2024
BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks
Pelletier S, Leclercq M, Roux-Dalvai F, de Geus M, Leslie S, Wang W, Lam T, Nairn A, Arnold S, Carlyle B, Precioso F, Droit A. BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks. Nature Communications 2024, 15: 3777. PMID: 38710683, PMCID: PMC11074280, DOI: 10.1038/s41467-024-48177-5.Peer-Reviewed Original ResearchConceptsLC-MS experimentsLC-MSLiquid chromatography mass spectrometry dataComplex biological samplesMass spectrometry dataLiquid chromatography mass spectrometryChromatography mass spectrometryMass spectrometrySpectrometry dataEffective removalBiological samplesExperimental conditionsBatch effect removalSample processing protocolBatch effectsSpectrometryBatch effect correction methodsCorrecting batch effectsRemoval of batch effects
2021
Supervised machine learning in the mass spectrometry laboratory: A tutorial
Lee ES, Durant TJS. Supervised machine learning in the mass spectrometry laboratory: A tutorial. Journal Of Mass Spectrometry And Advances In The Clinical Lab 2021, 23: 1-6. PMID: 34984411, PMCID: PMC8692990, DOI: 10.1016/j.jmsacl.2021.12.001.Peer-Reviewed Educational MaterialsCapabilities of MLMachine learning methodsHigh-dimensional datasetsClassification problemSupervised machineSupervised MLComputer scienceDiscrete data elementsLearning methodsResult qualityData elementsML practicesDatasetSoftwareScientist communityInherent relationshipTutorialMass spectrometry dataPromising synergyMS datasetsData analysisMass spectrometry laboratoriesDistant natureRecent yearsWorkflowOn the feasibility of deep learning applications using raw mass spectrometry data
Cadow J, Manica M, Mathis R, Reddel R, Robinson P, Wild P, Hains P, Lucas N, Zhong Q, Guo T, Aebersold R, Martínez M. On the feasibility of deep learning applications using raw mass spectrometry data. Bioinformatics 2021, 37: i245-i253. PMID: 34252933, PMCID: PMC8275322, DOI: 10.1093/bioinformatics/btab311.Peer-Reviewed Original ResearchConceptsRaw mass spectrometry dataDeep learning modelsRaw MS dataMass spectrometry dataClassification performanceDeep learningMS dataMass spectrometryLearning modelsSpectrometry dataApplication of deep learningMS imagesNatural image classificationDeep learning applicationsPrivacy of individualsTransfer learning techniqueData-independent-acquisitionMS2 spectraClassification taskData processing pipelinesClassification labelsImage classificationFeature vectorTransfer learningSample sparsityImproved methods for RNAseq-based alternative splicing analysis
Halperin RF, Hegde A, Lang JD, Raupach EA, Legendre C, Liang W, LoRusso P, Sekulic A, Sosman J, Trent J, Rangasamy S, Pirrotte P, Schork N. Improved methods for RNAseq-based alternative splicing analysis. Scientific Reports 2021, 11: 10740. PMID: 34031440, PMCID: PMC8144374, DOI: 10.1038/s41598-021-89938-2.Peer-Reviewed Original ResearchConceptsProtein-level effectsSplicing analysisSplice eventsSplice isoformsRNAseq dataAlternative splicing analysisTissue-specific splice variantsDifferential splicing analysisGene expression levelsPathogenic splicing variantProtein level expressionSequence readsSplicing variantsSplice variantsOncogenic mutationsMass spectrometry dataSplice alterationsExpression levelsRNAseqIsoformsMelanoma datasetSpectrometry dataNovel statistical approachAnalysis resourcesMass spectrometry
2020
Quantitative Proteomic Analysis of Chikungunya Virus-Infected Aedes aegypti Reveals Proteome Modulations Indicative of Persistent Infection
Cui Y, Liu P, Mooney BP, Franz AWE. Quantitative Proteomic Analysis of Chikungunya Virus-Infected Aedes aegypti Reveals Proteome Modulations Indicative of Persistent Infection. Journal Of Proteome Research 2020, 19: 2443-2456. PMID: 32375005, PMCID: PMC7419016, DOI: 10.1021/acs.jproteome.0c00173.Peer-Reviewed Original ResearchConceptsSerine-type endopeptidasesMetabolism related pathwaysQuantitative proteomic analysisFunctional enrichment analysisChikungunya virusRibosome biogenesisLabel-free quantificationRNA machineryNonpathogenic relationshipsNovel proteinProteome modulationProteomic analysisVesicular transportEnrichment analysisCHIKV infectionOxidative phosphorylationBiological pathwaysRelated pathwaysMass spectrometry dataPresence of CHIKVProteinMosquito samplesProteomeOral acquisitionMolecular interactions
2017
LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data
Koelmel J, Kroeger N, Ulmer C, Bowden J, Patterson R, Cochran J, Beecher C, Garrett T, Yost R. LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data. BMC Bioinformatics 2017, 18: 331. PMID: 28693421, PMCID: PMC5504796, DOI: 10.1186/s12859-017-1744-3.Peer-Reviewed Original ResearchConceptsLipid identificationNumerous biological functionsHigh-resolution tandem mass spectrometry dataMass spectrometry workflowsUncharacterized adductsBiological functionsLipid typeStructural detailsLipid-based biomarkersPathway perturbationsTandem mass spectrometry dataSpectral similarity scoreCorrect annotationLipid speciesMass spectrometry dataLiquid chromatography-tandem mass spectrometry (LC-MS/MS) workflowSilico FragmentMass spectrometry experimentsLipid candidatesEtiology of diseaseLipid moleculesAnnotationFatty acyl constituentsBiomarker discoveryDisease etiologyCorrigendum to “Common cases of improper lipid annotation using high-resolution tandem mass spectrometry data and corresponding limitations in biological interpretation” [Biochim. Biophys. Acta 1862(8) (2017) 766–770]
Koelmel J, Ulmer C, Jones C, Yost R, Bowden J. Corrigendum to “Common cases of improper lipid annotation using high-resolution tandem mass spectrometry data and corresponding limitations in biological interpretation” [Biochim. Biophys. Acta 1862(8) (2017) 766–770]. Biochimica Et Biophysica Acta (BBA) - Molecular And Cell Biology Of Lipids 2017, 1862: 1024. PMID: 28648965, DOI: 10.1016/j.bbalip.2017.06.013.Peer-Reviewed Original ResearchProteomics data on MAP Kinase Kinase 3 knock out bone marrow derived macrophages exposed to cigarette smoke extract
Srivastava R, Mannam P, Rauniyar N, Lam TT, Luo R, Lee PJ, Srivastava A. Proteomics data on MAP Kinase Kinase 3 knock out bone marrow derived macrophages exposed to cigarette smoke extract. Data In Brief 2017, 13: 320-325. PMID: 28653025, PMCID: PMC5476452, DOI: 10.1016/j.dib.2017.05.049.Peer-Reviewed Original ResearchMAP kinase kinase 3Yale Protein Expression DatabaseKinase 3Protein Expression DatabaseAbsolute quantitation (iTRAQ) reagentsIngenuity Pathway Analysis softwarePathway Analysis softwareTotal proteomeCellular proteinsIsobaric tagsProteomic dataMAP kinaseCanonical pathwaysMolecular networksCigarette smoke extractProteome DiscovererMass spectrometry dataBone marrowExpression databaseLC-MS/MSSpectrometry dataPathwaySmoke extractPhosphoproteomeProteomeCommon cases of improper lipid annotation using high-resolution tandem mass spectrometry data and corresponding limitations in biological interpretation
Koelmel J, Ulmer C, Jones C, Yost R, Bowden J. Common cases of improper lipid annotation using high-resolution tandem mass spectrometry data and corresponding limitations in biological interpretation. Biochimica Et Biophysica Acta (BBA) - Molecular And Cell Biology Of Lipids 2017, 1862: 766-770. PMID: 28263877, PMCID: PMC5584053, DOI: 10.1016/j.bbalip.2017.02.016.Peer-Reviewed Original Research
2016
SILAC based protein profiling data of MKK3 knockout mouse embryonic fibroblasts
Srivastava A, Shinn AS, Lam TT, Lee PJ, Mannam P. SILAC based protein profiling data of MKK3 knockout mouse embryonic fibroblasts. Data In Brief 2016, 7: 418-422. PMID: 26977448, PMCID: PMC4782019, DOI: 10.1016/j.dib.2016.02.034.Peer-Reviewed Original ResearchMouse embryonic fibroblastsYale Protein Expression DatabaseIngenuity Pathway AnalysisEmbryonic fibroblastsKnockout mouse embryonic fibroblastsProtein Expression DatabaseWT mouse embryonic fibroblastsQuantitative mass spectrometryWhole cell lysatesTotal proteomeIntegrated DiscoveryMAP kinasePathway analysisAltered pathwaysCell lysatesMass spectrometry dataSILACPhosphopeptide enrichmentProtein levelsExpression databaseProteinSpectrometry dataPathwayFibroblastsMass spectrometry
2015
Modeling of Imaging Mass Spectrometry Data and Testing by Permutation for Biomarkers Discovery in Tissues
Marczyk M, Drazek G, Pietrowska M, Widlak P, Polanska J, Polanski A. Modeling of Imaging Mass Spectrometry Data and Testing by Permutation for Biomarkers Discovery in Tissues. Procedia Computer Science 2015, 51: 693-702. DOI: 10.1016/j.procs.2015.05.186.Peer-Reviewed Original Research
2014
Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling
Benton HP, Ivanisevic J, Mahieu NG, Kurczy ME, Johnson CH, Franco L, Rinehart D, Valentine E, Gowda H, Ubhi BK, Tautenhahn R, Gieschen A, Fields MW, Patti GJ, Siuzdak G. Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling. Analytical Chemistry 2014, 87: 884-891. PMID: 25496351, PMCID: PMC4303330, DOI: 10.1021/ac5025649.Peer-Reviewed Original ResearchConceptsMass spectrometry data acquisitionSystems biology levelMass spectrometry analysisBioinformatics resourcesGlobal profilingTandem mass spectrometry dataProfiling datasetsMass spectrometry databaseMass spectrometry dataSpectrometry analysisProfilingData acquisitionSpectrometry dataRapid metabolite identificationMetabolomic profilingUntargeted metabolomicsBacterial samplesMetabolomicsSimultaneous data processingIdentificationMetabolite identificationMetabolomics workflowsFliesAutomatic searchAutonomous approach
2007
X!!Tandem, an Improved Method for Running X!Tandem in Parallel on Collections of Commodity Computers
Bjornson RD, Carriero NJ, Colangelo C, Shifman M, Cheung KH, Miller PL, Williams K. X!!Tandem, an Improved Method for Running X!Tandem in Parallel on Collections of Commodity Computers. Journal Of Proteome Research 2007, 7: 293-299. PMID: 17902638, PMCID: PMC3863625, DOI: 10.1021/pr0701198.Peer-Reviewed Original Research
2006
Computational Analysis of Mass Spectrometry Data Using Novel Combinatorial Methods
Fadiel A, Langston M, Peng X, Perkins A, Taylor H, Tuncalp O, Vitellot D, Pevsner P, Naftolin F. Computational Analysis of Mass Spectrometry Data Using Novel Combinatorial Methods. 2006, 266-273. DOI: 10.1109/aiccsa.2006.205100.Peer-Reviewed Original ResearchProteome profilingQuantitative proteome profilingProtein expression patternsTwo-dimensional electrophoresisCell proteomeMachinery functionsMatrix-Assisted Laser Desorption Ionization Mass SpectrometryLaser desorption ionization mass spectrometryMass spectrometryProteome profilesProteomic profilingProtein expression statusProteomic profilesAssisted Laser Desorption Ionization Mass SpectrometryExpression patternsDesorption ionization mass spectrometryMALDI mass spectrometryMass spectrometry dataProteomeNon-invasive sampling methodProfilingMALDI-MSSpectrometry dataComputational analysisExpression status
2005
Aligning Peaks Across Multiple Mass Spectrometry Data Sets Using A Scale-Space Based Approach
Yu W, Li X, Zhao H. Aligning Peaks Across Multiple Mass Spectrometry Data Sets Using A Scale-Space Based Approach. 2005, 126-127. DOI: 10.1109/csbw.2005.19.Peer-Reviewed Original ResearchHierarchical clustering methodScale-space approachAlignment frameworkBased ApproachClustering methodIntensity informationZ-informationAlignment of peaksCurrent approachesPeak alignmentMass spectrometry dataConcrete examplesInformationSpectrometry dataSpace limitationsExtra advantageFrameworkAlignmentParameter determinationSet
2003
Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data
Wu B, Abbott T, Fishman D, McMurray W, Mor G, Stone K, Ward D, Williams K, Zhao H. Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics 2003, 19: 1636-1643. PMID: 12967959, DOI: 10.1093/bioinformatics/btg210.Peer-Reviewed Original Research
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