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{ "count": 30461, "next": "?page=2", "previous": null, "list": [ { "name": "pyM2aia", "description": "pyM²aia is a Python package for memory-efficient access and processing of mass spectrometry image data. The I/O functionality is derived from the interactive desktop application M²aia. Special features are the batch generator utilities for deep learning applications.", "homepage": "https://m2aia.de/pym2aia.html", "biotoolsID": "pym2aia", "biotoolsCURIE": "biotools:pym2aia", "version": [], "otherID": [], "relation": [ { "biotoolsID": "m2aia", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3435", "term": "Standardisation and normalisation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] }, { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3839", "term": "ibd" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] }, { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3839", "term": "ibd" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3172", "term": "Metabolomics" }, { "uri": "http://edamontology.org/topic_0092", "term": "Data visualisation" }, { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python", "C++" ], "license": "BSD-3-Clause", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/m2aia/pym2aia", "type": [ "Issue tracker", "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/m2aia/pym2aia", "type": "Source code", "note": null, "version": null } ], "documentation": [ { "url": "https://data.jtfc.de/pym2aia/sphinx-build/html/index.html", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/gigascience/giab049", "pmid": "34282451", "pmcid": "PMC8290197", "type": [], "version": null, "note": "Cordes J; Enzlein T; Marsching C; Hinze M; Engelhardt S; Hopf C; Wolf I (July, 2021): M²aia - Interactive, fast and memory efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data https://doi.org/10.1093/gigascience/giab049", "metadata": { "title": "M2aia-Interactive, fast, and memory-efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data", "abstract": "Background: Mass spectrometry imaging (MSI) is a label-free analysis method for resolving bio-molecules or pharmaceuticals in the spatial domain. It offers unique perspectives for the examination of entire organs or other tissue specimens. Owing to increasing capabilities of modern MSI devices, the use of 3D and multi-modal MSI becomes feasible in routine applications-resulting in hundreds of gigabytes of data. To fully leverage such MSI acquisitions, interactive tools for 3D image reconstruction, visualization, and analysis are required, which preferably should be open-source to allow scientists to develop custom extensions. Findings: We introduce M2aia (MSI applications for interactive analysis in MITK), a software tool providing interactive and memory-efficient data access and signal processing of multiple large MSI datasets stored in imzML format. M2aia extends MITK, a popular open-source tool in medical image processing. Besides the steps of a typical signal processing workflow, M2aia offers fast visual interaction, image segmentation, deformable 3D image reconstruction, and multi-modal registration. A unique feature is that fused data with individual mass axes can be visualized in a shared coordinate system. We demonstrate features of M2aia by reanalyzing an N-glycan mouse kidney dataset and 3D reconstruction and multi-modal image registration of a lipid and peptide dataset of a mouse brain, which we make publicly available. Conclusions: To our knowledge, M2aia is the first extensible open-source application that enables a fast, user-friendly, and interactive exploration of large datasets. M2aia is applicable to a wide range of MSI analysis tasks.", "date": "2021-07-01T00:00:00Z", "citationCount": 17, "authors": [ { "name": "Cordes J." }, { "name": "Enzlein T." }, { "name": "Marsching C." }, { "name": "Hinze M." }, { "name": "Engelhardt S." }, { "name": "Hopf C." }, { "name": "Wolf I." } ], "journal": "GigaScience" } } ], "credit": [ { "name": "Jonas Cordes", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0003-3148-4282", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "jtfc", "additionDate": "2023-08-30T09:06:30.320364Z", "lastUpdate": "2025-06-05T12:19:20.571346Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "MSI-R", "description": "Adaptive Pixel Mass Recalibration for Mass Spectrometry Imaging Based on Locally Endogenous Biological Signals.\n\nUsing Biological Signals for Mass Recalibration of Mass Spectrometry Imaging Data.\n\nThis repository contains the python script performing the recalibration of mass spectrometry images (MSI) from the associated paper Using Biological Signals for Mass Recalibration of Mass Spectrometry Imaging Data.", "homepage": "https://github.com/LaRoccaRaphael/MSI_recalibration", "biotoolsID": "msi-r", "biotoolsCURIE": "biotools:msi-r", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3627", "term": "Mass spectra calibration" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] }, { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3839", "term": "ibd" } ] }, { "data": { "uri": "http://edamontology.org/data_2093", "term": "Data reference" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] }, { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3839", "term": "ibd" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_3382", "term": "Imaging" }, { "uri": "http://edamontology.org/topic_3172", "term": "Metabolomics" }, { "uri": "http://edamontology.org/topic_0749", "term": "Transcription factors and regulatory sites" } ], "operatingSystem": [], "language": [ "Python" ], "license": "Apache-2.0", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1021/acs.analchem.0c05071", "pmid": "33583182", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Adaptive pixel mass recalibration for mass spectrometry imaging based on locally endogenous biological signals", "abstract": "Mass spectrometry imaging (MSI) is a powerful and convenient method for revealing the spatial chemical composition of different biological samples. Molecular annotation of the detected signals is only possible if a high mass accuracy is maintained over the entire image and the m/z range. However, the change in the number of ions from pixel-to-pixel of the biological samples could lead to small fluctuations in the detected m/z-values, called mass shift. The use of internal calibration is known to offer the best solution to avoid, or at least to reduce, mass shifts. Their “a priori” selection for a global MSI acquisition is prone to false positive detection and therefore to poor recalibration. To fill this gap, this work describes an algorithm that recalibrates each spectrum individually by estimating its mass shift with the help of a list of pixel-specific internal calibrating ions, automatically generated in a data-adaptive manner (https://github.com/ LaRoccaRaphael/MSI_recalibration). Through a practical example, we applied the methodology to a zebrafish whole-body section acquired at a high mass resolution to demonstrate the impact of mass shift on data analysis and the capability of our algorithm to recalibrate MSI data. In addition, we illustrate the broad applicability of the method by recalibrating 31 different public MSI data sets from METASPACE from various samples and types of MSI and show that our recalibration significantly increases the numbers of METASPACE annotations (gaining from 20 up to 400 additional annotations), particularly the high-confidence annotations with a low false discovery rate.", "date": "2021-03-02T00:00:00Z", "citationCount": 14, "authors": [ { "name": "Rocca R.L." }, { "name": "Kune C." }, { "name": "Tiquet M." }, { "name": "Stuart L." }, { "name": "Eppe G." }, { "name": "Alexandrov T." }, { "name": "de Pauw E." }, { "name": "Quinton L." } ], "journal": "Analytical Chemistry" } } ], "credit": [], "owner": "Niclaskn", "additionDate": "2021-03-19T09:12:19Z", "lastUpdate": "2025-06-05T11:49:34.683716Z", "editPermission": { "type": "group", "authors": [ "sizhengZhao" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "MSIWarp", "description": "A General Approach to Mass Alignment in Mass Spectrometry Imaging.\n\nMSIWarp is a flexible tool to perform mass alignment of Mass Spectrometry Imaging (MSI) spectra. A key feature of MSIWarp is its compatibility with centroid spectra.", "homepage": "https://github.com/horvatovichlab/MSIWarp", "biotoolsID": "msiwarp", "biotoolsCURIE": "biotools:msiwarp", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2928", "term": "Alignment" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] }, { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3839", "term": "ibd" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] }, { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3839", "term": "ibd" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_3382", "term": "Imaging" }, { "uri": "http://edamontology.org/topic_0081", "term": "Structure analysis" } ], "operatingSystem": [], "language": [ "C++", "Python" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1021/acs.analchem.0c03833", "pmid": "33317272", "pmcid": "PMC7745203", "type": [], "version": null, "note": null, "metadata": { "title": "MSIWarp: A General Approach to Mass Alignment in Mass Spectrometry Imaging", "abstract": "Mass spectrometry imaging (MSI) is a technique that provides comprehensive molecular information with high spatial resolution from tissue. Today, there is a strong push toward sharing data sets through public repositories in many research fields where MSI is commonly applied; yet, there is no standardized protocol for analyzing these data sets in a reproducible manner. Shifts in the mass-to-charge ratio (m/z) of molecular peaks present a major obstacle that can make it impossible to distinguish one compound from another. Here, we present a label-free m/z alignment approach that is compatible with multiple instrument types and makes no assumptions on the sample's molecular composition. Our approach, MSIWarp (https://github.com/horvatovichlab/MSIWarp), finds an m/z recalibration function by maximizing a similarity score that considers both the intensity and m/z position of peaks matched between two spectra. MSIWarp requires only centroid spectra to find the recalibration function and is thereby readily applicable to almost any MSI data set. To deal with particularly misaligned or peak-sparse spectra, we provide an option to detect and exclude spurious peak matches with a tailored random sample consensus (RANSAC) procedure. We evaluate our approach with four publicly available data sets from both time-of-flight (TOF) and Orbitrap instruments and demonstrate up to 88% improvement in m/z alignment.", "date": "2020-12-15T00:00:00Z", "citationCount": 15, "authors": [ { "name": "Eriksson J.O." }, { "name": "Sanchez Brotons A." }, { "name": "Rezeli M." }, { "name": "Suits F." }, { "name": "Marko-Varga G." }, { "name": "Horvatovich P." } ], "journal": "Analytical Chemistry" } } ], "credit": [ { "name": "Peter Horvatovich", "email": "p.l.horvatovich@rug.nl", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "owner": "Niclaskn", "additionDate": "2021-01-18T12:22:18Z", "lastUpdate": "2025-06-05T11:41:35.873717Z", "editPermission": { "type": "group", "authors": [ "sizhengZhao" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "high" }, { "name": "MALDIquant", "description": "MALDIquant is a complete analysis pipeline for matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) and other two-dimensional mass spectrometry data. In addition to commonly used plotting and processing methods it includes distinctive features, namely baseline subtraction methods such as morphological filters (TopHat) or the statistics-sensitive non-linear iterative peak-clipping algorithm (SNIP), peak alignment using warping functions, handling of replicated measurements as well as allowing spectra with different resolutions.", "homepage": "https://cran.r-project.org/package=MALDIquant", "biotoolsID": "maldi_quant", "biotoolsCURIE": "biotools:maldi_quant", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3799", "term": "Quantification" }, { "uri": "http://edamontology.org/operation_3860", "term": "Spectrum calculation" }, { "uri": "http://edamontology.org/operation_3215", "term": "Peak detection" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] }, { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3839", "term": "ibd" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2048", "term": "Report" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] } ], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] }, { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3839", "term": "ibd" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2884", "term": "Plot" }, "format": [ { "uri": "http://edamontology.org/format_3603", "term": "PNG" } ] } ], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3627", "term": "Mass spectra calibration" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] }, { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3839", "term": "ibd" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3682", "term": "imzML metadata file" } ] }, { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3839", "term": "ibd" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Library", "Workflow" ], "topic": [ { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" } ], "operatingSystem": [], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/sgibb/MALDIquant/", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/sgibb/MALDIquant/issues", "type": [ "Issue tracker" ], "note": null }, { "url": "http://strimmerlab.org/software/maldiquant/", "type": [ "Other" ], "note": null } ], "download": [], "documentation": [ { "url": "https://cran.r-project.org/web/packages/MALDIquant/MALDIquant.pdf", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/bts447", "pmid": "22796955", "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Maldiquant: A versatile R package for the analysis of mass spectrometry data", "abstract": "MALDIquant is an R package providing a complete and modular analysis pipeline for quantitative analysis of mass spectrometry data. MALDIquant is specifically designed with application in clinical diagnostics in mind and implements sophisticated routines for importing raw data, preprocessing, non-linear peak alignment and calibration. It also handles technical replicates as well as spectra with unequal resolution. © The Author 2012. Published by Oxford University Press. All rights reserved.", "date": "2012-09-01T00:00:00Z", "citationCount": 499, "authors": [ { "name": "Gibb S." }, { "name": "Strimmer K." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Sebastian Gibb", "email": "mail@sebastiangibb.de", "url": null, "orcidid": "https://orcid.org/0000-0001-7406-4443", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Maintainer" ], "note": null }, { "name": "Korbinian Strimmer", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0001-7917-2056", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null } ], "owner": "Kigaard", "additionDate": "2021-05-26T20:42:18Z", "lastUpdate": "2025-06-05T11:28:57.497366Z", "editPermission": { "type": "group", "authors": [ "sizhengZhao" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Volumetry analysis (EUCAIM-SW-060_T-02-01-009)", "description": "Performs volumetric analysis of brain structures by segmenting and calculating the volume of grey matter, white matter, and CSF. Results support studies on neurodegeneration, development, or disease progression.", "homepage": "https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es", "biotoolsID": "volumetry_analysis", "biotoolsCURIE": "biotools:volumetry_analysis", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3365", "term": "Data architecture, analysis and design" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/", "type": [ "Social media" ], "note": null } ], "download": [], "documentation": [], "publication": [], "credit": [ { "name": "GIBI230 - HULAFE", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "casopon", "additionDate": "2025-05-29T10:58:25.526645Z", "lastUpdate": "2025-06-05T11:09:36.363997Z", "editPermission": { "type": "group", "authors": [ "pedromiguel_martinez_HULAFE" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Deep features extraction (EUCAIM-SW-058_T-02-01-007)", "description": "Extracts deep features from MR images using pretrained neural networks. These features can be used for classification, clustering, or survival prediction tasks in medical imaging.", "homepage": "https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es", "biotoolsID": "deep_features_extraction", "biotoolsCURIE": "biotools:deep_features_extraction", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3365", "term": "Data architecture, analysis and design" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/", "type": [ "Social media" ], "note": null } ], "download": [], "documentation": [], "publication": [], "credit": [ { "name": "GIBI230 - HULAFE", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "casopon", "additionDate": "2025-05-29T10:56:54.107090Z", "lastUpdate": "2025-06-05T11:09:20.805965Z", "editPermission": { "type": "group", "authors": [ "pedromiguel_martinez_HULAFE" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "R1 and T1 maps extraction (EUCAIM-SW-057_T-02-01-006)", "description": "Computes R1 and T1 maps from MR images, showing the rate and time of longitudinal relaxation. 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It streamlines the radiomics calculation process by generating a structured CSV file containing all extracted variables from medical images.\nThe dockerized software enables users to configure parameters like filters, bin width, resampling spacing, and normalization settings can be specified. The output radiomic variables provide quantitative information for further analysis in medical imaging research and machine learning applications. \nSpecially important the parameter selection of the band width. For robust and reproducible results, a bin width of 5 is commonly recommended, but it should be adjusted based on image resolution, modality, and noise levels.", "homepage": "https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es", "biotoolsID": "gibi230_radiomic_features_extraction", "biotoolsCURIE": "biotools:gibi230_radiomic_features_extraction", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [], "toolType": [], "topic": [], "operatingSystem": [], "language": [ "Python" ], "license": null, "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1148/ryai.230208", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Reproducibility Analysis of Radiomic Features on T2-weighted MR Images after Processing and Segmentation Alterations in Neuroblastoma Tumors", "abstract": "Purpose: To evaluate the reproducibility of radiomics features extracted from T2-weighted MR images in patients with neuroblastoma. Materials and Methods: A retrospective study included 419 patients (mean age, 29 months ± 34 [SD]; 220 male, 199 female) with neu-roblastic tumors diagnosed between 2002 and 2023, within the scope of the PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers (ie, PRIMAGE) project, involving 746 T2/T2*-weighted MRI sequences at diagnosis and/or after initial chemotherapy. Images underwent processing steps (denoising, inhomogeneity bias field cor-rection, normalization, and resampling). Tumors were automatically segmented, and 107 shape, first-order, and second-order radiomics features were extracted, considered as the reference standard. Subsequently, the previous image processing settings were modified, and volumetric masks were applied. New radiomics features were extracted and compared with the reference standard. Reproducibility was assessed using the concordance correlation coefficient (CCC); intrasubject repeatability was measured using the coefficient of variation (CoV). Results: When normalization was omitted, only 5% of the radiomics features demonstrated high reproducibility. Statistical analysis revealed significant changes in the normalization and resampling processes (P < .001). Inhomogeneities removal had the least impact on radiomics (83% of parameters remained stable). Shape features remained stable after mask modifications, with a CCC greater than 0.90. Mask modifications were the most favorable changes for achieving high CCC values, with a radiomics features stability of 70%. Only 7% of second-order radiomics features showed an excellent CoV of less than 0.10. Conclusion: Modifications in the T2-weighted MRI preparation process in patients with neuroblastoma resulted in changes in radiomics features, with normalization identified as the most influential factor for reproducibility. Inhomogeneities removal had the least impact on radiomics features.", "date": "2024-07-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Veiga-Canuto D." }, { "name": "Fernandez-Paton M." }, { "name": "Alberich L.C." }, { "name": "Pastor A.J." }, { "name": "Maya A.G." }, { "name": "Sierra J.M.C." }, { "name": "Nebot C.S." }, { "name": "de las Heras B.M." }, { "name": "Potschger U." }, { "name": "Taschner-Mandl S." }, { "name": "Neri E." }, { "name": "Canete A." }, { "name": "Ladenstein R." }, { "name": "Hero B." }, { "name": "Alberich-Bayarri A." }, { "name": "Marti-Bonmati L." } ], "journal": "Radiology: Artificial Intelligence" } } ], "credit": [], "owner": "pedromiguel_martinez_HULAFE", "additionDate": "2024-12-23T12:44:47.964606Z", "lastUpdate": "2025-06-05T11:07:12.219171Z", "editPermission": { "type": "group", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Perfusion maps extraction (EUCAIM-SW-053_T-02-01-002)", "description": "This tool extracts perfusion maps from dynamic imaging data (e.g., DCE-MRI) using pharmacokinetic models or semi-quantitative methods. 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HULAFE", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "casopon", "additionDate": "2025-05-29T11:01:13.461164Z", "lastUpdate": "2025-06-05T10:11:42.310864Z", "editPermission": { "type": "group", "authors": [ "pedromiguel_martinez_HULAFE" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Cluster based harmonization (EUCAIM-SW-044_T-01-03-006)", "description": "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.", "homepage": "https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es", "biotoolsID": "cluster_based_harmonization", "biotoolsCURIE": "biotools:cluster_based_harmonization", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0219", "term": "Data curation and archival" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/", "type": [ "Social media" ], "note": null } ], "download": [], "documentation": [], "publication": [], "credit": [ { "name": "GIBI230 - HULAFE", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "casopon", "additionDate": "2025-05-29T10:41:57.535794Z", "lastUpdate": "2025-06-05T08:56:13.538861Z", "editPermission": { "type": "group", "authors": [ "pedromiguel_martinez_HULAFE" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "CT-based neuroblastoma tumour detection and segmentation (EUCAIM-SW-022_T-01-02-005)", "description": "The tool performs an automatic segmentation of the possible neuroblastoma tumours on CT images.", "homepage": "https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es", "biotoolsID": "ct-based_neuroblastoma_tumour_detection_and_segmentation", "biotoolsCURIE": "biotools:ct-based_neuroblastoma_tumour_detection_and_segmentation", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_2640", "term": "Oncology" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/", "type": [ "Social media" ], "note": null } ], "download": [], "documentation": [], "publication": [], "credit": [ { "name": "GIBI230 - HULAFE", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "casopon", "additionDate": "2025-05-29T10:38:08.771699Z", "lastUpdate": "2025-06-05T08:55:29.869206Z", "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", "homepage": "https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es", "biotoolsID": "mr-based_glioblastoma_tumour_detection_and_segmentation", "biotoolsCURIE": "biotools:mr-based_glioblastoma_tumour_detection_and_segmentation", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3365", "term": "Data architecture, analysis and design" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/", "type": [ "Social media" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.3390/cancers14153648", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Comparative Multicentric Evaluation of Inter-Observer Variability in Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance Images", "abstract": "Tumor segmentation is one of the key steps in imaging processing. The goals of this study were to assess the inter-observer variability in manual segmentation of neuroblastic tumors and to analyze whether the state-of-the-art deep learning architecture nnU-Net can provide a robust solution to detect and segment tumors on MR images. A retrospective multicenter study of 132 patients with neuroblastic tumors was performed. Dice Similarity Coefficient (DSC) and Area Under the Receiver Operating Characteristic Curve (AUC ROC) were used to compare segmentation sets. Two more metrics were elaborated to understand the direction of the errors: the modified version of False Positive (FPRm) and False Negative (FNR) rates. Two radiologists manually segmented 46 tumors and a comparative study was performed. nnU-Net was trained-tuned with 106 cases divided into five balanced folds to perform cross-validation. The five resulting models were used as an ensemble solution to measure training (n = 106) and validation (n = 26) performance, independently. The time needed by the model to automatically segment 20 cases was compared to the time required for manual segmentation. The median DSC for manual segmentation sets was 0.969 (±0.032 IQR). The median DSC for the automatic tool was 0.965 (±0.018 IQR). The automatic segmentation model achieved a better performance regarding the FPRm. MR images segmentation variability is similar between radiologists and nnU-Net. Time leverage when using the automatic model with posterior visual validation and manual adjustment corresponds to 92.8%.", "date": "2022-08-01T00:00:00Z", "citationCount": 25, "authors": [ { "name": "Veiga-Canuto D." }, { "name": "Cerda-Alberich L." }, { "name": "Sanguesa Nebot C." }, { "name": "Martinez de las Heras B." }, { "name": "Potschger U." }, { "name": "Gabelloni M." }, { "name": "Carot Sierra J.M." }, { "name": "Taschner-Mandl S." }, { "name": "Duster V." }, { "name": "Canete A." }, { "name": "Ladenstein R." }, { "name": "Neri E." }, { "name": "Marti-Bonmati L." } ], "journal": "Cancers" } }, { "doi": "10.3390/cancers15051622", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Independent Validation of a Deep Learning nnU-Net Tool for Neuroblastoma Detection and Segmentation in MR Images", "abstract": "Objectives. To externally validate and assess the accuracy of a previously trained fully automatic nnU-Net CNN algorithm to identify and segment primary neuroblastoma tumors in MR images in a large children cohort. Methods. An international multicenter, multivendor imaging repository of patients with neuroblastic tumors was used to validate the performance of a trained Machine Learning (ML) tool to identify and delineate primary neuroblastoma tumors. The dataset was heterogeneous and completely independent from the one used to train and tune the model, consisting of 300 children with neuroblastic tumors having 535 MR T2-weighted sequences (486 sequences at diagnosis and 49 after finalization of the first phase of chemotherapy). The automatic segmentation algorithm was based on a nnU-Net architecture developed within the PRIMAGE project. For comparison, the segmentation masks were manually edited by an expert radiologist, and the time for the manual editing was recorded. Different overlaps and spatial metrics were calculated to compare both masks. Results. The median Dice Similarity Coefficient (DSC) was high 0.997; 0.944–1.000 (median; Q1–Q3). In 18 MR sequences (6%), the net was not able neither to identify nor segment the tumor. No differences were found regarding the MR magnetic field, type of T2 sequence, or tumor location. No significant differences in the performance of the net were found in patients with an MR performed after chemotherapy. The time for visual inspection of the generated masks was 7.9 ± 7.5 (mean ± Standard Deviation (SD)) seconds. Those cases where manual editing was needed (136 masks) required 124 ± 120 s. Conclusions. The automatic CNN was able to locate and segment the primary tumor on the T2-weighted images in 94% of cases. There was an extremely high agreement between the automatic tool and the manually edited masks. This is the first study to validate an automatic segmentation model for neuroblastic tumor identification and segmentation with body MR images. The semi-automatic approach with minor manual editing of the deep learning segmentation increases the radiologist’s confidence in the solution with a minor workload for the radiologist.", "date": "2023-03-01T00:00:00Z", "citationCount": 11, "authors": [ { "name": "Veiga-Canuto D." }, { "name": "Cerda-Alberich L." }, { "name": "Jimenez-Pastor A." }, { "name": "Carot Sierra J.M." }, { "name": "Gomis-Maya A." }, { "name": "Sanguesa-Nebot C." }, { "name": "Fernandez-Paton M." }, { "name": "Martinez de las Heras B." }, { "name": "Taschner-Mandl S." }, { "name": "Duster V." }, { "name": "Potschger U." }, { "name": "Simon T." }, { "name": "Neri E." }, { "name": "Alberich-Bayarri A." }, { "name": "Canete A." }, { "name": "Hero B." }, { "name": "Ladenstein R." }, { "name": "Marti-Bonmati L." } ], "journal": "Cancers" } } ], "credit": [ { "name": "GIBI230 - HULAFE", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "casopon", "additionDate": "2025-05-29T12:09:16.704255Z", "lastUpdate": "2025-06-05T08:54:02.757932Z", "editPermission": { "type": "group", "authors": [ "pedromiguel_martinez_HULAFE" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "MR-based DIPG tumour detection and segmentation (EUCAIM-SW-020_T-01-02-003)", "description": "The tool performs an automatic segmentation of the possible DIPG tumours on MR images.", "homepage": "https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es", "biotoolsID": "mr-based_dipg_tumour_detection_and_segmentation", "biotoolsCURIE": "biotools:mr-based_dipg_tumour_detection_and_segmentation", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_2640", "term": "Oncology" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "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://link.springer.com/article/10.1007/s10278-025-01557-9", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1007/s10278-025-01557-9", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "GIBI230 - HULAFE", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "casopon", "additionDate": "2025-05-29T09:19:51.404419Z", "lastUpdate": "2025-06-05T08:53:03.701212Z", "editPermission": { "type": "group", "authors": [ "pedromiguel_martinez_HULAFE" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "MR-based neuroblastoma tumour detection and segmentation (EUCAIM-SW-019_T-01-02-002)", "description": "The tool performs an automatic segmentation of the possible neuroblastoma tumours on MR images.", "homepage": "https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es", "biotoolsID": "mr-based_neuroblastoma_tumour_detection_and_segmentation", "biotoolsCURIE": "biotools:mr-based_neuroblastoma_tumour_detection_and_segmentation", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [], "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": [], "language": [], "license": null, "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "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://www.mdpi.com/2072-6694/14/15/3648", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.3390/cancers14153648", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Comparative Multicentric Evaluation of Inter-Observer Variability in Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance Images", "abstract": "Tumor segmentation is one of the key steps in imaging processing. The goals of this study were to assess the inter-observer variability in manual segmentation of neuroblastic tumors and to analyze whether the state-of-the-art deep learning architecture nnU-Net can provide a robust solution to detect and segment tumors on MR images. A retrospective multicenter study of 132 patients with neuroblastic tumors was performed. Dice Similarity Coefficient (DSC) and Area Under the Receiver Operating Characteristic Curve (AUC ROC) were used to compare segmentation sets. Two more metrics were elaborated to understand the direction of the errors: the modified version of False Positive (FPRm) and False Negative (FNR) rates. Two radiologists manually segmented 46 tumors and a comparative study was performed. nnU-Net was trained-tuned with 106 cases divided into five balanced folds to perform cross-validation. The five resulting models were used as an ensemble solution to measure training (n = 106) and validation (n = 26) performance, independently. The time needed by the model to automatically segment 20 cases was compared to the time required for manual segmentation. The median DSC for manual segmentation sets was 0.969 (±0.032 IQR). The median DSC for the automatic tool was 0.965 (±0.018 IQR). The automatic segmentation model achieved a better performance regarding the FPRm. MR images segmentation variability is similar between radiologists and nnU-Net. Time leverage when using the automatic model with posterior visual validation and manual adjustment corresponds to 92.8%.", "date": "2022-08-01T00:00:00Z", "citationCount": 25, "authors": [ { "name": "Veiga-Canuto D." }, { "name": "Cerda-Alberich L." }, { "name": "Sanguesa Nebot C." }, { "name": "Martinez de las Heras B." }, { "name": "Potschger U." }, { "name": "Gabelloni M." }, { "name": "Carot Sierra J.M." }, { "name": "Taschner-Mandl S." }, { "name": "Duster V." }, { "name": "Canete A." }, { "name": "Ladenstein R." }, { "name": "Neri E." }, { "name": "Marti-Bonmati L." } ], "journal": "Cancers" } }, { "doi": "10.3390/cancers15051622", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Independent Validation of a Deep Learning nnU-Net Tool for Neuroblastoma Detection and Segmentation in MR Images", "abstract": "Objectives. To externally validate and assess the accuracy of a previously trained fully automatic nnU-Net CNN algorithm to identify and segment primary neuroblastoma tumors in MR images in a large children cohort. Methods. An international multicenter, multivendor imaging repository of patients with neuroblastic tumors was used to validate the performance of a trained Machine Learning (ML) tool to identify and delineate primary neuroblastoma tumors. The dataset was heterogeneous and completely independent from the one used to train and tune the model, consisting of 300 children with neuroblastic tumors having 535 MR T2-weighted sequences (486 sequences at diagnosis and 49 after finalization of the first phase of chemotherapy). The automatic segmentation algorithm was based on a nnU-Net architecture developed within the PRIMAGE project. For comparison, the segmentation masks were manually edited by an expert radiologist, and the time for the manual editing was recorded. Different overlaps and spatial metrics were calculated to compare both masks. Results. The median Dice Similarity Coefficient (DSC) was high 0.997; 0.944–1.000 (median; Q1–Q3). In 18 MR sequences (6%), the net was not able neither to identify nor segment the tumor. No differences were found regarding the MR magnetic field, type of T2 sequence, or tumor location. No significant differences in the performance of the net were found in patients with an MR performed after chemotherapy. The time for visual inspection of the generated masks was 7.9 ± 7.5 (mean ± Standard Deviation (SD)) seconds. Those cases where manual editing was needed (136 masks) required 124 ± 120 s. Conclusions. The automatic CNN was able to locate and segment the primary tumor on the T2-weighted images in 94% of cases. There was an extremely high agreement between the automatic tool and the manually edited masks. This is the first study to validate an automatic segmentation model for neuroblastic tumor identification and segmentation with body MR images. The semi-automatic approach with minor manual editing of the deep learning segmentation increases the radiologist’s confidence in the solution with a minor workload for the radiologist.", "date": "2023-03-01T00:00:00Z", "citationCount": 11, "authors": [ { "name": "Veiga-Canuto D." }, { "name": "Cerda-Alberich L." }, { "name": "Jimenez-Pastor A." }, { "name": "Carot Sierra J.M." }, { "name": "Gomis-Maya A." }, { "name": "Sanguesa-Nebot C." }, { "name": "Fernandez-Paton M." }, { "name": "Martinez de las Heras B." }, { "name": "Taschner-Mandl S." }, { "name": "Duster V." }, { "name": "Potschger U." }, { "name": "Simon T." }, { "name": "Neri E." }, { "name": "Alberich-Bayarri A." }, { "name": "Canete A." }, { "name": "Hero B." }, { "name": "Ladenstein R." }, { "name": "Marti-Bonmati L." } ], "journal": "Cancers" } } ], "credit": [ { "name": "GIBI230 - HULAFE", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "casopon", "additionDate": "2025-05-29T09:08:44.174795Z", "lastUpdate": "2025-06-05T08:50:57.732945Z", "editPermission": { "type": "group", "authors": [ "pedromiguel_martinez_HULAFE" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Denoising-Inhomogeneity Correction Tool (EUCAIM-SW-015_T-01-01-015)", "description": "A customisable image pre-processing tool that performs 5 of the most common denoising filters and ANTS N4 bias field correction filter.", "homepage": "https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es", "biotoolsID": "denoising-inhomogeneity_correction_tool", "biotoolsCURIE": "biotools:denoising-inhomogeneity_correction_tool", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0219", "term": "Data curation and archival" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "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.google.com/file/d/1W0aHAIG_bdU9Z1J3aDNsb1r39WBuX9Ot/view?usp=drive_link", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1007/s10278-021-00512-8", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "MR Denoising Increases Radiomic Biomarker Precision and Reproducibility in Oncologic Imaging", "abstract": "Several noise sources, such as the Johnson–Nyquist noise, affect MR images disturbing the visualization of structures and affecting the subsequent extraction of radiomic data. We evaluate the performance of 5 denoising filters (anisotropic diffusion filter (ADF), curvature flow filter (CFF), Gaussian filter (GF), non-local means filter (NLMF), and unbiased non-local means (UNLMF)), with 33 different settings, in T2-weighted MR images of phantoms (N = 112) and neuroblastoma patients (N = 25). Filters were discarded until the most optimal solutions were obtained according to 3 image quality metrics: peak signal-to-noise ratio (PSNR), edge-strength similarity–based image quality metric (ESSIM), and noise (standard deviation of the signal intensity of a region in the background area). The selected filters were ADFs and UNLMs. From them, 107 radiomics features preservation at 4 progressively added noise levels were studied. The ADF with a conductance of 1 and 2 iterations standardized the radiomic features, improving reproducibility and quality metrics.", "date": "2021-10-01T00:00:00Z", "citationCount": 10, "authors": [ { "name": "Fernandez Paton M." }, { "name": "Cerda Alberich L." }, { "name": "Sanguesa Nebot C." }, { "name": "Martinez de las Heras B." }, { "name": "Veiga Canuto D." }, { "name": "Canete Nieto A." }, { "name": "Marti-Bonmati L." } ], "journal": "Journal of Digital Imaging" } } ], "credit": [ { "name": "GIBI230 - HULAFE", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "casopon", "additionDate": "2025-05-29T08:55:46.088021Z", "lastUpdate": "2025-06-05T08:49:36.367527Z", "editPermission": { "type": "group", "authors": [ "pedromiguel_martinez_HULAFE" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Time Coherence Tool (EUCAIM-SW-001_T-01-01-001)", "description": "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.\n \nStatus : Containerized", "homepage": "https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es", "biotoolsID": "time_coherence_tool", "biotoolsCURIE": "biotools:time_coherence_tool", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0092", "term": "Data visualisation" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": null, "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/", "type": [ "Social media" ], "note": null } ], "download": [], "documentation": [], "publication": [], "credit": [ { "name": "GIBI230 - HULAFE", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "pedromiguel_martinez_HULAFE", "additionDate": "2025-05-29T08:06:46.381063Z", "lastUpdate": "2025-06-05T08:48:31.083164Z", "editPermission": { "type": "group", "authors": [ "pedromiguel_martinez_HULAFE", "casopon" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "ML model for MR series categorisation (EUCAIM-SW-011_T-01-01-011)", "description": "AI tool that is able to perform a categorisation of MR 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...).", "homepage": "https://www.acim.lafe.san.gva.es/acim/?page_id=675&lang=es", "biotoolsID": "ml_model_for_mr_series_categorisation", "biotoolsCURIE": "biotools:ml_model_for_mr_series_categorisation", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3345", "term": "Data identity and mapping" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": null, "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "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://journalofbigdata.springeropen.com/articles/10.1186/s40537-025-01086-w", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1186/s40537-025-01086-w", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Automatic magnetic resonance imaging series labelling for large repositories", "abstract": "Large medical image repositories present challenges related to unstructured data. A data enrichment process allows the storage of additional information for fast identification of the content and properties of medical imaging studies. The aim of this study is to develop a metadata enrichment pipeline to facilitate the secondary use of medical images in a high-throughput environment. Our aim was to develop a categorization tool for the MR series to generate standardized tags that identify relevant image characteristics such as patient orientation, sequence type, weighting type, or the presence of fat suppression. Three models that make use of machine learning (ML) and DICOM tags are proposed. The dataset for their development consists of 4666 MR series from cancer patients, labeled by expert radiologists and acquired from different manufacturers, clinical centers and anatomical regions, covering as much variability as possible with the aim of making the models generalizable to other databases. Moreover, the inference performance of the end system has been evaluated on 25,596 MR series as well as the final model outputs with an external evaluation set of 1286 MR series. The weighting model achieves very reliable results with a f1 score of 88% in the validation set. Junk and chemical shift models achieved scores of 82% and 83% respectively. These results open the door to the automatic application of image post-processing and deep learning algorithms after accurate labeling, minimizing human intervention. Furthermore, the proposed solution can infer thousands of DICOM series in less than one minute. Thanks to the fast inference times provided by this solution, it fits well in a big data ecosystem, eliminating any performance issues on ingestion in a semi-real-time environment.", "date": "2025-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Gomis-Maya A." }, { "name": "Cerda-Alberich L." }, { "name": "Veiga-Canuto D." }, { "name": "Claudio-Fanni S." }, { "name": "Ten-Steve A." }, { "name": "Ribas-Despuig G." }, { "name": "Mallol-Rosello P.J." }, { "name": "Vila-Frances J." }, { "name": "Marti-Bonmati L." } ], "journal": "Journal of Big Data" } } ], "credit": [ { "name": "GIBI230 - HULAFE", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "casopon", "additionDate": "2025-05-29T08:46:55.065078Z", "lastUpdate": "2025-06-05T08:47:44.651751Z", "editPermission": { "type": "group", "authors": [ "pedromiguel_martinez_HULAFE" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "MRI Image intensity normalization", "description": "This tool enables image intensity harmonization across T2-weighted (T2w) MRI datasets from various anatomical regions, including the prostate, breast, and rectum.", "homepage": "https://quibim.com/news/understanding-mri-image-harmonization/", "biotoolsID": "mri_image_intensity_normalization", "biotoolsCURIE": "biotools:mri_image_intensity_normalization", "version": [ "0.1" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3695", "term": "Data filtering" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3442", "term": "MRI image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3442", "term": "MRI image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3444", "term": "MRI" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_3384", "term": "Medical imaging" } ], "operatingSystem": [ "Linux" ], "language": [ "Python" ], "license": "CC-BY-NC-ND-4.0", "collectionID": [ "EUCAIM" ], "maturity": "Emerging", "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://quibim.com/news/understanding-mri-image-harmonization/", "type": [ "General" ], "note": null } ], "publication": [], "credit": [], "owner": "xrafael", "additionDate": "2025-05-30T16:38:57.306019Z", "lastUpdate": "2025-06-05T07:33:17.653864Z", "editPermission": { "type": "group", "authors": [ "celia_martin" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "QP-Insights Uploader", "description": "This desktop application enables users to upload DICOM data along with associated clinical information to QP-Insights—the data management platform of the UPV Reference Node within EUCAIM.", "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/qp_insights_uploader/artifacts-tab", "biotoolsID": "qp-insights_uploader", "biotoolsCURIE": "biotools:qp-insights_uploader", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3431", "term": "Data deposition" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3424", "term": "Raw image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] }, { "data": { "uri": "http://edamontology.org/data_0956", "term": "Data index report" }, "format": [ { "uri": "http://edamontology.org/format_3580", "term": "rcc" }, { "uri": "http://edamontology.org/format_3468", "term": "xls" } ] } ], "output": [], "note": null, "cmd": null } ], "toolType": [ "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_3384", "term": "Medical imaging" }, { "uri": "http://edamontology.org/topic_3366", "term": "Data integration and warehousing" } ], "operatingSystem": [ "Mac" ], "language": [ "Python" ], "license": "CC-BY-NC-ND-4.0", "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [ { "url": "https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/qp_insights_uploader/artifacts-tab", "type": "Downloads page", "note": null, "version": null } ], "documentation": [ { "url": "https://eucaim.gitbook.io/enduserguide/6-userguide4members", "type": [ "User manual" ], "note": null } ], "publication": [], "credit": [ { "name": "Quibim", "email": null, "url": "https://quibim.com/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [], "note": null } ], "owner": "celia_martin", "additionDate": "2025-05-21T12:27:35.032026Z", "lastUpdate": "2025-06-05T07:29:04.541438Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "DICOM tags extractor", "description": "The tool scans the imaging DICOM files of a defined directory at the series level, and produces as output one single JSON file containing all the DICOM tags for each detected series.\nIf a list of selected DICOM tags is provided, the tool can also produce one single csv file containing the listed DICOM tags.\nTo use this tool, the user can download an executable file (.exe). Two execution modes are available : manual execution and command line execution.\nFor the tool to scan data successfully, it is important that : 1/ the extension “.dcm” is visible, as the tool only scans those files, and discard any other; 2/ each DICOM series has its own folder, as the tool gets a single .dcm file from each folder and extracts the tags.", "homepage": "https://www.bcplatforms.com/", "biotoolsID": "dicom_tags_extractor", "biotoolsCURIE": "biotools:dicom_tags_extractor", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0335", "term": "Data formatting" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3365", "term": "Data architecture, analysis and design" }, { "uri": "http://edamontology.org/topic_3384", "term": "Medical imaging" } ], "operatingSystem": [], "language": [], "license": "GPL-3.0", "collectionID": [ "EUCAIM" ], "maturity": "Emerging", "cost": "Free of charge (with restrictions)", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://www.bcplatforms.com/", "type": [ "Other" ], "note": null } ], "download": [ { "url": "https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories", "type": "Software package", "note": "1- Install ORAS tool (https://oras.land/docs/installation).\n2- Login in the registry using the command oras login harbor.eucaim.cancerimage.eu, and provide a username and a token\n3- Download the file by using the command oras pull harbor.eucaim.cancerimage.eu/ingestion-tools/dicom_tag_extraction:1.0", "version": "1.0" } ], "documentation": [], "publication": [], "credit": [ { "name": "BC|Platforms", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "lsaint-aubert", "additionDate": "2025-04-18T15:04:18.411979Z", "lastUpdate": "2025-06-04T11:29:09.107990Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Galaxy Image Analysis", "description": "Galaxy Image Analysis is mainly developed by the Biomedical Computer Vision (BMCV) Group at Heidelberg University. Its dedication is to provide tools for image analysis and image processing for the Galaxy platform.", "homepage": "https://github.com/BMCV/galaxy-image-analysis", "biotoolsID": "galaxy_image_analysis", "biotoolsCURIE": "biotools:galaxy_image_analysis", "version": [], "otherID": [], "relation": [ { "biotoolsID": "galaxy", "type": "includedIn" }, { "biotoolsID": "segmetrics", "type": "includes" }, { "biotoolsID": "superdsm", "type": "includes" }, { "biotoolsID": "rfove", "type": "includes" }, { "biotoolsID": "giatools", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3443", "term": "Image analysis" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3727", "term": "OME-TIFF" }, { "uri": "http://edamontology.org/format_3591", "term": "TIFF" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_2333", "term": "Binary format" } ] }, { "data": { "uri": "http://edamontology.org/data_0920", "term": "Genotype/phenotype report" }, "format": [ { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Suite" ], "topic": [ { "uri": "http://edamontology.org/topic_3382", "term": "Imaging" }, { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" } ], "operatingSystem": [ "Linux", "Mac" ], "language": [], "license": null, "collectionID": [ "de.NBI", "HD-HuB" ], "maturity": "Emerging", "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://github.com/BMCV/galaxy-image-analysis", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1016/j.jbiotec.2017.07.019", "pmid": "28757289", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Workflows for microscopy image analysis and cellular phenotyping", "abstract": "In large scale biological experiments, like high-throughput or high-content cellular screening, the amount and the complexity of images to be analyzed are steadily increasing. To handle and process these images, well defined image processing and analysis steps need to be performed by applying dedicated workflows. Multiple software tools have emerged with the aim to facilitate creation of such workflows by integrating existing methods, tools, and routines, and by adapting them to different applications and questions, as well as making them reusable and interchangeable. In this review, we describe workflow systems for the integration of microscopy image analysis techniques with focus on KNIME and Galaxy.", "date": "2017-11-10T00:00:00Z", "citationCount": 22, "authors": [ { "name": "Wollmann T." }, { "name": "Erfle H." }, { "name": "Eils R." }, { "name": "Rohr K." }, { "name": "Gunkel M." } ], "journal": "Journal of Biotechnology" } } ], "credit": [ { "name": "Thomas Wollmann", "email": "thomas.wollmann@bioquant.uni-heidelberg.de", "url": "https://www.bioquant.uni-heidelberg.de/bmcv", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "hdhub", "additionDate": "2017-03-21T13:32:41Z", "lastUpdate": "2025-06-04T09:45:37.769508Z", "editPermission": { "type": "group", "authors": [ "kostrykin" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "giatools", "description": "Tools required for Galaxy Image Analysis", "homepage": "https://github.com/BMCV/giatools", "biotoolsID": "giatools", "biotoolsCURIE": "biotools:giatools", "version": [], "otherID": [], "relation": [ { "biotoolsID": "galaxy_image_analysis", "type": "usedBy" } ], "function": [], "toolType": [], "topic": [], "operatingSystem": [], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [], "credit": [], "owner": "kostrykin", "additionDate": "2025-06-04T09:43:14.005502Z", "lastUpdate": "2025-06-04T09:44:58.197490Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "AITANA", "description": "AITANA is a platform to facilitate the investigation, development, training and inference of predictive models of Machine Learning and Artificial Intelligence, optimizing the computing resources with the use of a set of tools and methodologies. AITANA wants to minimize the times of training and find the solution of a Machine Learning project with the use of a complete platform to provide an abstraction for computational resources and infrastructure.", "homepage": "http://documentacion-publica-aitana-components-a52d2958cec90a353717180.pages.iti.upv.es/", "biotoolsID": "aitana", "biotoolsCURIE": "biotools:aitana", "version": [ "v6.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Command-line tool", "Plug-in" ], "topic": [ { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" } ], "operatingSystem": [], "language": [ "Python" ], "license": "Proprietary", "collectionID": [ "EUCAIM" ], "maturity": "Mature", "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "http://documentacion-publica-aitana-components-a52d2958cec90a353717180.pages.iti.upv.es/", "type": [ "Quick start guide", "API documentation" ], "note": null } ], "publication": [], "credit": [], "owner": "dsilveira", "additionDate": "2025-05-28T15:36:40.721814Z", "lastUpdate": "2025-06-04T09:20:17.042166Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Radiatus", "description": "An elastic platform for Big Data Analytics in the cloud that facilitates the configuration and deployment of robust, efficient, fault-tolerant and elastic services to undertake the different phases of exploiting large amounts of data, from model creation to putting them into production, decisively increasing accessibility to Big Data Analytics.", "homepage": "https://radiatus.iti.es/", "biotoolsID": "radiatus", "biotoolsCURIE": "biotools:radiatus", "version": [ "v5.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3365", "term": "Data architecture, analysis and design" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" } ], "operatingSystem": [], "language": [], "license": "Proprietary", "collectionID": [ "EUCAIM" ], "maturity": "Mature", "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [], "credit": [], "owner": "dsilveira", "additionDate": "2025-05-28T15:52:40.921469Z", "lastUpdate": "2025-06-04T09:19:28.702101Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "DICOM image similarity-duplicate checker", "description": "This module provides tools for analyzing and processing DICOM serial files with a focus on image comparison and deduplication. It searches directories for DICOM series, organizes them by patient ID and SeriesInstanceUID and excludes certain modalities. It detects duplicate files within series using metadata checks and performs perceptual matching by comparing perceptual hashes at the series level. For identified duplicate candidates, it performs a deeper content-based analysis using SSIM and cosine similarity metrics to confirm visual similarity. The module provides a detailed report on duplicates and potential matches, helping to ensure the consistency and integrity of DICOM records", "homepage": "https://www.med.auth.gr/en/content/laboratory-computing-medical-informatics-and-biomedical-imaging-technologies", "biotoolsID": "dicom_image_similarity-duplicate_checker", "biotoolsCURIE": "biotools:dicom_image_similarity-duplicate_checker", "version": [ "0.1" ], "otherID": [], "relation": [ { "biotoolsID": "data_integration_quality_check_tool_diqct", "type": "includedIn" } ], "function": [], "toolType": [ "Desktop application", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3572", "term": "Data quality management" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "Python" ], "license": "Proprietary", "collectionID": [ "eucaim" ], "maturity": "Mature", "cost": "Commercial", "accessibility": "Restricted access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://docs.google.com/document/d/1EnkkmFOGAo-WhPwx3OQ8vxSb3FKFn4b-/edit?rtpof=true&sd=true&tab=t.0", "type": [ "User manual" ], "note": null } ], "publication": [], "credit": [], "owner": "difoto", "additionDate": "2025-05-28T23:55:08.439649Z", "lastUpdate": "2025-06-04T09:02:13.793866Z", "editPermission": { "type": "group", "authors": [ "authEucaim" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "DICOM-SEG Annotation", "description": "This module provides a command line tool to validate DICOM SEG files against predefined requirements specified in an Excel file. It contains components for finding relevant DICOM files, loading and parsing validation requests and applying validation rules. The main validation process checks each DICOM file for compliance with the Type 1, 1C, 2, 2C and 3 attributes specified in the requirements file. A detailed report is generated highlighting issues such as missing, invalid or conditionally required attributes, including file paths and affected DICOM tags. 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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.\n\nStatus : Containerized", "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/dicom_file_integrity_checker/artifacts-tab", "biotoolsID": "dicom_file_integrity_checker_by_gibi230", "biotoolsCURIE": "biotools:dicom_file_integrity_checker_by_gibi230", "version": [ "1.2.9", "2.0.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3316", "term": "Computer science" }, { "uri": "http://edamontology.org/topic_3077", "term": "Data acquisition" }, { "uri": "http://edamontology.org/topic_3071", "term": "Data management" } ], "operatingSystem": [ "Linux", "Windows" ], "language": [ "Python" ], "license": null, "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "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.google.com/file/d/1s8-nVAvSuAGTHWwun4AQcgHtRWclsFg2/view?usp=sharing", "type": [ "General" ], "note": "Pedro-Miguel Martinez-Girones 1,*, Adrian Galiana-Bordera 1, Carina Soler-Pons 1, Ignacio Gomez-Rico-Junquero 1, Ignacio Blanquer-Espert 3, Leonor Cerda-Alberich 1 and Luis Marti-Bonmati 1,2\n1\tBiomedical Imaging Research Group (GIBI230), La Fe Health Research Institute (IIS La Fe), Avenida Fernando Abril Martorell, València, 46026, Spain.\n2\tLa Fe Medical Imaging Department, Avenida Fernando Abril Martorell, València, 46026, Spain.\n3\tComputing Science Department, Universitat Politècnica de València, Cami de Vera s/n, València, 46022, Spain." } ], "publication": [], "credit": [ { "name": "GIBI230 - HULAFE", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "pedromiguel_martinez_HULAFE", "additionDate": "2025-04-15T17:42:40.968987Z", "lastUpdate": "2025-06-04T08:07:34.085530Z", "editPermission": { "type": "group", "authors": [ "casopon" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Arabidopsis Co-expression Tool (ACT)", "description": "The Arabidopsis Coexpression Tool (ACT) is based on the coexpression analysis of 21273 Arabidopsis thaliana genes from gene pair correlation data of 3500 Affymetrix Arabidopsis ATH1 Genome Array Chip microarray samples.", "homepage": "https://www.michalopoulos.net/act/", "biotoolsID": "act", "biotoolsCURIE": "biotools:act", "version": [ "2.6" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2938", "term": "Dendrogram visualisation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1025", "term": "Gene identifier" }, "format": [] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3271", "term": "Gene tree" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [], "license": null, "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [ "Tools" ], "elixirNode": [ "Greece" ], "elixirCommunity": [ "Plant Sciences" ], "link": [], "download": [], "documentation": [ { "url": "https://www.michalopoulos.net/act2.6/help.php", "type": [ "FAQ" ], "note": null } ], "publication": [ { "doi": "10.3390/genes16030258", "pmid": "40149410", "pmcid": "PMC11942487", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "ACT2.6: Global Gene Coexpression Network in Arabidopsis thaliana Using WGCNA", "abstract": "Background/Objectives: Genes with similar expression patterns across multiple samples are considered coexpressed, and they may participate in similar biological processes or pathways. Gene coexpression networks depict the degree of similarity between the expression profiles of all genes in a set of samples. Gene coexpression tools allow for the prediction of functional gene partners or the assignment of roles to genes of unknown function. Weighted Gene Correlation Network Analysis (WGCNA) is an R package that provides a multitude of functions for constructing and analyzing a weighted or unweighted gene coexpression network. Methods: Previously preprocessed, high-quality gene expression data of 3500 samples of Affymetrix microarray technology from various tissues of the Arabidopsis thaliana plant model species were used to construct a weighted gene coexpression network, using WGCNA. Results: The gene dendrogram was used as the basis for the creation of a new Arabidopsis coexpression tool (ACT) version (ACT2.6). The dendrogram contains 21,273 leaves, each one corresponding to a single gene. Genes that are clustered in the same clade are coexpressed. WGCNA grouped the genes into 27 functional modules, all of which were positively or negatively correlated with specific tissues. Discussion: Genes known to be involved in common metabolic pathways were discovered in the same module. By comparing the current ACT version with the previous one, it was shown that the new version outperforms the old one in discovering the functional connections between gene partners. ACT2.6 is a major upgrade over the previous version and a significant addition to the collection of public gene coexpression tools.", "date": "2025-03-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Zogopoulos V.L." }, { "name": "Papadopoulos K." }, { "name": "Malatras A." }, { "name": "Iconomidou V.A." }, { "name": "Michalopoulos I." } ], "journal": "Genes" } }, { "doi": "10.1016/j.xpro.2022.101208", "pmid": "35243384", "pmcid": "PMC8885756", "type": [ "Method" ], "version": null, "note": null, "metadata": { "title": "Gene coexpression analysis in Arabidopsis thaliana based on public microarray data", "abstract": "Coexpressed genes tend to participate in related biological processes. Gene coexpression analysis allows the discovery of functional gene partners or the assignment of biological roles to genes of unknown function. In this protocol, we describe the steps necessary to create a gene coexpression tree for Arabidopsis thaliana, using publicly available Affymetrix CEL microarray data. Because the computational analysis described here is highly dependent on sample quality, we detail an automatic quality control approach. For complete details on the use and execution of this protocol, please refer to Zogopoulos et al. (2021).", "date": "2022-03-18T00:00:00Z", "citationCount": 6, "authors": [ { "name": "Zogopoulos V.L." }, { "name": "Malatras A." }, { "name": "Michalopoulos I." } ], "journal": "STAR Protocols" } }, { "doi": "10.3390/biology11071019", "pmid": "36101400", "pmcid": "PMC9312353", "type": [ "Review" ], "version": null, "note": null, "metadata": { "title": "Approaches in Gene Coexpression Analysis in Eukaryotes", "abstract": "Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied ex-tensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensi-ble account of the steps required for performing a complete gene coexpression analysis in eukary-otic organisms. We comment on the use of RNA‐Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs.", "date": "2022-07-01T00:00:00Z", "citationCount": 11, "authors": [ { "name": "Zogopoulos V.L." }, { "name": "Saxami G." }, { "name": "Malatras A." }, { "name": "Papadopoulos K." }, { "name": "Tsotra I." }, { "name": "Iconomidou V.A." }, { "name": "Michalopoulos I." } ], "journal": "Biology" } }, { "doi": "10.1093/nar/gkl204", "pmid": "16845059", "pmcid": "PMC1538833", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "Arabidopsis Co-expression Tool (ACT): Web server tools for microarray-based gene expression analysis", "abstract": "The Arabidopsis Co-expression Tool, ACT, ranks the genes across a large microarray dataset according to how closely their expression follows the expression of a query gene. A database stores pre-calculated co-expression results for ∼21 800 genes based on data from over 300 arrays. These results can be corroborated by calculation of co-expression results for user-defined sub-sets of arrays or experiments from the NASC/GARNet array dataset. Clique Finder (CF) identifies groups of genes which are consistently co-expressed with each other across a user-defined co-expression list. The parameters can be altered easily to adjust cluster size and the output examined for optimal inclusion of genes with known biological roles. Alternatively, a Scatter Plot tool displays the correlation coefficients for all genes against two user-selected queries on a scatter plot which can be useful for visual identification of clusters of genes with similar r-values. User-input groups of genes can be highlighted on the scatter plots. Inclusion of genes with known biology in sets of genes identified using CF and Scatter Plot tools allows inferences to be made about the roles of the other genes in the set and both tools can therefore be used to generate short lists of genes for further characterization. ACT is freely available at www.Arabidopsis.leeds.ac.uk/ACT. © The Author 2006. Published by Oxford University Press. All rights reserved.", "date": "2006-07-01T00:00:00Z", "citationCount": 130, "authors": [ { "name": "Manfield I.W." }, { "name": "Jen C.-H." }, { "name": "Pinney J.W." }, { "name": "Michalopoulos I." }, { "name": "Bradford J.R." }, { "name": "Gilmartin P.M." }, { "name": "Westhead D.R." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1111/j.1365-313x.2006.02681.x", "pmid": "16623895", "pmcid": null, "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "The Arabidopsis co-expression tool (ACT): A WWW-based tool and database for microarray-based gene expression analysis", "abstract": "We present a new WWW-based tool for plant gene analysis, the Arabidopsis Co-Expression Tool (ACT), based on a large Arabidopsis thaliana microarray data set obtained from the Nottingham Arabidopsis Stock Centre. The co-expression analysis tool allows users to identify genes whose expression patterns are correlated across selected experiments or the complete data set. Results are accompanied by estimates of the statistical significance of the correlation relationships, expressed as probability (P) and expectation (E) values. Additionally, highly ranked genes on a correlation list can be examined using the novel CLIQUE FINDER tool to determine the sets of genes most likely to be regulated in a similar manner. In combination, these tools offer three levels of analysis: creation of correlation lists of co-expressed genes, refinement of these lists using two-dimensional scatter plots, and dissection into cliques of co-regulated genes. We illustrate the applications of the software by analysing genes encoding functionally related proteins, as well as pathways involved in plant responses to environmental stimuli. These analyses demonstrate novel biological relationships underlying the observed gene co-expression patterns. To demonstrate the ability of the software to develop testable hypotheses on gene function within a defined biological process we have used the example of cell wall biosynthesis genes. The resource is freely available at http://www.arabidopsis.leeds.ac.uk/ACT/. © 2006 The Authors.", "date": "2006-04-01T00:00:00Z", "citationCount": 65, "authors": [ { "name": "Jen C.-H." }, { "name": "Manfield I.W." }, { "name": "Michalopoulos I." }, { "name": "Pinney J.W." }, { "name": "Willats W.G.T." }, { "name": "Gilmartin P.M." }, { "name": "Westhead D.R." } ], "journal": "Plant Journal" } }, { "doi": "10.1016/j.isci.2021.102848", "pmid": "34381973", "pmcid": "PMC8334378", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Arabidopsis Coexpression Tool: a tool for gene coexpression analysis in Arabidopsis thaliana", "abstract": "Gene coexpression analysis refers to the discovery of sets of genes which exhibit similar expression patterns across multiple transcriptomic data sets, such as microarray experiment data of public repositories. Arabidopsis Coexpression Tool (ACT), a gene coexpression analysis web tool for Arabidopsis thaliana, identifies genes which are correlated to a driver gene. Primary microarray data from ATH1 Affymetrix platform were processed with Single-Channel Array Normalization algorithm and combined to produce a coexpression tree which contains ∼21,000 A. thaliana genes. ACT was developed to present subclades of coexpressed genes, as well as to perform gene set enrichment analysis, being unique in revealing enriched transcription factors targeting coexpressed genes. ACT offers a simple and user-friendly interface producing working hypotheses which can be experimentally verified for the discovery of gene partnership, pathway membership, and transcriptional regulation. ACT analyses have been successful in identifying not only genes with coordinated ubiquitous expressions but also genes with tissue-specific expressions.", "date": "2021-08-20T00:00:00Z", "citationCount": 15, "authors": [ { "name": "Zogopoulos V.L." }, { "name": "Saxami G." }, { "name": "Malatras A." }, { "name": "Angelopoulou A." }, { "name": "Jen C.-H." }, { "name": "Duddy W.J." }, { "name": "Daras G." }, { "name": "Hatzopoulos P." }, { "name": "Westhead D.R." }, { "name": "Michalopoulos I." } ], "journal": "iScience" } } ], "credit": [ { "name": "David R Westhead", "email": "D.R.Westhead@leeds.ac.uk", "url": "https://biologicalsciences.leeds.ac.uk/molecular-and-cellular-biology/staff/154/professor-david-r-westhead", "orcidid": "https://orcid.org/0000-0002-0519-3820", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Ioannis Michalopoulos", "email": "imichalop@bioacademy.gr", "url": "https://www.michalopoulos.net/", "orcidid": "https://orcid.org/0000-0001-8991-8712", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "imichalop@bioacademy.gr", "additionDate": "2017-02-10T14:14:47Z", "lastUpdate": "2025-06-04T05:50:14.941224Z", "editPermission": { "type": "private", "authors": [ "imichalop@bioacademy.gr" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "CNSistent", "description": "Imputation, consistent segmentation, statistical analysis, and visualization of copy number profiles.", "homepage": "https://cnsistent.readthedocs.io", "biotoolsID": "cnsistent", "biotoolsCURIE": "biotools:cnsistent", "version": [ "0.7.4" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Command-line tool", "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3958", "term": "Copy number variation" }, { "uri": "http://edamontology.org/topic_2640", "term": "Oncology" } ], "operatingSystem": [], "language": [ "Python" ], "license": "MIT", "collectionID": [], "maturity": "Emerging", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [ { "url": "https://bitbucket.org/schwarzlab/cnsistent.git", "type": "Source code", "note": null, "version": null }, { "url": "https://pypi.org/project/CNSistent/", "type": "Software package", "note": null, "version": null } ], "documentation": [ { "url": "https://cnsistent.readthedocs.io/", "type": [ "Command-line options", "API documentation", "Quick start guide" ], "note": null } ], "publication": [ { "doi": "10.1101/2024.12.23.630118", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Adam Streck", "email": "adam.streck@gmail.com", "url": null, "orcidid": "https://orcid.org/0000-0002-7302-0147", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null } ], "owner": "AdamStreck", "additionDate": "2025-06-03T11:42:31.433057Z", "lastUpdate": "2025-06-03T11:54:45.306699Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "SAMtools", "description": "SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods.", "homepage": "http://www.htslib.org/", "biotoolsID": "samtools", "biotoolsCURIE": "biotools:samtools", "version": [ "1.0", "1.1", "1.2", "1.3", "1.3.1", "1.4", "1.4.1", "1.5", "1.6", "1.7", "1.8", "1.9", "1.10", "1.11", "1.12", "1.13", "1.14", "1.15", "1.15.1", "1.16", "1.16.1", "1.17", "1.18", "1.19", "1.19.1", "1.19.2", "1.20", "1.21", "1.22" ], "otherID": [], "relation": [ { "biotoolsID": "htslib", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0227", "term": "Indexing" }, { "uri": "http://edamontology.org/operation_3096", "term": "Data editing" }, { "uri": "http://edamontology.org/operation_1812", "term": "Data parsing" }, { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" }, { "uri": "http://edamontology.org/operation_0335", "term": "Data formatting" }, { "uri": "http://edamontology.org/operation_3802", "term": "Data sorting" }, { "uri": "http://edamontology.org/operation_3695", "term": "Data filtering" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0924", "term": "Sequence trace" }, "format": [ { "uri": "http://edamontology.org/format_2572", "term": "BAM" }, { "uri": "http://edamontology.org/format_2573", "term": "SAM" }, { "uri": "http://edamontology.org/format_3462", "term": "CRAM" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0924", "term": "Sequence trace" }, "format": [ { "uri": "http://edamontology.org/format_2572", "term": "BAM" }, { "uri": "http://edamontology.org/format_2573", "term": "SAM" }, { "uri": "http://edamontology.org/format_3462", "term": "CRAM" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Suite", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0102", "term": "Mapping" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" }, { "uri": "http://edamontology.org/topic_3325", "term": "Rare diseases" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "C" ], "license": "MIT", "collectionID": [ "Rare Disease", "Animal and Crop Genomics", "SAMtools" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/samtools/samtools", "type": [ "Repository" ], "note": null }, { "url": "http://www.htslib.org/support/#lists", "type": [ "Mailing list" ], "note": null }, { "url": "https://github.com/samtools/samtools/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [ { "url": "http://www.htslib.org/download/", "type": "Downloads page", "note": null, "version": null } ], "documentation": [ { "url": "http://www.htslib.org/doc/#howtos", "type": [ "Other" ], "note": "HowTos for samtools" }, { "url": "http://www.htslib.org/doc/#manual-pages", "type": [ "User manual" ], "note": null }, { "url": "http://www.htslib.org/download/", "type": [ "Installation instructions" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btp352", "pmid": "19505943", "pmcid": "PMC2723002", "type": [ "Primary" ], "version": null, "note": "The Sequence Alignment/Map format and SAMtools.", "metadata": { "title": "The Sequence Alignment/Map format and SAMtools", "abstract": "Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAM tools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. © 2009 The Author(s).", "date": "2009-08-01T00:00:00Z", "citationCount": 42239, "authors": [ { "name": "Li H." }, { "name": "Handsaker B." }, { "name": "Wysoker A." }, { "name": "Fennell T." }, { "name": "Ruan J." }, { "name": "Homer N." }, { "name": "Marth G." }, { "name": "Abecasis G." }, { "name": "Durbin R." } ], "journal": "Bioinformatics" } }, { "doi": "10.1093/gigascience/giab008", "pmid": "33590861", "pmcid": "PMC7931819", "type": [ "Primary" ], "version": null, "note": "Twelve years of SAMtools and BCFtools.", "metadata": { "title": "Twelve years of SAMtools and BCFtools", "abstract": "Background: SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods. Findings: The first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines. Conclusion: Both SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed >1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.", "date": "2021-02-01T00:00:00Z", "citationCount": 6224, "authors": [ { "name": "Danecek P." }, { "name": "Bonfield J.K." }, { "name": "Liddle J." }, { "name": "Marshall J." }, { "name": "Ohan V." }, { "name": "Pollard M.O." }, { "name": "Whitwham A." }, { "name": "Keane T." }, { "name": "McCarthy S.A." }, { "name": "Davies R.M." } ], "journal": "GigaScience" } }, { "doi": "10.1093/bioinformatics/btr509", "pmid": "21903627", "pmcid": "PMC3198575", "type": [], "version": null, "note": null, "metadata": { "title": "A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data", "abstract": "Motivation: Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. Results: We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. © The Author 2011. Published by Oxford University Press. All rights reserved.", "date": "2011-11-01T00:00:00Z", "citationCount": 4405, "authors": [ { "name": "Li H." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Richard Durbin", "email": "rd@sanger.ac.uk", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Contributor" ], "note": null }, { "name": "Wellcome Sanger Institute", "email": "samtools@sanger.ac.uk", "url": "https://www.sanger.ac.uk/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider", "Primary contact" ], "note": null }, { "name": "Samtools Help mailing list", "email": null, "url": "https://lists.sourceforge.net/lists/listinfo/samtools-help", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Project", "typeRole": [ "Support" ], "note": null } ], "owner": "awhitwham", "additionDate": "2017-01-13T13:16:12Z", "lastUpdate": "2025-06-03T09:08:10.199446Z", "editPermission": { "type": "group", "authors": [ "ELIXIR-EE", "animalandcropgenomics", "alice", "awhitwham", "sergitobara" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "BlobToolKit", "description": "BlobToolKit Viewer is a genome-scale dataset visualistion tool developed as part of the blobtoolkit project to allow browser-based identification and filtering of target and non-target data in genome assemblies.", "homepage": "https://blobtoolkit.genomehubs.org/view", "biotoolsID": "BlobToolKit", "biotoolsCURIE": "biotools:BlobToolKit", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0525", "term": "Genome assembly" }, { "uri": "http://edamontology.org/operation_3216", "term": "Scaffolding" }, { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" }, { "uri": "http://edamontology.org/operation_3431", "term": "Data deposition" }, { "uri": "http://edamontology.org/operation_1812", "term": "Data parsing" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_0196", "term": "Sequence assembly" }, { "uri": "http://edamontology.org/topic_0621", "term": "Model organisms" }, { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" }, { "uri": "http://edamontology.org/topic_0194", "term": "Phylogenomics" }, { "uri": "http://edamontology.org/topic_3500", "term": "Zoology" }, { "uri": "http://edamontology.org/topic_3050", "term": "Biodiversity" } ], "operatingSystem": [], "language": [ "JavaScript", "Python" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/blobtoolkit", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/blobtoolkit/specification", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/blobtoolkit/viewer", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://blobtoolkit.genomehubs.org/download", "type": "Downloads page", "note": null, "version": null } ], "documentation": [], "publication": [ { "doi": "10.1101/844852", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null }, { "doi": "10.1534/g3.119.400908", "pmid": "32071071", "pmcid": "PMC7144090", "type": [], "version": null, "note": null, "metadata": { "title": "BlobToolKit - interactive quality assessment of genome assemblies", "abstract": "Reconstruction of target genomes from sequence data produced by instruments that are agnostic as to the species-of-origin may be confounded by contaminant DNA. Whether introduced during sample processing or through co-extraction alongside the target DNA, if insufficient care is taken during the assembly process, the final assembled genome may be a mixture of data from several species. Such assemblies can confound sequence-based biological inference and, when deposited in public databases, may be included in downstream analyses by users unaware of underlying problems. We present BlobToolKit, a software suite to aid researchers in identifying and isolating non-target data in draft and publicly available genome assemblies. BlobToolKit can be used to process assembly, read and analysis files for fully reproducible interactive exploration in the browser-based Viewer. BlobToolKit can be used during assembly to filter non-target DNA, helping researchers produce assemblies with high biological credibility. We have been running an automated BlobToolKit pipeline on eukaryotic assemblies publicly available in the International Nucleotide Sequence Data Collaboration and are making the results available through a public instance of the Viewer at https://blobtoolkit.genomehubs.org/view. We aim to complete analysis of all publicly available genomes and then maintain currency with the flow of new genomes. We have worked to embed these views into the presentation of genome assemblies at the European Nucleotide Archive, providing an indication of assembly quality alongside the public record with links out to allow full exploration in the Viewer.", "date": "2020-04-01T00:00:00Z", "citationCount": 994, "authors": [ { "name": "Challis R." }, { "name": "Richards E." }, { "name": "Rajan J." }, { "name": "Cochrane G." }, { "name": "Blaxter M." } ], "journal": "G3: Genes, Genomes, Genetics" } } ], "credit": [], "owner": "Pub2Tools", "additionDate": "2020-01-14T09:05:51Z", "lastUpdate": "2025-06-03T09:01:03.782648Z", "editPermission": { "type": "public", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "HTSlib", "description": "The main purpose of HTSlib is to provide access to genomic information files, both alignment data (SAM, BAM, and CRAM formats) and variant data (VCF and BCF formats). The library also provides interfaces to access and index genome reference data in FASTA format and tab-delimited files with genomic coordinates. It is utilized and incorporated into both SAMtools and BCFtools.", "homepage": "http://www.htslib.org/", "biotoolsID": "htslib", "biotoolsCURIE": "biotools:htslib", "version": [ "1.0", "1.1", "1.2", "1.2.1", "1.3", "1.3.1", "1.3.2", "1.4", "1.4.1", "1.5", "1.6", "1.7", "1.8", "1.9", "1.10", "1.10.1", "1.10.2", "1.11", "1.12", "1.13", "1.14", "1.15", "1.15.1", "1.16", "1.17", "1.18", "1.19", "1.20", "1.21", "1.22" ], "otherID": [], "relation": [ { "biotoolsID": "samtools", "type": "usedBy" }, { "biotoolsID": "bcftools", "type": "usedBy" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2409", "term": "Data handling" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0924", "term": "Sequence trace" }, "format": [ { "uri": "http://edamontology.org/format_2573", "term": "SAM" }, { "uri": "http://edamontology.org/format_2572", "term": "BAM" }, { "uri": "http://edamontology.org/format_3462", "term": "CRAM" }, { "uri": "http://edamontology.org/format_1929", "term": "FASTA" }, { "uri": "http://edamontology.org/format_1930", "term": "FASTQ" } ] }, { "data": { "uri": "http://edamontology.org/data_3498", "term": "Sequence variations" }, "format": [ { "uri": "http://edamontology.org/format_3016", "term": "VCF" }, { "uri": "http://edamontology.org/format_3020", "term": "BCF" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0924", "term": "Sequence trace" }, "format": [ { "uri": "http://edamontology.org/format_2573", "term": "SAM" }, { "uri": "http://edamontology.org/format_2572", "term": "BAM" }, { "uri": "http://edamontology.org/format_3462", "term": "CRAM" }, { "uri": "http://edamontology.org/format_1929", "term": "FASTA" }, { "uri": "http://edamontology.org/format_1930", "term": "FASTQ" } ] }, { "data": { "uri": "http://edamontology.org/data_3498", "term": "Sequence variations" }, "format": [ { "uri": "http://edamontology.org/format_3016", "term": "VCF" }, { "uri": "http://edamontology.org/format_3020", "term": "BCF" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3071", "term": "Data management" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "C" ], "license": "MIT", "collectionID": [ "Animal and Crop Genomics" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/samtools/htslib", "type": [ "Repository" ], "note": null }, { "url": "http://www.htslib.org/support/#lists", "type": [ "Mailing list" ], "note": null }, { "url": "https://github.com/samtools/htslib/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [ { "url": "http://www.htslib.org/download/", "type": "Downloads page", "note": null, "version": null } ], "documentation": [ { "url": "http://www.htslib.org/doc/#manual-pages", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/gigascience/giab007", "pmid": "33594436", "pmcid": "PMC7931820", "type": [ "Primary" ], "version": null, "note": "HTSlib: C library for reading/writing high-throughput sequencing data.", "metadata": { "title": "HTSlib: C library for reading/writing high-Throughput sequencing data", "abstract": "Background: Since the original publication of the VCF and SAM formats, an explosion of software tools have been created to process these data files. To facilitate this a library was produced out of the original SAMtools implementation, with a focus on performance and robustness. The file formats themselves have become international standards under the jurisdiction of the Global Alliance for Genomics and Health. Findings: We present a software library for providing programmatic access to sequencing alignment and variant formats. It was born out of the widely used SAMtools and BCFtools applications. Considerable improvements have been made to the original code plus many new features including newer access protocols, the addition of the CRAM file format, better indexing and iterators, and better use of threading. Conclusion: Since the original Samtools release, performance has been considerably improved, with a BAM read-write loop running 5 times faster and BAM to SAM conversion 13 times faster (both using 16 threads, compared to Samtools 0.1.19). Widespread adoption has seen HTSlib downloaded >1 million times from GitHub and conda. The C library has been used directly by an estimated 900 GitHub projects and has been incorporated into Perl, Python, Rust, and R, significantly expanding the number of uses via other languages. HTSlib is open source and is freely available from htslib.org under MIT/BSD license.", "date": "2021-02-01T00:00:00Z", "citationCount": 189, "authors": [ { "name": "Bonfield J.K." }, { "name": "Marshall J." }, { "name": "Danecek P." }, { "name": "Li H." }, { "name": "Ohan V." }, { "name": "Whitwham A." }, { "name": "Keane T." } ], "journal": "GigaScience" } } ], "credit": [ { "name": "Wellcome Sanger Institute", "email": "samtools@sanger.ac.uk", "url": "https://www.sanger.ac.uk/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider", "Primary contact" ], "note": null }, { "name": "Samtools Help mailing list", "email": null, "url": "https://lists.sourceforge.net/lists/listinfo/samtools-help", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Project", "typeRole": [ "Support" ], "note": null } ], "owner": "awhitwham", "additionDate": "2017-08-20T16:07:58Z", "lastUpdate": "2025-06-03T09:00:20.313887Z", "editPermission": { "type": "group", "authors": [ "animalandcropgenomics", "smoe" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "BCFtools", "description": "BCFtools is a set of utilities that manipulate variant calls in the Variant Call Format (VCF) and its binary counterpart BCF. All commands work transparently with both VCFs and BCFs, both uncompressed and BGZF-compressed.", "homepage": "http://www.htslib.org/", "biotoolsID": "bcftools", "biotoolsCURIE": "biotools:bcftools", "version": [ "1.0", "1.1", "1.2", "1.3", "1.3.1", "1.4", "1.4.1", "1.5", "1.6", "1.7", "1.8", "1.9", "1.10", "1.10.1", "1.10.2", "1.11", "1.12", "1.13", "1.14", "1.15", "1.15.1", "1.16", "1.17", "1.18", "1.19", "1.20", "1.21", "1.22" ], "otherID": [], "relation": [ { "biotoolsID": "htslib", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2409", "term": "Data handling" }, { "uri": "http://edamontology.org/operation_3227", "term": "Variant calling" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3498", "term": "Sequence variations" }, "format": [ { "uri": "http://edamontology.org/format_3016", "term": "VCF" }, { "uri": "http://edamontology.org/format_3020", "term": "BCF" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3498", "term": "Sequence variations" }, "format": [ { "uri": "http://edamontology.org/format_3016", "term": "VCF" }, { "uri": "http://edamontology.org/format_3020", "term": "BCF" } ] } ], "note": "Multiple data munging operations.", "cmd": null } ], "toolType": [ "Command-line tool", "Suite" ], "topic": [ { "uri": "http://edamontology.org/topic_0199", "term": "Genetic variation" }, { "uri": "http://edamontology.org/topic_2885", "term": "DNA polymorphism" }, { "uri": "http://edamontology.org/topic_3517", "term": "GWAS study" }, { "uri": "http://edamontology.org/topic_3516", "term": "Genotyping experiment" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "C" ], "license": "MIT", "collectionID": [ "BCFtools" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/samtools/bcftools", "type": [ "Repository" ], "note": null }, { "url": "http://www.htslib.org/support/#lists", "type": [ "Mailing list" ], "note": null }, { "url": "https://github.com/samtools/bcftools/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [ { "url": "http://www.htslib.org/download/", "type": "Downloads page", "note": null, "version": null } ], "documentation": [ { "url": "http://www.htslib.org/doc/bcftools.html", "type": [ "General" ], "note": null }, { "url": "http://www.htslib.org/workflow/#mapping_to_variant", "type": [ "Other" ], "note": "A workflow for BCFtools." } ], "publication": [ { "doi": "10.1093/bioinformatics/btp352", "pmid": "19505943", "pmcid": "PMC2723002", "type": [ "Primary" ], "version": null, "note": "The Sequence Alignment/Map format and SAMtools.", "metadata": { "title": "The Sequence Alignment/Map format and SAMtools", "abstract": "Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAM tools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. © 2009 The Author(s).", "date": "2009-08-01T00:00:00Z", "citationCount": 42239, "authors": [ { "name": "Li H." }, { "name": "Handsaker B." }, { "name": "Wysoker A." }, { "name": "Fennell T." }, { "name": "Ruan J." }, { "name": "Homer N." }, { "name": "Marth G." }, { "name": "Abecasis G." }, { "name": "Durbin R." } ], "journal": "Bioinformatics" } }, { "doi": "10.1093/gigascience/giab008", "pmid": "33590861", "pmcid": "PMC7931819", "type": [ "Primary" ], "version": null, "note": "Twelve years of SAMtools and BCFtools.", "metadata": { "title": "Twelve years of SAMtools and BCFtools", "abstract": "Background: SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods. Findings: The first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines. Conclusion: Both SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed >1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.", "date": "2021-02-01T00:00:00Z", "citationCount": 6224, "authors": [ { "name": "Danecek P." }, { "name": "Bonfield J.K." }, { "name": "Liddle J." }, { "name": "Marshall J." }, { "name": "Ohan V." }, { "name": "Pollard M.O." }, { "name": "Whitwham A." }, { "name": "Keane T." }, { "name": "McCarthy S.A." }, { "name": "Davies R.M." } ], "journal": "GigaScience" } } ], "credit": [ { "name": "Wellcome Sanger Institute", "email": "samtools@sanger.ac.uk", "url": "https://www.sanger.ac.uk/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Samtools Help mailing list", "email": null, "url": "https://lists.sourceforge.net/lists/listinfo/samtools-help", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Project", "typeRole": [ "Support" ], "note": null } ], "owner": "awhitwham", "additionDate": "2015-08-24T08:35:55Z", "lastUpdate": "2025-06-03T08:59:30.745713Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "OMA", "description": "Project that aims to identify orthologs among publicly available, complete genomes. With many hundreds of genomes analyzed to date, it is one of the largest projects of its kind.", "homepage": "http://omabrowser.org/oma/home/", "biotoolsID": "oma", "biotoolsCURIE": "biotools:oma", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2423", "term": "Prediction and recognition" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3021", "term": "UniProt accession" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_3671", "term": "Text" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_2974", "term": "Protein sequence (raw)" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_0842", "term": "Identifier" }, "format": [] } ], "output": [], "note": "orthology prediction and paralogy OMA group", "cmd": null } ], "toolType": [ "Web application", "Database portal", "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_3299", "term": "Evolutionary biology" }, { "uri": "http://edamontology.org/topic_0797", "term": "Comparative genomics" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [ "Data" ], "elixirNode": [ "Switzerland" ], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://omabrowser.org/oma/about/", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/nar/gku1158", "pmid": "25399418", "pmcid": "PMC4383958", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "The OMA orthology database in 2015: Function predictions, better plant support, synteny view and other improvements", "abstract": "The Orthologous Matrix (OMA) project is a method and associated database inferring evolutionary relationships amongst currently 1706 complete proteomes (i.e. the protein sequence associated for every protein-coding gene in all genomes). In this update article, we present six major new developments in OMA: (i) a new web interface; (ii) Gene Ontology function predictions as part of the OMA pipeline; (iii) better support for plant genomes and in particular homeologs in the wheat genome; (iv) a new synteny viewer providing the genomic context of orthologs; (v) statically computed hierarchical orthologous groups subsets downloadable in OrthoXML format; and (vi) possibility to export parts of the all-against-all computations and to combine them with custom data for 'client-side' orthology prediction. OMA can be accessed through the OMA Browser and various programmatic interfaces at http://omabrowser.org.", "date": "2015-01-28T00:00:00Z", "citationCount": 160, "authors": [ { "name": "Altenhoff A.M." }, { "name": "Sunca N." }, { "name": "Glover N." }, { "name": "Train C.-M." }, { "name": "Sueki A." }, { "name": "Pilizota I." }, { "name": "Gori K." }, { "name": "Tomiczek B." }, { "name": "Muller S." }, { "name": "Redestig H." }, { "name": "Gonnet G.H." }, { "name": "Dessimoz C." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1093/nar/gkad1020", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "OMA or tholog y in 2024: impro v ed prokary ot e co v erãg e,ãncestralãnd extant GO enrichment,ã revamped synteny viewerãnd more in the OMA Ecosystem", "abstract": "In this update paper, we present the latest de v elopments in the OMA browser knowledgebase, whichãims to provide high-quality orthology inferencesãnd facilitate the study of gene families, genomesãnd their evolution. First, we discuss theãddition of new species in the database, particularlyãn expanded representation of prokaryotic species. The OMA browser now offers Ancestral Genome pagesãndãn Ancestral Gene Order vie w er,ãllo wing users to e xplore the e v olutionar y histor yãnd gene content ofãncestral genomes. Weãlso introduceã re vãmped L ocal Synten y Vie w er to compare genomic neighborhoodsãcross both e xtantãndãncestral genomes. Hierarchical Orthologous Groups (H O Gs)ãre nowãnnotated with Gene Ontologyãnnotations,ãnd users can easily perform extant orãncestral GO enrichments. Finally, we recap new tools in the OMA Ecosystem, including OMAmer for proteome mapping, OMArk for proteome qualityãssessment, OMAMO for model organism selectionãnd Read2Tree for phylogenetic species tree construction from reads. These new features provide exciting opportunities for orthologyãnalysisãnd comparative genomics. OMA isãccessibleãt https://omabrowser.org .", "date": "2024-01-05T00:00:00Z", "citationCount": 24, "authors": [ { "name": "Altenhoff A.M." }, { "name": "Vesztrocy A.W." }, { "name": "Bernard C." }, { "name": "Train C.-M." }, { "name": "Nicheperovich A." }, { "name": "Banos S.P." }, { "name": "Julca I." }, { "name": "Moi D." }, { "name": "Nevers Y." }, { "name": "Majidian S." }, { "name": "Dessimoz C." }, { "name": "Glover N.M." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1093/nar/gkaa1007", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "OMA orthology in 2021: Website overhaul, conserved isoforms, ancestral gene order and more", "abstract": "OMA is an established resource to elucidate evolutionary relationships among genes from currently 2326 genomes covering all domains of life. OMA provides pairwise and groupwise orthologs, functional annotations, local and global gene order conservation (synteny) information, among many other functions. This update paper describes the reorganisation of the database into gene-, group- and genome-centric pages. Other new and improved features are detailed, such as reporting of the evolutionarily best conserved isoforms of alternatively spliced genes, the inferred local order of ancestral genes, phylogenetic profiling, better cross-references, fast genome mapping, semantic data sharing via RDF, as well as a special coronavirus OMA with 119 viruses from the Nidovirales order, including SARS-CoV-2, the agent of the COVID-19 pandemic. We conclude with improvements to the documentation of the resource through primers, tutorials and short videos. OMA is accessible at https://omabrowser.org.", "date": "2021-01-08T00:00:00Z", "citationCount": 135, "authors": [ { "name": "Altenhoff A.M." }, { "name": "Train C.-M." }, { "name": "Gilbert K.J." }, { "name": "Mediratta I." }, { "name": "de Farias T.M." }, { "name": "Moi D." }, { "name": "Nevers Y." }, { "name": "Radoykova H.-S." }, { "name": "Rossier V." }, { "name": "Vesztrocy A.W." }, { "name": "Glover N.M." }, { "name": "Dessimoz C." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1093/nar/gkx1019", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "The OMA orthology database in 2018: Retrieving evolutionary relationships among all domains of life through richer web and programmatic interfaces", "abstract": "The Orthologous Matrix (OMA) is a leading resource to relate genes across many species from all of life. In this update paper, we review the recent algorithmic improvements in the OMA pipeline, describe increases in species coverage (particularly in plants and early-branching eukaryotes) and introduce several new features in the OMA web browser. Notable improvements include: (i) a scalable, interactive viewer for hierarchical orthologous groups; (ii) protein domain annotations and domain-based links between orthologous groups; (iii) functionality to retrieve phylogenetic marker genes for a subset of species of interest; (iv) a new synteny dot plot viewer; and (v) an overhaul of the programmatic access (REST API and semantic web), which will facilitate incorporation of OMA analyses in computational pipelines and integration with other bioinformatic resources. OMA can be freely accessed at https://omabrowser.org.", "date": "2018-01-01T00:00:00Z", "citationCount": 168, "authors": [ { "name": "Altenhoff A.M." }, { "name": "Glover N.M." }, { "name": "Train C.-M." }, { "name": "Kaleb K." }, { "name": "Warwick Vesztrocy A." }, { "name": "Dylus D." }, { "name": "De Farias T.M." }, { "name": "Zile K." }, { "name": "Stevenson C." }, { "name": "Long J." }, { "name": "Redestig H." }, { "name": "Gonnet G.H." }, { "name": "Dessimoz C." } ], "journal": "Nucleic Acids Research" } } ], "credit": [ { "name": "SIB", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": null, "email": "contact@omabrowser.org", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Christophe Dessimoz", "email": "Christophe.Dessimoz@unil.ch", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "adrian.altenhoff", "additionDate": "2016-06-24T11:36:59Z", "lastUpdate": "2025-06-03T08:27:37.764262Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "ToolDistillator", "description": "ToolDistillator is a tool to extract information from output files of specific tools, expose it as JSON files, and aggregate over several tools.\nIt can produce both a single file to each tool or a summarized file from a set of reports.", "homepage": "https://gitlab.com/ifb-elixirfr/abromics/tooldistillator", "biotoolsID": "tooldistillator", "biotoolsCURIE": "biotools:tooldistillator", "version": [ "0.8.4.1", "0.8.5.0", "0.9.0", "0.9.1", "0.9.2", "0.9.3" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_1812", "term": "Data parsing" } ], "input": [], "output": [ { "data": { "uri": "http://edamontology.org/data_2048", "term": "Report" }, "format": [ { "uri": "http://edamontology.org/format_3464", "term": "JSON" } ] } ], "note": null, "cmd": null } ], "toolType": [], "topic": [ { "uri": "http://edamontology.org/topic_3301", "term": "Microbiology" }, { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://gitlab.com/ifb-elixirfr/abromics/tooldistillator/-/blob/main/README.md?ref_type=heads", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.5281/ZENODO.8282656", "pmid": null, "pmcid": null, "type": [ "Other" ], "version": null, "note": null, "metadata": null } ], "credit": [], "owner": "bebatut", "additionDate": "2024-04-25T12:18:28.403118Z", "lastUpdate": "2025-06-03T06:31:14.660645Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "bdct", "description": "Estimator of epidemiological parameters under the Birth-Death with Contact Tracing (BD-CT) model from phylogenetic trees and a non-parametric CT detection test.", "homepage": "https://github.com/evolbioinfo/bdct", "biotoolsID": "bdpn", "biotoolsCURIE": "biotools:bdpn", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3658", "term": "Statistical inference" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0872", "term": "Phylogenetic tree" }, "format": [ { "uri": "http://edamontology.org/format_1910", "term": "newick" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2526", "term": "Text data" }, "format": [ { "uri": "http://edamontology.org/format_3751", "term": "DSV" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3305", "term": "Public health and epidemiology" }, { "uri": "http://edamontology.org/topic_3293", "term": "Phylogenetics" } ], "operatingSystem": [], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/evolbioinfo/bdct", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/evolbioinfo/bdct", "type": "Source code", "note": null, "version": null }, { "url": "https://pypi.org/project/bdct", "type": "Downloads page", "note": null, "version": null }, { "url": "https://hub.docker.com/r/evolbioinfo/bdct", "type": "Container file", "note": null, "version": null } ], "documentation": [ { "url": "https://pypi.org/project/bdct", "type": [ "Command-line options" ], "note": null } ], "publication": [ { "doi": "10.1371/journal.pcbi.1012461", "pmid": "40440423", "pmcid": null, "type": [ "Method" ], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Anna Zhukova", "email": "anna.zhukova@pasteur.fr", "url": "https://research.pasteur.fr/en/member/anna-zhukova/", "orcidid": "https://orcid.org/0000-0003-2200-7935", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "azhukova", "additionDate": "2024-09-06T14:19:58.602565Z", "lastUpdate": "2025-06-02T14:34:59.109392Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "wtv", "description": "A CLI/python library that selects characteristic qualitative ions based on \nhttps://github.com/yuanhonglun/WTV_2.0", "homepage": "https://recetox.github.io/wtv/", "biotoolsID": "wtv", "biotoolsCURIE": "biotools:wtv", "version": [ "0.0.2" ], "otherID": [], "relation": [ { "biotoolsID": "wtv", "type": "isNewVersionOf" } ], "function": [], "toolType": [ "Command-line tool" ], "topic": [], "operatingSystem": [], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://recetox.github.io/wtv/", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1016/j.molp.2024.04.012", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "WTV2.0: A high-coverage plant volatilomics method with a comprehensive selective ion monitoring acquisition mode", "abstract": "Volatilomics is essential for understanding the biological functions and fragrance contributions of plant volatiles. However, the annotation coverage achieved using current untargeted and widely targeted volatomics (WTV) methods has been limited by low sensitivity and/or low acquisition coverage. Here, we introduce WTV 2.0, which enabled the construction of a high-coverage library containing 2111 plant volatiles, and report the development of a comprehensive selective ion monitoring (cSIM) acquisition method, including the selection of characteristic qualitative ions with the minimal ion number for each compound and an optimized segmentation method, that can acquire the smallest but sufficient number of ions for most plant volatiles, as well as the automatic qualitative and semi-quantitative analysis of cSIM data. Importantly, the library and acquisition method we developed can be self-expanded by incorporating compounds not present in the library, utilizing the obtained cSIM data. We showed that WTV 2.0 increases the median signal-to-noise ratio by 7.6-fold compared with the untargeted method, doubled the annotation coverage compared with the untargeted and WTV 1.0 methods in tomato fruit, and led to the discovery of menthofuran as a novel flavor compound in passion fruit. WTV 2.0 is a Python library with a user-friendly interface and is applicable to profiling of volatiles and primary metabolites in any species.", "date": "2024-06-03T00:00:00Z", "citationCount": 12, "authors": [ { "name": "Yuan H." }, { "name": "Jiangfang Y." }, { "name": "Liu Z." }, { "name": "Su R." }, { "name": "Li Q." }, { "name": "Fang C." }, { "name": "Huang S." }, { "name": "Liu X." }, { "name": "Fernie A.R." }, { "name": "Luo J." } ], "journal": "Molecular Plant" } } ], "credit": [], "owner": "acquayefrank", "additionDate": "2025-06-02T12:24:30.535794Z", "lastUpdate": "2025-06-02T13:00:49.495177Z", "editPermission": { "type": "group", "authors": [ "recetox-specdatri" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "SynGenes", "description": "SynGenes is a Python class for standardizing Mitochondrial or Chloroplast gene nomenclatures, this class is capable of recognizing and converting the different nomenclature variations into a standardized form.", "homepage": "https://luanrabelo.github.io/SynGenes/", "biotoolsID": "syngenes", "biotoolsCURIE": "biotools:syngenes", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2421", "term": "Database search" }, { "uri": "http://edamontology.org/operation_3435", "term": "Standardisation and normalisation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [], "topic": [ { "uri": "http://edamontology.org/topic_0089", "term": "Ontology and terminology" }, { "uri": "http://edamontology.org/topic_3053", "term": "Genetics" }, { "uri": "http://edamontology.org/topic_2229", "term": "Cell biology" }, { "uri": "http://edamontology.org/topic_3297", "term": "Biotechnology" }, { "uri": "http://edamontology.org/topic_3293", "term": "Phylogenetics" } ], "operatingSystem": [ "Mac", "Windows", "Linux" ], "language": [ "Python" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://pypi.org/project/SynGenes/", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/s12859-024-05781-y", "pmid": "38649820", "pmcid": "PMC11036621", "type": [], "version": null, "note": null, "metadata": { "title": "SynGenes: a Python class for standardizing nomenclatures of mitochondrial and chloroplast genes and a web form for enhancing searches for evolutionary analyses", "abstract": "Background: The reconstruction of the evolutionary history of organisms has been greatly influenced by the advent of molecular techniques, leading to a significant increase in studies utilizing genomic data from different species. However, the lack of standardization in gene nomenclature poses a challenge in database searches and evolutionary analyses, impacting the accuracy of results obtained. Results: To address this issue, a Python class for standardizing gene nomenclatures, SynGenes, has been developed. It automatically recognizes and converts different nomenclature variations into a standardized form, facilitating comprehensive and accurate searches. Additionally, SynGenes offers a web form for individual searches using different names associated with the same gene. The SynGenes database contains a total of 545 gene name variations for mitochondrial and 2485 for chloroplasts genes, providing a valuable resource for researchers. Conclusions: The SynGenes platform offers a solution for standardizing gene nomenclatures of mitochondrial and chloroplast genes and providing a standardized search solution for specific markers in GenBank. Evaluation of SynGenes effectiveness through research conducted on GenBank and PubMedCentral demonstrated its ability to yield a greater number of outcomes compared to conventional searches, ensuring more comprehensive and accurate results. This tool is crucial for accurate database searches, and consequently, evolutionary analyses, addressing the challenges posed by non-standardized gene nomenclature. Graphical abstract: (Figure presented.)", "date": "2024-12-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Rabelo L.P." }, { "name": "Sodre D." }, { "name": "de Sousa R.P.C." }, { "name": "Watanabe L." }, { "name": "Gomes G." }, { "name": "Sampaio I." }, { "name": "Vallinoto M." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Luan Pinto Rabelo", "email": "mvallino@ufpa.br", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "owner": "Pub2Tools", "additionDate": "2025-05-31T07:43:57.081940Z", "lastUpdate": "2025-05-31T07:43:57.084502Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "FastSpar", "description": "FastSpar is a C++ implementation of the SparCC algorithm for fast correlation estimation from compositional count data such as microbiome OTU tables.", "homepage": "https://github.com/scwatts/fastspar", "biotoolsID": "fastspar", "biotoolsCURIE": "biotools:fastspar", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" }, { "uri": "http://edamontology.org/topic_3697", "term": "Microbial ecology" } ], "operatingSystem": [], "language": [], "license": "MIT", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [], "credit": [], "owner": "minaHA", "additionDate": "2025-05-30T12:24:45.929338Z", "lastUpdate": "2025-05-30T12:53:30.248280Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "PhyloPhlAn", "description": "PhyloPhlAn is a comprehensive and scalable pipeline for phylogenetic profiling of microbial genomes and metagenomes. 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