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{ "count": 8959, "next": "?page=2", "previous": null, "list": [ { "name": "RAPTOR", "description": "An intelligent framework for RNA-seq analysis pipeline selection and benchmarking. RAPTOR profiles RNA-seq data characteristics (biological coefficient of variation, sequencing depth, library size distribution, sample size, zero-inflation patterns) and employs a multi-criteria scoring algorithm to recommend optimal pipelines from eight production-ready workflows. 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Recommends from 8 pipelines: STAR-RSEM-DESeq2 (high accuracy), HISAT2-StringTie-Ballgown (novel transcripts), Salmon-edgeR (balanced), Kallisto-Sleuth (ultra-fast), STAR-HTSeq-limma (complex designs), STAR-featureCounts-NOISeq (non-parametric), Bowtie2-RSEM-EBSeq (Bayesian), HISAT2-Cufflinks-Cuffdiff (legacy). Priority modes: accuracy, speed, memory, or balanced.", "cmd": "raptor run --pipeline 3 --data your_fastq/ --output results/" }, { "operation": [ { "uri": "http://edamontology.org/operation_2428", "term": "Validation" }, { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" }, { "uri": "http://edamontology.org/operation_2424", "term": "Comparison" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3494", "term": "DNA sequence" }, "format": [ { "uri": "http://edamontology.org/format_2182", "term": "FASTQ-like format (text)" } ] }, { "data": { "uri": "http://edamontology.org/data_0925", "term": "Sequence assembly" }, "format": [ { "uri": "http://edamontology.org/format_2305", "term": "GFF" } ] }, { "data": { "uri": "http://edamontology.org/data_3112", "term": "Gene expression matrix" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2048", "term": "Report" }, "format": [ { "uri": "http://edamontology.org/format_2331", "term": "HTML" } ] }, { "data": { "uri": "http://edamontology.org/data_1772", "term": "Score" }, "format": [ { "uri": "http://edamontology.org/format_3751", "term": "DSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2884", "term": "Plot" }, "format": [ { "uri": "http://edamontology.org/format_3603", "term": "PNG" }, { "uri": "http://edamontology.org/format_3508", "term": "PDF" } ] }, { "data": { "uri": "http://edamontology.org/data_0928", "term": "Gene expression profile" }, "format": [ { "uri": "http://edamontology.org/format_3751", "term": "DSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2082", "term": "Matrix" }, "format": [ { "uri": "http://edamontology.org/format_3603", "term": "PNG" }, { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] } ], "note": "Comprehensive benchmarking framework comparing 8 RNA-seq pipelines. Evaluates: (1) Accuracy metrics: sensitivity, specificity, precision, F1, ROC AUC, concordance; (2) Efficiency metrics: runtime, memory, CPU utilization, disk space; (3) Reproducibility metrics: between-run concordance, fold-change correlation, p-value correlation. Benchmark datasets: simulated data (Polyester), SEQC/MAQC validation, and user data. Runtime: 2-4 hours (small datasets) to 8-24 hours (large datasets). 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"10.1038/s41592-025-02819-8", "pmid": null, "pmcid": null, "type": [], "version": null, "note": "MMseqs2-GPU", "metadata": { "title": "GPU-accelerated homology search with MMseqs2", "abstract": "", "date": "2025-10-01T00:00:00Z", "citationCount": 0, "authors": [], "journal": "Nature Methods" } } ], "credit": [], "owner": "milot-mirdita", "additionDate": "2019-07-03T16:03:27Z", "lastUpdate": "2025-11-10T08:28:33.612233Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "SERtool", "description": "Detection of Sequence-Enriched Regions (SER) in Proteins and Nucleic Acids", "homepage": "https://github.com/rennmeng/SERtool", "biotoolsID": "sertool", "biotoolsCURIE": "biotools:sertool", "version": [ "1.0.1" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0291", "term": "Sequence clustering" } ], "input": [], "output": [], "note": "Independent scan for 20 amino acids", "cmd": "python SERtool.py A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y --input test.fasta --start 20 --point 18 20 30 50" }, { "operation": [ { "uri": "http://edamontology.org/operation_0291", "term": "Sequence clustering" } ], "input": [], "output": [], "note": "Same class D-E analysis", "cmd": "python SERtool.py D-E --input test.fasta --start 20 --point 18 20 30 50" }, { "operation": [ { "uri": "http://edamontology.org/operation_0291", "term": "Sequence clustering" } ], "input": [], "output": [], "note": "Wildcard: all X-S motifs (e.g., DS, RS)", "cmd": "python SERtool.py .S --input test.fasta --start 15 --point 5 10 10 30" } ], "toolType": [ "Command-line tool", "Bioinformatics portal", "Web service" ], "topic": [ { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/topic_0160", "term": "Sequence sites, features and motifs" } ], "operatingSystem": [ "Linux" ], "language": [ "Python", "Rust" ], "license": "MIT", "collectionID": [], "maturity": "Legacy", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/rennmeng/SERtool", "type": [ "Repository" ], "note": null }, { "url": "https://www.sertool.net/", "type": [ "Service" ], "note": null } ], "download": [ { "url": "https://www.sertool.net/download", "type": "Downloads page", "note": null, "version": "1.0.1" } ], "documentation": [ { "url": "https://www.sertool.net/help", "type": [ "User manual" ], "note": null } ], "publication": [], "credit": [ { "name": "Meng Ren", "email": "rennmeng@smail.nju.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "rennmeng", "additionDate": "2025-09-25T00:18:49.762990Z", "lastUpdate": "2025-11-09T12:54:28.385022Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "PolySTest", "description": "Robust statistical testing of quantitative proteomics data. 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Optimizes cluster assignment by taking into account individual feature variance .", "homepage": "http://computproteomics.bmb.sdu.dk/app_direct/VSClust/", "biotoolsID": "vsclust", "biotoolsCURIE": "biotools:vsclust", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0533", "term": "Expression profile pathway mapping" }, { "uri": "http://edamontology.org/operation_0313", "term": "Expression profile clustering" }, { "uri": "http://edamontology.org/operation_3664", "term": "Statistical modelling" }, { "uri": "http://edamontology.org/operation_3960", "term": "Principal component analysis" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2603", "term": "Expression data" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3768", "term": "Clustered expression profiles" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] }, { "data": { "uri": "http://edamontology.org/data_3932", "term": "Q-value" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool", "Web service" ], "topic": [ { "uri": "http://edamontology.org/topic_0622", "term": "Genomics" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_3391", "term": "Omics" }, { "uri": "http://edamontology.org/topic_3172", "term": "Metabolomics" }, { "uri": "http://edamontology.org/topic_2269", "term": "Statistics and probability" }, { "uri": "http://edamontology.org/topic_3473", "term": "Data mining" } ], "operatingSystem": [ "Windows", "Mac", "Linux" ], "language": [ "R" ], "license": "GPL-2.0", "collectionID": [ "Proteomics" ], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://bitbucket.org/veitveit/vsclust/src/master/", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "https://bitbucket.org/veitveit/vsclust", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/bty224", "pmid": "29635359", "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "VSClust: Feature-based variance-sensitive clustering of omics data", "abstract": "", "date": "2018-09-01T00:00:00Z", "citationCount": 27, "authors": [], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Veit Schwämmle", "email": "veits@bmb.sdu.dk", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "veits@bmb.sdu.dk", "additionDate": "2018-06-02T07:03:53Z", "lastUpdate": "2025-11-08T12:32:14.933981Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "Prostate zone segmentation tool", "description": "This tool automatically segments the the prostate into two zones: the central and transition zones (TZ+CZ) and the peripheral zone (PZ). It takes as input a T2-weighted image and produces a segmentation in the same input format.\n\nThree datasets were used to train this model: ProstateX (n=152), Prostate158 (n=119) and ProstateNet (the ProCAncer-I dataset; n=532). We used the T2-weighted images available in each dataset and trained a standard three-class nnU-Net model with three classes: background, peripheral zone and the combination of the transition and central zones as annotated by radiologists. Using 149 cases from the three previously noted datasets, this model achieved DSC=0.81 (95% CI=[0.59, 0.92]) for PZ and 0.87 (95% CI=[0.60, 0.96]) for CZ+TZ. In a clinical validation using ProCAncer-I data, this model achieved, for PZ and TZ+CZ, DSC=0.65 and DSC=0.77, respectively, comparable to the observed between-radiologist DSC for a subset of cases (DSC=0.61 and DSC=0.73, respectively).", "homepage": "https://github.com/josegcpa/nnunet_serve", "biotoolsID": "prostate_zone_segmentation_tool", "biotoolsCURIE": "biotools:prostate_zone_segmentation_tool", "version": [], "otherID": [], "relation": [ { "biotoolsID": "nnunet", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3442", "term": "MRI image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": "Image annotation for individual DICOM and Nifti images: Please note that it is easier to mount the input and output paths path and keep `--study_path` always as `/data/input` and `--output_dir` always as `/data/output`. This way only `--series_folders` requires any change. `--series_folders` can refer both to a Nifti file (if the input is Nifti) or the a DICOM series folder.\n\nIf the input is DICOM, `--is_dicom` should be used.", "cmd": "docker run --shm-size=1gb -v $(pwd)/example:/data/input:ro -v $(pwd)/test_output:/data/output:rw --gpus=\"all\" test-eucaim:latest nnunet-predict --study_path /data/input --series_folders <series-folder> --output_dir /data/output" }, { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3442", "term": "MRI image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "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_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": "Batch image annotation for DICOM and Nifti images using dataset JSON and data directory supports two methods: using a dataset JSON and using a data directory. \n\nThe dataset JSON-based method relies on having a dataset JSON which has a list of dictionaries that follow this structure: `{\"study_path\": str, \"series_folders\": [[str]], \"output_dir\": str}`. These are analogous to the previous modes. Be mindful that paths should be specified accordingly for the Docker file-system. The dataset JSON should be specified using `--data_json`.\n\nThe data directory-based method relies on having a directory structured as patient/study/series, where each series follows an nnU-Net-like convention (i.e. `<series_id>_0000`, `<series_id>_0001`, etc.). In this case, it should be `<series-id>_0000`. The data directory should be specified using `--data_dir` and requires the specification of an `--output_dir`.\n\nIf the input is DICOM, `--is_dicom` should be specified.", "cmd": "docker run --shm-size=1gb -v $(pwd)/example_dataset:/data/input:ro -v $(pwd)/test_output:/data/output:rw --gpus=\"all\" test-eucaim:latest nnunet-predict-batch {{--data_json /data/input/<dataset_json>.json|--data_dir /data/input --output_dir /data/output}}" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3063", "term": "Medical informatics" } ], "operatingSystem": [ "Linux" ], "language": [ "Python" ], "license": "CC-BY-NC-4.0", "collectionID": [ "EUCAIM" ], "maturity": "Emerging", "cost": "Free of charge (with restrictions)", "accessibility": "Open access (with restrictions)", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/josegcpa/nnunet_serve", "type": [ "Repository" ], "note": "This is the nnunet_serve repository, containing the generic nnU-Net building and serving functionalities used by the Computational Clinical Imaging Group at Champalimaud Foundation." } ], "download": [], "documentation": [ { "url": "https://github.com/josegcpa/nnunet_serve", "type": [ "API documentation" ], "note": "This is the repository containing all of the code together with a comprehensive README for deployment and examplar files." } ], "publication": [ { "doi": "10.1016/j.compbiomed.2024.108216", "pmid": "38442555", "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data", "abstract": "", "date": "2024-03-01T00:00:00Z", "citationCount": 9, "authors": [], "journal": "Computers in Biology and Medicine" } } ], "credit": [ { "name": "José Guilherme de Almeida", "email": "jose.almeida@research.fchampalimaud.org", "url": "https://josegcpa.net", "orcidid": "https://orcid.org/0000-0002-1887-0157", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Documentor", "Maintainer" ], "note": null }, { "name": "Nickolas Papanikolaou", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0003-3298-2072", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Support" ], "note": null }, { "name": "Nuno Rodrigues", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null } ], "owner": "josegcpa", "additionDate": "2025-11-07T18:15:49.962044Z", "lastUpdate": "2025-11-07T18:17:59.196564Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "HCC segmentation tool for contrast-enhanced abdominal CT", "description": "Features an nnU-Net based segmentation tool capable of performing HCC segmentation for both arterial and portal venous phase in polyphasic contrast-enhanced abdominal CT.\n\nPlease be mindful that the usage instructions below are for the image in the EUCAIM Harbor.", "homepage": "https://github.com/josegcpa/nnunet_serve", "biotoolsID": "hcc_segmentation_tool_for_contrast-enhanced_abdominal_ct", "biotoolsCURIE": "biotools:hcc_segmentation_tool_for_contrast-enhanced_abdominal_ct", "version": [], "otherID": [], "relation": [ { "biotoolsID": "nnunet", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3442", "term": "MRI image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": "Image annotation for individual DICOM and Nifti images: Please note that it is easier to mount the input and output paths path and keep `--study_path` always as `/data/input` and `--output_dir` always as `/data/output`. This way only `--series_folders` requires any change. `--series_folders` can refer both to a Nifti file (if the input is Nifti) or the a DICOM series folder.\n\nIf the input is DICOM, `--is_dicom` should be used.", "cmd": "docker run --shm-size=1gb -v $(pwd)/example:/data/input:ro -v $(pwd)/test_output:/data/output:rw --gpus=\"all\" test-eucaim:latest nnunet-predict --study_path /data/input --series_folders <series-folder> --output_dir /data/output" }, { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3442", "term": "MRI image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": "Batch image annotation for DICOM and Nifti images using dataset JSON and data directory supports two methods: using a dataset JSON and using a data directory. \n\nThe dataset JSON-based method relies on having a dataset JSON which has a list of dictionaries that follow this structure: `{\"study_path\": str, \"series_folders\": [[str]], \"output_dir\": str}`. These are analogous to the previous modes. Be mindful that paths should be specified accordingly for the Docker file-system. The dataset JSON should be specified using `--data_json`.\n\nThe data directory-based method relies on having a directory structured as patient/study/series, where each series follows an nnU-Net-like convention (i.e. `<series_id>_0000`, `<series_id>_0001`, etc.). In this case, it should be `<series-id>_0000`. 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Published by Oxford University Press.Summary: Harmonizing quality control (QC) of large-scale second and third-generation sequencing datasets is key for enabling downstream computational and biological analyses. We present Alfred, an efficient and versatile command-line application that computes multi-sample QC metrics in a read-group aware manner, across a wide variety of sequencing assays and technologies. In addition to standard QC metrics such as GC bias, base composition, insert size and sequencing coverage distributions it supports haplotype-aware and allele-specific feature counting and feature annotation. The versatility of Alfred allows for easy pipeline integration in high-throughput settings, including DNA sequencing facilities and large-scale research initiatives, enabling continuous monitoring of sequence data quality and characteristics across samples. 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Despite the advancements in untargeted liquid chromatography-mass spectrometry (LC–MS) to achieve a high-throughput profile of metabolites from complex biological resources, only a small fraction of these metabolites can be annotated with confidence. Many novel computational methods and tools have been developed to enable chemical structure annotation to known and unknown compounds such as in silico generated spectra and molecular networking. Here, we present an automated and reproducible Metabolome Annotation Workflow (MAW) for untargeted metabolomics data to further facilitate and automate the complex annotation by combining tandem mass spectrometry (MS2) input data pre-processing, spectral and compound database matching with computational classification, and in silico annotation. MAW takes the LC-MS2 spectra as input and generates a list of putative candidates from spectral and compound databases. The databases are integrated via the R package Spectra and the metabolite annotation tool SIRIUS as part of the R segment of the workflow (MAW-R). The final candidate selection is performed using the cheminformatics tool RDKit in the Python segment (MAW-Py). Furthermore, each feature is assigned a chemical structure and can be imported to a chemical structure similarity network. MAW is following the FAIR (Findable, Accessible, Interoperable, Reusable) principles and has been made available as the docker images, maw-r and maw-py. The source code and documentation are available on GitHub (https://github.com/zmahnoor14/MAW) The performance of MAW is evaluated on two case studies. MAW can improve candidate ranking by integrating spectral databases with annotation tools like SIRIUS which contributes to an efficient candidate selection procedure. The results from MAW are also reproducible and traceable, compliant with the FAIR guidelines. 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[ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_3172", "term": "Metabolomics" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_0153", "term": "Lipids" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python", "Java" ], "license": "AGPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1038/S41592-022-01486-3", "pmid": "35637304", "pmcid": "PMC9262714", "type": [], "version": null, "note": null, "metadata": { "title": "MSNovelist: de novo structure generation from mass spectra", "abstract": "© 2022, The Author(s).Current methods for structure elucidation of small molecules rely on finding similarity with spectra of known compounds, but do not predict structures de novo for unknown compound classes. We present MSNovelist, which combines fingerprint prediction with an encoder–decoder neural network to generate structures de novo solely from tandem mass spectrometry (MS2) spectra. In an evaluation with 3,863 MS2 spectra from the Global Natural Product Social Molecular Networking site, MSNovelist predicted 25% of structures correctly on first rank, retrieved 45% of structures overall and reproduced 61% of correct database annotations, without having ever seen the structure in the training phase. Similarly, for the CASMI 2016 challenge, MSNovelist correctly predicted 26% and retrieved 57% of structures, recovering 64% of correct database annotations. Finally, we illustrate the application of MSNovelist in a bryophyte MS2 dataset, in which de novo structure prediction substantially outscored the best database candidate for seven spectra. MSNovelist is ideally suited to complement library-based annotation in the case of poorly represented analyte classes and novel compounds.", "date": "2022-07-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Stravs M.A." }, { "name": "Duhrkop K." }, { "name": "Bocker S." }, { "name": "Zamboni N." } ], "journal": "Nature Methods" } } ], "credit": [ { "name": "Nicola Zamboni", "email": "zamboni@imsb.biol.ethz.ch", "url": null, "orcidid": "https://orcid.org/0000-0003-1271-1021", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Sebastian Böcker", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-9304-8091", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Kai Dührkop", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-9056-0540", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Michael A. Stravs", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-1426-8572", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "Jennifer", "additionDate": "2022-09-05T21:19:03.847868Z", "lastUpdate": "2025-11-03T16:47:18.329938Z", "editPermission": { "type": "group", "authors": [ "recetox-specdatri" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "peakPantheR", "description": "R package for large-scale targeted extraction and integration of annotated metabolic features in LC-MS profiling datasets.", "homepage": "https://phenomecentre.github.io/peakPantheR.github.io/", "biotoolsID": "peakpanther", "biotoolsCURIE": "biotools:peakpanther", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3803", "term": "Natural product identification" }, { "uri": "http://edamontology.org/operation_3937", "term": "Feature extraction" }, { "uri": "http://edamontology.org/operation_3215", "term": "Peak detection" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3172", "term": "Metabolomics" }, { "uri": "http://edamontology.org/topic_3407", "term": "Endocrinology and metabolism" }, { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://bioconductor.org/packages/peakPantheR/", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/phenomecentre/metabotyping-dementia-urine", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/phenomecentre/peakPantheR/", "type": "Source code", "note": null, "version": null } ], "documentation": [ { "url": "https://phenomecentre.github.io/peakPantheR.github.io/", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btab433", "pmid": "34125879", "pmcid": "PMC8665750", "type": [], "version": null, "note": null, "metadata": { "title": "Extraction and integration of annotated metabolic features in LC-MS profiling datasets", "abstract": "Summary: Untargeted liquid chromatography-mass spectrometry (LC-MS) profiling assays are capable of measuring thousands of chemical compounds in a single sample, but unreliable feature extraction and metabolite identification remain considerable barriers to their interpretation and usefulness. peakPantheR (Peak Picking and ANnoTation of High-resolution Experiments in R) is an R package for the targeted extraction and integration of annotated features from LC-MS profiling experiments. It takes advantage of chromatographic and spectral databases and prior information of sample matrix composition to generate annotated and interpretable metabolic phenotypic datasets and power workflows for real-time data quality assessment.", "date": "2021-12-15T00:00:00Z", "citationCount": 21, "authors": [ { "name": "Wolfer A.M." }, { "name": "Correia G.D.S." }, { "name": "Sands C.J." }, { "name": "Camuzeaux S." }, { "name": "Yuen A.H.Y." }, { "name": "Chekmeneva E." }, { "name": "Takats Z." }, { "name": "Pearce J.T.M." }, { "name": "Lewis M.R." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Arnaud M Wolfer", "email": "phenomecentre@imperial.ac.uk", "url": null, "orcidid": "https://orcid.org/0000-0001-5856-3218", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Goncalo Correia", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0001-8271-9294", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Jake Pearce", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Caroline Sands", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "Jennifer", "additionDate": "2021-10-28T20:02:19.622064Z", "lastUpdate": "2025-11-03T16:47:17.380949Z", "editPermission": { "type": "group", "authors": [ "recetox-specdatri" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "PeakForest", "description": "A multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management.", "homepage": "https://peakforest.org", "biotoolsID": "peakforest", "biotoolsCURIE": "biotools:peakforest", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3803", "term": "Natural product identification" }, { "uri": "http://edamontology.org/operation_3801", "term": "Spectral library search" }, { "uri": "http://edamontology.org/operation_3431", "term": "Deposition" }, { "uri": "http://edamontology.org/operation_2422", "term": "Data retrieval" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3172", "term": "Metabolomics" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_0593", "term": "NMR" }, { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_0219", "term": "Data submission, annotation and curation" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Java", "JavaScript" ], "license": "Other", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/peakforest", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1007/S11306-022-01899-3", "pmid": "35699774", "pmcid": "PMC9197906", "type": [], "version": null, "note": null, "metadata": { "title": "PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management", "abstract": "© 2022, The Author(s).Introduction: Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories. Objectives: To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management. Methods: We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles. Results: PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases. Conclusion: PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest.", "date": "2022-06-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Paulhe N." }, { "name": "Canlet C." }, { "name": "Damont A." }, { "name": "Peyriga L." }, { "name": "Durand S." }, { "name": "Deborde C." }, { "name": "Alves S." }, { "name": "Bernillon S." }, { "name": "Berton T." }, { "name": "Bir R." }, { "name": "Bouville A." }, { "name": "Cahoreau E." }, { "name": "Centeno D." }, { "name": "Costantino R." }, { "name": "Debrauwer L." }, { "name": "Delabriere A." }, { "name": "Duperier C." }, { "name": "Emery S." }, { "name": "Flandin A." }, { "name": "Hohenester U." }, { "name": "Jacob D." }, { "name": "Joly C." }, { "name": "Jousse C." }, { "name": "Lagree M." }, { "name": "Lamari N." }, { "name": "Lefebvre M." }, { "name": "Lopez-Piffet C." }, { "name": "Lyan B." }, { "name": "Maucourt M." }, { "name": "Migne C." }, { "name": "Olivier M.-F." }, { "name": "Rathahao-Paris E." }, { "name": "Petriacq P." }, { "name": "Pinelli J." }, { "name": "Roch L." }, { "name": "Roger P." }, { "name": "Roques S." }, { "name": "Tabet J.-C." }, { "name": "Tremblay-Franco M." }, { "name": "Traikia M." }, { "name": "Warnet A." }, { "name": "Zhendre V." }, { "name": "Rolin D." }, { "name": "Jourdan F." }, { "name": "Thevenot E." }, { "name": "Moing A." }, { "name": "Jamin E." }, { "name": "Fenaille F." }, { "name": "Junot C." }, { "name": "Pujos-Guillot E." }, { "name": "Giacomoni F." } ], "journal": "Metabolomics" } } ], "credit": [ { "name": "Franck Giacomoni", "email": "Franck.Giacomoni@inrae.fr", "url": null, "orcidid": "https://orcid.org/0000-0001-6063-4214", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Estelle Pujos-Guillot", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Christophe Junot", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "François Fenaille", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "Jennifer", "additionDate": "2022-09-04T23:10:37.868759Z", "lastUpdate": "2025-11-03T16:47:12.424836Z", "editPermission": { "type": "group", "authors": [ "recetox-specdatri" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "BUDDY", "description": "Open-source cheminformatic software platform developed for MS-based metabolomics research.", "homepage": "https://github.com/HuanLab/BUDDY", "biotoolsID": "buddy", "biotoolsCURIE": "biotools:buddy", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3646", "term": "Peptide database search" }, { "uri": "http://edamontology.org/operation_3644", "term": "de Novo sequencing" }, { "uri": "http://edamontology.org/operation_2425", "term": "Optimisation and refinement" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_3172", "term": "Metabolomics" }, { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_2258", "term": "Cheminformatics" }, { "uri": "http://edamontology.org/topic_3407", "term": "Endocrinology and metabolism" }, { "uri": "http://edamontology.org/topic_3292", "term": "Biochemistry" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "C#" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://philipbear.github.io/BUDDY_Metabolomics", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1038/s41592-023-01850-x", "pmid": "37055660", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "BUDDY: molecular formula discovery via bottom-up MS/MS interrogation", "abstract": "A substantial fraction of metabolic features remains undetermined in mass spectrometry (MS)-based metabolomics, and molecular formula annotation is the starting point for unraveling their chemical identities. Here we present bottom-up tandem MS (MS/MS) interrogation, a method for de novo formula annotation. Our approach prioritizes MS/MS-explainable formula candidates, implements machine-learned ranking and offers false discovery rate estimation. Compared with the mathematically exhaustive formula enumeration, our approach shrinks the formula candidate space by 42.8% on average. Method benchmarking on annotation accuracy was systematically carried out on reference MS/MS libraries and real metabolomics datasets. Applied on 155,321 recurrent unidentified spectra, our approach confidently annotated >5,000 novel molecular formulae absent from chemical databases. Beyond the level of individual metabolic features, we combined bottom-up MS/MS interrogation with global optimization to refine formula annotations while revealing peak interrelationships. This approach allowed the systematic annotation of 37 fatty acid amide molecules in human fecal data. All bioinformatics pipelines are available in a standalone software, BUDDY (https://github.com/HuanLab/BUDDY)", "date": "2023-06-01T00:00:00Z", "citationCount": 21, "authors": [ { "name": "Xing S." }, { "name": "Shen S." }, { "name": "Xu B." }, { "name": "Li X." }, { "name": "Huan T." } ], "journal": "Nature Methods" } } ], "credit": [ { "name": "Tao Huan", "email": "thuan@chem.ubc.ca", "url": null, "orcidid": "http://orcid.org/0000-0001-6295-2435", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "owner": "Pub2Tools", "additionDate": "2023-12-01T15:46:02.196263Z", "lastUpdate": "2025-11-03T16:45:48.375514Z", "editPermission": { "type": "group", "authors": [ "recetox-specdatri" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "tidyMass", "description": "An Object-oriented Reproducible Analysis Framework for LC-MS Data.", "homepage": "https://www.tidymass.org/", "biotoolsID": "tidymass", "biotoolsCURIE": "biotools:tidymass", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3172", "term": "Metabolomics" }, { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "R" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/tidymass", "type": [ "Repository" ], "note": null }, { "url": "https://tidymass.tidymass.org/", "type": [ "Other" ], "note": null } ], "download": [ { "url": "https://hub.docker.com/r/jaspershen/tidymass-case-study", "type": "Container file", "note": null, "version": null }, { "url": "https://hub.docker.com/r/jaspershen/tidymass", "type": "Container file", "note": null, "version": null }, { "url": "https://www.tidymass.org/start/tidymass_docker/", "type": "Container file", "note": null, "version": null } ], "documentation": [], "publication": [ { "doi": "10.1038/s41467-022-32155-w", "pmid": "35902589", "pmcid": "PMC9334349", "type": [], "version": null, "note": null, "metadata": { "title": "TidyMass an object-oriented reproducible analysis framework for LC–MS data", "abstract": "Reproducibility, traceability, and transparency have been long-standing issues for metabolomics data analysis. Multiple tools have been developed, but limitations still exist. Here, we present the tidyMass project (https://www.tidymass.org/) a comprehensive R-based computational framework that can achieve the traceable, shareable, and reproducible workflow needs of data processing and analysis for LC-MS-based untargeted metabolomics. TidyMass is an ecosystem of R packages that share an underlying design philosophy, grammar, and data structure, which provides a comprehensive, reproducible, and object-oriented computational framework. The modular architecture makes tidyMass a highly flexible and extensible tool, which other users can improve and integrate with other tools to customize their own pipeline.", "date": "2022-12-01T00:00:00Z", "citationCount": 28, "authors": [ { "name": "Shen X." }, { "name": "Yan H." }, { "name": "Wang C." }, { "name": "Gao P." }, { "name": "Johnson C.H." }, { "name": "Snyder M.P." } ], "journal": "Nature Communications" } } ], "credit": [ { "name": "Caroline H. Johnson", "email": "caroline.johnson@yale.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Michael P. Snyder", "email": "mpsnyder@stanford.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Xiaotao Shen", "email": null, "url": null, "orcidid": "http://orcid.org/0000-0002-9608-9964", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Hong Yan", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "Jennifer", "additionDate": "2022-11-25T21:43:37.501728Z", "lastUpdate": "2025-11-03T16:45:44.856713Z", "editPermission": { "type": "group", "authors": [ "recetox-specdatri" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" } ] }