstituteDeveloperFANTASIAFANTASIA (Functional ANnoTAtion based on embedding space SImilArity) is a pipeline for protein annotating via GO term transference using the embedding space. FANTASIA’s latest developments include additional protein language models and provide enhanced functionalities.https://github.com/CBBIO/fantasiav2biotools:fantasiav24.0LITECommand-line toolhttp://edamontology.org/topic_3945Molecular evolutionhttp://edamontology.org/topic_0218Natural language processinghttp://edamontology.org/topic_0085Functional genomicshttp://edamontology.org/topic_4010Open scienceLinuxPythonSQLMITMatureFree of chargeOpen accessTools3D-BioInfoProteomicsSpainhttp://edamontology.org/operation_3672Gene functional annotationhttps://github.com/CBBIO/FANTASIARepositoryMain repository contains documentation from latest versionhttps://github.com/MetazoaPhylogenomicsLab/FANTASIARepositoryMain repository contains documentation from linitial version based on Bioembeddings implementation.https://www.earthbiogenome.org/report-on-annotation-recommended-toolsSoftware catalogueRecommended tool for the Earth Biogenome Projecthttps://github.com/CBBIO/FANTASIA-LiteSoftware catalogueThis is the LITE version of FANTASIA for a quick and custom annotationhttps://gitlab.com/ifb-elixirfr/cluster/tools/-/tree/master/tools/fantasia/0.1.0?ref_type=headsRepositoryThis is a repository for FANTASIA-LITE available in Elixir Francehttps://fantasia.readthedocs.io/en/latest/GeneralFull documetnation with user cases, benchmarking, and cluster implementations10.1093/nargab/lqae078Usage10.1007/978-1-0716-4623-6_840601255Method2.8.0This protocol describes the use of the first version of FANTASIA10.1038/s42003-025-08651-240813894PMC12354702Primary4.0Application of FANTASIA to annotate 24 million of genes of 1000 animal speciesFrancisco Miguel Pérez Canalesfmpercan@upo.eshttp://www.bioinfocb.es/PersonPrimary contactDeveloperDocumentorMaintainerSupportProviderProgrammerAna M Rojas Mendozaa.rojas.m@csic.eshttp://www.bioinfocb.es/PersonPrimary contactContributorDocumentorSupportScientific concept and functionalitiesRosa Fernandezrosa.fernandez@ibe.upf-csic.eshttps://www.metazomics.com/ContributorDocumentorScientific concept and functionalitiesFrancisco J. Ruiz Motafraruimot@alum.us.eshttp://www.bioinfocb.es/PersonDeveloperJunior developerGemma Martinez Redondogemma.martinez@ibe.upf-csic.eshttps://www.metazomics.com/PersonContributorDeveloperContributed as developer of the first version of FANTASIA V1Alex Dominguez Rodriguezadomrod4@upo.eshttp://www.bioinfocb.es/PersonDeveloperPrimary contactMaintainerDocumentorImplemented the LITE version of FANTASIAPoly PipelineA data analysis pipeline for Spatial Transcriptomics data tailored to polyploid organisms.https://github.com/capuccino26/POLY_PIPELINEpoly_pipelinebiotools:poly_pipelinev1.0.0doi:10.5281/zenodo.18655692doiv1.0.0Command-line toolScripthttp://edamontology.org/topic_3170RNA-SeqLinuxRBashPythonMITMatureFree of chargeOpen accessToolsPlant Scienceshttp://edamontology.org/operation_0313Expression profile clusteringhttp://edamontology.org/data_3917Count matrix

http://edamontology.org/data_3768Clustered expression profiles

Clustering of GEF files.ANALYSIS=1http://edamontology.org/operation_3925Network visualisationhttp://edamontology.org/data_3917Count matrix

http://edamontology.org/data_2600Pathway or network

Generation of Nodes and Edges.ANALYSIS=3http://edamontology.org/operation_0336Format validationhttp://edamontology.org/data_3112Gene expression matrix

http://edamontology.org/data_3112Gene expression matrix

Data converter.ANALYSIS=0http://edamontology.org/operation_3800RNA-Seq quantificationhttp://edamontology.org/data_3112Gene expression matrix

http://edamontology.org/data_3917Count matrix

Generation of count matrix.ANALYSIS=1http://edamontology.org/operation_3695Data filteringhttp://edamontology.org/data_3112Gene expression matrix

http://edamontology.org/data_3112Gene expression matrix

Filtering of GEF files.ANALYSIS=1https://github.com/capuccino26/POLY_PIPELINERepositoryGithub Repositoryhttps://zenodo.org/records/18655692OtherZenodo Inserthttps://github.com/capuccino26/POLY_PIPELINE/releases/tag/v1.0.0Source codeGithub Releasev1.0.0https://github.com/capuccino26/POLY_PIPELINE#poly_pipelineUser manualGithub Documentation10.5281/zenodo.18655692Otherv1.0.0Zenodo InsertionPedro Cristovão Carvalhopccrvl@gmail.comhttps://cafinvest.com.br/resume.phphttps://orcid.org/0000-0001-9242-6911PersonDeveloperPrimary contactMaintainerProviderSupportMain developer of the pipelineSegmentation evaluationThis tool calculates segmentation metrics (Dice score, intersection over union, Hausdorff distance, and normalised surface distance) between two sets of masks. It can be used to quickly calculate a number of metrics for segmentation models.https://github.com/josegcpa/eucaim-metrics/tree/mastersegmentation_evaluationbiotools:segmentation_evaluationv0.1Command-line toolhttp://edamontology.org/topic_3063Medical informaticshttp://edamontology.org/topic_3444MRILinuxWindowsMacCC-BY-4.0EUCAIMEmergingFree of chargeOpen accesshttp://edamontology.org/operation_3443Image analysishttp://edamontology.org/data_3424Raw imagehttp://edamontology.org/format_3549nii

http://edamontology.org/data_2048Reporthttp://edamontology.org/format_3464JSON

This is the core command which can be used to calculate image segmentation metrics. For more options please run `uv run eval_segmentation --help`uv run eval_segmentation \ --pred test_data/predicted/ \ --gt test_data/groundtruth/ \ --output test.json \ --n_classes 2 \ --verbose \ --params '{"normalised_surface_distance": {"max_distance": 100.0}}'https://github.com/josegcpa/eucaim-metrics/tree/masterRepositoryhttps://github.com/josegcpa/eucaim-metrics/tree/masterQuick start guideAPI documentationJosé Guilherme de Almeidajose.almeida@research.fchampalimaud.orghttps://orcid.org/0000-0002-1887-0157PersonPrimary contactDeveloperDocumentorMaintainerNickolas Papanikolaouhttps://orcid.org/0000-0003-3298-2072PersonContributordecompTumor2SigR package for identification of mutational signatures active in individual tumors. It decomposes an individual tumor genome into a given set of Alexandrov-type or Shiraishi-type signatures, thus quantifying the contribution of the corresponding mutational processes to the somatic mutations identified in the tumor.http://rmpiro.net/decompTumor2Sig/decompTumor2Sigbiotools:decompTumor2Sig2.0.0Libraryhttp://edamontology.org/topic_0199Genetic variationhttp://edamontology.org/topic_2640Oncologyhttp://edamontology.org/topic_3168SequencingLinuxWindowsMacRGPL-2.0MatureFree of chargehttp://edamontology.org/operation_0239Sequence motif recognitionhttp://edamontology.org/operation_1812Parsinghttps://github.com/rmpiro/decompTumor2Sig/issuesIssue trackerhttps://github.com/rmpiro/decompTumor2SigRepositoryhttps://bioconductor.org/packages/release/bioc/html/decompTumor2Sig.htmlSoftware cataloguehttp://rmpiro.net/index.html#downloadsDownloads pagehttps://github.com/rmpiro/decompTumor2Sig/blob/master/decompTumor2Sig-manual.pdfUser manual10.1186/s12859-019-2688-630999866PMC6472187PrimaryProf. Dr. Rosario M. Piror.piro@fu-berlin.dePersonPrimary contactDeveloperSarekSarek is part of nf-core and has changed to the nf-core-sarek id.https://nf-co.re/sareksarekbiotools:sarek2.0.02.1.02.2.02.2.12.2.22.32.3.FIX1Command-line toolWorkflowhttp://edamontology.org/topic_2640Oncologyhttp://edamontology.org/topic_0622Genomicshttp://edamontology.org/topic_3673Whole genome sequencinghttp://edamontology.org/topic_0769WorkflowsLinuxGroovyMITMatureOpen accesshttp://edamontology.org/operation_3731Sample comparisonhttp://edamontology.org/operation_0361Sequence annotationhttp://edamontology.org/operation_3227Variant callinghttp://edamontology.org/data_3497DNA sequence (raw)http://edamontology.org/format_1930FASTQhttp://edamontology.org/data_3494DNA sequencehttp://edamontology.org/format_2572BAM

http://edamontology.org/data_3498Sequence variationshttp://edamontology.org/format_3016VCF

https://github.com/nf-core/sarekRepositoryGithub Repositoryhttps://nfcore.slack.com/channels/sarekSocial mediaSlack channelhttps://nf-co.re/sarekGeneralhttps://nf-co.re/sarek/usageUser manualhttps://nf-co.re/usage/installationInstallation instructionshttps://github.com/nf-core/sarek/blob/master/LICENSETerms of usecawisNewVersionOfnf-core-sarekhasNewVersion10.1101/316976OtherThis is the publication about the nf-core framework10.12688/f1000research.16665.2OtherThis is the publication about the nf-core framework10.1038/s41587-020-0439-x32055031OtherThis is the publication about the nf-core frameworkMaxime U. Garciamaxime.garcia@scilifelab.sehttps://maxulysse.github.io/https://orcid.org/0000-0003-2827-9261PersonPrimary contactDeveloperMaintainerSzilvester Juhosszilveszter.juhos@scilifelab.sePersonPrimary contactDeveloperMaintainerScience for Life Laboratory - SciLifeLabhttps://www.scilifelab.se/InstituteNational Genomics Infrastructure - NGIhttps://ngisweden.scilifelab.se/DivisionNational Bioinformatics Infrastructure Sweden - NBIShttps://nbis.se/DivisionBarntumörbankenhttps://ki.se/forskning/barntumorbankenInstituteExposome MapsExposome Maps is a data catalogue of geospatially resolved datasets on environmental exposure estimates. It contains more than 20,000 datasets on several Exposome dimensions including Physico-Chemical Environment, Built Environment, Food Environment, and Social Environment. It provides harmonized data collections across Europe for following environmental factors: PM10, PM2.5, NO2, O3, Temperature, Ultra Violet Radiation, Road Traffic Noise, Urbanicity, NDVI (Normalized Difference Vegetation Index ), MSAVI (Modified Soil Adjusted Vegetation Index), Green spaces, Blue spaces, Imperviousness, Light at night, Walkability Index.https://exposome.uu.nl/exposome_mapsbiotools:exposome_mapshttp://edamontology.org/topic_4012FAIR datahttp://edamontology.org/topic_3855Environmental sciencesOtherEXPANSEEHENMatureFree of charge (with restrictions)Restricted accesshttps://expanseproject.eu/OtherProject information pagehttps://surfdrive.surf.nl/files/index.php/s/uqUORDrd428H2F9User manualDescription of Environmental variables available through Exposome Mapshttps://surfdrive.surf.nl/files/index.php/s/fI0Gmuu5O1MJAI9Terms of useExposome-NL data platform Data Access and Publication Policy10.1126/science.aay316410.1097/EE9.0000000000000162Roel VermeulenR.C.H.Vermeulen@uu.nlhttps://orcid.org/0000-0003-4082-8163Primary contactZimbo Boudewijnsz.s.r.m.boudewijns@uu.nlhttps://orcid.org/0000-0002-9383-9571MaintainerLloyd Rogal.q.roga@uu.nlDeveloperImage duplication checkA CLI tool for detecting duplicate DICOM images by analyzing pixel data, generating content-based hashes, and comparing them to identify identical images, even when metadata, file format, or size differshttps://docs.google.com/presentation/d/1guNJkm0E5YSsbIVKaaEevFnNGG4WR_m6XGsL695e3_A/edit?slide=id.g370e96e3b29_0_111#slide=id.g370e96e3b29_0_111image_duplicate_check_toolbiotools:image_duplicate_check_toolv1.0.0Command-line toolhttp://edamontology.org/topic_3384Medical imagingMacLinuxJavaScriptMITEUCAIMEmergingFree of chargeOpen accesshttps://github.com/helghast79/eucaim-image-duplication-check-toolRepositoryhttps://github.com/helghast79/eucaim-image-duplication-check-tool/releases/tag/v1.0.0Downloads pagescroll to bottom of github release page to find the executable download for each OSv1.0.0https://github.com/helghast79/eucaim-image-duplication-check-tool/blob/main/README.mdGeneralCommand-line optionsInstallation instructionsUser manualRelease notesMiguel Chambelmiguel.chambel@research.fchampalimaud.orghttps://orcid.org/0000-0002-6099-4110PersonDeveloperContributorDocumentorJosé Guilherme de Almeidajose.almeida@research.fchampalimaud.orghttps://josegcpa.net/https://orcid.org/0000-0002-1887-0157PersonMaintainerSupportNickolas Papanikolaouhttps://orcid.org/0000-0003-3298-2072PersonContributor