2024
A supervised Bayesian factor model for the identification of multi-omics signatures
Gygi J, Konstorum A, Pawar S, Aron E, Kleinstein S, Guan L. A supervised Bayesian factor model for the identification of multi-omics signatures. Bioinformatics 2024, 40: btae202. PMID: 38603606, PMCID: PMC11078774, DOI: 10.1093/bioinformatics/btae202.Peer-Reviewed Original ResearchMeSH KeywordsBayes TheoremComputational BiologyCOVID-19FemaleGenomicsHumansMultiomicsSupervised Machine LearningConceptsMulti-omics signaturesBayesian factor modelMulti-omics dataMulti-omics integrationSupplementary dataOmics datasetsMulti-omicsProfiling datasetsR packageDiverse assaysImproved biological understandingProfiling assaysSignature discoveryBioinformaticsProfiling studiesBiological understandingDimensionality reductionBiological responsesBiological signaturesCombination of dimensionality reduction
2021
Immunophenotyping assessment in a COVID-19 cohort (IMPACC): A prospective longitudinal study
, , Rouphael N, Maecker H, Montgomery R, Diray-Arce J, Kleinstein S, Altman M, Bosinger S, Eckalbar W, Guan L, Hough C, Krammer F, Langelier C, Levy O, McEnaney K, Peters B, Rahman A, Rajan J, Sigelman S, Steen H, van Bakel H, Ward A, Wilson M, Woodruff P, Zamecnik C, Augustine A, Ozonoff A, Reed E, Becker P, Higuita N, Altman M, Atkinson M, Baden L, Becker P, Bime C, Brakenridge S, Calfee C, Cairns C, Corry D, Davis M, Augustine A, Ehrlich L, Haddad E, Erle D, Fernandez-Sesma A, Hafler D, Hough C, Kheradmand F, Kleinstein S, Kraft M, Levy O, McComsey G, Melamed E, Messer W, Metcalf J, Montgomery R, Nadeau K, Ozonoff A, Peters B, Pulendran B, Reed E, Rouphael N, Sarwal M, Schaenman J, Sekaly R, Shaw A, Simon V. Immunophenotyping assessment in a COVID-19 cohort (IMPACC): A prospective longitudinal study. Science Immunology 2021, 6: eabf3733. PMID: 34376480, PMCID: PMC8713959, DOI: 10.1126/sciimmunol.abf3733.Peer-Reviewed Original ResearchConceptsCOVID-19 cohortProspective longitudinal studyHost immune responseLongitudinal studyCOVID-19Identification of biomarkersHospitalized patientsRespiratory secretionsClinical criteriaDisease progressionImmune responseRadiographic dataImmunologic assaysEffective therapeuticsOptimal timingStudy designBiologic samplingSuch interventionsCohortSeveritySample collectionAssay protocolsPatients
2020
Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data
Nouri N, Kleinstein SH. Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data. PLOS Computational Biology 2020, 16: e1007977. PMID: 32574157, PMCID: PMC7347241, DOI: 10.1371/journal.pcbi.1007977.Peer-Reviewed Original ResearchA structured model for immune exposures
Vita R, Overton JA, Dunn P, Cheung KH, Kleinstein SH, Sette A, Peters B. A structured model for immune exposures. Database 2020, 2020: baaa016. PMID: 32283555, PMCID: PMC7153954, DOI: 10.1093/database/baaa016.Peer-Reviewed Original ResearchMeSH KeywordsAntibodiesAntigensBiological OntologiesComputational BiologyData CurationDatabases, FactualEpitopesHumansImmune SystemImmune System DiseasesConceptsImmune exposure
2019
Gene set meta-analysis with Quantitative Set Analysis for Gene Expression (QuSAGE)
Meng H, Yaari G, Bolen CR, Avey S, Kleinstein SH. Gene set meta-analysis with Quantitative Set Analysis for Gene Expression (QuSAGE). PLOS Computational Biology 2019, 15: e1006899. PMID: 30939133, PMCID: PMC6461294, DOI: 10.1371/journal.pcbi.1006899.Peer-Reviewed Original ResearchMeSH KeywordsComputational BiologyGene ExpressionGene Expression ProfilingHumansInfluenza, HumanProbabilitySoftwareVaccination
2018
The CAIRR Pipeline for Submitting Standards-Compliant B and T Cell Receptor Repertoire Sequencing Studies to the National Center for Biotechnology Information Repositories
Bukhari SAC, O’Connor M, Martínez-Romero M, Egyedi AL, Willrett D, Graybeal J, Musen MA, Rubelt F, Cheung KH, Kleinstein SH. The CAIRR Pipeline for Submitting Standards-Compliant B and T Cell Receptor Repertoire Sequencing Studies to the National Center for Biotechnology Information Repositories. Frontiers In Immunology 2018, 9: 1877. PMID: 30166985, PMCID: PMC6105692, DOI: 10.3389/fimmu.2018.01877.Peer-Reviewed Original ResearchConceptsMetadata qualityInformation repositoryAdaptive immune receptor repertoiresLarge-scale dataWeb–based templateSoftware frameworkData annotationData standardsEffective sharingAIRR-seq dataReceptor repertoireData submittersCell receptorSequence filesAdaptive immune responsesRepositoryImmune receptor repertoiresMetadataData setsT cell receptorArchive databaseB cell receptorOptimized Threshold Inference for Partitioning of Clones From High-Throughput B Cell Repertoire Sequencing Data
Nouri N, Kleinstein SH. Optimized Threshold Inference for Partitioning of Clones From High-Throughput B Cell Repertoire Sequencing Data. Frontiers In Immunology 2018, 9: 1687. PMID: 30093903, PMCID: PMC6070604, DOI: 10.3389/fimmu.2018.01687.Peer-Reviewed Original Research
2017
Multiple network-constrained regressions expand insights into influenza vaccination responses
Avey S, Mohanty S, Wilson J, Zapata H, Joshi SR, Siconolfi B, Tsang S, Shaw AC, Kleinstein SH. Multiple network-constrained regressions expand insights into influenza vaccination responses. Bioinformatics 2017, 33: i208-i216. PMID: 28881994, PMCID: PMC5870750, DOI: 10.1093/bioinformatics/btx260.Peer-Reviewed Original ResearchHierarchical Clustering Can Identify B Cell Clones with High Confidence in Ig Repertoire Sequencing Data
Gupta NT, Adams KD, Briggs AW, Timberlake SC, Vigneault F, Kleinstein SH. Hierarchical Clustering Can Identify B Cell Clones with High Confidence in Ig Repertoire Sequencing Data. The Journal Of Immunology 2017, 198: 2489-2499. PMID: 28179494, PMCID: PMC5340603, DOI: 10.4049/jimmunol.1601850.Peer-Reviewed Original Research
2016
Solving Immunology?
Vodovotz Y, Xia A, Read EL, Bassaganya-Riera J, Hafler DA, Sontag E, Wang J, Tsang JS, Day JD, Kleinstein SH, Butte AJ, Altman MC, Hammond R, Sealfon SC. Solving Immunology? Trends In Immunology 2016, 38: 116-127. PMID: 27986392, PMCID: PMC5695553, DOI: 10.1016/j.it.2016.11.006.Peer-Reviewed Original ResearchRecurrent genetic defects in classical Hodgkin lymphoma cell lines
Hudnall SD, Meng H, Lozovatsky L, Li P, Strout M, Kleinstein SH. Recurrent genetic defects in classical Hodgkin lymphoma cell lines. Leukemia & Lymphoma 2016, 57: 2890-2900. PMID: 27121023, DOI: 10.1080/10428194.2016.1177179.Peer-Reviewed Original ResearchConceptsMitosis-related genesSingle nucleotide variantsCHL cell linesCell linesRecurrent genetic defectsPathogenic single nucleotide variantsHL cell linesMitotic genesChromosome duplicationClassical Hodgkin lymphoma cell linesGenomic instabilityGenetic analysisWhole-exome sequencingNucleotide variantsGenesHodgkin's lymphoma cell linesLymphoma cell linesNumber variantsKaryotypic analysisGenetic defectsWealth of informationPoor growthVariantsDuplicationLines
2015
Practical guidelines for B-cell receptor repertoire sequencing analysis
Yaari G, Kleinstein SH. Practical guidelines for B-cell receptor repertoire sequencing analysis. Genome Medicine 2015, 7: 121. PMID: 26589402, PMCID: PMC4654805, DOI: 10.1186/s13073-015-0243-2.Peer-Reviewed Original ResearchMeSH KeywordsComputational BiologyGuidelines as TopicHigh-Throughput Nucleotide SequencingHumansReceptors, Antigen, B-CellSequence AnalysisInteractive Big Data Resource to Elucidate Human Immune Pathways and Diseases
Gorenshteyn D, Zaslavsky E, Fribourg M, Park CY, Wong AK, Tadych A, Hartmann BM, Albrecht RA, García-Sastre A, Kleinstein SH, Troyanskaya OG, Sealfon SC. Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases. Immunity 2015, 43: 605-614. PMID: 26362267, PMCID: PMC4753773, DOI: 10.1016/j.immuni.2015.08.014.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsBayes TheoremComputational BiologyGene Regulatory NetworksHost-Pathogen InteractionsHumansImmune SystemImmune System DiseasesInternetProtein Interaction MappingProtein Interaction MapsReproducibility of ResultsSignal TransductionSupport Vector MachineTranscriptomeVirus DiseasesConceptsPublic high-throughput dataGenome-scale experimentsDisease-associated genesHigh-throughput datasetsHigh-throughput dataData-driven hypothesesGenetic studiesImmune pathwaysGenesImmunological diseasesFunctional relationshipBiomedical research effortsImportant interactionsMolecular entitiesImmune systemHuman immune systemProteinExponential growthPathwayData resourcesBayesian integrationRelevant insightsGrowthCompendiumIdentificationAnalysis of gene–environment interactions in postnatal development of the mammalian intestine
Rakoff-Nahoum S, Kong Y, Kleinstein SH, Subramanian S, Ahern PP, Gordon JI, Medzhitov R. Analysis of gene–environment interactions in postnatal development of the mammalian intestine. Proceedings Of The National Academy Of Sciences Of The United States Of America 2015, 112: 1929-1936. PMID: 25691701, PMCID: PMC4343130, DOI: 10.1073/pnas.1424886112.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsComputational BiologyGene-Environment InteractionIntestinesMiceMice, KnockoutReceptors, Interleukin-1Toll-Like ReceptorsConceptsTLR/IL-1RToll-like receptorsPostnatal developmentIntestinal gene expressionMyeloid differentiation factor 88Domain-containing adapter-inducing interferonDifferentiation factor 88Adapter-inducing interferonMast cell homeostasisIntestinal ontogenyReceptor family membersFactor 88WT miceGene expression programsSmooth muscle developmentGene-environment interactionsIL-1RIntestinal physiologyImmune systemKnockout littermatesPostnatal transitionMicrobial colonizationIntestinal contentsGene expressionPubertal maturation
2014
Computational resources for high-dimensional immune analysis from the Human Immunology Project Consortium
Brusic V, Gottardo R, Kleinstein SH, Davis MM. Computational resources for high-dimensional immune analysis from the Human Immunology Project Consortium. Nature Biotechnology 2014, 32: 146-148. PMID: 24441472, PMCID: PMC4294529, DOI: 10.1038/nbt.2777.Peer-Reviewed Original Research
2013
Reconstruction of regulatory networks through temporal enrichment profiling and its application to H1N1 influenza viral infection
Zaslavsky E, Nudelman G, Marquez S, Hershberg U, Hartmann BM, Thakar J, Sealfon SC, Kleinstein SH. Reconstruction of regulatory networks through temporal enrichment profiling and its application to H1N1 influenza viral infection. BMC Bioinformatics 2013, 14: s1. PMID: 23734902, PMCID: PMC3633009, DOI: 10.1186/1471-2105-14-s6-s1.Peer-Reviewed Original ResearchConceptsRegulatory networksTranscription factorsExtensive genetic reprogrammingUnderlying transcriptional networksGene expression patternsAntiviral responseGene expression changesNovel antiviral factorTranscriptional cascadeTranscriptional networksDendritic cellsPromoter analysisRegulatory connectionsGenetic reprogrammingTranscriptional programsExpression patternsNetwork reconstruction methodsExpression changesCellular responsesExpression kineticsMonocyte-derived human dendritic cellsAntiviral stateHuman monocyte-derived dendritic cellsSuch virus infectionsImmune antagonists
2011
Biomedical Model Fitting and Error Analysis
Costa KD, Kleinstein SH, Hershberg U. Biomedical Model Fitting and Error Analysis. Science Signaling 2011, 4: tr9. PMID: 21954296, PMCID: PMC3272496, DOI: 10.1126/scisignal.2001983.Peer-Reviewed Original ResearchMeSH KeywordsComputational BiologyModels, BiologicalNonlinear DynamicsResearch DesignStatistics as TopicConceptsMathematical modelAppropriate mathematical modelModel parametersError analysisFit parameter valuesLinearization schemeNonlinear modelGoodness of fitNonlinear dataModel fittingBest fitParameter valuesInverse modelingComputational methodsParticular applicationSuch constantsExperimental dataFittingBiomedical systemsProblemFitModelParametersConstantsSeries of measurementsCell subset prediction for blood genomic studies
Bolen CR, Uduman M, Kleinstein SH. Cell subset prediction for blood genomic studies. BMC Bioinformatics 2011, 12: 258. PMID: 21702940, PMCID: PMC3213685, DOI: 10.1186/1471-2105-12-258.Peer-Reviewed Original ResearchMeSH KeywordsComputational BiologyGene Expression ProfilingHepatitis C, ChronicHumansLeukocytes, MononuclearPolymorphism, Single NucleotideConceptsPeripheral blood mononuclear cellsTotal peripheral blood mononuclear cellsGene signatureSubset-specific genesBlood mononuclear cellsPatient blood samplesPersonalized treatment decisionsSpecific cell subsetsHCV patientsPBMC subsetsNK cellsStandard therapyCell subsetsMononuclear cellsT cellsTreatment decisionsTherapy responseBlood samplesB cellsMyeloid cellsCellular sourceTranscriptional profilingDisease mechanismsGene expression profilesCells
2008
Getting Started in Computational Immunology
Kleinstein SH. Getting Started in Computational Immunology. PLOS Computational Biology 2008, 4: e1000128. PMID: 18769677, PMCID: PMC2518523, DOI: 10.1371/journal.pcbi.1000128.Peer-Reviewed Original Research