SlideScopeDesktop viewer for microscopy and whole slide pathology images on Windows and macOS. Opens whole slide scanner formats (Aperio SVS, Hamamatsu NDPI, MIRAX MRXS, Leica SCN, Ventana BIF) alongside acquisition formats (Zeiss CZI, Nikon ND2, DICOM, TIFF/OME-TIFF) in a single application, providing pyramid navigation of gigapixel images, metadata inspection, calibrated measurements, annotations, and on-device segmentation and object counting.https://slidescope.science/slidescopebiotools:slidescope1.8.50Desktop applicationhttp://edamontology.org/topic_3382Imaginghttp://edamontology.org/topic_3383Bioimaginghttp://edamontology.org/topic_3384Medical imagingWindowsMacProprietaryMatureCommercialRestricted accesshttps://slidescope.science/en/downloads/Binarieshttps://slidescope.science/en/learn/GeneralTrace4HarmonizationHarmonize numerical values extracted from medical images (e.g. acquired with different models of image-acquisition system)http://deeptracetech.com/trace4harmonizationbiotools:trace4harmonizationv1.0.00Desktop applicationLibraryCommand-line toolhttp://edamontology.org/topic_3474Machine learninghttp://edamontology.org/topic_2269Statistics and probabilityLinuxWindowsPythonMATLABProprietaryEUCAIMEmergingCommercialRestricted accesshttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/trace4harmonization/artifacts-tabRepositoryHarbor Linkhttps://drive.google.com/file/d/1_kb8y5ExGnjFM7pL9Ml7hUtPIWzX8umX/viewUser manualmgnifamBuild protein families from sequence clusters by iterative HMM search over very large databases — streaming, deterministic, pyHMMER-based.https://github.com/vagkaratzas/mgnifammgnifambiotools:mgnifam3.1.0Brain Metastasis SegmenterThe Brain Metastasis Segmenter is a software tool developed by researchers from Universidad Politécnica de Madrid and Children’s National Hospital for the segmentation and analysis of brain metastases in magnetic resonance imaging (MRI). Built in Python, it enables precise quantitative analysis of brain metastases in MRI scans to support clinical decision-making in both diagnosis and prognosis.https://github.com/BIT-UPM/EUCAIM/tree/main/brain_metastasis_segmenterbrain_metastasis_segmenterbiotools:brain_metastasis_segmenterv1.0Web applicationCommand-line toolWorkflowhttp://edamontology.org/topic_3444MRIhttp://edamontology.org/topic_3474Machine learningLinuxMacWindowsPythonCC-BY-NC-ND-4.0EUCAIMMatureFree of chargeOpen accesshttp://edamontology.org/operation_3553Image annotationhttps://segmenter.hope4kids.io/OtherMain app webpagehttps://github.com/Pediatric-Accelerated-Intelligence-Lab/HOPE-Segmenter-KidsRepositoryGitHub repositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/brain-metastasis-segmenter/info-tabRepositoryEUCAIM harbor linkhttps://docs.hope4kids.io/HOPE-Segmenter-Kids/API documentationCitation instructionsTerms of useCode of conductRelease notesUser manualGeneralQuick start guidehttps://youtu.be/J9iKgc3IRZQTraining material10.1007/978-3-031-76163-8_2010.1109/ISBI56570.2024.1063546910.48550/arXiv.2412.0409410.48550/arXiv.2412.04111Daniel Capellán-Martíndaniel.capellan@upm.eshttps://orcid.org/0000-0002-9743-0845Primary contactDeveloperContributorSupportAbhijeet Paridapabhijeet@childrensnational.orghttps://orcid.org/0000-0002-4978-0576DeveloperContributorSupportMaintainerZhifan Jiangzjiang@childrensnational.orgContributorDeveloperSupportMaría Jesus Ledesma-Carbayomj.ledesma@upm.eshttps://orcid.org/0000-0001-6846-3923Primary contactContributorMarius George Lingurarumlingura@childrensnational.orgPrimary contactContributorIntercranial Meningioma SegmenterThe Intercranial Meningioma Segmenter is a software tool developed by researchers from Universidad Politécnica de Madrid and Children’s National Hospital for the segmentation and analysis of intercranial meningiomas in magnetic resonance imaging (MRI). Built in Python, it enables precise quantitative analysis of intercranial meningiomas in brain MRI scans to support clinical decision-making in both diagnosis and prognosis.https://github.com/BIT-UPM/EUCAIM/tree/main/intercranial_meningioma_segmenterintercranial_meningioma_segmenterbiotools:intercranial_meningioma_segmenterv1.0Web applicationWorkflowCommand-line toolhttp://edamontology.org/topic_3444MRIhttp://edamontology.org/topic_3474Machine learningMacLinuxWindowsPythonCC-BY-NC-ND-4.0EUCAIMMatureFree of chargeOpen accesshttp://edamontology.org/operation_3553Image annotationhttps://segmenter.hope4kids.io/OtherMain app webpagehttps://github.com/Pediatric-Accelerated-Intelligence-Lab/HOPE-Segmenter-KidsRepositoryGitHub repositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/intracranial-meningioma-segmenter/info-tabRepositoryEUCAIM harbor linkhttps://docs.hope4kids.io/HOPE-Segmenter-Kids/API documentationCitation instructionsTerms of useCode of conductRelease notesUser manualGeneralQuick start guidehttps://youtu.be/aLwU_a4xyxcTraining material10.1007/978-3-031-76163-8_2010.1109/ISBI56570.2024.1063546910.48550/arXiv.2412.0409410.48550/arXiv.2412.04111Daniel Capellán-Martíndaniel.capellan@upm.eshttps://orcid.org/0000-0002-9743-0845Primary contactDeveloperContributorSupportAbhijeet Paridapabhijeet@childrensnational.orghttps://orcid.org/0000-0002-4978-0576DeveloperContributorSupportMaintainerZhifan Jiangzjiang@childrensnational.orgContributorDeveloperSupportMaría Jesus Ledesma-Carbayomj.ledesma@upm.eshttps://orcid.org/0000-0001-6846-3923Primary contactContributorMarius George Lingurarumlingura@childrensnational.orgPrimary contactContributorSub-Saharan Africa Brain Glioma SegmenterThe Sub-Saharan Africa Brain Glioma Segmenter is a software tool developed by researchers from Universidad Politécnica de Madrid and Children’s National Hospital for the segmentation and analysis of intercranial meningiomas in magnetic resonance imaging (MRI). Built in Python, it enables precise quantitative analysis of gliomas in brain MRI scans to support clinical decision-making in both diagnosis and prognosis. This tool was trained on a cohort of patients from Sub-Saharan Africa.https://github.com/BIT-UPM/EUCAIM/tree/main/sub_saharan_africa_brain_glioma_segmentersub-saharan_africa_brain_glioma_segmenterbiotools:sub-saharan_africa_brain_glioma_segmenterv1.0Web applicationWorkflowCommand-line toolhttp://edamontology.org/topic_3444MRIhttp://edamontology.org/topic_3474Machine learningMacLinuxWindowsPythonCC-BY-NC-ND-4.0EUCAIMMatureFree of chargeOpen accesshttp://edamontology.org/operation_3553Image annotationhttps://segmenter.hope4kids.io/OtherMain app webpagehttps://github.com/Pediatric-Accelerated-Intelligence-Lab/HOPE-Segmenter-KidsRepositoryGitHub repositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/ssa-brain-glioma-segmenter/info-tabRepositoryEUCAIM harbor linkhttps://docs.hope4kids.io/HOPE-Segmenter-Kids/API documentationCitation instructionsTerms of useCode of conductRelease notesUser manualGeneralQuick start guidehttps://youtu.be/91h-7Ulb1kETraining material10.1007/978-3-031-76163-8_2010.1109/ISBI56570.2024.1063546910.48550/arXiv.2412.0409410.48550/arXiv.2412.04111Daniel Capellán-Martíndaniel.capellan@upm.eshttps://orcid.org/0000-0002-9743-0845Primary contactDeveloperContributorSupportAbhijeet Paridapabhijeet@childrensnational.orghttps://orcid.org/0000-0002-4978-0576DeveloperContributorSupportMaintainerZhifan Jiangzjiang@childrensnational.orgContributorDeveloperSupportMaría Jesus Ledesma-Carbayomj.ledesma@upm.eshttps://orcid.org/0000-0001-6846-3923Primary contactContributorMarius George Lingurarumlingura@childrensnational.orgPrimary contactContributorBrain Glioma SegmenterThe Brain Glioma Segmenter is a software tool developed by researchers from Universidad Politécnica de Madrid and Children’s National Hospital for the segmentation and analysis of brain gliomas in magnetic resonance imaging (MRI). Built in Python, it enables precise quantitative analysis of brain gliomas in MRI scans to support clinical decision-making in both diagnosis and prognosis.https://github.com/BIT-UPM/EUCAIM/tree/main/brain_glioma_segmenterbrain_glioma_segmenterbiotools:brain_glioma_segmenterv1.0Web applicationWorkflowCommand-line toolhttp://edamontology.org/topic_3444MRIhttp://edamontology.org/topic_3474Machine learningLinuxMacWindowsPythonCC-BY-NC-ND-4.0EUCAIMMatureFree of chargeOpen accesshttp://edamontology.org/operation_3553Image annotationhttps://segmenter.hope4kids.io/OtherMain app webpagehttps://github.com/Pediatric-Accelerated-Intelligence-Lab/HOPE-Segmenter-KidsRepositoryGitHub repositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/brain-glioma-segmenter/info-tabRepositoryEUCAIM harbor linkhttps://docs.hope4kids.io/HOPE-Segmenter-Kids/API documentationCitation instructionsTerms of useCode of conductRelease notesUser manualGeneralQuick start guidehttps://youtu.be/RC4_6HHXX1QTraining material10.1007/978-3-031-76163-8_2010.1109/ISBI56570.2024.1063546910.48550/arXiv.2412.0409410.48550/arXiv.2412.04111Daniel Capellán-Martíndaniel.capellan@upm.eshttps://orcid.org/0000-0002-9743-0845Primary contactContributorDeveloperSupportAbhijeet Paridapabhijeet@childrensnational.orghttps://orcid.org/0000-0002-4978-0576ContributorDeveloperSupportMaintainerZhifan Jiangzjiang@childrensnational.orgContributorDeveloperSupportMaría Jesus Ledesma-Carbayomj.ledesma@upm.eshttps://orcid.org/0000-0001-6846-3923Primary contactContributorMarius George Lingurarumlingura@childrensnational.orgPrimary contactContributorPediatric Brain Tumor SegmenterThe Pediatric Brain Tumor Segmenter is a software tool developed by researchers from Universidad Politécnica de Madrid and Children’s National Hospital for the segmentation and analysis of pediatric brain tumors in magnetic resonance imaging (MRI). Built in Python, it enables precise quantitative analysis of pediatric brain tumors in MRI scans to support clinical decision-making in both diagnosis and prognosis.https://github.com/BIT-UPM/EUCAIM/tree/main/pediatric_brain_tumor_segmenterpediatric_brain_tumor_segmenterbiotools:pediatric_brain_tumor_segmenterv1.0Web applicationCommand-line toolWorkflowhttp://edamontology.org/topic_3444MRIhttp://edamontology.org/topic_3474Machine learningLinuxMacWindowsPythonCC-BY-NC-SA-4.0EUCAIMMatureFree of chargeOpen accesshttp://edamontology.org/operation_3553Image annotationhttps://segmenter.hope4kids.io/OtherMain app webpagehttps://github.com/Pediatric-Accelerated-Intelligence-Lab/HOPE-Segmenter-KidsRepositoryGitHub repositoryhttps://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/pediatric-brain-tumor-segmenter/info-tabRepositoryEUCAIM harbor linkhttps://docs.hope4kids.io/HOPE-Segmenter-Kids/API documentationCitation instructionsTerms of useCode of conductRelease notesUser manualGeneralQuick start guidehttps://youtu.be/7sgCydcDpnsTraining material10.1007/978-3-031-76163-8_2010.1109/ISBI56570.2024.1063546910.48550/arXiv.2412.0409410.48550/arXiv.2412.04111Daniel Capellán-Martíndaniel.capellan@upm.eshttps://orcid.org/0000-0002-9743-0845PersonPrimary contactDeveloperContributorSupportAbhijeet Paridapabhijeet@childrensnational.orghttps://orcid.org/0000-0002-4978-0576DeveloperContributorSupportMaintainerZhifan Jiangzjiang@childrensnational.orgContributorDeveloperSupportMaría Jesus Ledesma-Carbayomj.ledesma@upm.eshttps://orcid.org/0000-0001-6846-3923Primary contactContributorMarius George Lingurarumlingura@childrensnational.orgPrimary contactContributornf-core-proteinfamiliesnf-core/proteinfamilies is a bioinformatics pipeline that generates protein families from amino acid sequences and/or updates existing families with new sequences. It takes a protein fasta file as input, clusters the sequences and then generates protein family Hiden Markov Models (HMMs) along with their multiple sequence alignments (MSAs). Optionally, paths to existing family HMMs and MSAs can be given (must have matching base filenames one-to-one) in order to update with new sequences in case of matching hits.https://github.com/nf-core/proteinfamiliesnf-core_proteinfamiliesbiotools:nf-core_proteinfamilies2.0.02.1.02.2.02.5.0RRID:SCR_027374rrid1.3.0Workflowhttp://edamontology.org/topic_0623Gene and protein familiesGroovyMITnf-coreMatureFree of chargeOpen accesshttps://github.com/nf-core/proteinfamiliesRepository10.1101/2025.08.12.670010BrownotateBrownotate is an application designed for generating a protein sequence database for a given species. It can be run as a command-line tool or as a web application using Flask.https://github.com/LSMBO/BrownotateBrownotatebiotools:Brownotate1.4.0Desktop applicationCommand-line toolhttp://edamontology.org/topic_0078Proteinshttp://edamontology.org/topic_3489Database managementLinuxPerlShellPythonNot licensedEmerginghttp://edamontology.org/operation_3561Database comparisonhttp://edamontology.org/operation_0338Sequence database searchhttp://edamontology.org/data_1233Sequence set (protein)

http://edamontology.org/data_0857Sequence search results

http://edamontology.org/data_1233Sequence set (protein)

https://github.com/LSMBO/brownotate-appRepository10.1002/pmic.70094LipidXplorerLipidXplorer is a software that supports the quantitative characterization of complex lipidomes by interpreting large datasets of shotgun mass spectra.https://lifs-tools.org/lipidxplorerlipidxplorerbiotools:lipidxplorer1.2.81.2.8.11.2.91.5.0Desktop applicationhttp://edamontology.org/topic_0153Lipidomicshttp://edamontology.org/topic_3172MetabolomicsLinuxWindowsMacPythonBioInfra.ProtLIFSde.NBIGermanyhttp://edamontology.org/operation_3694Mass spectrum visualisationhttp://edamontology.org/operation_3803Natural product identificationhttp://edamontology.org/operation_3629Deisotopinghttp://edamontology.org/data_2536Mass spectrometry datahttp://edamontology.org/format_3654mzXMLhttp://edamontology.org/format_3752CSVhttp://edamontology.org/format_3244mzMLhttp://edamontology.org/format_3652dtahttp://edamontology.org/data_0943Mass spectrometry spectrahttp://edamontology.org/format_3654mzXMLhttp://edamontology.org/format_3752CSVhttp://edamontology.org/format_3244mzMLhttp://edamontology.org/format_3652dta

http://edamontology.org/data_2536Mass spectrometry datahttp://edamontology.org/format_3620xlsx

https://lifs-tools.org/lipidxplorerSource codehttps://lifs-tools.org/wiki/index.php/LipidXplorer_DocumentationGeneral10.1371/journal.pone.0029851Primary21247462BioInfra.ProtInstituteProviderdominik.kopczynski@univie.ac.athttp://groups.google.com/group/lipidxplorer-discussion-groupPersonPrimary contacttsdfA package to read, modify and write TSDF data in Python.https://biomarkersparkinson.github.io/tsdftsdfbiotools:tsdfv0.6.2Libraryhttp://edamontology.org/topic_3306BiophysicsLinuxWindowsMacPythonApache-2.0Maturehttp://edamontology.org/operation_3434Conversionhttp://edamontology.org/operation_3096Data editinghttp://edamontology.org/data_3870Trajectory datahttp://edamontology.org/format_3464JSON

http://edamontology.org/data_3870Trajectory data

https://biomarkersparkinson.github.io/tsdfGovernance10.48550/arXiv.2211.11294bioformatsBio-Formats is a Java library for reading and writing data in life sciences image file formats. It is developed by the Open Microscopy Environment.https://www.openmicroscopy.org/bio-formatsbioformatsbiotools:bioformatsv8.5.0Plug-inCommand-line toolhttp://edamontology.org/topic_3382Imaginghttp://edamontology.org/topic_0611Electron microscopyLinuxMacWindowsJavaPythonMATLABShellGPL-2.0Maturehttp://edamontology.org/operation_3434Conversionhttp://edamontology.org/data_3424Raw image

http://edamontology.org/data_3424Raw imagehttp://edamontology.org/format_3727OME-TIFF

10.1083/jcb.201004104PrimaryTreeShrinkTreeShrink: Fast and accurate detection of outlier long branches in collections of phylogenetic treeshttps://uym2.github.io/TreeShrinkTreeShrinkbiotools:TreeShrinkv1.4.0Command-line toolhttp://edamontology.org/topic_0084PhylogenyLinuxMacWindowsRPythonShellGPL-3.0Maturehttp://edamontology.org/operation_0555Consensus tree constructionhttps://uym2.github.io/TreeShrinkGeneral10.1186/s12864-018-4620-2Usage10.1007/978-3-319-67979-2_7PrimaryOpenChromOpen source tool for mass spectrometry and chromatography.https://www.openchrom.net/openchrombiotools:openchromDesktop applicationhttp://edamontology.org/topic_0092Data visualisationhttp://edamontology.org/topic_3370Analytical chemistryLinuxMacWindowsJavaEPL-2.0ms-utilsMaturehttp://edamontology.org/operation_0495Local alignmenthttp://edamontology.org/operation_3694Mass spectrum visualisationhttp://edamontology.org/operation_2424Comparisonhttp://edamontology.org/operation_3203Chromatogram visualisationhttp://edamontology.org/operation_3215Peak detectionhttp://edamontology.org/operation_3214Spectral analysishttp://edamontology.org/data_0943Mass spectrometry spectrahttp://edamontology.org/format_4039MSPhttp://edamontology.org/format_3834mzDatahttp://edamontology.org/format_1632SCFhttp://edamontology.org/format_3815Molfilehttp://edamontology.org/format_3331BLAST XML results formathttp://edamontology.org/format_3858Waters RAWhttp://edamontology.org/format_3752CSVhttp://edamontology.org/format_3018ZTRhttp://edamontology.org/format_1637dathttp://edamontology.org/format_3710WIFF formathttp://edamontology.org/format_3712Thermo RAWhttp://edamontology.org/format_3859JCAMP-DXhttp://edamontology.org/format_3650NetCDFhttp://edamontology.org/format_3244mzMLhttp://edamontology.org/format_4023cmlhttp://edamontology.org/format_3000AB1http://edamontology.org/format_3825nmrMLhttp://edamontology.org/format_3654mzXMLhttp://edamontology.org/format_3814SDFhttp://edamontology.org/format_3836BLAST XML v2 results format

http://edamontology.org/data_0944Peptide mass fingerprinthttp://edamontology.org/format_3620xlsxhttp://edamontology.org/format_3650NetCDFhttp://edamontology.org/format_3244mzMLhttp://edamontology.org/format_3752CSV

http://edamontology.org/data_2968Imagehttp://edamontology.org/format_3508PDFhttp://edamontology.org/format_3603PNGhttp://edamontology.org/format_3604SVG

https://github.com/openchrom/openchrom/issuesIssue trackerhttps://github.com/openchrom/openchrom/discussionsDiscussion forumhttps://ticket.lablicate.com/Helpdeskhttps://openchrom.net/downloadDownloads pagehttps://github.com/openchrom/openchromSource codehttps://lablicate.com/marketplace/extensions/handbookUser manualhttps://github.com/OpenChrom/openchrom/wiki/faqFAQ10.1186/1471-2105-11-40520673335Philip WenigPersonMaintainerMatthias Mailänderhttps://orcid.org/0000-0001-6114-0304PersonContributordoubletfinderDoubletFinder is an R package that predicts doublets in single-cell RNA sequencing data.https://github.com/chris-mcginnis-ucsf/DoubletFinderdoubletfinderbiotools:doubletfinder2.0.6Scripthttp://edamontology.org/topic_3308Transcriptomicshttp://edamontology.org/topic_4028Single-cell sequencingMacWindowsLinuxRCC0-1.0MatureOpen accesshttp://edamontology.org/operation_3800RNA-Seq quantification10.1016/j.cels.2019.03.00330954475PMC6853612xmapbridgexmapBridge can plot graphs in the X:Map genome browser. This package exports plotting files in a suitable format.https://bioconductor.org/packages/xmapbridgexmapbridgebiotools:xmapbridge1.70.0LibraryCommand-line toolhttp://edamontology.org/topic_0102MappingMacLinuxWindowsRLGPL-3.0BioConductorhttp://edamontology.org/operation_3208Genome visualisationhttp://edamontology.org/data_2887Nucleic acid sequence recordhttp://edamontology.org/format_3161MAGE-ML

http://edamontology.org/data_2166Sequence composition plothttp://edamontology.org/format_3617Graph format

http://www.bioconductor.orgMirrorhttp://xmap.picr.man.ac.ukMirrorhttp://www.bioconductor.orgSource codehttp://xmap.picr.man.ac.ukSource codehttps://bioconductor.org/packages/xmapbridgeUser manualTim YatesXDEMulti-level model for cross-study detection of differential gene expression.https://bioconductor.org/packages/XDExdebiotools:xde2.58.0Command-line toolLibraryhttp://edamontology.org/topic_3572Data quality managementhttp://edamontology.org/topic_0203Gene expressionhttp://edamontology.org/topic_2269Statistics and probabilityMacWindowsLinuxRLGPL-2.0BioConductorhttp://edamontology.org/operation_2428Validationhttp://edamontology.org/operation_2495Gene expression analysishttp://edamontology.org/operation_3664Statistical modellinghttp://edamontology.org/operation_3436Aggregationhttps://bioconductor.org/packages/release/bioc/src/contrib/XDE_2.58.0.tar.gzSource codehttps://bioconductor.org/packages/XDEUser manualA.B. NobelG. ParmigianiR.B. Scharpfand H. TjelmelandviperInference of protein activity from gene expression data, including the VIPER and msVIPER algorithmshttps://bioconductor.org/packages/viperviperbiotools:viper1.46.0LibraryCommand-line toolhttp://edamontology.org/topic_3473Data mininghttp://edamontology.org/topic_0204Gene regulationhttp://edamontology.org/topic_3510Protein sites, features and motifshttp://edamontology.org/topic_0749Transcription factors and regulatory siteshttp://edamontology.org/topic_3308TranscriptomicsMacLinuxWindowsRBioConductorMaturehttp://edamontology.org/operation_3630Protein quantificationhttp://edamontology.org/operation_3562Network simulationhttp://edamontology.org/operation_0314Gene expression profilinghttps://bioconductor.org/packages/release/bioc/src/contrib/viper_1.46.0.tar.gzSource codehttps://bioconductor.org/packages/viperUser manualMariano J AlvarezVariantFilteringFilter genetic variants using different criteria such as inheritance model, amino acid change consequence, minor allele frequencies across human populations, splice site strength, conservation, etc.https://bioconductor.org/packages/VariantFilteringvariantfilteringbiotools:variantfiltering1.48.0LibraryCommand-line toolhttp://edamontology.org/topic_2885DNA polymorphismMacLinuxWindowsRArtistic-2.0BioConductorhttp://edamontology.org/operation_3225Variant classificationhttp://edamontology.org/operation_3208Genome visualisationhttp://edamontology.org/operation_3661SNP annotationhttp://edamontology.org/operation_3202Polymorphism detectionhttp://edamontology.org/data_0863Sequence alignmenthttp://edamontology.org/format_3016VCFhttp://edamontology.org/data_3498Sequence variationshttp://edamontology.org/format_3016VCFhttps://github.com/rcastelo/VariantFilteringMirrorhttps://github.com/rcastelo/VariantFilteringSource codehttps://bioconductor.org/packages/VariantFilteringUser manualDei Martinez ElurbePersonContributorJoan FernandezPersonContributorPau PuigdevallPersonContributorRobert CasteloPersonDeveloperMaintainerVanillaICEHidden Markov Models for characterizing chromosomal alteration in high throughput SNP arrays.https://bioconductor.org/packages/VanillaICEvanillaicebiotools:vanillaice1.74.0LibraryCommand-line toolhttp://edamontology.org/topic_3569Applied mathematicshttp://edamontology.org/topic_3175DNA structural variationhttp://edamontology.org/topic_0622Genomicshttp://edamontology.org/topic_3518Microarray experimentMacLinuxWindowsRLGPL-2.0BioConductorhttp://edamontology.org/operation_3196Genotypinghttp://edamontology.org/operation_2238Statistical calculationhttp://edamontology.org/operation_3228Structural variation detectionhttps://bioconductor.org/packages/release/bioc/src/contrib/VanillaICE_1.74.0.tar.gzSource codehttps://bioconductor.org/packages/VanillaICEUser manualRobert ScharpfPersonDeveloperMaintainerunifiedWMWqPCRThis packages implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data.https://bioconductor.org/packages/unifiedWMWqPCRunifiedwmwqpcrbiotools:unifiedwmwqpcr1.48.0LibraryCommand-line toolhttp://edamontology.org/topic_3519PCR experimentMacLinuxWindowsRGPL-2.0BioConductorhttp://edamontology.org/operation_3223Differential gene expression analysishttp://edamontology.org/data_2603Gene expression datahttp://edamontology.org/format_3475TSV

http://edamontology.org/data_2603Gene expression datahttp://edamontology.org/format_3475TSVhttp://edamontology.org/format_3033Matrix formathttp://edamontology.org/format_2331HTML

https://bioconductor.org/packages/release/bioc/src/contrib/unifiedWMWqPCR_1.48.0.tar.gzSource codehttps://bioconductor.org/packages/unifiedWMWqPCRUser manualJan R. De Neve & Joris MeystweeDEseqDifferential expression analysis of RNA-seq using the Poisson-Tweedie (PT) family of distributions. PT distributions are described by a mean, a dispersion and a shape parameter and include Poisson and NB distributions, among others, as particular cases. An important feature of this family is that, while the Negative Binomial (NB) distribution only allows a quadratic mean-variance relationship, the PT distributions generalizes this relationship to any orde.https://bioconductor.org/packages/tweeDEseqtweedeseqbiotools:tweedeseq1.58.0Command-line toolLibraryhttp://edamontology.org/topic_0203Gene expressionhttp://edamontology.org/topic_3170RNA-Seqhttp://edamontology.org/topic_2269Statistics and probabilityMacWindowsLinuxRGPL-2.0-or-laterBioConductorhttp://edamontology.org/operation_3763Service invocationhttp://edamontology.org/operation_3680RNA-Seq analysishttp://edamontology.org/operation_3223Differential gene expression analysishttp://edamontology.org/operation_2238Statistical calculationhttp://www.creal.cat/jrgonzalez/software.htmMirrorhttps://github.com/isglobal-brge/tweeDEseq/Source codehttps://bioconductor.org/packages/tweeDEseqUser manualDolors Pelegri-SisoPersonDeveloperMaintainerJuan R. GonzalezPersonDeveloperMikel EsnaolaPersonDeveloperRobert CasteloPersonDeveloperTurboNormA fast scatterplot smoother based on B-splines with second-order difference penalty. Functions for microarray normalization of single-colour data i.e. Affymetrix/Illumina and two-colour data supplied as marray MarrayRaw-objects or limma RGList-objects are available.https://bioconductor.org/packages/TurboNormturbonormbiotools:turbonorm1.60.0Command-line toolLibraryhttp://edamontology.org/topic_3572Data quality managementhttp://edamontology.org/topic_0092Data visualisationhttp://edamontology.org/topic_3518Microarray experimentMacWindowsLinuxRLGPL-2.0-or-laterBioConductorhttp://edamontology.org/operation_3443Image analysishttp://edamontology.org/operation_3553Image annotationhttp://edamontology.org/operation_3438Calculationhttp://www.humgen.nl/MicroarrayAnalysisGroup.htmlMirrorhttp://www.humgen.nl/MicroarrayAnalysisGroup.htmlSource codehttps://bioconductor.org/packages/TurboNormUser manualMaarten van Iterson and Chantal van LeeuwenTOASTThis package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include 1. detect cell-type specific or cross-cell type differential signals 2. tree-based differential analysis 3. improve variable selection in reference-free deconvolution 4. partial reference-free deconvolution with prior knowledge.https://bioconductor.org/packages/TOASTTOASTbiotools:TOAST1.26.0http://edamontology.org/topic_3295Epigeneticshttp://edamontology.org/topic_3518Microarray experimenthttp://edamontology.org/topic_3170RNA-SeqRGPL-2.0BioConductorhttp://edamontology.org/operation_3223Differential gene expression analysishttp://edamontology.org/operation_3629Deisotopinghttp://edamontology.org/operation_2495Expression analysishttps://bioconductor.org/packages/release/bioc/src/contrib/TOAST_1.26.0.tar.gzSource codehttps://bioconductor.org/packages/TOASTUser manualZiyi Li and Weiwei Zhang and Luxiao Chen and Hao WutilingArrayThe package provides functionality that can be useful for the analysis of high-density tiling microarray data (such as from Affymetrix genechips) for measuring transcript abundance and architecture. The main functionalities of the package are: 1. the class 'segmentation' for representing partitionings of a linear series of data; 2. the function 'segment' for fitting piecewise constant models using a dynamic programming algorithm that is both fast and exact; 3. the function 'confint' for calculating confidence intervals using the strucchange package; 4. the function 'plotAlongChrom' for generating pretty plots; 5. the function 'normalizeByReference' for probe-sequence dependent response adjustment from a (set of) reference hybridizations.https://bioconductor.org/packages/tilingArraytilingarraybiotools:tilingarray1.90.0Command-line toolLibraryhttp://edamontology.org/topic_3518Microarray experimentMacWindowsLinuxRArtistic-2.0BioConductorhttp://edamontology.org/operation_0337Visualisationhttp://edamontology.org/operation_2238Statistical calculationhttps://bioconductor.org/packages/release/bioc/src/contrib/tilingArray_1.90.0.tar.gzSource codehttps://bioconductor.org/packages/tilingArrayUser manualJoern Toedling with contributions from Matt RitchieWolfgang HuberZhenyu XuternarynetGene-regulatory network (GRN) modeling seeks to infer dependencies between genes and thereby provide insight into the regulatory relationships that exist within a cell. This package provides a computational Bayesian approach to GRN estimation from perturbation experiments using a ternary network model, in which gene expression is discretized into one of 3 states: up, unchanged, or down). The ternarynet package includes a parallel implementation of the replica exchange Monte Carlo algorithm for fitting network models, using MPI.https://bioconductor.org/packages/ternarynetternarynetbiotools:ternarynet1.56.0Command-line toolLibraryhttp://edamontology.org/topic_2229Cell biologyhttp://edamontology.org/topic_0203Gene expressionhttp://edamontology.org/topic_2269Statistics and probabilityMacWindowsLinuxRGPL-2.0-or-laterBioConductorhttp://edamontology.org/operation_3439Pathway or network predictionhttp://edamontology.org/data_3108Experimental measurementhttp://edamontology.org/format_2330Textual formathttp://edamontology.org/format_3033Matrix formathttp://edamontology.org/data_3112Gene expression matrixhttp://edamontology.org/format_2330Textual formathttp://edamontology.org/format_3033Matrix format

http://edamontology.org/data_0951Statistical estimate scorehttp://edamontology.org/format_3033Matrix format

https://bioconductor.org/packages/release/bioc/src/contrib/ternarynet_1.56.0.tar.gzSource codehttps://bioconductor.org/packages/ternarynetUser manualAnthony AlmudevarDavid BurtonHarry SternMatthew N. McCallTEQCTarget capture experiments combine hybridization-based (in solution or on microarrays) capture and enrichment of genomic regions of interest (e.g. the exome) with high throughput sequencing of the captured DNA fragments. This package provides functionalities for assessing and visualizing the quality of the target enrichment process, like specificity and sensitivity of the capture, per-target read coverage and so on.https://bioconductor.org/packages/TEQCteqcbiotools:teqc4.34.0Command-line toolLibraryhttp://edamontology.org/topic_3053Geneticshttp://edamontology.org/topic_3518Microarray experimentWindowsLinuxMacRGPL-2.0-or-laterBioConductorhttp://edamontology.org/operation_2428Validationhttps://bioconductor.org/packages/release/bioc/src/contrib/TEQC_4.34.0.tar.gzSource codehttps://bioconductor.org/packages/TEQCUser manualE. LowyG. RomaM. HummelS. BonninTCCThis package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages.https://bioconductor.org/packages/TCCtccbiotools:tcc1.52.0Command-line toolLibraryhttp://edamontology.org/topic_0203Gene expressionhttp://edamontology.org/topic_3170RNA-SeqMacWindowsLinuxRGPL-2.0BioConductorhttp://edamontology.org/operation_3223Differential gene expression analysishttps://bioconductor.org/packages/release/bioc/src/contrib/TCC_1.52.0.tar.gzSource codehttps://bioconductor.org/packages/TCCUser manualJianqiang SunKentaro ShimizuTomoaki Nishiyamaand Koji KadotasurvcompAssessment and Comparison for Performance of Risk Prediction (Survival) Models.https://bioconductor.org/packages/survcompsurvcompbiotools:survcomp1.62.0LibraryCommand-line toolhttp://edamontology.org/topic_3303Medicinehttp://edamontology.org/topic_3518Microarray experimenthttp://edamontology.org/topic_0634Pathologyhttp://edamontology.org/topic_2269Statistics and probabilityMacLinuxWindowsRArtistic-2.0BioConductorhttp://edamontology.org/operation_2424Comparisonhttp://www.pmgenomics.ca/bhklab/Mirrorhttp://www.mybiosoftware.com/survcomp-performance-assessment-and-comparison-for-survival-analysis.htmlSoftware cataloguehttp://www.pmgenomics.ca/bhklab/Source codehttps://bioconductor.org/packages/survcompUser manualBenjamin Haibe-KainsPersonDeveloperMaintainerCatharina OlsenPersonDeveloperChristos SotiriouPersonDeveloperGianluca BontempiPersonDeveloperJohn QuackenbushPersonDeveloperMarkus SchroederPersonDeveloperSamuel BrandersPersonDeveloperZhaleh SafikhaniPersonDeveloperstructDefines and includes a set of class-based templates for developing and implementing data processing and analysis workflows, with a strong emphasis on statistics and machine learning. The templates can be used and where needed extended to 'wrap' tools and methods from other packages into a common standardised structure to allow for effective and fast integration. Model objects can be combined into sequences, and sequences nested in iterators using overloaded operators to simplify and improve readability of the code. Ontology lookup has been integrated and implemented to provide standardised definitions for methods, inputs and outputs wrapped using the class-based templates.https://bioconductor.org/packages/structstructbiotools:struct1.24.0http://edamontology.org/topic_3474Machine learninghttp://edamontology.org/topic_3172Metabolomicshttp://edamontology.org/topic_3520Proteomics experimenthttp://edamontology.org/topic_2269Statistics and probabilityhttp://edamontology.org/topic_0769WorkflowsRGPL-3.0BioConductorhttp://edamontology.org/operation_3891Essential dynamicshttp://edamontology.org/operation_3435Standardisation and normalisationhttp://edamontology.org/operation_0337Visualisationhttp://bioconductor.org/packages/structToolboxRepositoryhttps://github.com/computational-metabolomicsRepositoryhttps://bioconductor.org/packages/release/bioc/src/contrib/struct_1.24.0.tar.gzSource codehttps://bioconductor.org/packages/structUser manual10.1093/bioinformatics/btaa1031Gavin Rhys LloydPersonDeveloperMaintainerRalf Johannes Maria WeberPersonDeveloperSTRINGdbThe STRINGdb package provides an R interface to STRING, a protein-protein interaction database and functional enrichment analysis tool (https://string-db.org).https://bioconductor.org/packages/STRINGdbstringdbbiotools:stringdb2.24.0LibraryCommand-line toolhttp://edamontology.org/topic_0128Protein interactionshttp://edamontology.org/topic_0078ProteinsMacLinuxWindowsRGPL-2.0BioConductorhttp://edamontology.org/operation_2421Database searchhttps://bioconductor.org/packages/release/bioc/src/contrib/STRINGdb_2.24.0.tar.gzSource codehttps://bioconductor.org/packages/STRINGdbUser manualAndrea FranceschinistatTargetA streamlined tool provides a graphical user interface for quality control based signal drift correction (QC-RFSC), integration of data from multi-batch MS-based experiments, and the comprehensive statistical analysis in metabolomics and proteomics.https://bioconductor.org/packages/statTargetstattargetbiotools:stattarget1.42.0LibraryCommand-line toolhttp://edamontology.org/topic_3572Data quality managementhttp://edamontology.org/topic_3172Metabolomicshttp://edamontology.org/topic_3520Proteomics experimentMacLinuxWindowsRLGPL-3.0-or-laterBioConductorProteomicshttp://edamontology.org/operation_2238Statistical calculationhttp://edamontology.org/operation_2428Validationhttp://edamontology.org/operation_3659Regression analysishttps://github.com/13479776/statTargetMirrorhttps://stattarget.github.ioSource codehttps://bioconductor.org/packages/statTargetUser manualbiocstyleusesHemi LuanspillRChannel interference in mass cytometry can cause spillover and may result in miscounting of protein markers. We develop a nonparametric finite mixture model and use the mixture components to estimate the probability of spillover. We implement our method using expectation-maximization to fit the mixture model.https://bioconductor.org/packages/spillRspillrbiotools:spillr1.8.0http://edamontology.org/topic_3934Cytometryhttp://edamontology.org/topic_3520Proteomics experimenthttp://edamontology.org/topic_2269Statistics and probabilityRLGPL-3.0BioConductorhttp://edamontology.org/operation_0337Visualisationhttp://edamontology.org/operation_3659Regression analysishttp://edamontology.org/operation_2929Protein fragment weight comparisonhttps://bioconductor.org/packages/release/bioc/src/contrib/spillR_1.8.0.tar.gzSource codehttps://bioconductor.org/packages/spillRUser manualAlexander G. ReisachPersonDeveloperChristof SeilerPersonDeveloperMarco GuazziniPersonDeveloperMaintainerSebastian WeichwaldPersonDeveloperSPIAThis package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study.https://bioconductor.org/packages/SPIAspiabiotools:spia2.64.0LibraryCommand-line toolhttp://edamontology.org/topic_0203Gene expressionhttp://edamontology.org/topic_3518Microarray experimenthttp://edamontology.org/topic_0602Molecular interactions, pathways and networksMacLinuxWindowsRBioConductorhttp://edamontology.org/operation_3223Differential gene expression analysishttp://edamontology.org/operation_2497Pathway or network analysishttp://bioinformatics.oxfordjournals.org/cgi/reprint/btn577v1Mirrorhttp://bioinformatics.oxfordjournals.org/cgi/reprint/btn577v1Source codehttps://bioconductor.org/packages/SPIAUser manualAdi Laurentiu TarcaPurvesh KathrisoGGiThe soGGi package provides a toolset to create genomic interval aggregate/summary plots of signal or motif occurence from BAM and bigWig files as well as PWM, rlelist, GRanges and GAlignments Bioconductor objects. soGGi allows for normalisation, transformation and arithmetic operation on and between summary plot objects as well as grouping and subsetting of plots by GRanges objects and user supplied metadata. Plots are created using the GGplot2 libary to allow user defined manipulation of the returned plot object. Coupled together, soGGi features a broad set of methods to visualise genomics data in the context of groups of genomic intervals such as genes, superenhancers and transcription factor binding events.https://bioconductor.org/packages/soGGisoggibiotools:soggi1.44.0LibraryCommand-line toolhttp://edamontology.org/topic_3169ChIP-seqhttp://edamontology.org/topic_0092Data visualisationhttp://edamontology.org/topic_0160Sequence sites, features and motifsMacLinuxWindowsRGPL-3.0-or-laterBioConductorhttp://edamontology.org/operation_0337Visualisationhttp://bioconductor/packages/release/bioc/src/contrib/soGGi_1.6.0.tar.gzSource codehttps://bioconductor.org/packages/soGGiUser manualDoug BarrowsGopuraja DharmalingamTom CarrollSNPRelateData management of large-scale whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language.https://bioconductor.org/packages/SeqArraysnprelatebiotools:snprelate1.52.1Command-line toolLibraryhttp://edamontology.org/topic_2885DNA polymorphismhttp://edamontology.org/topic_3517GWAS studyhttp://edamontology.org/topic_3053GeneticsMacWindowsLinuxRGPL-3.0BioConductorhttp://edamontology.org/operation_3197Genetic variation analysishttp://edamontology.org/operation_2238Statistical calculationhttp://corearray.sourceforge.net/tutorials/SNPRelate/Mirrorhttp://github.com/zhengxwen/SNPRelateMirrorhttps://github.com/zhengxwen/SeqArraySource codehttps://bioconductor.org/packages/SeqArrayUser manual10.1093/bioinformatics/bts60610.1093/bioinformatics/btx145Cathy LauriePersonContributorDavid LevinePersonContributorStephanie GogartenPersonDeveloperXiuwen ZhengPersonDeveloperMaintainersnmSNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest.https://bioconductor.org/packages/snmsnmbiotools:snm1.60.0Command-line toolLibraryhttp://edamontology.org/topic_3572Data quality managementhttp://edamontology.org/topic_0203Gene expressionhttp://edamontology.org/topic_3518Microarray experimentMacWindowsLinuxRLGPL-2.0-or-laterBioConductorhttp://edamontology.org/operation_3435Standardisation and normalisationhttps://bioconductor.org/packages/release/bioc/src/contrib/snm_1.60.0.tar.gzSource codehttps://bioconductor.org/packages/snmUser manualBrig Mecham and John D. Storeyslalomslalom is a scalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity, thereby allowing to identify biological drivers of cell-to-cell variability and model confounding factors. The method uses Bayesian factor analysis with a latent variable model to identify active pathways (selected by the user, e.g. KEGG pathways) that explain variation in a single-cell RNA-seq dataset. This an R/C++ implementation of the f-scLVM Python package. See the publication describing the method at https://doi.org/10.1186/s13059-017-1334-8.https://bioconductor.org/packages/slalomslalombiotools:slalom1.34.0Libraryhttp://edamontology.org/topic_0203Gene expressionhttp://edamontology.org/topic_3170RNA-Seqhttp://edamontology.org/topic_3168Sequencinghttp://edamontology.org/topic_3308TranscriptomicsMacWindowsLinuxRGPL-2.0BioConductorFree of chargehttp://edamontology.org/operation_3435Standardisation and normalisationhttp://edamontology.org/operation_0337Visualisationhttp://bioconductor.org/packages/slalom/Mirrorhttps://bioconductor.org/packages/release/bioc/src/contrib/slalom_1.34.0.tar.gzSource codehttps://bioconductor.org/packages/slalomUser manualDavis McCarthyPersonDeveloperMaintainerFlorian BuettnerPersonDeveloperJohn MarioniPersonDeveloperNaruemon PratanwanichPersonDeveloperOliver SteglePersonDeveloperSingleCellExperimentDefines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries.https://bioconductor.org/packages/SingleCellExperimentsinglecellexperimentbiotools:singlecellexperiment1.34.0Libraryhttp://edamontology.org/topic_3341Clone libraryWindowsLinuxMacRGPL-3.0BioConductorhttp://edamontology.org/operation_0224Query and retrievalhttp://bioconductor.org/packages/SingleCellExperiment/Mirrorhttps://git.bioconductor.org/packages/SingleCellExperimentRepositoryhttps://bioconductor.org/packages/release/bioc/src/contrib/SingleCellExperiment_1.34.0.tar.gzSource codehttps://bioconductor.org/packages/SingleCellExperimentUser manualgithub: lazappi)Aaron LunPersonDeveloperDavide RissoPersonDeveloperMaintainerKeegan KorthauerPersonContributorKevin Rue-AlbrechtPersonContributorLuke ZappiaPersonContributorsincellCell differentiation processes are achieved through a continuum of hierarchical intermediate cell-states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies.https://bioconductor.org/packages/sincellsincellbiotools:sincell1.44.0Command-line toolLibraryhttp://edamontology.org/topic_0099RNAhttp://edamontology.org/topic_3170RNA-Seqhttp://edamontology.org/topic_0637Taxonomyhttp://edamontology.org/topic_3308TranscriptomicsWindowsLinuxMacRGPL-2.0-or-laterBioConductorhttp://edamontology.org/operation_3680RNA-Seq analysishttp://edamontology.org/data_0872Phylogenetic treehttp://edamontology.org/format_2572BAMhttp://edamontology.org/data_0863Sequence alignmenthttp://edamontology.org/format_2572BAM

http://edamontology.org/data_2589Hierarchyhttp://edamontology.org/format_2067Sequence distance matrix format

http://bioconductor.org/Mirrorhttp://www.mybiosoftware.com/sincell-analysis-of-cell-state-hierarchies-from-single-cell-rna-seq.htmlSoftware cataloguehttp://bioconductor.org/Source codehttps://bioconductor.org/packages/sincellUser manualAmalio TelentiAntonio RausellMiguel JuliaSIMFinds associations between two human genomic datasets.https://bioconductor.org/packages/SIMsimbiotools:sim1.82.0LibraryCommand-line toolhttp://edamontology.org/topic_0092Data visualisationhttp://edamontology.org/topic_0622GenomicsMacLinuxWindowsRGPL-2.0-or-laterBioConductorhttp://edamontology.org/operation_3215Peak detectionhttp://edamontology.org/operation_3649Target-Decoyhttp://edamontology.org/data_0943Mass spectrometry spectrahttp://edamontology.org/format_3245Mass spectrometry data format

http://edamontology.org/data_1772Scorehttp://edamontology.org/format_3475TSV

https://bioconductor.org/packages/release/bioc/src/contrib/SIM_1.82.0.tar.gzSource codehttps://bioconductor.org/packages/SIMUser manualRenee X. de Menezes and Judith M. BoersightsSIGHTS is a suite of normalization methods, statistical tests, and diagnostic graphical tools for high throughput screening (HTS) assays. HTS assays use microtitre plates to screen large libraries of compounds for their biological, chemical, or biochemical activity.https://bioconductor.org/packages/sightssightsbiotools:sights1.38.0LibraryCommand-line toolSuitehttp://edamontology.org/topic_3343Compound libraries and screeninghttp://edamontology.org/topic_3572Data quality managementhttp://edamontology.org/topic_0092Data visualisationhttp://edamontology.org/topic_2269Statistics and probabilityMacLinuxWindowsRGPL-3.0BioConductorhttp://edamontology.org/operation_0337Visualisationhttp://edamontology.org/operation_2238Statistical calculationhttp://edamontology.org/operation_3435Standardisation and normalisationhttps://eg-r.github.io/sights/Mirrorhttps://eg-r.github.io/sights/Source codehttps://bioconductor.org/packages/sightsUser manualCarl MuriePersonDeveloperElika GargPersonDeveloperMaintainerHeydar EnshaPersonContributorRobert NadonPersonDeveloperSigFugeAlgorithm for testing significance of clustering in RNA-seq data.https://bioconductor.org/packages/SigFugesigfugebiotools:sigfuge1.50.0LibraryCommand-line toolhttp://edamontology.org/topic_0092Data visualisationhttp://edamontology.org/topic_0203Gene expressionhttp://edamontology.org/topic_0099RNAhttp://edamontology.org/topic_3170RNA-SeqMacLinuxWindowsRGPL-3.0BioConductorhttp://edamontology.org/operation_3432Clusteringhttp://bioconductor/packages/release/bioc/src/contrib/SigFuge_1.12.0.tar.gzSource codehttps://bioconductor.org/packages/SigFugeUser manualChristopher CabanskiPatrick KimesSGCPSGC is a semi-supervised pipeline for gene clustering in gene co-expression networks. SGC consists of multiple novel steps that enable the computation of highly enriched modules in an unsupervised manner. But unlike all existing frameworks, it further incorporates a novel step that leverages Gene Ontology information in a semi-supervised clustering method that further improves the quality of the computed modules.https://bioconductor.org/packages/SGCPsgcpbiotools:sgcp1.11.1http://edamontology.org/topic_0203Gene expressionhttp://edamontology.org/topic_0602Molecular interactions, pathways and networkshttp://edamontology.org/topic_0089Ontology and terminologyhttp://edamontology.org/topic_3170RNA-Seqhttp://edamontology.org/topic_0769WorkflowsGPL-3.0BioConductorhttp://edamontology.org/operation_3463Expression correlation analysishttp://edamontology.org/operation_3432Clusteringhttp://edamontology.org/operation_3766Weighted correlation network analysishttps://github.com/na396/SGCPSource codehttps://bioconductor.org/packages/SGCPUser manualIoannis KoutisPersonDeveloperNiloofar AghaieAbianePersonDeveloperMaintainerSeqGSEAThe package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively.https://bioconductor.org/packages/SeqGSEAseqgseabiotools:seqgsea1.52.0LibraryCommand-line toolhttp://edamontology.org/topic_0203Gene expressionhttp://edamontology.org/topic_3170RNA-SeqMacLinuxWindowsRGPL-3.0-or-laterBioConductorhttp://edamontology.org/operation_2436Gene-set enrichment analysishttps://bioconductor.org/packages/release/bioc/src/contrib/SeqGSEA_1.52.0.tar.gzSource codehttps://bioconductor.org/packages/SeqGSEAUser manualXi WangseqCATThe seqCAT package uses variant calling data (in the form of VCF files) from high throughput sequencing technologies to authenticate and validate the source, function and characteristics of biological samples used in scientific endeavours.https://bioconductor.org/packages/seqCATseqcatbiotools:seqcat1.34.0Libraryhttp://edamontology.org/topic_0199Genetic variationhttp://edamontology.org/topic_3168SequencingMacLinuxWindowsRMITBioConductorFree of chargehttp://edamontology.org/operation_3197Genetic variation analysishttp://bioconductor.org/packages/seqCAT/Mirrorhttps://bioconductor.org/packages/release/bioc/src/contrib/seqCAT_1.34.0.tar.gzSource codehttps://bioconductor.org/packages/seqCATUser manualErik FasteriusPersonDeveloperMaintainerSeqArrayScalable implementation of generalized mixed models with highly optimized C++ implementation and integration with Genomic Data Structure (GDS) files. It is designed for single variant tests and set-based aggregate tests in large-scale Phenome-wide Association Studies (PheWAS) with millions of variants and samples, controlling for sample structure and case-control imbalance. The implementation is based on the SAIGE R package (v0.45, Zhou et al. 2018 and Zhou et al. 2020), and it is extended to include the state-of-the-art ACAT-O set-based tests. Benchmarks show that SAIGEgds is significantly faster than the SAIGE R package. Optional OpenCL-based GPU acceleration is supported for the GRM cross-product computation in null model fitting and for GRM construction.https://bioconductor.org/packages/SAIGEgdsseqarraybiotools:seqarray2.12.0Command-line toolLibraryhttp://edamontology.org/topic_0199Genetic variationhttp://edamontology.org/topic_0622GenomicsMacWindowsLinuxRGPL-3.0BioConductorhttp://edamontology.org/operation_3227Variant callinghttp://github.com/zhengxwen/SeqArrayMirrorhttps://github.com/AbbVie-ComputationalGenomics/SAIGEgdsSource codehttps://bioconductor.org/packages/SAIGEgdsUser manual10.1038/s41588-018-0184-y10.1093/bioinformatics/btaa73110.1093/bioinformatics/btx145J. Wade DavisPersonContributorWei ZhouPersonContributorXiuwen ZhengPersonDeveloperMaintainersemisupImplements a parametric semi-supervised mixture model. The permutation test detects markers with main or interactive effects, without distinguishing them. Possible applications include genome-wide association analysis and differential expression analysis.https://bioconductor.org/packages/semisupsemisupbiotools:semisup1.36.0Libraryhttp://edamontology.org/topic_2885DNA polymorphismhttp://edamontology.org/topic_0199Genetic variationhttp://edamontology.org/topic_3053Geneticshttp://edamontology.org/topic_3518Microarray experimenthttp://edamontology.org/topic_3168SequencingWindowsLinuxMacRGPL-3.0BioConductorFree of chargehttp://edamontology.org/operation_2990Classificationhttp://edamontology.org/operation_3432Clusteringhttp://edamontology.org/operation_2424Comparisonhttps://github.com/rauschenberger/semisup/issuesIssue trackerhttps://github.com/rauschenberger/semisupMirrorhttps://github.com/rauschenberger/semisupSource codehttps://bioconductor.org/packages/semisupUser manualArmin RauschenbergerPersonDeveloperMaintainerscDDThis package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions.https://bioconductor.org/packages/scDDscddbiotools:scdd1.36.0Libraryhttp://edamontology.org/topic_3170RNA-SeqMacWindowsLinuxRGPL-2.0BioConductorFree of chargehttp://edamontology.org/operation_3432Clusteringhttp://edamontology.org/operation_2424Comparisonhttp://edamontology.org/operation_3223Differential gene expression analysishttp://edamontology.org/operation_0337Visualisationhttps://github.com/kdkorthauer/scDD/issuesIssue trackerhttp://bioconductor.org/packages/scDD/Mirrorhttps://github.com/kdkorthauer/scDDMirrorhttps://github.com/kdkorthauer/scDDSource codehttps://bioconductor.org/packages/scDDUser manualKeegan KorthauerPersonDeveloperMaintainer