pretext-to-asmpretext-to-asm takes the AGP output from PretextView and assembles it into FASTA format to produce a new assembly.https://github.com/sanger-tol/agp-tpf-utilspretext-to-asmbiotools:pretext-to-asm1.4.01.4.1Command-line toolMITMatureOpen accesshttp://edamontology.org/operation_0226AnnotationSCUBASCUBA visually examines single cell RNA-seq data using gene co-expression networks. Instead of clustering cells, SCUBA groups co-expressed genes into automatically annotated modules, then shows how their activity shifts between conditions.https://scuba-nw.orgscuba-nwbiotools:scuba-nwDesktop applicationWeb applicationhttp://edamontology.org/topic_4028Single-cell sequencingWindowsMacLinuxPythonProprietaryMatureFree of charge (with restrictions)Open accesshttps://scuba-nw.orgServiceBioSyn Lab CalculatorsFree browser-based calculators for molecular biology, biochemistry, cell culture and molecular cloning work. Twenty-plus tools cover molarity and dilution, stock and working solutions, DNA and protein molecular weight, primer melting temperature, PCR master mix setup, qPCR efficiency and delta-delta Ct, A260 and A280 concentration, plasmid molecular weight, ligation ratio, transformation efficiency, hemocytometer cell counting, trypan blue viability, seeding density, population doubling time and CFU plate counts, organised into laboratory, DNA & RNA, PCR & qPCR, cell culture, protein and molecular cloning categories. Every result page states the equation, defines each variable and walks a worked example. Each calculator is paired with a method guide explaining the formula, its assumptions and the common mistakes. All computation runs locally in the browser: no input data is uploaded, nothing is stored, and no account is required.https://biosyn-inc.com/biosyn-lab-calculatorsbiotools:biosyn-lab-calculatorsWeb applicationSuitehttp://edamontology.org/topic_3292Biochemistryhttp://edamontology.org/topic_3047Molecular biologyhttp://edamontology.org/topic_0077Nucleic acidshttp://edamontology.org/topic_0078Proteinshttp://edamontology.org/topic_2229Cell biologyhttp://edamontology.org/topic_0608Cell and tissue culturehttp://edamontology.org/topic_3321Molecular geneticshttp://edamontology.org/topic_0203Gene expressionJavaScriptFreewareEmergingFree of chargeOpen accesshttp://edamontology.org/operation_3438Calculationhttp://edamontology.org/data_2140Concentrationhttp://edamontology.org/format_2330Textual format

http://edamontology.org/data_2140Concentrationhttp://edamontology.org/format_2330Textual format

Concentration and solution preparation: molarity from mass, volume or moles; C1V1 = C2V2 dilutions; stock solutions from solid or liquid; working solutions; percent w/v, v/v and w/w; ppm to mg/L.http://edamontology.org/operation_2238Statistical calculationhttp://edamontology.org/data_2140Concentrationhttp://edamontology.org/format_2330Textual format

http://edamontology.org/data_2140Concentrationhttp://edamontology.org/format_2330Textual format

qPCR amplification efficiency from a standard curve by regressing Cq on log10 concentration, the -3.32 slope equivalence for 100% efficiency and the 90-110% acceptance window; population doubling time from two cell counts via t.ln2/ln(Nt/N0).http://edamontology.org/operation_3434Conversionhttp://edamontology.org/data_2140Concentrationhttp://edamontology.org/format_2330Textual format

http://edamontology.org/data_2140Concentrationhttp://edamontology.org/format_2330Textual format

Unit conversions across molarity, amount, mass and volume (M/mM/uM/nM, mol/mmol/umol/nmol, g/mg/ug/ng, L/mL/uL) plus ug-to-nmol, ug/mL-to-nM and mg/mL-to-uM conversions.http://edamontology.org/operation_3438Calculationhttp://edamontology.org/operation_3267Sequence coordinate conversionhttp://edamontology.org/data_2044Sequencehttp://edamontology.org/format_2330Textual formathttp://edamontology.org/data_1249Sequence lengthhttp://edamontology.org/format_2330Textual format

http://edamontology.org/data_1249Sequence lengthhttp://edamontology.org/format_2330Textual format

Nucleic acid and protein sizing: DNA oligo molecular weight from length or sequence using free-acid residue masses with the terminal correction and 5'-OH versus 5'-phosphate handling; plasmid molecular weight from length in bp; protein molecular weight by summing residue masses and adding one water; protein isoelectric point from ionizable groups and pKa values; GC content of a DNA or RNA sequence.http://edamontology.org/operation_2495Expression analysishttp://edamontology.org/data_0932Oligonucleotide probe datahttp://edamontology.org/format_2330Textual format

http://edamontology.org/data_2140Concentrationhttp://edamontology.org/format_2330Textual format

Relative expression: delta-delta Ct (2^-ddCt) fold change with reference-gene normalisation, and the Livak method's efficiency and error preconditions.http://edamontology.org/operation_3435Standardisation and normalisationhttp://edamontology.org/operation_3438Calculationhttp://edamontology.org/data_1532Protein optical densityhttp://edamontology.org/format_2330Textual format

http://edamontology.org/data_2140Concentrationhttp://edamontology.org/format_2330Textual format

Spectrophotometric normalisation: nucleic acid concentration from A260 using the 50/33/40 factors with dilution-factor and path-length correction, and protein concentration from A280 with epsilon derived from Trp, Tyr and Cys content.http://edamontology.org/operation_3438Calculationhttp://edamontology.org/data_2140Concentrationhttp://edamontology.org/format_2331HTML

http://edamontology.org/data_2140Concentrationhttp://edamontology.org/format_2331HTML

Cell culture and microbiology: hemocytometer counts to cells/mL using the 1e-4 mL chamber volume and border-cell rule; trypan blue viability percentage; seeding density from target density and growth area; CFU/mL from plate counts with the 25-250 countable range.http://edamontology.org/operation_3438Calculationhttp://edamontology.org/operation_3434Conversionhttp://edamontology.org/data_1249Sequence lengthhttp://edamontology.org/format_2330Textual format

http://edamontology.org/data_2140Concentrationhttp://edamontology.org/format_2330Textual format

Molecular cloning: ligation insert-to-vector molar ratio converted from fragment sizes and vector mass into nanograms of insert, and transformation efficiency in CFU per microgram with the plated-volume dilution factor applied.http://edamontology.org/operation_2238Statistical calculationhttp://edamontology.org/operation_3438Calculationhttp://edamontology.org/data_2139Nucleic acid melting temperaturehttp://edamontology.org/format_2330Textual format

http://edamontology.org/data_2139Nucleic acid melting temperaturehttp://edamontology.org/format_2330Textual format

PCR setup: per-reaction and total master mix volumes solved with C1V1 = C2V2, scaled by reaction count plus overage, and primer melting temperature by both the Wallace rule and the SantaLucia 1998 nearest-neighbour method.https://biosyn-inc.com/laboratory/Software catalogueLaboratory calculators.https://biosyn-inc.com/dna-rna/Software catalogueDNA and RNA calculators.https://biosyn-inc.com/pcr/Software cataloguePCR and qPCR calculators.https://biosyn-inc.com/cell-culture/Software catalogueCell culture calculators.https://biosyn-inc.com/protein/Software catalogueProtein calculators.https://biosyn-inc.com/cloning/Software catalogueMolecular cloning calculators.https://biosyn-inc.com/guides/what-is-molarity/User manualWhat molarity means: moles per litre, M vs mM, and why temperature changes the answer.https://biosyn-inc.com/guides/how-to-calculate-molarity/User manualMolarity in three steps with worked examples for NaCl, glucose and a 10 mM reagent.https://biosyn-inc.com/guides/c1v1-equation/User manualC1V1 = C2V2 explained: all four rearrangements, the unit-matching rule, and 10x stock mapping.https://biosyn-inc.com/guides/how-to-calculate-a-dilution/User manualFind stock volume, diluent volume, dilution factor or final concentration, with ratio and percentage tables.https://biosyn-inc.com/guides/how-to-make-a-serial-dilution/User manualPlan a serial dilution: tube-by-tube concentrations, transfer and diluent volumes, pipetting errors.https://biosyn-inc.com/guides/how-to-make-a-stock-solution/User manualMass = MW x M x V, the 80% rule, hydrate and purity corrections, per-compound recipes.https://biosyn-inc.com/guides/how-to-calculate-dna-molecular-weight/User manualFree-acid residue masses, terminal correction, 5'-OH vs 5'-phosphate, ug-to-nmol.https://biosyn-inc.com/guides/how-to-calculate-dna-concentration/User manualA260 to concentration: the 50/33/40 factors, dilution factor, path length, ug/mL to nM.https://biosyn-inc.com/guides/how-to-calculate-gc-content/User manualGC% = (G + C) / length x 100 with IUPAC degenerate-base rules.https://biosyn-inc.com/guides/how-to-calculate-primer-tm/User manualWallace rule and SantaLucia 1998 nearest-neighbour method with a worked example.https://biosyn-inc.com/guides/how-to-choose-annealing-temperature/User manualTm-5 empirical start, Rychlik Ta-optimum formula, gradient windows, mismatched primers.https://biosyn-inc.com/guides/how-to-make-a-pcr-master-mix/User manualSolve each component with C1V1=C2V2, scale by reactions plus overage, balance the water.https://biosyn-inc.com/guides/how-to-calculate-qpcr-efficiency/User manualRegress Cq on log10 concentration, why slope -3.32 is 100%, the 90-110% window.https://biosyn-inc.com/guides/how-to-calculate-delta-delta-ct/User manualLivak 2^-ddCt by hand: normalise to a reference gene, subtract control, check efficiency first.https://biosyn-inc.com/guides/how-to-calculate-protein-molecular-weight/User manualSum residue masses and add one water; average vs monoisotopic; sources.https://biosyn-inc.com/guides/how-to-calculate-protein-isoelectric-point/User manualIonizable groups, pKa values, the net-charge sum and why iteration is required.https://biosyn-inc.com/guides/how-to-determine-protein-concentration-a280/User manualDerive epsilon280 from Trp/Tyr/Cys, apply Beer-Lambert, convert to mg/mL and uM.https://biosyn-inc.com/guides/how-to-count-cells-with-hemocytometer/User manualThe 1e-4 mL chamber volume, why you multiply by 10^4, the border-cell rule.https://biosyn-inc.com/guides/how-to-calculate-cell-viability/User manualTrypan blue counts: viability % = live/total x 100, viable density, the ~90% threshold.https://biosyn-inc.com/guides/how-to-calculate-seeding-density/User manualTarget density to cells per well, growth-area conversion, C1V1=C2V2 for the stock volume.https://biosyn-inc.com/guides/how-to-calculate-doubling-time/User manualTd = t.ln2/ln(Nt/N0), the log2 form, specific growth rate, PDL vs passage number.https://biosyn-inc.com/guides/how-to-calculate-cfu/User manualCFU/mL from colony counts: dilution factor, plated volume, the 25-250 countable range.https://biosyn-inc.com/guides/how-to-calculate-plasmid-molecular-weight/User manual660 g/mol.bp convention, then pmol/ug, molarity, molecules and copy number.https://biosyn-inc.com/guides/how-to-calculate-ligation-ratio/User manualInsert:vector molar ratio from fragment sizes and vector mass, or reversed from masses used.https://biosyn-inc.com/guides/how-to-calculate-transformation-efficiency/User manualCFU/ug: plated-volume dilution factor, DNA mass to ug, read against the expected range.https://biosyn-inc.com/guides/how-to-calculate-molar-mass/User manualSum atomic masses, handle hydrates, convert grams to moles; H2O, NaCl and glucose examples.https://biosyn-inc.com/guides/molarity-vs-molality/User manualMoles per litre vs per kg of solvent, the temperature difference, and M vs m vs N.https://biosyn-inc.com/guides/percent-solution-wv-vv-ww/User manualThe four percent-solution types compared, plus converting w/v to w/w using density.https://biosyn-inc.com/guides/ppm-to-mg-l/User manualWhy 1 ppm is about 1 mg/L only in dilute water; crosswalk to ug/mL, ppb, % and mg/m3.https://biosyn-inc.com/guides/how-to-prepare-a-working-solution/User manualTurn a stock into a working solution: dilution-factor table, workflow, ratio crosswalk.https://biosyn-inc.com/laboratory/GeneralLaboratory calculator index: molarity, dilution, stock solutions, percent and ppm.https://biosyn-inc.com/dna-rna/GeneralDNA and RNA index: GC content, primer Tm, molecular weight, A260 concentration.https://biosyn-inc.com/pcr/GeneralPCR and qPCR index: primer Tm, master mix setup, qPCR standard-curve efficiency.https://biosyn-inc.com/cell-culture/GeneralCell culture index: counting, viability, seeding density, doubling time, CFU plate counts.https://biosyn-inc.com/protein/GeneralProtein index: molecular weight, isoelectric point, A280 concentration.https://biosyn-inc.com/cloning/GeneralMolecular cloning index: plasmid MW, ligation ratio, transformation efficiency.Phylogeny AppFree web application for viewing, annotating and sharing phylogenetic trees. Opens Newick, NEXUS and phyloXML trees in the browser with no installation or account, joins CSV or TSV metadata to colour tips, shows BEAST HPD bars and support values, and exports publication-ready SVG, PNG and PDF figures or shareable and embeddable interactive views. Phylogeny.app is the continuation of the original phylogeny.io app. It carries forward the original project's interactive tree viewing, annotation, and sharing work while updating the viewer and administration for the current site.https://phylogeny.appphylogeny.appbiotools:phylogeny.appv1.0.1Web applicationhttp://edamontology.org/topic_3293Phylogeneticshttp://edamontology.org/topic_0084Phylogenyhttp://edamontology.org/topic_0194PhylogenomicsTypeScriptFreewareEmergingFree of chargeOpen accesshttp://edamontology.org/operation_0567Phylogenetic tree visualisationhttps://phylogeny.app/aboutGeneralCitation instructions10.7287/peerj.preprints.2579v1PreprintInteractive web-based visualization of phylogenetic trees using Phylogeny.IO10.1093/nar/gkz35631114886PMC6602505PrimaryInteractive web-based visualization and sharing of phylogenetic trees using phylogeny.IONikola Jovanovicnixony@gmail.comhttps://nikolajovanovic.netPersonDeveloperSasha Mikheyevhttps://scholar.google.com/citations?user=d1Q6iL0AAAAJ&hl=enPersonContributorSasha (Alexander) Mikheyev is a Russian-American-Australian evolutionary biologist. He is a professor in the Research School of Biology, and Associate Dean (International) for the College of Science and Medicine.IQA DCE toolThe purpose of the tool is to provide a complete framework for automatic detection of image quality for Breast DCE MR images. The assessment is performed on the first post-contrast dynamic phase in order to assess the most clinically relevant sequence among a number of identical acquisitions. Two categories are available for image classification, i.e. high and low quality, depending on the level of noise, degree of blurring and presence of artifacts.https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/iqa_dce_tool/info-tabiqa_dce_toolbiotools:iqa_dce_tool1.0Command-line toolhttp://edamontology.org/topic_3444MRIhttp://edamontology.org/topic_3572Data quality managementhttp://edamontology.org/topic_2640OncologyWindowsMacLinuxPythonEUPL-1.2EUCAIMMatureFree of chargeOpen accesshttps://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/iqa_dce_toolContainer file1.0https://drive.eucaim.cancerimage.eu/s/NeAGoB5GGjSXeinUser manualhttps://www.youtube.com/watch?v=z2sSu1eIXTg&list=PL3Q1XjQpjfg_GEmwPDrQeESh6nqCMnYyR&index=5Training material10.3390/jimaging11110417Computational BioMedicine Laboratory, Foundation for Research and Technology Hellas (FORTH)https://www.ics.forth.gr/cbml/?lang=enInstituteProviderToolyard BioFree collection of 67 DNA and protein sequence tools and 44 lab calculators that run in the web browser, including reverse complement, translation, ORF finder, restriction maps, pairwise alignment, primer Tm, CRISPR guide finder, codon optimization, plasmid maps, and molarity, dilution, qPCR ΔΔCt and IC50 calculators. Sequences are processed on the user's device and never uploaded.https://webtoolyard.com/seq/toolyard_biobiotools:toolyard_bioWeb applicationhttp://edamontology.org/topic_0080Sequence analysishttp://edamontology.org/topic_3047Molecular biologyhttp://edamontology.org/topic_3912Genetic engineeringWindowsMacLinuxFreewareMatureFree of chargeOpen accesshttp://edamontology.org/operation_0292Sequence alignmenthttp://edamontology.org/data_3494DNA sequencehttp://edamontology.org/format_1929FASTAhttp://edamontology.org/data_2976Protein sequencehttp://edamontology.org/format_1929FASTA

http://edamontology.org/data_0863Sequence alignment

http://edamontology.org/operation_0431Restriction site recognitionhttp://edamontology.org/data_3494DNA sequencehttp://edamontology.org/format_1929FASTAhttp://edamontology.org/operation_0308PCR primer designhttp://edamontology.org/data_3494DNA sequencehttp://edamontology.org/format_1929FASTAhttp://edamontology.org/operation_0286Codon usage analysishttp://edamontology.org/data_3494DNA sequencehttp://edamontology.org/format_1929FASTAhttp://edamontology.org/operation_0233Sequence conversionhttp://edamontology.org/data_3494DNA sequencehttp://edamontology.org/format_1927EMBL formathttp://edamontology.org/format_1936GenBank format

http://edamontology.org/data_3494DNA sequencehttp://edamontology.org/format_1929FASTA

http://edamontology.org/operation_0236Sequence composition calculationhttp://edamontology.org/data_3494DNA sequencehttp://edamontology.org/format_1929FASTAhttp://edamontology.org/data_2976Protein sequencehttp://edamontology.org/format_1929FASTATaproot Solutions LLChttps://webtoolyard.com/support/Catalyst Evidence GraphEvidence graph of findings on what compounds, supplements, nutrients and foods do in the human body. Each finding is extracted from a published study, anchored to the verbatim sentence it was drawn from, and given an evidence strength computed from the study design and population. Findings are linked to reference data on proteins, pathways and reactions, and relations inferred from reference databases are labelled as hypotheses. The graph is searchable through a web portal, a REST API and a Model Context Protocol server.https://catalystproject.ai/catalyst_evidence_graphbiotools:catalyst_evidence_graph0.2.0Database portalWeb APIhttp://edamontology.org/topic_3390Nutritional sciencehttp://edamontology.org/topic_0202Pharmacologyhttp://edamontology.org/topic_3292Biochemistryhttp://edamontology.org/topic_0154Small moleculeshttp://edamontology.org/topic_2840ToxicologySQLTypeScriptOtherEmergingFree of charge (with restrictions)Open accesshttp://edamontology.org/operation_2421Database searchhttp://edamontology.org/operation_0224Query and retrievalhttp://edamontology.org/data_0990Compound namehttp://edamontology.org/data_1174ChEBI ID

http://edamontology.org/data_2042Evidencehttp://edamontology.org/format_3464JSON

Find a compound, outcome or protein by name or synonym and return its curated study findings. Each finding has a computed evidence strength (strong, moderate, limited, very limited or insufficient), the verbatim sentence it was drawn from, the source paper, the population and dose, and a permalink.http://edamontology.org/operation_2422Data retrievalhttp://edamontology.org/data_1174ChEBI ID

http://edamontology.org/data_2600Pathway or networkhttp://edamontology.org/format_3464JSON

Relations that public reference databases record for a compound or protein (transporters, enzymes, pathways, expression), labelled as hypotheses, not study results.http://edamontology.org/operation_3928Pathway analysishttp://edamontology.org/data_1174ChEBI ID

http://edamontology.org/data_2978Reaction datahttp://edamontology.org/format_3464JSON

http://edamontology.org/data_2600Pathway or networkhttp://edamontology.org/format_3464JSON

Reactions a compound or protein takes part in, and the compounds reachable from a compound within three reaction steps, with the enzymes involved. Derived from Rhea and Human-GEM; reported as inferred connectivity, not measured effects.https://catalystproject.ai/mcpServicehttpsModel Context Protocol server (streamable HTTP, read-only, no authentication)://catalystproject.ai/mcphttps://catalystproject.ai/apiServiceQuery interface to the graphhttps://github.com/colorcatalyst/catalyst-mcpRepositorySource of the assistant and editor connectors (MCP client configuration, ChatGPT/Claude plugins, VS Code extension)https://github.com/colorcatalyst/catalyst-mcp/issuesIssue trackerhttps://open-vsx.org/extension/catalystproject/catalyst-evidence-graphSoftware catalogueVS Code / Cursor extensionhttps://open-vsx.org/api/catalystproject/catalyst-evidence-graph/0.2.0/file/catalystproject.catalyst-evidence-graph-0.2.0.vsixBinariesEditor extension that registers the MCP server0.2.0https://catalystproject.ai/connectInstallation instructionsQuick start guidehttps://catalystproject.ai/apiAPI documentationhttps://catalystproject.ai/how-it-worksGeneralhttps://catalystproject.ai/evidence-keyUser manualhttps://catalystproject.ai/termsTerms of usechebiusesrheausesuniprotkbusesreactomeusespubmeduseswikipathwaysusesCatalysthello@catalystproject.aihttps://catalystproject.aiProjectDeveloperMaintainerProviderPrimary contactBioManagerA free, open-source lab management app for a biology lab's animal colonies (mice, zebrafish, flies, worms and any other organism), plasmids, samples, orders, reagents, antibodies and viruses, with a calendar and a lab notebook. It runs as a desktop app for one person or as a server for a whole lab.https://biomanager.orgbiomanagerbiotools:biomanagerDesktop applicationWeb applicationhttp://edamontology.org/topic_0607Laboratory information managementhttp://edamontology.org/topic_3386Laboratory animal sciencehttp://edamontology.org/topic_0621Model organismshttp://edamontology.org/topic_3071Data managementWindowsLinuxMacPythonJavaScriptMITFree of chargeOpen accesshttp://edamontology.org/operation_2409Data handlinghttps://github.com/gaspolymerase/biomanagerRepositoryhttps://github.com/gaspolymerase/biomanager/issuesIssue trackerhttps://github.com/gaspolymerase/biomanager/releases/latestDownloads pagehttps://biomanager.org/guideUser manualVarnaOpsNo-code web platform for reproducible single-cell RNA-seq analysis that scales to millions of cells. Users upload an AnnData (.h5ad) count matrix, choose a tissue, and receive a complete HTML report plus the processed results: cell-type prediction by an scANVI classifier trained on 15 human and mouse reference atlases with per-cell confidence, quality control, marker analysis, differential expression across conditions, transcription factor, pathway and gene-set activity scoring, and novel-population detection.https://varnaops.comvarnaopsbiotools:varnaopsWeb applicationhttp://edamontology.org/topic_4028Single-cell sequencinghttp://edamontology.org/topic_0203Gene expressionhttp://edamontology.org/topic_3308Transcriptomicshttp://edamontology.org/topic_3474Machine learninghttp://edamontology.org/topic_3170RNA-SeqProprietaryEmergingCommercialOpen access (with restrictions)http://edamontology.org/operation_0226Annotationhttp://edamontology.org/operation_2990Classificationhttp://edamontology.org/data_0928Gene expression profilehttp://edamontology.org/format_3590HDF5

http://edamontology.org/data_0928Gene expression profilehttp://edamontology.org/format_3590HDF5

http://edamontology.org/data_2048Reporthttp://edamontology.org/format_2331HTML

Cell type, annotation confidence, out-of-distribution score, novel populations, QC metrics; transcription factor, pathway and gene-set activity per cell type.http://edamontology.org/operation_2990Classificationhttp://edamontology.org/data_0928Gene expression profilehttp://edamontology.org/format_3590HDF5

http://edamontology.org/data_0928Gene expression profilehttp://edamontology.org/format_3590HDF5

http://edamontology.org/data_2048Reporthttp://edamontology.org/format_2331HTML

Cell type prediction with a per-cell confidence score.http://edamontology.org/operation_0313Expression profile clusteringhttp://edamontology.org/data_0928Gene expression profilehttp://edamontology.org/format_3590HDF5

http://edamontology.org/data_3768Clustered expression profileshttp://edamontology.org/format_3590HDF5

Novel-population detection and validation of predicted cell types against unsupervised clusters.http://edamontology.org/operation_2424Comparisonhttp://edamontology.org/data_0928Gene expression profilehttp://edamontology.org/format_3590HDF5

http://edamontology.org/data_0928Gene expression profilehttp://edamontology.org/format_3752CSV

http://edamontology.org/data_2048Reporthttp://edamontology.org/format_2331HTML

Differential expression and differential TF, pathway and gene-set activity between user-defined conditions (donor and condition columns) and between cell types; dendrogram of cell types and clusters from distances in the reference model's latent space.http://edamontology.org/operation_2428Validationhttp://edamontology.org/data_0928Gene expression profilehttp://edamontology.org/format_3590HDF5

http://edamontology.org/data_0928Gene expression profilehttp://edamontology.org/format_3752CSV

Predicted cell types validated against curated marker panels and unsupervised clusters; cell-level quality control (counts, detected genes, mitochondrial fraction, doublets).http://edamontology.org/operation_0337Visualisationhttp://edamontology.org/data_0928Gene expression profilehttp://edamontology.org/format_3590HDF5

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3603PNG

http://edamontology.org/data_2048Reporthttp://edamontology.org/format_2331HTML

Interactive UMAPs, marker dotplots, volcano plots, activity heatmaps and the cell-type dendrogram, in a self-contained HTML report.http://edamontology.org/operation_2436Gene-set enrichment analysishttp://edamontology.org/operation_3928Pathway analysishttp://edamontology.org/data_0928Gene expression profilehttp://edamontology.org/format_3590HDF5

http://edamontology.org/data_0928Gene expression profilehttp://edamontology.org/format_3752CSV

http://edamontology.org/data_2048Reporthttp://edamontology.org/format_2331HTML

Transcription factor, pathway and gene-set activity scores per cell type.https://varnaops.com/privacy.htmlOtherPrivacy noticehttps://varnaops.comServiceSign in and run analyses.https://www.linkedin.com/company/varnaops/Social mediaLinkedIn company pagehttps://x.com/VarnaOpsSocial mediaX company pagehttps://bsky.app/profile/varnaops.comSocial mediaBluesky company pagehttps://varnaops.com/methods.htmlGeneralMethods: references, model, QC, analyseshttps://varnaops.com/terms.htmlTerms of useVarnaOpssupport@varnaops.comhttps://varnaops.comInstituteProviderLeonardo Golinellileonardo@varnaops.comPersonPrimary contactDeveloperPeptide Calculator ProFree browser-based calculators for peptide sequences and solutions. From a one-letter amino acid sequence it computes average molecular weight, monoisotopic mass, molecular formula and m/z for charge states, with N-terminal acetylation, C-terminal amidation and disulfide options. It also computes net charge at a chosen pH, isoelectric point (pI) and GRAVY using EMBOSS pKa values. A concentration calculator converts peptide mass and volume to mg/mL, mM or µM from the molecular weight. All calculations run client-side and no login is required.https://peptidecalculatorpro.org/peptide_calculator_probiotools:peptide_calculator_proWeb applicationhttp://edamontology.org/topic_0123Protein propertieshttp://edamontology.org/topic_3292Biochemistryhttp://edamontology.org/topic_3314ChemistryLinuxWindowsMacJavaScriptProprietaryFree of chargeOpen accesshttp://edamontology.org/operation_0398Protein molecular weight calculationhttp://edamontology.org/data_2976Protein sequencehttp://edamontology.org/format_1964plain text format (unformatted)

http://edamontology.org/data_1519Peptide molecular weights

http://edamontology.org/operation_0403Protein isoelectric point calculationhttp://edamontology.org/data_2976Protein sequencehttp://edamontology.org/format_1964plain text format (unformatted)

http://edamontology.org/data_1528Protein isoelectric point

http://edamontology.org/data_0845Molecular charge

http://edamontology.org/operation_3434Conversion

http://edamontology.org/data_2140Concentration

https://peptidecalculatorpro.org/peptide-molecular-weight-calculator/OtherPeptide molecular weight calculatorhttps://peptidecalculatorpro.org/peptide-net-charge-calculator/OtherPeptide net charge and isoelectric point calculatorhttps://peptidecalculatorpro.org/peptide-concentration-calculator/OtherPeptide concentration calculatorProtBedClient-side computational platform for quantitative mass spectrometry proteomics and untargeted metabolomics analysis with automated ML benchmarking.https://protbed.com/protbedbiotools:protbed1.0.0Web applicationhttp://edamontology.org/topic_3172Metabolomicshttp://edamontology.org/topic_0121Proteomicshttp://edamontology.org/topic_3474Machine learninghttp://edamontology.org/topic_3360BiomarkersLinuxWindowsMacJavaScriptTypeScriptFreewareMatureFree of charge (with restrictions)Open accesshttp://edamontology.org/operation_0226Annotationhttp://edamontology.org/operation_0337Visualisationhttp://edamontology.org/operation_3435Standardisation and normalisationhttp://edamontology.org/operation_3432Clusteringhttp://edamontology.org/operation_2409Data handlinghttp://edamontology.org/operation_2945Data analysishttp://edamontology.org/operation_2990Classificationhttp://edamontology.org/operation_3935Dimensionality reductionhttp://edamontology.org/data_2536Mass spectrometry datahttp://edamontology.org/format_3752CSVhttp://edamontology.org/format_3475TSV

http://edamontology.org/data_2526Text data

http://edamontology.org/data_2129File format name

http://edamontology.org/data_1636Heat map

http://edamontology.org/data_2884Plot

http://edamontology.org/operation_3432Clusteringhttp://edamontology.org/operation_0571Expression data visualisationhttps://protbed.com/contactHelpdeskUser contact and platform support formhttps://protbed.com/benchmarksOtherComparative benchmarks against MetaboAnalyst, Perseus, and other omics platformshttps://protbed.com/benchmarks/perseus-for-macDiscussion forumWorkflow guide for zero-install MaxQuant and DIA proteomics analysishttps://anasbedraoui.comSocial mediaLead developer portfolio and research profileDr. Anas Bedraouianas@protbed.comhttps://anasbedraoui.com/https://orcid.org/0000-0003-4069-5575PersonPrimary contactDeveloperMaintainerLead developer and corresponding author of the ProtBed platform.spaCRspaCR links pooled CRISPR-Cas9 perturbations to single-cell spatial phenotypes in high-content microscopy images. It segments cells, nuclei, pathogens and organelles with Cellpose, measures morphology, intensity, texture and colocalization features into a SQLite project, trains image and measurement classifiers, maps sequencing reads to wells and gRNAs, and estimates per-guide and per-gene effects by regression.https://einarolafsson.github.io/spacr/spacrbiotools:spacr1.5.1.3doi:10.5281/zenodo.21343316doiLibraryDesktop applicationCommand-line toolhttp://edamontology.org/topic_3383Bioimaginghttp://edamontology.org/topic_3385Light microscopyhttp://edamontology.org/topic_2229Cell biologyhttp://edamontology.org/topic_0625Genotype and phenotypehttp://edamontology.org/topic_0085Functional genomicshttp://edamontology.org/topic_3474Machine learningLinuxWindowsMacPythonBSD-3-ClauseEmergingFree of chargeOpen accesshttp://edamontology.org/operation_3937Feature extractionhttp://edamontology.org/operation_3443Image analysishttp://edamontology.org/operation_3799Quantificationhttp://edamontology.org/data_3424Raw imagehttp://edamontology.org/format_3591TIFFhttp://edamontology.org/format_3603PNGhttp://edamontology.org/format_3727OME-TIFF

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3591TIFFhttp://edamontology.org/format_3603PNG

http://edamontology.org/data_2082Matrixhttp://edamontology.org/format_3752CSVhttp://edamontology.org/format_3621SQLite format

Mask and Measure modules: Cellpose segmentation of cells, nuclei, pathogens and organelles, followed by morphology, intensity, texture, spatial and colocalization measurement of every object into a SQLite project, with object crops.spacr-run mask --settings settings.csv ; spacr-run measure --settings settings.csvhttp://edamontology.org/operation_2990Classificationhttp://edamontology.org/operation_3553Image annotationhttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3591TIFFhttp://edamontology.org/format_3603PNG

http://edamontology.org/data_1772Scorehttp://edamontology.org/format_3752CSVhttp://edamontology.org/format_3621SQLite format

Annotate and Classify modules: keyboard-driven labelling of object crops with active-learning queues, then training of image or measurement-based classifiers, with held-out performance recorded alongside each checkpoint.spacr-run classify --settings settings.csvhttp://edamontology.org/operation_3933Demultiplexinghttp://edamontology.org/operation_3198Read mappinghttp://edamontology.org/operation_3196Genotypinghttp://edamontology.org/data_2977Nucleic acid sequencehttp://edamontology.org/format_1930FASTQ

http://edamontology.org/data_2082Matrixhttp://edamontology.org/format_3752CSVhttp://edamontology.org/format_3621SQLite format

Map Barcodes module: assigns sequencing reads to plate wells and to gRNA barcodes, and reports per-well guide abundance with collision and coverage QC.spacr-run map_barcodes --settings settings.csvhttp://edamontology.org/operation_3658Statistical inferencehttp://edamontology.org/operation_3659Regression analysishttp://edamontology.org/data_2082Matrixhttp://edamontology.org/format_3752CSVhttp://edamontology.org/format_3621SQLite format

http://edamontology.org/data_0920Genotype/phenotype reporthttp://edamontology.org/format_3752CSV

Regression module: estimates guide, gene, condition and control effects with model families suited to continuous, fractional and count responses, and returns a ranked gene-level hit list.spacr-run regression --settings settings.csvhttp://edamontology.org/operation_3935Dimensionality reductionhttp://edamontology.org/operation_3432Clusteringhttp://edamontology.org/operation_0337Visualisationhttp://edamontology.org/data_2082Matrixhttp://edamontology.org/format_3752CSVhttp://edamontology.org/format_3621SQLite format

http://edamontology.org/data_2884Plothttp://edamontology.org/format_3603PNG

UMAP, gating, graph-building and QC dashboard tools for exploring single-object embeddings and per-plate quality.https://github.com/EinarOlafsson/spacrRepositorySource code repository.https://github.com/EinarOlafsson/spacr/issuesIssue trackerBug reports and feature requests.https://pypi.org/project/spacr/Software catalogueReleased package on PyPI.https://anaconda.org/conda-forge/spacrSoftware catalogueconda-forge package.https://pypi.org/project/spacr/Software packagepip install "spacr[qt]"1.5.0.9https://anaconda.org/conda-forge/spacrSoftware packageconda install conda-forge::spacrhttps://github.com/EinarOlafsson/spacrSource codeGit repository; the main branch carries the latest release.https://github.com/EinarOlafsson/spacr/releases/latestDownloads pageSelf-contained desktop installers for Linux, macOS and Windows.https://einarolafsson.github.io/spacr/GeneralProject documentation site.https://einarolafsson.github.io/spacr/api/spacr/index.htmlAPI documentationGenerated Python API reference.https://einarolafsson.github.io/spacr/tutorials/Training materialInteractive walkthroughs of the core workflow.https://github.com/EinarOlafsson/spacr/blob/main/docs/source/installer_guide.rstInstallation instructionsInstallers, extras, update, uninstall and troubleshooting.https://github.com/EinarOlafsson/spacr/blob/main/CITATION.cffCitation instructionsMachine-readable citation metadata.https://github.com/EinarOlafsson/spacr/blob/main/CONTRIBUTING.mdContributions policyhttps://github.com/EinarOlafsson/spacr/blob/main/CODE_OF_CONDUCT.mdCode of conduct10.64898/2026.07.08.737057PrimaryA pooled image-based CRISPR screen identifies EAF1 as a T. gondii modulator of ESCRT subversion. bioRxiv, 2026.10.1016/j.celrep.2025.11569440349346UsageIRE1a promotes phagosomal calcium flux to enhance macrophage fungicidal activity. Cell Reports, 2025.10.1128/msphere.01011-2440172222UsageMetabolic adaptability and nutrient scavenging in Toxoplasma gondii. mSphere, 2025.10.64898/2026.07.20.739547UsageToxoplasma GRA8 engages the host ESCRT accessory protein ALG-2. bioRxiv, 2026.10.5281/zenodo.21343316OtherArchived software releases, concept DOI for all versions.Einar Birnir Olafssonolafsson@med.umich.comhttps://orcid.org/0000-0002-3388-061XPersonPrimary contactDeveloperMaintainerLead developer and maintainer.Christophe-Sebastien Arnoldhttps://orcid.org/0000-0002-2392-5837PersonContributorJacob A. Kellermeierhttps://orcid.org/0000-0001-9211-7406PersonContributorPatrick A. Rimplehttps://orcid.org/0009-0000-6834-8343PersonContributorHargobinder Kaurhttps://orcid.org/0000-0002-8275-2091PersonContributorYifan Wanghttps://orcid.org/0000-0001-9451-0041PersonContributorJonathan Z. Sextonhttps://orcid.org/0000-0002-9244-5888PersonContributorStaffan Svardhttps://orcid.org/0000-0002-7392-1746PersonContributorName is 'Staffan Svard' here; the CITATION.cff spelling is 'Svard' with a-diaeresis.Vern B. Carruthershttps://orcid.org/0000-0001-6859-8895PersonContributorMatthew J. O'Mearahttps://orcid.org/0000-0002-3128-5331PersonContributorxcmsFramework for processing and visualization of chromatographically separated and single-spectra mass spectral data. Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. Preprocesses data for high-throughput, untargeted analyte profiling.https://bioconductor.org/packages/xcmsxcmsbiotools:xcms4.10.1LibraryWeb applicationCommand-line toolhttp://edamontology.org/topic_0092Data visualisationhttp://edamontology.org/topic_3172MetabolomicsLinuxWindowsMacRGPL-2.0-or-laterGalaxy ToolsBioConductorBiocondaRforMassSpectrometryMatureOpen accessMetabolomicsCzech RepublicFranceGermanyhttp://edamontology.org/operation_3215Peak detectionhttp://edamontology.org/operation_2428Validationhttp://edamontology.org/operation_3695Data filteringhttp://edamontology.org/operation_3694Mass spectrum visualisationhttp://edamontology.org/operation_3628Chromatographic alignmenthttp://edamontology.org/operation_3634Label-free quantificationhttp://edamontology.org/data_2536Mass spectrometry datahttp://edamontology.org/format_3654mzXMLhttp://edamontology.org/format_3244mzMLhttp://edamontology.org/format_3650NetCDFhttp://edamontology.org/data_0943Mass spectrumhttp://edamontology.org/format_3654mzXMLhttp://edamontology.org/format_3244mzMLhttp://edamontology.org/format_3650NetCDF

http://edamontology.org/data_2536Mass spectrometry datahttp://edamontology.org/format_3248mzQuantMLhttp://edamontology.org/format_3650NetCDF

http://edamontology.org/operation_3203Chromatogram visualisationhttp://edamontology.org/data_2536Mass spectrometry datahttp://edamontology.org/format_3244mzML

http://edamontology.org/data_2884Plothttp://edamontology.org/format_3547Image format

https://github.com/sneumann/xcmsRepositoryLink to the repository hosting the R code.https://usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/lecorguille/xcms_xcmsset/abims_xcms_xcmsSet/3.12.0+galaxy1Galaxy servicehttp://metlin.scripps.edu/download/Mirrorhttps://github.com/sneumann/xcmsSource codehttps://bioconductor.org/packages/xcmsUser manualcamerausedByramclustrusedByworkflow4metabolomicsusedBy10.1021/acs.analchem.5c04338Carl BruniusPersonContributorChristopher ConleyPersonDeveloperColin A. SmithPersonDeveloperJohannes RainerPersonDeveloperMichael WittingPersonContributorPablo VangeenderhuysenPersonContributorPaul BentonPersonDeveloperPhilippine LouailPersonDeveloperRalf TautenhahnPersonDeveloperSteffen NeumannPersonDeveloperMaintainerWilliam KumlerPersonDeveloperTopconfectsRank results by confident effect sizes, while maintaining False Discovery Rate and False Coverage-statement Rate control. Topconfects is an alternative presentation of TREAT results with improved usability, eliminating p-values and instead providing confidence bounds. The main application is differential gene expression analysis, providing genes ranked in order of confident log2 fold change, but it can be applied to any collection of effect sizes with associated standard errors.https://bioconductor.org/packages/topconfectsTopconfectsbiotools:Topconfects1.28.0Libraryhttp://edamontology.org/topic_3518Microarray experimenthttp://edamontology.org/topic_2640Oncologyhttp://edamontology.org/topic_3170RNA-SeqMacWindowsLinuxRLGPL-2.1BioConductorMatureFree of chargeOpen accesshttp://edamontology.org/operation_3680RNA-Seq analysishttp://edamontology.org/operation_3800RNA-Seq quantificationhttp://edamontology.org/operation_3223Differential gene expression profilinghttps://github.com/pfh/topconfectsSource codehttps://bioconductor.org/packages/topconfectsUser manual10.1186/s13059-019-1674-7Paul HarrisonPersonDeveloperMaintainersurfaltrAn R/Bioconductor package to benchmark surface protein isoforms by rapid prediction and visualization of transmembrane topologies.https://bioconductor.org/packages/surfaltrsurfaltrbiotools:surfaltr1.18.0Libraryhttp://edamontology.org/topic_2229Cell biologyhttp://edamontology.org/topic_0820Membrane and lipoproteinshttp://edamontology.org/topic_0166Protein structural motifs and surfaceshttp://edamontology.org/topic_0121Proteomicshttp://edamontology.org/topic_3320RNA splicingLinuxWindowsMacRMITBioConductorFree of chargeOpen accesshttp://edamontology.org/operation_0264Alternative splicing predictionhttp://edamontology.org/operation_2241Transmembrane protein visualisationhttp://edamontology.org/operation_0269Transmembrane protein predictionhttps://bioconductor.org/packages/release/bioc/src/contrib/surfaltr_1.18.0.tar.gzSource codehttps://bioconductor.org/packages/surfaltrUser manual10.18129/B9.bioc.surfaltrAditi MerchantPersonDeveloperPooja GangrasPersonDeveloperMaintainerSplicingFactoryThe SplicingFactory R package uses transcript-level expression values to analyze splicing diversity based on various statistical measures, like Shannon entropy or the Gini index. These measures can quantify transcript isoform diversity within samples or between conditions. Additionally, the package analyzes the isoform diversity data, looking for significant changes between conditions.https://bioconductor.org/packages/SplicingFactorysplicingfactorybiotools:splicingfactory1.20.0Libraryhttp://edamontology.org/topic_3512Gene transcriptshttp://edamontology.org/topic_3320RNA splicinghttp://edamontology.org/topic_3170RNA-Seqhttp://edamontology.org/topic_3308TranscriptomicsLinuxMacWindowsRGPL-3.0BioConductorFree of chargeOpen accesshttp://edamontology.org/operation_3196Genotypinghttp://edamontology.org/operation_0314Gene expression profilinghttp://edamontology.org/operation_0264Alternative splicing predictionhttp://edamontology.org/operation_3800RNA-Seq quantificationhttps://github.com/esebesty/SplicingFactoryRepositoryhttps://github.com/esebesty/SplicingFactorySource codehttps://bioconductor.org/packages/SplicingFactoryUser manual10.1093/bioinformatics/btab648Endre SebestyenPersonDeveloperMaintainerPeter A. SzikoraPersonDeveloperTamas PorPersonDeveloperspicyRThe spicyR package provides a framework for performing inference on changes in spatial relationships between pairs of cell types for cell-resolution spatial omics technologies. spicyR consists of three primary steps: (i) summarizing the degree of spatial localization between pairs of cell types for each image; (ii) modelling the variability in localization summary statistics as a function of cell counts and (iii) testing for changes in spatial localizations associated with a response variable.https://bioconductor.org/packages/spicyRspicyrbiotools:spicyr1.24.0Libraryhttp://edamontology.org/topic_2229Cell biologyhttp://edamontology.org/topic_3934Cytometryhttp://edamontology.org/topic_0625Genotype and phenotypehttp://edamontology.org/topic_3382Imaginghttp://edamontology.org/topic_0634Pathologyhttp://edamontology.org/topic_3308TranscriptomicsLinuxWindowsMacRGPL-2.0BioConductorFree of chargeOpen accesshttp://edamontology.org/operation_2426Modelling and simulationhttp://edamontology.org/operation_3446Cell migration analysishttp://bioconductor.org/packages/release/bioc/html/spicyR.htmlRepositoryhttps://github.com/nickcee/spicyRPaperRepositoryhttps://ellispatrick.github.io/spicyR/Source codehttps://github.com/SydneyBioX/spicyRSource codehttps://sydneybiox.github.io/spicyR/Source codehttps://bioconductor.org/packages/spicyRUser manual10.1093/bioinformatics/btac268Alex QinPersonContributorEllis PatrickPersonDeveloperMaintainerFarhan AmeenPersonContributorNicholas RobertsonPersonContributorNicolas CanetePersonDeveloperShreya RaoPersonContributorscTensorscTensor detects many-to-many cell–cell interactions from single cell RNA-sequencing data. The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor.https://bioconductor.org/packages/scTensorsctensorbiotools:sctensor2.22.0Libraryhttp://edamontology.org/topic_2229Cell biologyhttp://edamontology.org/topic_3173Epigenomicshttp://edamontology.org/topic_3172Metabolomicshttp://edamontology.org/topic_3170RNA-SeqRArtistic-2.0BioConductorFree of chargeOpen accesshttp://edamontology.org/operation_3435Standardisation and normalisationhttp://edamontology.org/operation_3625Relation extractionhttp://edamontology.org/operation_0335Data formattinghttps://bioconductor.org/packages/release/bioc/src/contrib/scTensor_2.22.0.tar.gzSource codehttps://bioconductor.org/packages/scTensorUser manual10.18129/B9.bioc.scTensorKoki TsuyuzakiPersonDeveloperMaintainerKozo NishidaPersonDeveloperscFeaturesscFeatures constructs multi-view representations of single-cell and spatial data. scFeatures is a tool that generates multi-view representations of single-cell and spatial data through the construction of a total of 17 feature types. These features can then be used for a variety of analyses using other software in Biocondutor.https://bioconductor.org/packages/scFeaturesscfeaturesbiotools:scfeatures1.12.0Libraryhttp://edamontology.org/topic_0091Bioinformaticshttp://edamontology.org/topic_2229Cell biologyhttp://edamontology.org/topic_0634PathologyLinuxMacWindowsRGPL-3.0BioConductorFree of chargeOpen accesshttps://github.com/SydneyBioX/scFeatures/Source codehttps://sydneybiox.github.io/scFeatures/Source codehttps://bioconductor.org/packages/scFeaturesUser manual10.1093/bioinformatics/btac590Ellis PatrickPersonDeveloperJean Yee Hwa YangPersonDeveloperPengyi YangPersonDeveloperYingxin LinPersonDeveloperYue CaoPersonDeveloperMaintainerReUseDataR/Bioconductor tool for reusable and reproducible genomic data management.https://bioconductor.org/packages/ReUseDatareusedatabiotools:reusedata1.12.0Libraryhttp://edamontology.org/topic_0091Bioinformaticshttp://edamontology.org/topic_0219Data curation and archivalhttp://edamontology.org/topic_3489Database managementhttp://edamontology.org/topic_3372Software engineeringMacWindowsLinuxRCWLGPL-3.0BioConductorFree of chargeOpen accesshttp://edamontology.org/operation_3431Data depositionhttp://edamontology.org/operation_3436Aggregationhttp://edamontology.org/operation_0335Data formattinghttps://rcwl.org/dataRecipes/Otherhttps://github.com/rworkflow/ReUseDataSource codehttps://bioconductor.org/packages/ReUseDataUser manual10.18129/B9.bioc.ReUseDataQian LiuPersonDeveloperMaintainerRareVariantVisA suite for analysis of rare genomic variants in whole genome sequencing data. It annotates, filters and presents genomic variants in a global, per chromosome way.https://bioconductor.org/packages/RareVariantVisrarevariantvisbiotools:rarevariantvis2.40.0LibraryCommand-line toolhttp://edamontology.org/topic_0199Genetic variationhttp://edamontology.org/topic_0622Genomicshttp://edamontology.org/topic_3325Rare diseaseshttp://edamontology.org/topic_3168SequencingLinuxWindowsMacRArtistic-2.0BioConductorMatureFree of chargeOpen accesshttp://edamontology.org/operation_3197Genetic variation analysishttps://bioconductor.org/packages/release/bioc/src/contrib/RareVariantVis_2.40.0.tar.gzSource codehttps://bioconductor.org/packages/RareVariantVisUser manual10.1093/bioinformatics/btw35927288501PrimaryAdam Gudys and Tomasz StokowypeakPantheRAn automated pipeline for the detection, integration and reporting of predefined features across a large number of mass spectrometry data files. It enables the real time annotation of multiple compounds in a single file, or the parallel annotation of multiple compounds in multiple files. A graphical user interface as well as command line functions will assist in assessing the quality of annotation and update fitting parameters until a satisfactory result is obtained.https://bioconductor.org/packages/peakPantheRpeakpantherbiotools:peakpanther1.26.0Libraryhttp://edamontology.org/topic_3407Endocrinology and metabolismhttp://edamontology.org/topic_3172Metabolomicshttp://edamontology.org/topic_3520Proteomics experimenthttp://edamontology.org/topic_0154Small moleculeshttp://edamontology.org/topic_0769WorkflowsLinuxMacWindowsRGPL-3.0BioConductorFree of chargeOpen accesshttp://edamontology.org/operation_3937Feature extractionhttp://edamontology.org/operation_3803Natural product identificationhttp://edamontology.org/operation_3215Peak detectionhttps://bioconductor.org/packages/peakPantheR/Repositoryhttps://github.com/phenomecentre/metabotyping-dementia-urineRepositoryhttps://github.com/phenomecentre/peakPantheRSource codehttps://bioconductor.org/packages/peakPantheRUser manual10.1093/bioinformatics/btab433Arnaud WolferPersonDeveloperMaintainerCaroline SandsPersonContributorGoncalo CorreiaPersonDeveloperJake PearcePersonContributorOmadaWe have developed a suite of tools to automate RNAseq clustering processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning based functions. The efficiency of each tool was tested with four datasets characterised by different expression signal strengths. Our toolkit’s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Even in datasets with less clear biological distinctions, stable subgroups with different expression profiles and clinical associations were found.https://bioconductor.org/packages/omadaomadabiotools:omada1.14.0Scripthttp://edamontology.org/topic_3307Computational biologyRGPL-3.0BioConductorFree of chargeOpen accesshttp://edamontology.org/operation_3432Clusteringhttp://edamontology.org/data_2603Expression datahttps://bioconductor.org/packages/release/bioc/html/omada.htmlRepositoryBioconductor repohttps://bioconductor.org/packages/release/bioc/src/contrib/omada_1.14.0.tar.gzSource codehttps://bioconductor.org/packages/omadaUser manual10.18129/B9.bioc.omadaSokratis KariotisPersonDeveloperMaintainerOGREOGRE calculates overlap between user defined genomic region datasets. Any regions can be supplied i.e. genes, SNPs, or reads from sequencing experiments. Key numbers help analyse the extend of overlaps which can also be visualized at a genomic level.https://bioconductor.org/packages/OGREogrebiotools:ogre1.16.0Libraryhttp://edamontology.org/topic_3169ChIP-seqhttp://edamontology.org/topic_2885DNA polymorphismhttp://edamontology.org/topic_3295Epigeneticshttp://edamontology.org/topic_0622Genomicshttp://edamontology.org/topic_0749Transcription factors and regulatory sitesRArtistic-2.0BioConductorFree of chargeOpen accesshttp://edamontology.org/operation_1812Data parsinghttp://edamontology.org/operation_0362Genome annotationhttp://edamontology.org/operation_0337Visualisationhttps://github.com/svenbioinf/OGRERepositoryhttps://github.com/svenbioinf/OGRE/Source codehttps://bioconductor.org/packages/OGREUser manual10.18129/B9.bioc.OGREJörg GromollPersonContributorMarius WöstePersonContributorSandra LaurentinoPersonContributorSarah SandmannPersonContributorSven BerresPersonDeveloperMaintainernnSVGnnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes.https://bioconductor.org/packages/nnSVGnnsvgbiotools:nnsvg1.16.1Libraryhttp://edamontology.org/topic_3168Sequencinghttp://edamontology.org/topic_2269Statistics and probabilityhttp://edamontology.org/topic_3308TranscriptomicsMacWindowsLinuxRMITBioConductorFree of chargeOpen accesshttp://edamontology.org/operation_3936Feature selectionhttp://edamontology.org/operation_3695Data filteringhttp://edamontology.org/operation_3435Standardisation and normalisationhttps://github.com/lmweber/nnSVGRepositoryhttps://github.com/lmweber/nnSVG-analysesRepositoryhttps://github.com/lmweber/nnSVGSource codehttps://bioconductor.org/packages/nnSVGUser manual10.1038/s41467-023-39748-zLukas M. WeberPersonDeveloperMaintainerStephanie C. HicksPersonDevelopermetaMSMS-based metabolomics data processing and compound annotation pipeline.https://bioconductor.org/packages/metaMSmetamsbiotools:metams1.48.0LibraryCommand-line toolhttp://edamontology.org/topic_3172MetabolomicsLinuxMacWindowsRGPL-2.0-or-laterBioConductorOpen accessMetabolomicshttp://edamontology.org/operation_3214Spectral analysishttp://edamontology.org/data_0943Mass spectrumhttp://edamontology.org/format_2333Binary format

http://edamontology.org/data_2603Expression datahttp://edamontology.org/format_3752CSV

http://edamontology.org/data_0943Mass spectrumhttp://edamontology.org/format_4039MSP

https://github.com/yguitton/metaMSSource codehttps://bioconductor.org/packages/metaMSUser manualElisabete CarvalhoPersonContributorGeorg WeingartPersonContributorJulien Saint-VannePersonContributorMatthias ScholzPersonContributorNir ShahafPersonContributorPietro FranceschiPersonDeveloperRon WehrensPersonDeveloperYann GuittonPersonContributorMaintainerMaAsLin2MaAsLin2 is comprehensive R package for efficiently determining multivariable association between phenotypes, environments, exposures, covariates and microbial meta’omic features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, and offers a variety of data exploration, normalization, and transformation methods.https://bioconductor.org/packages/Maaslin2maaslin2biotools:maaslin21.26.0http://edamontology.org/topic_3174Metagenomicshttp://edamontology.org/topic_2269Statistics and probabilityRMITBioConductorOpen accesshttp://edamontology.org/operation_3435Standardisation and normalisationhttp://edamontology.org/operation_3695Data filteringhttp://edamontology.org/operation_2238Statistical calculationhttp://edamontology.org/operation_0337Visualisationhttp://huttenhower.sph.harvard.edu/maaslin2Source codehttps://bioconductor.org/packages/Maaslin2User manual10.1371/journal.pcbi.1009442Ali RahnavardPersonDeveloperHimel MallickPersonDeveloperSagun MaharjanPersonDeveloperMaintainerThomas KuntzPersonDeveloperIsoformSwitchAnalyzeRAnalysis of alternative splicing and isoform switches with predicted functional consequences (e.g. gain/loss of protein domains etc.) from quantification of all types of RNA-seq (short/long) by tools such as Kallisto, Salmon, StringTie, Tallon, IsoQuant etc.https://bioconductor.org/packages/IsoformSwitchAnalyzeRisoformswitchanalyzerbiotools:isoformswitchanalyzer2.12.0Libraryhttp://edamontology.org/topic_3307Computational biologyhttp://edamontology.org/topic_3512Gene transcriptsLinuxMacWindowsRGPL-2.0-or-laterBioConductorMatureFree of chargeOpen accesshttp://edamontology.org/operation_2451Sequence comparisonhttp://edamontology.org/operation_2403Sequence analysisThe basic input is a replicate transcript/isoform expression matrix as well as the genomic coordinates of the transcript/isoforms. The output is a list of (prioritized) isoform switches with predicted consequences.http://bioconductor.org/packages/IsoformSwitchAnalyzeR/Repositoryhttp://bioconductor.org/packages/IsoformSwitchAnalyzeR/Source codehttps://bioconductor.org/packages/IsoformSwitchAnalyzeRUser manual10.1093/bioinformatics/btz24710.1158/1541-7786.MCR-16-0459Chunxu HanPersonContributorElena Iriondo DelgadoPersonContributorJeroen GilisPersonContributorKristoffer Vitting-SeerupPersonDeveloperMaintaineridpr‘idpr’ aims to integrate tools for the computational analysis of intrinsically disordered proteins (IDPs) within R. This package is used to identify known characteristics of IDPs for a sequence of interest with easily reported and dynamic results. Additionally, this package includes tools for IDP-based sequence analysis to be used in conjunction with other R packages. Described in McFadden WM & Yanowitz JL (2022). "idpr: A package for profiling and analyzing Intrinsically Disordered Proteins in R." PloS one, 17(4), e0266929. <https://doi.org/10.1371/journal.pone.0266929>.https://bioconductor.org/packages/idpridprbiotools:idpr1.22.0Libraryhttp://edamontology.org/topic_3538Protein disordered structurehttp://edamontology.org/topic_0736Protein folds and structural domainshttp://edamontology.org/topic_0123Protein propertieshttp://edamontology.org/topic_0121Proteomicshttp://edamontology.org/topic_0154Small moleculesMacWindowsLinuxRLGPL-3.0-or-laterBioConductorFree of chargeOpen accesshttp://edamontology.org/operation_3904Protein disorder predictionhttp://edamontology.org/operation_0474Protein structure predictionhttp://edamontology.org/operation_2574Protein hydropathy calculationhttp://edamontology.org/operation_0470Protein secondary structure prediction (coils)https://github.com/wmm27/idprRepositoryhttps://bioconductor.org/packages/release/bioc/src/contrib/idpr_1.22.0.tar.gzSource codehttps://bioconductor.org/packages/idprUser manual10.1371/journal.pone.0266929Judith L. YanowitzPersonContributorDeveloperMichael BuszczakPersonContributorWilliam M. McFaddenPersonDeveloperMaintainerGenomicDistributionsAn R package that provides functions for calculating and visualizing a variety of statistics for a collection of genomic ranges.https://bioconductor.org/packages/GenomicDistributionsgenomicdistributionsbiotools:genomicdistributions1.20.0Libraryhttp://edamontology.org/topic_3169ChIP-seqhttp://edamontology.org/topic_3173Epigenomicshttp://edamontology.org/topic_0204Gene regulationLinuxMacWindowsRBSD-2-ClauseBioConductorFree of chargeOpen accesshttp://edamontology.org/operation_3435Standardisation and normalisationhttp://edamontology.org/operation_3096Data editinghttp://edamontology.org/operation_0337Visualisationhttp://code.databio.org/GenomicDistributions/Repositoryhttps://bioconductor.org/packages/release/bioc/html/GenomicDistributions.htmlRepositoryhttp://code.databio.org/GenomicDistributionsSource codehttps://bioconductor.org/packages/GenomicDistributionsUser manual10.1186/S12864-022-08467-YBingjie XuePersonDeveloperJason SmithPersonDeveloperJohn LawsonPersonDeveloperJohn StubbsPersonDeveloperJose VerdezotoPersonDeveloperJose VerdezotoPersonDeveloperKristyna KupkovaPersonDeveloperMaintainerMichal StolarczykPersonDeveloperNathan C. SheffieldPersonDeveloperSophia RogersPersonDeveloperTessa DanehyPersonDeveloperGBScleanRGBScleanR is a package for quality check, filtering, and error correction of genotype data derived from next generation sequcener (NGS) based genotyping platforms. GBScleanR takes Variant Call Format (VCF) file as input. The main function of this package is `estGeno()` which estimates the true genotypes of samples from given read counts for genotype markers using a hidden Markov model with incorporating uneven observation ratio of allelic reads. This implementation gives robust genotype estimation even in noisy genotype data usually observed in Genotyping-By-Sequnencing (GBS) and similar methods, e.g. RADseq. The current implementation accepts genotype data of a diploid population at any generation of multi-parental cross, e.g. biparental F2 from inbred parents, biparental F2 from outbred parents, and 8-way recombinant inbred lines (8-way RILs) which can be refered to as MAGIC population.https://bioconductor.org/packages/GBScleanRgbscleanrbiotools:gbscleanr2.6.1Libraryhttp://edamontology.org/topic_2885DNA polymorphismhttp://edamontology.org/topic_3473Data mininghttp://edamontology.org/topic_0625Genotype and phenotypehttp://edamontology.org/topic_0160Sequence sites, features and motifshttp://edamontology.org/topic_3168SequencingRGPL-3.0BioConductorFree of chargeOpen accesshttp://edamontology.org/operation_0487Haplotype mappinghttp://edamontology.org/operation_0484SNP detectionhttp://edamontology.org/operation_3196Genotypinghttps://github.com/tomoyukif/GBScleanRSource codehttps://bioconductor.org/packages/GBScleanRUser manual10.1093/genetics/iyad055Tomoyuki FurutaPersonDeveloperMaintainercsdRThis package contains functionality to run differential gene co-expression across two different conditions. The algorithm is inspired by Voigt et al. 2017 and finds Conserved, Specific and Differentiated genes (hence the name CSD). This package include efficient and variance calculation by bootstrapping and Welford's algorithm.https://bioconductor.org/packages/csdRcsdrbiotools:csdr1.18.0Libraryhttp://edamontology.org/topic_0085Functional genomicshttp://edamontology.org/topic_0204Gene regulationLinuxWindowsMacGPL-3.0BioConductorMatureFree of chargeOpen accesshttps://github.com/AlmaasLab/csdRRepositoryhttps://www.bioconductor.org/packages/release/bioc/html/csdR.htmlSoftware cataloguehttps://almaaslab.github.io/csdRSource codehttps://github.com/AlmaasLab/csdRSource codehttps://bioconductor.org/packages/csdRUser manual10.1186/s12859-022-04605-1Jakob Peder PettersenPersonDeveloperMaintainerCalibraCurveA highly useful and flexible tool for calibration of targeted MS‐based measurements. CalibraCurve enables an automated batch-mode determination of dynamic linear ranges and quantification limits for both targeted proteomics and similar assays. The software uses a variety of measures to assess the accuracy of the calibration and provides intuitive visualizations.https://bioconductor.org/packages/CalibraCurvecalibracurvebiotools:calibracurve1.2.0WorkflowDesktop applicationScriptPlug-inhttp://edamontology.org/topic_3678Experimental design and studieshttp://edamontology.org/topic_0121ProteomicsLinuxWindowsMacRBSD-3-ClauseBioConductorBioInfra.ProtCUBiMed.RUBde.NBIMatureFree of chargeOpen accessToolsProteomicsGermanyhttp://edamontology.org/operation_2945Data analysishttp://edamontology.org/data_2526Text datahttp://edamontology.org/format_3752CSV

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http://edamontology.org/operation_0337Visualisationhttps://github.com/mpc-bioinformatics/CalibraCurveRepositoryhttps://github.com/mpc-bioinformatics/CalibraCurveSource codehttps://bioconductor.org/packages/CalibraCurveUser manual10.1002/pmic.201900143Karin SchorkPersonDeveloperMaintainerMarkus StepathPersonDeveloperMartin EisenacherPersonContributorDeveloperMichael KohlPersonDeveloperRobin GrugelPersonDevelopercageminerThis package aims to integrate GWAS-derived SNPs and coexpression networks to mine candidate genes associated with a particular phenotype. For that, users must define a set of guide genes, which are known genes involved in the studied phenotype. Additionally, the mined candidates can be given a score that favor candidates that are hubs and/or transcription factors. The scores can then be used to rank and select the top n most promising genes for downstream experiments.https://bioconductor.org/packages/cageminercageminerbiotools:cageminer1.18.0Libraryhttp://edamontology.org/topic_2885DNA polymorphismhttp://edamontology.org/topic_3517GWAS studyhttp://edamontology.org/topic_0203Gene expressionhttp://edamontology.org/topic_0625Genotype and phenotypehttp://edamontology.org/topic_0602Molecular interactions, pathways and networksLinuxWindowsMacRGPL-3.0BioConductorFree of chargeOpen accesshttp://edamontology.org/operation_3463Expression correlation analysishttp://edamontology.org/operation_2436Gene-set enrichment analysishttp://edamontology.org/operation_0314Gene expression profilinghttps://github.com/almeidasilvaf/cageminerRepositoryhttps://github.com/almeidasilvaf/cageminerSource codehttps://bioconductor.org/packages/cageminerUser manual10.1093/insilicoplants/diac018Fabrício Almeida-SilvaPersonDeveloperMaintainerThiago VenancioPersonDeveloperProstate zone segmentation toolThis tool automatically segments the the prostate into two zones: the central and transition zones (TZ+CZ) and the peripheral zone (PZ). It takes as input a T2-weighted image and produces a segmentation in the same input format. Three datasets were used to train this model: ProstateX (n=152), Prostate158 (n=119) and ProstateNet (the ProCAncer-I dataset; n=532). We used the T2-weighted images available in each dataset and trained a standard three-class nnU-Net model with three classes: background, peripheral zone and the combination of the transition and central zones as annotated by radiologists. Using 149 cases from the three previously noted datasets, this model achieved DSC=0.81 (95% CI=[0.59, 0.92]) for PZ and 0.87 (95% CI=[0.60, 0.96]) for CZ+TZ. In a clinical validation using ProCAncer-I data, this model achieved, for PZ and TZ+CZ, DSC=0.65 and DSC=0.77, respectively, comparable to the observed between-radiologist DSC for a subset of cases (DSC=0.61 and DSC=0.73, respectively).https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/champ-prostate-zone-segmentationprostate_zone_segmentation_toolbiotools:prostate_zone_segmentation_toolCommand-line toolhttp://edamontology.org/topic_3063Medical informaticsLinuxPythonCC-BY-NC-4.0EUCAIMEmergingFree of charge (with restrictions)Open access (with restrictions)http://edamontology.org/operation_3553Image annotationhttp://edamontology.org/data_3442MRI imagehttp://edamontology.org/format_3549niihttp://edamontology.org/format_3548DICOM format

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Image annotation for individual DICOM and Nifti images: Please note that it is easier to mount the input and output paths path and keep `--study_path` always as `/data/input` and `--output_dir` always as `/data/output`. This way only `--series_folders` requires any change. `--series_folders` can refer both to a Nifti file (if the input is Nifti) or the a DICOM series folder. If the input is DICOM, `--is_dicom` should be used.docker run --shm-size=1gb -v $(pwd)/example:/data/input:ro -v $(pwd)/test_output:/data/output:rw --gpus="all" <image-name> bash nnunet-server-entrypoint.sh nnunet-predict --study_path /data/study --series_folders <path-to-series> --output_dir /outhttp://edamontology.org/operation_3553Image annotationhttp://edamontology.org/data_3442MRI imagehttp://edamontology.org/format_3549niihttp://edamontology.org/format_3548DICOM format

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Batch image annotation for DICOM and Nifti images using dataset JSON and data directory supports two methods: using a dataset JSON and using a data directory. The dataset JSON-based method relies on having a dataset JSON which has a list of dictionaries that follow this structure: `{"study_path": str, "series_folders": [[str]], "output_dir": str}`. These are analogous to the previous modes. Be mindful that paths should be specified accordingly for the Docker file-system. The dataset JSON should be specified using `--data_json`. The data directory-based method relies on having a directory structured as patient/study/series, where each series follows an nnU-Net-like convention (i.e. `<series_id>_0000`, `<series_id>_0001`, etc.). In this case, it should be `<series-id>_0000`. The data directory should be specified using `--data_dir` and requires the specification of an `--output_dir`. If the input is DICOM, `--is_dicom` should be specified.docker run --shm-size=1gb -v $(pwd)/example:/data/input:ro -v $(pwd)/test_output:/data/output:rw --gpus="all" <image-name> bash nnunet-server-entrypoint.sh nnunet-predict-batch --data_dir /datahttps://github.com/josegcpa/nnunet_serveRepositoryThis is the nnunet_serve repository, containing the generic nnU-Net building and serving functionalities used by the Computational Clinical Imaging Group at Champalimaud Foundation. This should also be used as documentation.https://kdrive.infomaniak.com/app/share/1928123/f7bf2733-0454-47b0-a29c-0beacb51fe68OtherContains the EUCAIM model card for this model.https://gist.github.com/josegcpa/47ba1bf2bb425db111393e2c135d97e6API documentationContains the documentation required to run the Docker image.https://youtu.be/WdjWF4w_ZD8?si=ptrzfCNEUT0_uI5xTraining materialShort video on how to use.nnunetuses10.1016/j.compbiomed.2024.10821638442555PrimaryJosé Guilherme de Almeidajose.almeida@research.fchampalimaud.orghttps://josegcpa.nethttps://orcid.org/0000-0002-1887-0157PersonPrimary contactDeveloperDocumentorMaintainerNickolas Papanikolaouhttps://orcid.org/0000-0003-3298-2072PersonSupportNuno RodriguesPersonDeveloperImage duplication checkA CLI tool for detecting duplicate DICOM images by analyzing pixel data, generating content-based hashes, and comparing them to identify identical images, even when metadata, file format, or size differs. Supports large datasets, LMDB-based indexing, and multiple operation modes for flexible workflows.https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/eucaim-image-duplication-check-tool/artifacts-tabimage_duplicate_check_toolbiotools:image_duplicate_check_toolv1.0.0Command-line toolhttp://edamontology.org/topic_3384Medical imagingMacLinuxJavaScriptMITEUCAIMEmergingFree of chargeOpen accesshttps://github.com/helghast79/eucaim-image-duplication-check-toolRepositoryhttps://github.com/helghast79/eucaim-image-duplication-check-tool/releases/tag/v1.0.0Downloads pagescroll to bottom of github release page to find the executable download for each OSv1.0.0https://github.com/helghast79/eucaim-image-duplication-check-tool/blob/main/README.mdGeneralCommand-line optionsInstallation instructionsUser manualRelease noteshttps://docs.google.com/presentation/d/1guNJkm0E5YSsbIVKaaEevFnNGG4WR_m6XGsL695e3_A/edit?usp=sharingQuick start guideMiguel Chambelmiguel.chambel@research.fchampalimaud.orghttps://orcid.org/0000-0002-6099-4110PersonDeveloperContributorDocumentorJosé Guilherme de Almeidajose.almeida@research.fchampalimaud.orghttps://josegcpa.net/https://orcid.org/0000-0002-1887-0157PersonMaintainerSupportNickolas Papanikolaouhttps://orcid.org/0000-0003-3298-2072PersonContributorSPICE GUIGene editing, protein design, and project management — all in one place, all on your machine.https://github.com/SPICE-Protein/SPICE_GUIspice_guibiotools:spice_gui0.0.1Desktop applicationhttp://edamontology.org/topic_4019Bioscienceshttp://edamontology.org/topic_3332Computational chemistryAndroidMacWindowsLinuxTypeScriptRustApache-2.0Free of chargeOpen accesshttps://github.com/SPICE-Protein/SPICE_GUI/actionsDownloads pageREMAGRecovery of Eukaryotic Metagenome-Assembled Genomes using contrastive learning. A specialized metagenomic binning tool designed for recovering high-quality eukaryotic genomes from mixed prokaryotic-eukaryotic samples.https://github.com/danielzmbp/remagremagbiotools:remagCommand-line toolhttp://edamontology.org/topic_3174Metagenomicshttp://edamontology.org/topic_0196Sequence assemblyhttp://edamontology.org/topic_3837Metagenomic sequencingPythonMITFree of chargeOpen accesshttp://edamontology.org/operation_0361Sequence annotationhttp://edamontology.org/data_1234Sequence set (nucleic acid)http://edamontology.org/format_1929FASTAhttp://edamontology.org/data_1383Nucleic acid sequence alignmenthttp://edamontology.org/format_2572BAM

http://edamontology.org/data_1234Sequence set (nucleic acid)http://edamontology.org/format_1929FASTA

http://edamontology.org/data_1234Sequence set (nucleic acid)http://edamontology.org/format_3475TSV

10.64898/2026.03.05.709928Daniel Gómez-PérezPersonSébastien RaguideauPersonRobert JamesPersonSally WarringPersonFalk HildebrandPersonChristopher QuincePersonOlivers mTOR AtlasHand-curated knowledge base of mTOR biology, linking 428 primary studies to genes, drugs, diseases, pathways and outcomes. Every study carries a code for the kind of study behind it - synthesis of human data, human, animal, molecular or review - which names the system a finding was established in rather than ranking it, alongside open research questions. The data are also available through a read-only JSON API (OpenAPI 3.1) and an MCP server for AI assistants.https://mtor-atlas.orgolivers_mtor_atlasbiotools:olivers_mtor_atlasWeb APIDatabase portalhttp://edamontology.org/topic_3053Geneticshttp://edamontology.org/topic_3047Molecular biologyhttp://edamontology.org/topic_2259Systems biologyhttp://edamontology.org/topic_3376Medicines research and developmenthttp://edamontology.org/topic_2640Oncologyhttp://edamontology.org/topic_0202Pharmacologyhttp://edamontology.org/topic_2229Cell biologyhttp://edamontology.org/topic_0602Molecular interactions, pathways and networksCC-BY-4.0EmergingFree of chargeOpen accesshttps://github.com/open-mtor-atlas/atlasRepositoryhttps://fairsharing.org/8905OtherFAIRsharing registry recordhttps://www.wikidata.org/wiki/Q141256074OtherWikidata itemhttps://github.com/open-mtor-atlas/atlas/tree/main/mcpRepositoryMCP server for AI assistants (npm: mtor-atlas-mcp; MCP Registry: io.github.open-mtor-atlas/mtor-atlas)https://github.com/open-mtor-atlas/atlasSource codehttps://doi.org/10.5281/zenodo.22059963OtherArchived snapshot of the full dataset on Zenodohttps://mtor-atlas.org/data/Downloads pageCSV and JSON exports of studies and pathway entities (CC BY 4.0)https://mtor-atlas.org/api/openapi.jsonAPI specificationOpenAPI 3.1 description of the JSON APIhttps://mtor-atlas.org/about/Generalhttps://mtor-atlas.org/data/GeneralData exports and field descriptionshttps://mtor-atlas.org/api/API documentationRead-only JSON API, no key required (CC BY 4.0)10.5281/zenodo.22059963Primary10.6084/m9.figshare.33772297OtherCompanion article (Figshare)Oliver Bartonoliver.barton1113@gmail.comhttps://mtor-atlas.org/about/https://orcid.org/0009-0008-2025-2148PersonPrimary contactDeveloperMaintainerRadiomics Feature-based HarmonizationThe tool is designed to perform harmonization at the feature-level. Feature-based harmonization method aims to reduce the variability in the radiomics features due to different scanners, acquisition protocols and conditions by using empirical Bayesian methods to estimate differences in radiomics values and then expressing them in a common space (location/scale adjustment). The tool offers two methods: (1) ComBat method, which shifts the radiomics features to the overall mean and pooled variance of all centers, and (2) M-ComBat method, which shifts the radiomics features to the mean and variance of the chosen reference center with the most samples.https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/feat_harmonization/info-tabfeature-based_harmonizationbiotools:feature-based_harmonization1.41.51.5-femCommand-line toolhttp://edamontology.org/topic_3071Data managementhttp://edamontology.org/topic_0219Data curation and archivalhttp://edamontology.org/topic_3384Medical imagingMacWindowsLinuxPythonEUPL-1.2eucaimEmergingFree of chargeOpen accesshttp://edamontology.org/operation_3435Standardisation and normalisationhttp://edamontology.org/data_2526Text datahttp://edamontology.org/format_3752CSVhttp://edamontology.org/data_2526Text datahttp://edamontology.org/format_3752CSV

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-h, --help show help / usage information -M, --manufacturerModelName If specified, the manufacturer Model variable instead of the (default)manufacturer variable will be used as center-effect. -c, --combat If specified, the ComBat method will be used. If not, the M-ComBat method will be used.docker run --rm \ -v "your_input_path:/data/input" \ -v "your_output_path/:/data/output" harbor.eucaim.cancerimage.eu/processing-tools/feat_harmonization:latest /data/input /data/output [-h] [-M] [-c]https://cbml-gitlab.ics.forth.gr/adovrou/featuresharmonization/-/tree/eucaim?ref_type=headsRepositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/feat_harmonization/artifacts-tabSoftware catalogueLink to EUCAIM's harbor registryhttps://drive.eucaim.cancerimage.eu/s/7EgLZerXZF3EesEUser manualhttps://www.youtube.com/watch?v=ghi3fW3p62wTraining materialComputational BioMedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH)https://www.ics.forth.gr/cbml/InstituteAikaterini Dovroudovrou@ics.forth.grhttps://orcid.org/0000-0001-5242-6060PersonPrimary contactDeveloperDocumentorSupportStelios Sfakianakisssfak@ics.forth.grhttps://orcid.org/0000-0001-8424-4157PersonSupportValia Kalokyrivkalokyri@ics.forth.grhttps://orcid.org/0000-0002-5245-8238PersonSupportFruitDBaseFruitDBase was conceived to address a gap in the bioinformatics landscape: the lack of a species-centric, quality-controlled transcriptomic resource for Prunus dulcis (almond). While general-purpose repositories such as NCBI SRA, ENA, and GDR provide valuable raw and genomic data, they lack the curation and integration needed for efficient comparative studies. The project currently provides a curated expression atlas for P. dulcis, based on uniformly processed RNA-seq data and supported by interactive exploration tools. For other Prunus species, FruitDBase offers access to available public SRA datasets, laying the groundwork for future atlas development across the genus.https://fruitdbase.csic.esfruitdbasebiotools:fruitdbasev.1.1.0Database portalBioinformatics portalhttp://edamontology.org/topic_0091Bioinformaticshttp://edamontology.org/topic_0780Plant biologyhttp://edamontology.org/topic_3308TranscriptomicsWindowsPHPGPL-3.0Free of chargeOpen accesshttps://github.com/evalopezf/FruitDBase/issuesIssue trackerhttps://fruitdbase.csic.es/Repositoryhttps://fruitdbase.csic.es/about.phpGeneralhttps://fruitdbase.csic.es/documentation.phpUser manualEva María López Fernándezemlopez@cebas.csic.eshttps://evalopezf.github.io/portfolio-digital/https://orcid.org/0009-0006-7539-6398PersonMaintainerMomentum CSIC Programme: Develop Your Digital Talent (European Commission – NextGenerationEU)https://momentum.csic.es/Funding agencyThis research work was funded by the European Commission-NextGenerationEU, through Momentum CSIC Programme: Develop Your Digital TalentN4 Bias FilterThe tool is designed to perform image pre-processing to reduce the bias field effect, improving the quality of the image. Two main functionalities are offered: (1) Apply N4 filter to image/images (either with the default parameters values or with parameters values defined by the user) (2) Find the optimal configuration of the N4 filter for specific image/images by measuring the Full Width at Half Maximum (FWHM) of the periprostatic fat distribution.https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/n4filter/info-tabn4_bias_filterbiotools:n4_bias_filter1.71.8Workflowhttp://edamontology.org/topic_3474Machine learninghttp://edamontology.org/topic_0092Data visualisationhttp://edamontology.org/topic_3572Data quality managementMacWindowsLinuxPythonEUPL-1.2eucaimEmergingFree of chargeOpen accesshttp://edamontology.org/operation_3695Data filteringhttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM format

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http://edamontology.org/operation_3443Image analysishttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM format

http://edamontology.org/data_3671Texthttp://edamontology.org/format_3620xlsx

https://cbml-gitlab.ics.forth.gr/adovrou/n4biasfilter/-/tree/eucaim?ref_type=headsRepositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/n4filter/info-tabSoftware catalogueLink to EUCAIM's harborhttps://drive.eucaim.cancerimage.eu/s/KgnaA8JAMYedFpdUser manualhttps://www.youtube.com/watch?v=OXbpJcRntOMTraining material10.1016/j.mri.2023.03.01237004467MethodComputational BioMedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH)https://www.ics.forth.gr/cbml/InstituteAikaterini Dovroudovrou@ics.forth.grhttps://orcid.org/0000-0001-5242-6060PersonPrimary contactDeveloperDocumentorSupportStelios Sfakianakisssfak@ics.forth.grhttps://orcid.org/0000-0001-8424-4157PersonSupportValia Kalokyrivkalokyri@ics.forth.grhttps://orcid.org/0000-0002-5245-8238PersonSupportDeep Learning Noise Reduction (DLNR)A fully convolutional (with no pooling layers) model was trained on a set of noisy images with the ground truth being the original image without the (synthetic) noise. Different levels of noise and types were incorporated into the training set. The experiments showed reduction in noise levels, but it can impact image quality when T2ws without noise is provided to the model.https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/deep-learning-noise-reduction/info-tabdeep_learning_noise_reduction_dlnrbiotools:deep_learning_noise_reduction_dlnr2.0Command-line toolhttp://edamontology.org/topic_3572Data quality managementLinuxMacWindowsEUPL-1.2EUCAIMMatureFree of chargeOpen accesshttps://github.com/trivizakis/DLNRRepositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/deep-learning-noise-reduction/info-tabSoftware cataloguehttps://drive.eucaim.cancerimage.eu/f/2363User manualhttps://www.youtube.com/watch?v=DsX42vSAKv0Training material10.1109/SACI66288.2025.11030174Computational BioMedicine Laboratory, Foundation for Research and Technology Hellas (FORTH)https://www.ics.forth.gr/cbml/?lang=enInstituteProviderEleftherios Trivizakistrivizakis@ics.forth.grDeveloperLethe DICOM AnonymizerA DICOM Anonymization pipeline in a Docker container. This pipeline is designed to anonymize DICOM files according to the EUCAIM standard and includes the following steps: Step 1 (Optional): Perform OCR on DICOM pixel data to remove sensitive information (burned-in information). Step 2: Deidentify DICOM metadata using the RSNA CTP Anonymizer and the EUCAIM anonymization script. Step 3 (Optional): Deidentify clinical data provided in CSV files so that the referenced patient id is anonymized the same way CTP does in Step 2.https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/lethe-dicom-anonymizer/info-tablethe_dicom_anonymizerbiotools:lethe_dicom_anonymizer0.9.120.10.10.11.20.11.3Desktop applicationCommand-line toolhttp://edamontology.org/topic_3384Medical imaginghttp://edamontology.org/topic_4044Data protectionhttp://edamontology.org/topic_4012FAIR dataMacWindowsLinuxPythonEUPL-1.2EUCAIMEmergingFree of chargeOpen accesshttp://edamontology.org/operation_3283Anonymisationhttp://edamontology.org/data_3424Raw imagehttp://edamontology.org/format_3548DICOM format

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

* site_id TEXT The SITE-ID provided by the EUCAIM Technical team [required] │ │ input_dir [INPUT_DIR] Input directory to read DICOM files from [default: /input] │ │ output_dir [OUTPUT_DIR] Output directory to write processed DICOM files to │ │ [default: /output] │docker run -it -v <INPUT-DIR>:/input -v <OUTPUT-DIR>:/output harbor.eucaim.cancerimage.eu/ingestion-tools/lethe-dicom-anonymizer run <SITE-ID>https://github.com/cbml-forth/lethe_anon_pipelineRepositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/lethe-dicom-anonymizer/info-tabSoftware cataloguehttps://github.com/cbml-forth/lethe_anon_pipeline/blob/main/README.mdUser manualhttps://www.youtube.com/watch?v=tebt0Z-m_QATraining material10.5281/zenodo.19097245Computational BioMedicine Laboratory, Foundation for Research and Technology Hellas (FORTH)https://www.ics.forth.gr/cbml/?lang=enInstituteProviderStelios Sfakianakishttps://orcid.org/0000-0001-8424-4157PersonPrimary contactDeveloperMaintainerSupportValia Kalokyrihttps://orcid.org/0000-0002-5245-8238PersonContributorSupportEUCAIM Wizard ToolThe EUCAIM Wizard Tool performs an analysis of data re-identification risks of imaging and clinical data that follow the EUCAIM CDM. It includes and uses an EUCAIM specific configuration of the ARX Data Anonymization Tool (biotools:arx), by supporting a wide variety of privacy and risk models as well methods for analyzing the usefulness of output data.https://github.com/cbml-forth/eucaim_wizard_tooleucaim_wizard_toolbiotools:eucaim_wizard_toolbeta1.0Desktop applicationhttp://edamontology.org/topic_3384Medical imaginghttp://edamontology.org/topic_4044Data protectionhttp://edamontology.org/topic_2640OncologyMacWindowsLinuxJavaEUPL-1.2EUCAIMEmergingFree of chargeOpen accesshttp://edamontology.org/operation_3283Anonymisationhttps://github.com/cbml-forth/eucaim_wizard_toolRepositoryhttps://drive.eucaim.cancerimage.eu/apps/files/files/1833?dir=/Applications/eucaim_wizard_toolRepositoryEUCAIM Drive repositoryhttps://drive.eucaim.cancerimage.eu/s/FYwDc66kXqFRebaUser manualhttps://www.youtube.com/watch?v=MDwKyuvw8q4Training materialarxincludesarxusesComputational BioMedicine Laboratory, Foundation for Research and Technology Hellas (FORTH)https://www.ics.forth.gr/cbml/?lang=enInstituteProviderKaterina Nikiforakinikiforakik@gmail.comPersonPrimary contactDocumentorSupportValia Kalokyrivkalokyri@ics.forth.grhttps://orcid.org/0000-0002-5245-8238PersonSupportBiologically motivated normalization techniquesThe tool is designed to perform normalization at the image-level. This normalization method aims to reduce the variability in the intensity values of the Magnetic Resonance (MR) prostate images due to different scanners, acquisition protocols and conditions, based on the intensity values of specific tissues. This tool implements three biologically-motivated intensity normalization techniques: (1) The fat-based normalization method, (2) The muscle-based normalization method, and (3) The single tissue (fat or muscle) piece-wise normalization method.https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/bio_intensity_norm/info-tabbiologically_motivated_normalization_techniquesbiotools:biologically_motivated_normalization_techniques1.61.7Command-line toolhttp://edamontology.org/topic_3474Machine learninghttp://edamontology.org/topic_0092Data visualisationhttp://edamontology.org/topic_3071Data managementhttp://edamontology.org/topic_0219Data curation and archivalMacWindowsLinuxPythonEUPL-1.2eucaimEmergingFree of chargeOpen accesshttp://edamontology.org/operation_3443Image analysishttp://edamontology.org/data_2968Imagehttp://edamontology.org/format_3548DICOM format

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

-h, --help: show this help message and exit -f, --fat-based: If specified, the fat-based normalization algorithm is applied -m, --muscle-based: If specified, the muscle-based normalization algorithm is applied -p, --piece-wise: If specified, the single tissue piece-wise normalization algorithm is applied, Default = fat tissue piece-wisedocker run --rm \ -v "your_input_path:/data/input" \ -v "your_output_path:/data/output" harbor.eucaim.cancerimage.eu/processing-tools/bio_intensity_norm:1.7 /data/input /data/output [-h] [-f] [-m] [-p]https://cbml-gitlab.ics.forth.gr/adovrou/biointensitynorm/-/tree/eucaim?ref_type=headsRepositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/bio_intensity_norm/artifacts-tabSoftware catalogueLink to EUCAIM's Harborhttps://cbml-gitlab.ics.forth.gr/adovrou/biointensitynorm/-/tree/eucaim?ref_type=headsQuick start guidehttps://www.youtube.com/watch?v=NtwamIRRJ6oTraining materialhttps://drive.eucaim.cancerimage.eu/s/SYLB7HYksEwaKF4User manualComputational BioMedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH)https://www.ics.forth.gr/cbml/InstituteAikaterini Dovroudovrou@ics.forth.grhttps://orcid.org/0000-0001-5242-6060PersonPrimary contactDeveloperDocumentorSupportStelios Sfakianakisssfak@ics.forth.grhttps://orcid.org/0000-0001-8424-4157PersonSupportValia Kalokyrivkalokyri@ics.forth.grhttps://orcid.org/0000-0002-5245-8238PersonSupportmonocleMonocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well.https://bioconductor.org/packages/monoclemonoclebiotools:monocle2.40.0LibraryCommand-line toolhttp://edamontology.org/topic_2229Cell biologyhttp://edamontology.org/topic_0203Gene expressionhttp://edamontology.org/topic_3519PCR experimenthttp://edamontology.org/topic_3170RNA-SeqMacWindowsLinuxRArtistic-2.0BioConductorMatureFree of chargeOpen accesshttp://edamontology.org/operation_3439Pathway or network predictionhttp://edamontology.org/operation_3562Network simulationhttp://edamontology.org/data_2603Gene expression datahttp://edamontology.org/format_1213rna

http://edamontology.org/data_1713Fate maphttp://edamontology.org/format_2032Workflow format

https://github.com/cole-trapnell-lab/monocle-releaseRepositoryhttps://bioconductor.org/packages/release/bioc/src/contrib/monocle_2.40.0.tar.gzSource codehttps://bioconductor.org/packages/monocleUser manualCole TrapnellMetCircMetCirc comprises a workflow to interactively explore high-resolution MS/MS metabolomics data. MetCirc uses the Spectra object infrastructure defined in the package Spectra that stores MS/MS spectra. MetCirc offers functionality to calculate similarity between precursors based on the normalised dot product, neutral losses or user-defined functions and visualise similarities in a circular layout. Within the interactive framework the user can annotate MS/MS features based on their similarity to (known) related MS/MS features.https://bioconductor.org/packages/MetCircmetcircbiotools:metcirc1.42.0LibraryCommand-line toolhttp://edamontology.org/topic_3172Metabolomicshttp://edamontology.org/topic_0602Molecular interactions, pathways and networkshttp://edamontology.org/topic_2259Systems biologyLinuxMacWindowsRGPL-3.0-or-laterBioConductorFree of chargeOpen accesshttp://edamontology.org/operation_3694Mass spectrum visualisationhttp://edamontology.org/operation_3925Network visualisationhttp://edamontology.org/operation_3801Spectral library searchhttp://edamontology.org/operation_3432Clusteringhttp://edamontology.org/data_0943Mass spectrumhttp://edamontology.org/format_4039MSPhttp://edamontology.org/format_3244mzML

http://edamontology.org/data_2855Distance matrixhttp://edamontology.org/format_3751DSV

http://edamontology.org/data_2884Plothttp://edamontology.org/format_3547Image format

https://github.com/tnaake/MetCirc/Repositoryhttps://bioconductor.org/packages/release/bioc/src/contrib/MetCirc_1.42.0.tar.gzSource codehttps://bioconductor.org/packages/MetCircUser manualJohannes RainerThomas NaakemetaseqR2Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way.https://bioconductor.org/packages/metaseqR2metaseqr2biotools:metaseqr21.24.0WorkflowCommand-line toolhttp://edamontology.org/topic_0203Gene expressionhttp://edamontology.org/topic_3170RNA-SeqLinuxMacWindowsRGPL-3.0-or-laterBioConductorMatureFree of chargeOpen accesshttp://edamontology.org/operation_3435Standardisation and normalisationhttps://github.com/pmoulos/metaseqR2/issuesIssue trackerhttps://github.com/pmoulos/metaseqR2Repositoryhttp://www.fleming.grSource codehttps://bioconductor.org/packages/metaseqR2User manualPanagiotis MoulosPersonDeveloperMaintainerCAMERAAnnotation of peaklists generated by xcms, rule based annotation of isotopes and adducts, isotope validation, EIC correlation based tagging of unknown adducts and fragmentshttps://bioconductor.org/packages/CAMERAcamerabiotools:camera1.68.0LibraryCommand-line toolhttp://edamontology.org/topic_3172MetabolomicsMacWindowsLinuxRGPL-2.0-or-laterBioConductorOpen accessGalaxyMetabolomicshttp://edamontology.org/operation_3214Spectral analysishttp://edamontology.org/operation_0226Annotationhttp://edamontology.org/data_2536Mass spectrometry datahttp://edamontology.org/format_2333Binary format

http://edamontology.org/data_2082Matrixhttp://edamontology.org/format_3751DSV

http://msbi.ipb-halle.de/msbi/CAMERA/Mirrorhttp://msbi.ipb-halle.de/msbi/CAMERA/Source codehttps://bioconductor.org/packages/CAMERAUser manualworkflow4metabolomicsusedByCarsten KuhlHendrik TreutlerRalf TautenhahnSteffen Neumann {ckuhl|htreutle|sneumann}@ipb-halle.dertautenh@scripps.edu