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
Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data
Gupta NT, Vander Heiden JA, Uduman M, Gadala-Maria D, Yaari G, Kleinstein SH. Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics 2015, 31: 3356-3358. PMID: 26069265, PMCID: PMC4793929, DOI: 10.1093/bioinformatics/btv359.Peer-Reviewed Original ResearchConceptsHigh-throughput sequencing technologyB cell immunoglobulinLarge-scale characterizationLineage treesSpecialized computational methodsSelection pressureSequencing technologiesSomatic diversityClonal populationsIg repertoireSomatic hypermutationIg sequencesDiversityNon-commercial useSuite of utilitiesRepertoire diversityGermlineComputational methodsAllelesHypermutationA 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 miceAutomated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles
Gadala-Maria D, Yaari G, Uduman M, Kleinstein SH. Automated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles. Proceedings Of The National Academy Of Sciences Of The United States Of America 2015, 112: e862-e870. PMID: 25675496, PMCID: PMC4345584, DOI: 10.1073/pnas.1417683112.Peer-Reviewed Original Research
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