2023
IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data
Peres A, Lees W, Rodriguez O, Lee N, Polak P, Hope R, Kedmi M, Collins A, Ohlin M, Kleinstein S, Watson C, Yaari G. IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data. Nucleic Acids Research 2023, 51: e86-e86. PMID: 37548401, PMCID: PMC10484671, DOI: 10.1093/nar/gkad603.Peer-Reviewed Original Research
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
2019
Inferred Allelic Variants of Immunoglobulin Receptor Genes: A System for Their Evaluation, Documentation, and Naming
Ohlin M, Scheepers C, Corcoran M, Lees WD, Busse CE, Bagnara D, Thörnqvist L, Bürckert JP, Jackson KJL, Ralph D, Schramm CA, Marthandan N, Breden F, Scott J, Matsen F, Greiff V, Yaari G, Kleinstein SH, Christley S, Sherkow JS, Kossida S, Lefranc MP, van Zelm MC, Watson CT, Collins AM. Inferred Allelic Variants of Immunoglobulin Receptor Genes: A System for Their Evaluation, Documentation, and Naming. Frontiers In Immunology 2019, 10: 435. PMID: 30936866, PMCID: PMC6431624, DOI: 10.3389/fimmu.2019.00435.Peer-Reviewed Original ResearchMeSH KeywordsAllelesBase SequenceDatabases, GeneticDatasets as TopicGene LibraryGenes, ImmunoglobulinGenetic VariationGerm-Line MutationHigh-Throughput Nucleotide SequencingHumansImmunoglobulin Heavy ChainsImmunoglobulin Variable RegionPolymerase Chain ReactionSequence AlignmentSequence Homology, Nucleic AcidTerminology as TopicV(D)J RecombinationVDJ ExonsConceptsGene databaseInternational ImMunoGeneTics information systemAdaptive immune receptor repertoire sequencingLymphocyte receptor genesAllelic variantsGermline genesReceptor geneAIRR CommunityVertebrate speciesGenetic variationIg diversityAIRR-seq dataJ genesIg genesAllelic sequencesGenesIGHV genesEffector moleculesUnprecedented insightsB-cell lineageBiological interpretationT cell receptorReference databaseGene variationRepertoire studiesIdentification of Subject-Specific Immunoglobulin Alleles From Expressed Repertoire Sequencing Data
Gadala-Maria D, Gidoni M, Marquez S, Heiden J, Kos JT, Watson CT, O'Connor KC, Yaari G, Kleinstein SH. Identification of Subject-Specific Immunoglobulin Alleles From Expressed Repertoire Sequencing Data. Frontiers In Immunology 2019, 10: 129. PMID: 30814994, PMCID: PMC6381938, DOI: 10.3389/fimmu.2019.00129.Peer-Reviewed Original Research
2017
Comment on “A Database of Human Immune Receptor Alleles Recovered from Population Sequencing Data”
Watson CT, Matsen FA, Jackson KJL, Bashir A, Smith ML, Glanville J, Breden F, Kleinstein SH, Collins AM, Busse CE. Comment on “A Database of Human Immune Receptor Alleles Recovered from Population Sequencing Data”. The Journal Of Immunology 2017, 198: 3371-3373. PMID: 28416712, DOI: 10.4049/jimmunol.1700306.Peer-Reviewed Original Research
2016
Recurrent 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
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 methodsAllelesHypermutationAutomated 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