2024
nf-core/airrflow: An adaptive immune receptor repertoire analysis workflow employing the Immcantation framework.
Gabernet G, Marquez S, Bjornson R, Peltzer A, Meng H, Aron E, Lee N, Jensen C, Ladd D, Polster M, Hanssen F, Heumos S, , Yaari G, Kowarik M, Nahnsen S, Kleinstein S. nf-core/airrflow: An adaptive immune receptor repertoire analysis workflow employing the Immcantation framework. PLOS Computational Biology 2024, 20: e1012265. PMID: 39058741, PMCID: PMC11305553, DOI: 10.1371/journal.pcbi.1012265.Peer-Reviewed Original ResearchImmcantation frameworkAIRR-seqAIRR-seq dataAdaptive immune receptor repertoire sequencingSingle-cell sequencing datasetsClonal inferenceSequencing errorsSequencing datasetsNextflow workflowClonal relationshipReceptor sequencesRepertoire sequencingHigh-throughput processingSequenceImmune challengeAnalysis workflowT-cell receptor sequencingNextflowB cellsSARS-CoV-2Experimental toolResponse to SARS-CoV-2Infectious diseasesInferring B Cell Phylogenies from Paired H and L Chain BCR Sequences with Dowser.
Jensen C, Sumner J, Kleinstein S, Hoehn K. Inferring B Cell Phylogenies from Paired H and L Chain BCR Sequences with Dowser. The Journal Of Immunology 2024, 212: 1579-1588. PMID: 38557795, PMCID: PMC11073909, DOI: 10.4049/jimmunol.2300851.Peer-Reviewed Original ResearchConceptsPhylogenetic treeL chainsBranch lengthsBCR sequencesTree-building methodsSingle-cell sequencing dataHistory of mutationsSingle-cell sequencingPhylogenetic methodsSequence dataSequencing technologiesL chain sequencesTree accuracyEvolutionary processSingle-cellPhylogenyImmune responseSomatic hypermutationSequenceClonesMutationsB cell clonesHuman immune responseTreesBCR
2020
Position-Dependent Differential Targeting of Somatic Hypermutation
Zhou JQ, Kleinstein SH. Position-Dependent Differential Targeting of Somatic Hypermutation. The Journal Of Immunology 2020, 205: 3468-3479. PMID: 33188076, PMCID: PMC7726104, DOI: 10.4049/jimmunol.2000496.Peer-Reviewed Original ResearchConceptsSomatic hypermutationSHM targetingIg sequencesSame DNA motifTranscription start siteAllele-specific effectsInfluence of selectionGene familyVariable gene familiesDNA motifsSequence neighborhoodError-prone repairStart siteAb diversityDNA lesionsDifferential targetingUnique motifMotifSequenceTargetingHypermutationEffective humoral immunityIntrinsic biasesAffinity maturationLarge collection
2016
A Model of Somatic Hypermutation Targeting in Mice Based on High-Throughput Ig Sequencing Data
Cui A, Di Niro R, Vander Heiden JA, Briggs AW, Adams K, Gilbert T, O'Connor KC, Vigneault F, Shlomchik MJ, Kleinstein SH. A Model of Somatic Hypermutation Targeting in Mice Based on High-Throughput Ig Sequencing Data. The Journal Of Immunology 2016, 197: 3566-3574. PMID: 27707999, PMCID: PMC5161250, DOI: 10.4049/jimmunol.1502263.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsB-LymphocytesCells, CulturedClonal Selection, Antigen-MediatedDNA RepairFemaleGerminal CenterHigh-Throughput Nucleotide SequencingHumansImmunoglobulin Heavy ChainsImmunoglobulin Variable RegionMiceMice, Inbred BALB CMice, TransgenicModels, GeneticMutationMutation RateSomatic Hypermutation, ImmunoglobulinConceptsSpecific DNA motifsSimilar biological processesObserved mutation patternDNA repair activityIg sequencesNonfunctional sequencesDNA motifsMutation patternsHigh mutation frequencySelection pressureUnselected mutationsSequencing dataBiological processesFunctional sequencesRepair activityTransition mutationsSomatic hypermutation patternsGerminal center B cellsSomatic hypermutationNext-generation methodsHypermutation patternsMutation frequencyMutationsSequenceMotif
2015
A model of somatic hypermutation targeting in mice based on high-throughput immunoglobulin sequencing data (TECH2P.910)
Cui A, Diniro R, Briggs A, Adams K, Vander Heiden J, O'Connor K, Vigneault F, Shlomchik M, Kleinstein S. A model of somatic hypermutation targeting in mice based on high-throughput immunoglobulin sequencing data (TECH2P.910). The Journal Of Immunology 2015, 194: 206.20-206.20. DOI: 10.4049/jimmunol.194.supp.206.20.Peer-Reviewed Original ResearchSimilar biological processesNon-functional sequencesObserved mutation patternDNA repair activityMutation patternsDNA motifsNext-generation sequencingHigh mutation frequencyUnselected mutationsSelection pressureSequencing dataBiological processesFunctional sequencesImmunoglobulin genesRepair activitySomatic hypermutation patternsSomatic hypermutationHypermutation patternsMutation frequencyIg sequencesT transitionSequenceHeavy chain transgenic miceMotifTransgenic mice
2014
Integrating B Cell Lineage Information into Statistical Tests for Detecting Selection in Ig Sequences
Uduman M, Shlomchik MJ, Vigneault F, Church GM, Kleinstein SH. Integrating B Cell Lineage Information into Statistical Tests for Detecting Selection in Ig Sequences. The Journal Of Immunology 2014, 192: 867-874. PMID: 24376267, PMCID: PMC4363135, DOI: 10.4049/jimmunol.1301551.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsAntibody AffinityAntibody DiversityB-Lymphocyte SubsetsCell LineageClonal Selection, Antigen-MediatedComputer SimulationConfounding Factors, EpidemiologicGene Rearrangement, B-LymphocyteGenes, ImmunoglobulinHumansMiceModels, ImmunologicalModels, StatisticalROC CurveSequence Analysis, DNASomatic Hypermutation, ImmunoglobulinVDJ ExonsConceptsLineage treesHigh-throughput sequencing technologyLineage tree shapesCell lineage informationIg sequencesRatio of replacementTree-shape analysisStatistical frameworkSequence-based methodsBinomial statistical analysisExperimental data setsIndicators of selectionSequencing technologiesLineage informationSequencing depthNumber of generationsData setsHybrid methodVivo selectionSilent mutationsTree shapeStatistical testsSequenceShape analysisMutations
2012
Quantifying selection in high-throughput Immunoglobulin sequencing datasets (58.4)
Yaari G, Uduman M, Kleinstein S. Quantifying selection in high-throughput Immunoglobulin sequencing datasets (58.4). The Journal Of Immunology 2012, 188: 58.4-58.4. DOI: 10.4049/jimmunol.188.supp.58.4.Peer-Reviewed Original Research
2011
Somatic hypermutation targeting is influenced by location within the immunoglobulin V region
Cohen RM, Kleinstein SH, Louzoun Y. Somatic hypermutation targeting is influenced by location within the immunoglobulin V region. Molecular Immunology 2011, 48: 1477-1483. PMID: 21592579, PMCID: PMC3109224, DOI: 10.1016/j.molimm.2011.04.002.Peer-Reviewed Original ResearchConceptsObserved mutation patternSpecific DNA motifsBiased codon usageImmunoglobulin V genesMutation accumulationGene positionCodon usageMutation patternsDNA motifsPositive selectionPosition-specific effectsImmunoglobulin V regionsNegative selectionB cellsMutationsMutation frequencyV geneGenesPeripheral B cellsSubstitution typeV regionsTargetingSpecific targetingCellsSequence
2006
Mutation parameters from DNA sequence data using graph theoretic measures on lineage trees
Magori-Cohen R, Louzoun Y, Kleinstein SH. Mutation parameters from DNA sequence data using graph theoretic measures on lineage trees. Bioinformatics 2006, 22: e332-e340. PMID: 16873490, DOI: 10.1093/bioinformatics/btl239.Peer-Reviewed Original ResearchConceptsLineage treesDNA sequencesDNA sequence dataSomatic hypermutationMaximum likelihood analysisTree shapeBioinformatics methodsSequence dataLethal mutationsMutation rateNumber of generationsLikelihood analysisMutation parametersB cellsSynthetic treeClonal expansionTreesSequenceMutationsHypermutationAffinity maturationCellsImportant linkClonesUnexpected locations