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{ "count": 1074, "next": "?page=2", "previous": null, "list": [ { "name": "ExomeFlow", "description": "ExomeFlow 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.\n\nIt 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. 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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.", "homepage": "https://github.com/Vidhusv/FRAMEXPLORER", "biotoolsID": "framexplore", "biotoolsCURIE": "biotools:framexplore", "version": [ "1.0.0" ], "otherID": [ { "value": "doi:10.5281/zenodo.20135079", "type": "doi", "version": null } ], "relation": [], "function": [], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_4019", "term": "Biosciences" } ], "operatingSystem": [ "Linux", "Mac", "Windows" ], "language": [ "Python" ], "license": "MIT", "collectionID": [], "maturity": "Emerging", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/Vidhusv/FRAMEXPLORER", "type": [ "Repository" ], "note": "GitHub repository with source code, documentation, and logo" }, { "url": "https://github.com/Vidhusv/FRAMEXPLORER/issues", "type": [ "Issue tracker" ], "note": "Report bugs, suggest features, or ask questions" } ], "download": [ { "url": "https://github.com/Vidhusv/FRAMEXPLORER/archive/refs/heads/main.zip", "type": "Source code", "note": "Latest version of the complete repository as a ZIP file", "version": "1.0.0" }, { "url": "https://raw.githubusercontent.com/Vidhusv/FRAMEXPLORER/main/FrameXplore.ipynb", "type": "Binaries", "note": "Direct download of the Colab notebook (.ipynb file). 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Nature Methods, 2022.", "metadata": null } ], "credit": [ { "name": "Vidhu S Vijay", "email": "vidhuvijay2003@gmail.com", "url": "https://github.com/Vidhusv", "orcidid": "https://orcid.org/0009-0009-1182-2089", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer", "Maintainer" ], "note": "MS Bioinformatics student at Amrita Vishwa Vidyapeetham; creator and maintainer of FrameXplore." } ], "owner": "vidhu", "additionDate": "2026-05-13T05:45:59.510938Z", "lastUpdate": "2026-05-13T06:23:55.557041Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "GlyComboCLI", "description": "GlyComboCLI 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. 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The tool automatically extends a multiple sequence alignment that may represent only a fragment of a longer repetitive sequence family.", "homepage": "https://github.com/Dfam-consortium/RepeatAfterMe", "biotoolsID": "repeatafterme", "biotoolsCURIE": "biotools:repeatafterme", "version": [ "v0.0.7" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0495", "term": "Local alignment" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3494", "term": "DNA sequence" }, "format": [ { "uri": "http://edamontology.org/format_3009", "term": "2bit" } ] }, { "data": { "uri": "http://edamontology.org/data_1017", "term": "Sequence range" }, "format": [ { "uri": "http://edamontology.org/format_3585", "term": "bed6" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0863", "term": "Sequence alignment" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "note": null, "cmd": "./RAMExtend -ranges test/extension-test2.tsv -twobit test/extension-test2.2bit" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0622", "term": "Genomics" } ], "operatingSystem": [ "Mac", "Linux" ], "language": [ "C" ], "license": "CC0-1.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [], "credit": [ { "name": "Robert Hubley", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null } ], "owner": "Pub2Tools", "additionDate": "2026-05-08T10:33:02.567199Z", "lastUpdate": "2026-05-08T10:33:02.569259Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "MR-based neuroblastoma tumour detection and segmentation (EUCAIM-SW-019_T-01-02-002)", "description": "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.\n\nTRAINING & VALIDATION COHORTS:\n\nInitial Development (Veiga-Canuto 2022):\n-Training: 106 patients, 5-fold CV (median DSC 0.965 ± 0.018).\n-Internal validation: 26 patients (median DSC 0.918 ± 0.067).\n-Sources: La Fe (Spain), SIOPEN HR-NBL1/LINES, St. Anna (Austria), Pisa (Italy).\n-Mean age: 37.6 ± 39.3 months.\n-Median tumor volume: 116,518 mm³.\n\nExternal Validation (Veiga-Canuto 2023):\n-300 patients, 535 independent T2 MRI scans (486 at diagnosis, 49 post-chemotherapy).\n-Performance: median DSC 0.997 (0.944–1.000), 94% successful detection.\n-Sources: 12 European countries (HR-NBL1/SIOPEN 119, LINES/SIOPEN 107, German Registry 62, others 12).\n-Heterogeneous data: 1.5T (435), 3T (100); Siemens (318), Philips (109), GE (105), Canon (3).", "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/mr_based_neuroblastoma_tumour_detection_and_segmentation/info-tab", "biotoolsID": "mr-based_neuroblastoma_tumour_detection_and_segmentation", "biotoolsCURIE": "biotools:mr-based_neuroblastoma_tumour_detection_and_segmentation", "version": [ "2.0.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] }, { "data": { "uri": "http://edamontology.org/data_2048", "term": "Report" }, "format": [ { "uri": "http://edamontology.org/format_3464", "term": "JSON" }, { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": null, "cmd": "docker run --rm \\\n -v \"<input_path>:/input\" \\\n -v \"<output_path>:/output\" \\\n --gpus all \\\n harbor.eucaim.cancerimage.eu/processing-tools/mr_based_neuroblastoma_tumour_detection_and_segmentation:2.0.0 \\\n --mode dicom-seg \\\n --series-selector /output/config/series_to_segment.csv \\\n --seg-series-number 2302001 \\\n --seg-algorithm-name \"nnUNet_Neuroblastoma_Primage_training\" \\\n --seg-coordinating-center \"EUCAIM Consortium\" \\\n --keep-nifti false" } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_2640", "term": "Oncology" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_3365", "term": "Data architecture, analysis and design" } ], "operatingSystem": [ "Mac", "Windows", "Linux" ], "language": [ "Python" ], "license": "CC-BY-NC-ND-4.0", "collectionID": [ "EUCAIM" ], "maturity": "Mature", "cost": "Free of charge (with restrictions)", "accessibility": "Open access (with restrictions)", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/mr_based_neuroblastoma_tumour_detection_and_segmentation/info-tab", "type": [ "Software catalogue" ], "note": null }, { "url": "https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/", "type": [ "Social media" ], "note": null } ], "download": [], "documentation": [ { "url": "https://drive.eucaim.cancerimage.eu/s/tS2W8nb38Zs2ZzK", "type": [ "User manual" ], "note": null }, { "url": "https://www.mdpi.com/2072-6694/14/15/3648", "type": [ "General" ], "note": null }, { "url": "https://drive.eucaim.cancerimage.eu/s/44BxMSNpxwgsZiE", "type": [ "Terms of use" ], "note": null }, { "url": "https://www.youtube.com/watch?v=c7XOEGRA9aQ&list=PL3Q1XjQpjfg_GEmwPDrQeESh6nqCMnYyR&index=12", "type": [ "Training material" ], "note": null } ], "publication": [ { "doi": "10.3390/cancers14153648", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": null }, { "doi": "10.3390/cancers15051622", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "GIBI230 - 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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.", "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/cluster_based_harmonization/info-tab", "biotoolsID": "cluster_based_harmonization", "biotoolsCURIE": "biotools:cluster_based_harmonization", "version": [ "1.1.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3432", "term": "Clustering" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2526", "term": "Text data" }, "format": [ { "uri": "http://edamontology.org/format_3620", "term": "xlsx" }, { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2526", "term": "Text data" }, "format": [ { "uri": "http://edamontology.org/format_3464", "term": "JSON" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2526", "term": "Text data" }, "format": [ { "uri": "http://edamontology.org/format_3620", "term": "xlsx" }, { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2526", "term": "Text data" }, "format": [ { "uri": "http://edamontology.org/format_3620", "term": "xlsx" }, { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2526", "term": "Text data" }, "format": [ { "uri": "http://edamontology.org/format_3835", "term": "TIDE TXT" } ] } ], "note": null, "cmd": "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.json" }, { "operation": [ { "uri": "http://edamontology.org/operation_3432", "term": "Clustering" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2526", "term": "Text data" }, "format": [ { "uri": "http://edamontology.org/format_3620", "term": "xlsx" }, { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2526", "term": "Text data" }, "format": [ { "uri": "http://edamontology.org/format_3620", "term": "xlsx" }, { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2526", "term": "Text data" }, "format": [ { "uri": "http://edamontology.org/format_3620", "term": "xlsx" }, { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2526", "term": "Text data" }, "format": [ { "uri": "http://edamontology.org/format_3835", "term": "TIDE TXT" } ] } ], "note": null, "cmd": "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 merge" } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0219", "term": "Data curation and archival" }, { "uri": "http://edamontology.org/topic_3382", "term": "Imaging" } ], "operatingSystem": [ "Windows", "Mac", "Linux" ], "language": [ "Python" ], "license": "CC-BY-NC-ND-4.0", "collectionID": [ "EUCAIM" ], "maturity": "Emerging", "cost": "Free of charge (with restrictions)", "accessibility": "Open access (with restrictions)", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/cluster_based_harmonization/artifacts-tab", "type": [ "Software catalogue" ], "note": null }, { "url": "https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/", "type": [ "Social media" ], "note": null } ], "download": [], "documentation": [ { "url": "https://drive.eucaim.cancerimage.eu/s/Kz3jfQYQZjezpxK", "type": [ "User manual" ], "note": null }, { "url": "https://drive.eucaim.cancerimage.eu/s/5bMA2jsZTfS8eoZ", "type": [ "Terms of use" ], "note": null }, { "url": "https://www.youtube.com/watch?v=0GJqNJv-Qf8&list=PL3Q1XjQpjfg_GEmwPDrQeESh6nqCMnYyR&index=31", "type": [ "Training material" ], "note": null }, { "url": "https://drive.eucaim.cancerimage.eu/s/LkczHCMZH5nd8QR", "type": [ "General" ], "note": null } ], "publication": [], "credit": [ { "name": "GIBI230 - 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Updated version available in the project repository.", "metadata": null } ], "credit": [ { "name": "Francisco Javier Lobo-Cabrera", "email": "fjlobcab@upo.es", "url": "https://scholar.google.com/citations?user=D4NAEvEAAAAJ&hl=en", "orcidid": "https://orcid.org/0000-0002-9592-6863", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer" ], "note": "PhD in Biotechnology, with publications in computational biology." }, { "name": "José A. 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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).", "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/champ-prostate-zone-segmentation", "biotoolsID": "prostate_zone_segmentation_tool", "biotoolsCURIE": "biotools:prostate_zone_segmentation_tool", "version": [], "otherID": [], "relation": [ { "biotoolsID": "nnunet", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3442", "term": "MRI image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" }, { "uri": "http://edamontology.org/format_3549", "term": "nii" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" }, { "uri": "http://edamontology.org/format_3549", "term": "nii" } ] } ], "note": "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.\n\nIf the input is DICOM, `--is_dicom` should be used.", "cmd": "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/output" }, { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3442", "term": "MRI image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" }, { "uri": "http://edamontology.org/format_3549", "term": "nii" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3442", "term": "MRI image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" }, { "uri": "http://edamontology.org/format_3549", "term": "nii" } ] } ], "note": "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. \n\nThe 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`.\n\nThe 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`.\n\nIf the input is DICOM, `--is_dicom` should be specified.", "cmd": "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}}" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3063", "term": "Medical informatics" } ], "operatingSystem": [ "Linux" ], "language": [ "Python" ], "license": "CC-BY-NC-4.0", "collectionID": [ "EUCAIM" ], "maturity": "Emerging", "cost": "Free of charge (with restrictions)", "accessibility": "Open access (with restrictions)", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/josegcpa/nnunet_serve", "type": [ "Repository" ], "note": "This 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." }, { "url": "https://kdrive.infomaniak.com/app/share/1928123/f7bf2733-0454-47b0-a29c-0beacb51fe68", "type": [ "Other" ], "note": "Contains the EUCAIM model card for this model." } ], "download": [], "documentation": [ { "url": "https://github.com/josegcpa/nnunet_serve", "type": [ "API documentation" ], "note": "This is the repository containing all of the code together with a comprehensive README for deployment and examplar files." } ], "publication": [ { "doi": "10.1016/j.compbiomed.2024.108216", "pmid": "38442555", "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "José Guilherme de Almeida", "email": "jose.almeida@research.fchampalimaud.org", "url": "https://josegcpa.net", "orcidid": "https://orcid.org/0000-0002-1887-0157", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Documentor", "Maintainer" ], "note": null }, { "name": "Nickolas Papanikolaou", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0003-3298-2072", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Support" ], "note": null }, { "name": "Nuno Rodrigues", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null } ], "owner": "josegcpa", "additionDate": "2025-11-07T18:15:49.962044Z", "lastUpdate": "2026-04-29T10:36:29.272637Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "Sub-Saharan Africa Brain Glioma Segmenter", "description": "The 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). 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HULAFE", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Matias Fernandez-Paton", "email": "matias_fernandez@iislafe.es", "url": null, "orcidid": "https://orcid.org/0000-0001-9374-1411", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Pedro-Miguel Martinez-Girones", "email": "pedromiguel_martinez@iislafe.es", "url": null, "orcidid": "https://orcid.org/0000-0002-9506-9451", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Documentor", "Support" ], "note": null }, { "name": "Adrian Galiana-Bordera", "email": "adrian_galiana@iislafe.es", "url": null, "orcidid": "https://orcid.org/0000-0002-8324-8284", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Carina Soler-Pons", "email": "carina_soler@iislafe.es", "url": null, "orcidid": "https://orcid.org/0009-0000-2991-1391", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Documentor", "Support" ], "note": null }, { "name": "Leonor Cerda-Alberich", "email": "leonor_cerda@iislafe.es", "url": null, "orcidid": "https://orcid.org/0000-0002-5567-4278", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Provider" ], "note": null }, { "name": "Luis Marti-Bonmati", "email": "luis_marti@iislafe.es", "url": null, "orcidid": "https://orcid.org/0000-0002-8234-010X", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Provider" ], "note": null } ], "owner": "casopon", "additionDate": "2025-05-29T09:19:51.404419Z", "lastUpdate": "2026-04-27T11:03:42.973594Z", "editPermission": { "type": "group", "authors": [ "pedromiguel_martinez_HULAFE" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "MR-based glioblastoma tumour detection and segmentation (EUCAIM-SW-021_T-01-02-004)", "description": "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). 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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 plots", "cmd": null } ], "toolType": [ "Web API", "Suite" ], "topic": [ { "uri": "http://edamontology.org/topic_2814", "term": "Protein structure analysis" }, { "uri": "http://edamontology.org/topic_0736", "term": "Protein folds and structural domains" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [ "BiB tools", "CBU tools", "UiB tools", "ELIXIR-NO", "ELIXIR-Norway" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [ "Norway" ], "elixirCommunity": [ "3D-BioInfo" ], "link": [ { "url": "https://github.com/reuter-group/webnma3", "type": [ "Repository", "Issue tracker" ], "note": null }, { "url": "https://elixir.no/helpdesk", "type": [ "Helpdesk" ], "note": "Helpdesk and support for ELIXIR Norway services." } ], "download": [], "documentation": [ { "url": "http://apps.cbu.uib.no/webnma3/howto/single", "type": [ "Quick start guide" ], "note": null }, { "url": "http://apps.cbu.uib.no/webnma3/qanda", "type": [ "FAQ" ], "note": null }, { "url": "http://apps.cbu.uib.no/webnma3/about", "type": [ "General", "Citation instructions" ], "note": null } ], "publication": [ { "doi": "10.1186/s12859-014-0427-6", "pmid": "25547242", "pmcid": "PMC4339738", "type": [ "Primary" ], "version": "2.0", "note": null, "metadata": { "title": "WEBnmat v2.0: Web server and services for comparing protein flexibility", "abstract": "Background: Normal mode analysis (NMA) using elastic network models is a reliable and cost-effective computational method to characterise protein flexibility and by extension, their dynamics. Further insight into the dynamics-function relationship can be gained by comparing protein motions between protein homologs and functional classifications. This can be achieved by comparing normal modes obtained from sets of evolutionary related proteins. Results: We have developed an automated tool for comparative NMA of a set of pre-aligned protein structures. The user can submit a sequence alignment in the FASTA format and the corresponding coordinate files in the Protein Data Bank (PDB) format. The computed normalised squared atomic fluctuations and atomic deformation energies of the submitted structures can be easily compared on graphs provided by the web user interface. The web server provides pairwise comparison of the dynamics of all proteins included in the submitted set using two measures: the Root Mean Squared Inner Product and the Bhattacharyya Coefficient. The Comparative Analysis has been implemented on our web server for NMA, WEBnmat, which also provides recently upgraded functionality for NMA of single protein structures. This includes new visualisations of protein motion, visualisation of inter-residue correlations and the analysis of conformational change using the. In addition, programmatic access to WEBnmat is now available through a SOAP-based web service. WEBnmat is available at. Conclusion: WEBnmat v2.0 is an online tool offering unique capability for comparative NMA on multiple protein structures. Along with a convenient web interface, powerful computing resources, and several methods for mode analyses, WEBnmat facilitates the assessment of protein flexibility within protein families and superfamilies. These analyses can give a good view of how the structures move and how the flexibility is conserved over the different structures.", "date": "2014-12-30T00:00:00Z", "citationCount": 87, "authors": [ { "name": "Tiwari S.P." }, { "name": "Fuglebakk E." }, { "name": "Hollup S.M." }, { "name": "Skjaerven L." }, { "name": "Cragnolini T." }, { "name": "Grindhaug S.H." }, { "name": "Tekle K.M." }, { "name": "Reuter N." } ], "journal": "BMC Bioinformatics" } }, { "doi": "10.1186/1471-2105-6-52", "pmid": "15762993", "pmcid": "PMC1274249", "type": [ "Primary" ], "version": "1.0", "note": null, "metadata": { "title": "WEBnm@: A web application for normal mode analyses of proteins", "abstract": "Background: Normal mode analysis (NMA) has become the method of choice to investigate the slowest motions in macromolecular systems. NMA is especially useful for large biomolecular assemblies, such as transmembrane channels or virus capsids. NMA relies on the hypothesis that the vibrational normal modes having the lowest frequencies (also named soft modes) describe the largest movements in a protein and are the ones that are functionally relevant. Results: We developed a web-based server to perform normal modes calculations and different types of analyses. Starting from a structure file provided by the user in the PDB format, the server calculates the normal modes and subsequently offers the user a series of automated calculations; normalized squared atomic displacements, vector field representation and animation of the first six vibrational modes. Each analysis is performed independently from the others and results can be visualized using only a web browser. No additional plug-in or software is required. For users who would like to analyze the results with their favorite software, raw results can also be downloaded. The application is available on http://www.bioinfo.no/tools/normalmodes. We present here the underlying theory, the application architecture and an illustration of its features using a large transmembrane protein as an example. Conclusion: We built an efficient and modular web application for normal mode analysis of proteins. Non specialists can easily and rapidly evaluate the degree of flexibility of multi-domain protein assemblies and characterize the large amplitude movements of their domains. © 2005 Hollup et al; licensee BioMed Central Ltd.", "date": "2005-03-11T00:00:00Z", "citationCount": 105, "authors": [ { "name": "Hollup S.M." }, { "name": "Salensminde G." }, { "name": "Reuter N." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Nathalie Reuter", "email": "Nathalie.Reuter@uib.no", "url": "http://www.cbu.uib.no/reuter/", "orcidid": "https://orcid.org/0000-0002-3649-7675", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer" ], "note": null }, { "name": "Sandhya P Tiwari", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-0747-3826", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Kidane M Tekle", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Tristan Cragnolini", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Svenn H Grindhaug", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Lars Skjærven", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Gisle Salensminde", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Edvin Fuglebakk", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Siv M Hollup", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Department of Molecular Biology, University of Bergen, Norway", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Computational Biology Unit, Department of Informatics, University of Bergen, Norway", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "UiB", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null } ], "owner": "UiB", "additionDate": "2016-03-17T13:51:10Z", "lastUpdate": "2026-03-11T08:56:55.566213Z", "editPermission": { "type": "group", "authors": [ "eca008", "korbinib" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "QMRITools", "description": "QMRITools is a collection of tools for quantitative muscle MRI analysis, enabling the assessment of neuromuscular disorders and conditions. 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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.", "homepage": "https://randomnumberlab.org", "biotoolsID": "random_number_lab", "biotoolsCURIE": "biotools:random_number_lab", "version": [], "otherID": [ { "value": "RRID:SCR_028010", "type": "rrid", "version": null } ], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0364", "term": "Random sequence generation" } ], "input": [], "output": [], "note": "Generation of sequences of random numbers or elements, spatial point patterns, random walks", "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" } ], "input": [], "output": [], "note": "Visualization of random distributions and random processes", "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_2409", "term": "Data handling" } ], "input": [], "output": [], "note": "Randomization and permutation of data in a privacy-preserving way", "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_2269", "term": "Statistics and probability" }, { "uri": "http://edamontology.org/topic_3678", "term": "Experimental design and studies" } ], "operatingSystem": [ "Linux", "iOS", "Mac", "Android", "Windows" ], "language": [ "JavaScript" ], "license": null, "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [], "credit": [ { "name": "Thomas Amthor", "email": "hello@randomnumberlab.org", "url": null, "orcidid": "https://orcid.org/0000-0002-1456-6131", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer", "Maintainer" ], "note": null } ], "owner": "thomas9", "additionDate": "2026-02-27T17:39:14.144907Z", "lastUpdate": "2026-02-27T18:10:10.460700Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "PLAST", "description": "PLAST 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.", "homepage": "https://github.com/tischulz1/plast", "biotoolsID": "pangenome-blast", "biotoolsCURIE": "biotools:pangenome-blast", "version": [ "0.0.1-0.2.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0495", "term": "Local alignment" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3494", "term": "DNA sequence" }, "format": [ { "uri": "http://edamontology.org/format_1211", "term": "unambiguous pure nucleotide" } ] }, { "data": { "uri": "http://edamontology.org/data_1234", "term": "Sequence set (nucleic acid)" }, "format": [ { "uri": "http://edamontology.org/format_3975", "term": "GFA 1" }, { "uri": "http://edamontology.org/format_2333", "term": "Binary format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1383", "term": "Nucleic acid sequence alignment" }, "format": [ { "uri": "http://edamontology.org/format_1333", "term": "BLAST results" } ] } ], "note": "In order to search for alignments within the pangenome graph, \nA 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.", "cmd": "PLAST Search -i pangenomeGraphCommonFilePrefix -q fileContainingOneQueryPerLine" }, { "operation": [ { "uri": "http://edamontology.org/operation_0227", "term": "Indexing" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0850", "term": "Sequence set" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" }, { "uri": "http://edamontology.org/format_1930", "term": "FASTQ" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0850", "term": "Sequence set" }, "format": [ { "uri": "http://edamontology.org/format_3975", "term": "GFA 1" }, { "uri": "http://edamontology.org/format_2333", "term": "Binary format" } ] } ], "note": "If a pangenome graph already exists and only an index has to be built, FASTA/FASTQ files are not needed.", "cmd": "PLAST Build -i pangenomeGraphCommonFilePrefix -R *.fasta" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0797", "term": "Comparative genomics" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" } ], "operatingSystem": [ "Linux", "Mac", "Windows" ], "language": [ "C++" ], "license": "GPL-3.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://gitlab.ub.uni-bielefeld.de/gi/plast", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/tischulz1/plast", "type": [ "Mirror" ], "note": null } ], "download": [], "documentation": [ { "url": "https://gitlab.ub.uni-bielefeld.de/gi/plast/-/blob/master/README.md", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btab077", "pmid": "33532821", "pmcid": "PMC8388040", "type": [ "Primary", "Method", "Benchmarking study" ], "version": null, "note": null, "metadata": { "title": "Detecting high-scoring local alignments in pangenome graphs", "abstract": "Motivation: Increasing amounts of individual genomes sequenced per species motivate the usage of pangenomic approaches. Pangenomes may be represented as graphical structures, e.g. compacted colored de Bruijn graphs, which offer a low memory usage and facilitate reference-free sequence comparisons. While sequence-to-graph mapping to graphical pangenomes has been studied for some time, no local alignment search tool in the vein of BLAST has been proposed yet. Results: We present a new heuristic method to find maximum scoring local alignments of a DNA query sequence to a pangenome represented as a compacted colored de Bruijn graph. Our approach additionally allows a comparison of similarity among sequences within the pangenome. We show that local alignment scores follow an exponential-tail distribution similar to BLAST scores, and we discuss how to estimate its parameters to separate local alignments representing sequence homology from spurious findings. An implementation of our method is presented, and its performance and usability are shown. Our approach scales sublinearly in running time and memory usage with respect to the number of genomes under consideration. This is an advantage over classical methods that do not make use of sequence similarity within the pangenome.", "date": "2021-08-15T00:00:00Z", "citationCount": 6, "authors": [ { "name": "Schulz T." }, { "name": "Wittler R." }, { "name": "Rahmann S." }, { "name": "Hach F." }, { "name": "Stoye J." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Bielefeld University", "email": null, "url": "https://www.uni-bielefeld.de/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Genome Informatics", "email": null, "url": "https://gi.cebitec.uni-bielefeld.de/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Tizian Schulz", "email": "plast-service@cebitec.uni-bielefeld.de", "url": null, "orcidid": "https://orcid.org/0000-0003-0744-7078", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null } ], "owner": "tizian", "additionDate": "2021-05-06T06:18:52Z", "lastUpdate": "2026-02-26T11:22:02.485629Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Helix Insight", "description": "AI-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. 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For more options please run `uv run eval_segmentation --help`", "cmd": "uv run eval_segmentation \\\n --pred test_data/predicted/ \\\n --gt test_data/groundtruth/ \\\n --output test.json \\\n --n_classes 2 \\\n --verbose \\\n --params '{\"normalised_surface_distance\": {\"max_distance\": 100.0}}'" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3063", "term": "Medical informatics" }, { "uri": "http://edamontology.org/topic_3444", "term": "MRI" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [], "license": "CC-BY-4.0", "collectionID": [ "EUCAIM" ], "maturity": "Emerging", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/josegcpa/eucaim-metrics/tree/master", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "https://github.com/josegcpa/eucaim-metrics/tree/master", "type": [ "Quick start guide", "API documentation" ], "note": null } ], "publication": [], "credit": [ { "name": "José Guilherme de Almeida", "email": "jose.almeida@research.fchampalimaud.org", "url": null, "orcidid": "https://orcid.org/0000-0002-1887-0157", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Documentor", "Maintainer" ], "note": null }, { "name": "Nickolas Papanikolaou", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0003-3298-2072", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Contributor" ], "note": null } ], "owner": "josegcpa", "additionDate": "2026-02-12T14:54:22.759092Z", "lastUpdate": "2026-02-12T15:01:23.363666Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "decompTumor2Sig", "description": "R 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.", "homepage": "http://rmpiro.net/decompTumor2Sig/", "biotoolsID": "decompTumor2Sig", "biotoolsCURIE": "biotools:decompTumor2Sig", "version": [ "2.0.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0239", "term": "Sequence motif recognition" }, { "uri": "http://edamontology.org/operation_1812", "term": "Parsing" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0199", "term": "Genetic variation" }, { "uri": "http://edamontology.org/topic_2640", "term": "Oncology" }, { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "R" ], "license": "GPL-2.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/rmpiro/decompTumor2Sig/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "https://github.com/rmpiro/decompTumor2Sig", "type": [ "Repository" ], "note": null }, { "url": "https://bioconductor.org/packages/release/bioc/html/decompTumor2Sig.html", "type": [ "Software catalogue" ], "note": null } ], "download": [ { "url": "http://rmpiro.net/index.html#downloads", "type": "Downloads page", "note": null, "version": null } ], "documentation": [ { "url": "https://github.com/rmpiro/decompTumor2Sig/blob/master/decompTumor2Sig-manual.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1186/s12859-019-2688-6", "pmid": "30999866", "pmcid": "PMC6472187", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "DecompTumor2Sig: Identification of mutational signatures active in individual tumors", "abstract": "© 2019 The Author(s).Background: The somatic mutations found in a tumor have in most cases been caused by multiple mutational processes such as those related to extrinsic carcinogens like cigarette smoke, and those related to intrinsic processes like age-related spontaneous deamination of 5-methylcytosine. The effect of such mutational processes can be modeled by mutational signatures, of which two different conceptualizations exist: the model introduced by Alexandrov et al., Nature 500:415-421, 2013, and the model introduced by Shiraishi et al., PLoS Genetics 11(12):e1005657, 2015. The initial identification and definition of mutational signatures requires large sets of tumor samples. Results: Here, we present decompTumor2Sig, an easy to use R package that can decompose 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. Until now, such tools were available only for Alexandrov signatures. We demonstrate the correctness and usefulness of our approach with three test cases, using somatic mutations from 21 breast cancer genomes, from 435 tumor genomes of ten different tumor entities, and from simulated tumor genomes, respectively. Conclusions: The decompTumor2Sig package is freely available and has been accepted for inclusion in Bioconductor.", "date": "2019-04-18T00:00:00Z", "citationCount": 8, "authors": [ { "name": "Kruger S." }, { "name": "Piro R.M." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Prof. Dr. Rosario M. 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