Featured Publications
Differential richness inference for 16S rRNA marker gene surveys
Kumar M, Slud E, Hehnly C, Zhang L, Broach J, Irizarry R, Schiff S, Paulson J. Differential richness inference for 16S rRNA marker gene surveys. Genome Biology 2022, 23: 166. PMID: 35915508, PMCID: PMC9344657, DOI: 10.1186/s13059-022-02722-x.Peer-Reviewed Original ResearchConceptsMarker gene surveysRRNA marker gene surveysGene surveysMicrobial assemblagesSpecies discoveryMicrobial taxaMicrobial communitiesMicrobiome surveysRichness estimationSequencing readsDiversity measuresTaxaGenus abundanceMicrobiome dataR packageAbundanceRichnessDiscoveryDiversityReadsAssemblagesObserved numberExperimental evidenceAccumulationInferenceDifferential abundance analysis for microbial marker-gene surveys
Paulson J, Stine O, Bravo H, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nature Methods 2013, 10: 1200-1202. PMID: 24076764, PMCID: PMC4010126, DOI: 10.1038/nmeth.2658.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsArea Under CurveCluster AnalysisComputer SimulationDatabases, GeneticGene Expression ProfilingGenetic MarkersGenetic VariationHumansIntestinesMetagenomicsMiceMicrobiotaModels, GeneticModels, StatisticalNormal DistributionPhenotypeRNA, Ribosomal, 16SSequence Analysis, DNASoftware
2024
Lubricating gel influence on vaginal microbiome sampling
Amitai Komem D, Hadar R, Paulson J, Mordechai Y, Eskandarian H, Efroni G, Amir A, Haberman Y, Tsur A. Lubricating gel influence on vaginal microbiome sampling. Scientific Reports 2024, 14: 18223. PMID: 39107405, PMCID: PMC11303677, DOI: 10.1038/s41598-024-68948-w.Peer-Reviewed Original ResearchConceptsMicrobial compositionVaginal samplesVaginal microbiome samplesBeta diversityVaginal microbiome studiesMicrobiome studiesMicrobiome samplesTaxa abundanceGynecological examinationPregnant womenLubricant gelReduce painMicrobial dataGel exposureEmergency roomSterile swabsEffect of gelMicrobial analysis
2023
mbQTL: an R/Bioconductor package for microbial quantitative trait loci (QTL) estimation
Movassagh M, Schiff S, Paulson J. mbQTL: an R/Bioconductor package for microbial quantitative trait loci (QTL) estimation. Bioinformatics 2023, 39: btad565. PMID: 37707523, PMCID: PMC10516520, DOI: 10.1093/bioinformatics/btad565.Peer-Reviewed Original ResearchConceptsSingle nucleotide variationsRNA sequencingMicrobial abundance dataQuantitative trait lociSingle nucleotide polymorphism dataRibosomal RNA sequencingField of genomicsWhole-genome sequencingEvidence of interplayMutational profileTrait lociMicrobial communitiesMicrobial abundancePolymorphism dataMicrobial populationsGenome sequencingAbundance dataFirst R packageHuman geneticsBioconductor packageGenetic variantsMicrobiome dataSequencingR packageAbundance
2022
Disentangling the genetic basis of rhizosphere microbiome assembly in tomato
Oyserman B, Flores S, Griffioen T, Pan X, van der Wijk E, Pronk L, Lokhorst W, Nurfikari A, Paulson J, Movassagh M, Stopnisek N, Kupczok A, Cordovez V, Carrión V, Ligterink W, Snoek B, Medema M, Raaijmakers J. Disentangling the genetic basis of rhizosphere microbiome assembly in tomato. Nature Communications 2022, 13: 3228. PMID: 35710629, PMCID: PMC9203511, DOI: 10.1038/s41467-022-30849-9.Peer-Reviewed Original ResearchConceptsMicrobiome assemblyGenetic basisRhizosphere microbiome assemblyMetagenome-assembled genomesGene content analysisQuantitative trait lociDomestication sweepsDomesticated tomatoRhizosphere microbiomePutative plantPlant geneticsTrait lociBacterial genesPlant growthHybrid populationsQuantitative traitsBreeding programsMbp regionGenetic variation associatesPlant polysaccharidesDifferential recruitmentTomatoStreptomycesTraitsPivotal role
2021
Vaginal microbiome topic modeling of laboring Ugandan women with and without fever
Movassagh M, Bebell L, Burgoine K, Hehnly C, Zhang L, Moran K, Sheldon K, Sinnar S, Mbabazi-Kabachelor E, Kumbakumba E, Bazira J, Ochora M, Mulondo R, Nsubuga B, Weeks A, Gladstone M, Olupot-Olupot P, Ngonzi J, Roberts D, Meier F, Irizarry R, Broach J, Schiff S, Paulson J. Vaginal microbiome topic modeling of laboring Ugandan women with and without fever. Npj Biofilms And Microbiomes 2021, 7: 75. PMID: 34508087, PMCID: PMC8433417, DOI: 10.1038/s41522-021-00244-1.Peer-Reviewed Original ResearchConceptsIntrapartum feverClinical variablesHigh prevalenceVaginal microbiomeUgandan womenLonger labour durationMaternal clinical featuresYoung maternal ageDuration of pregnancyOnset of laborMicrobial communitiesVaginal microbial communitiesAfebrile mothersFebrile mothersPeripartum courseMaternal feverNeonatal outcomesLabor durationClinical featuresMaternal ageVaginal microbesFeverOutcome riskVeillonella genusMicrobiome influencesNasal Microbiota and Infectious Complications After Elective Surgical Procedures
Hsiao C, Paulson J, Singh S, Mongodin E, Carroll K, Fraser C, Rock P, Faraday N. Nasal Microbiota and Infectious Complications After Elective Surgical Procedures. JAMA Network Open 2021, 4: e218386. PMID: 33914049, PMCID: PMC8085724, DOI: 10.1001/jamanetworkopen.2021.8386.Peer-Reviewed Original ResearchMeSH KeywordsAgedBacteremiaCardiac Surgical ProceduresCase-Control StudiesCraniotomyElective Surgical ProceduresFemaleHumansMaleMicrobiotaMiddle AgedNosePneumoniaPostoperative ComplicationsRisk AssessmentRisk FactorsRNA, Ribosomal, 16SSpinal FusionStaphylococcus aureusSurgical Wound InfectionVascular Surgical ProceduresConceptsBaseline clinical characteristicsSurgical proceduresElective surgical proceduresInfectious complicationsInfectious outcomesPostoperative infectionClinical characteristicsComposite of surgical site infectionsNasal carriage of Staphylococcus aureusNasal microbiotaNo history of autoimmune diseaseCarriage of Staphylococcus aureusHistory of autoimmune diseaseProspective cohort of patientsNasal microbiomeTertiary care university hospitalOccurrence of postoperative infectionsImmune-modulating medicationsPostoperative infectious complicationsHigher oddsSurgical site infectionCohort of patientsIntracranial surgical proceduresAssociated with higher oddsNasal carriage
2020
MicrobiomeExplorer: an R package for the analysis and visualization of microbial communities
Reeder J, Huang M, Kaminker J, Paulson J. MicrobiomeExplorer: an R package for the analysis and visualization of microbial communities. Bioinformatics 2020, 37: 1317-1318. PMID: 32960962, PMCID: PMC8193707, DOI: 10.1093/bioinformatics/btaa838.Peer-Reviewed Original ResearchmicrobiomeDASim: Simulating longitudinal differential abundance for microbiome data
Williams J, Bravo H, Tom J, Paulson J. microbiomeDASim: Simulating longitudinal differential abundance for microbiome data. F1000Research 2020, 8: 1769. PMID: 32148761, PMCID: PMC7047923, DOI: 10.12688/f1000research.20660.2.Peer-Reviewed Original Research
2019
Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome
Carrión V, Perez-Jaramillo J, Cordovez V, Tracanna V, de Hollander M, Ruiz-Buck D, Mendes L, van Ijcken W, Gomez-Exposito R, Elsayed S, Mohanraju P, Arifah A, van der Oost J, Paulson J, Mendes R, van Wezel G, Medema M, Raaijmakers J. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science 2019, 366: 606-612. PMID: 31672892, DOI: 10.1126/science.aaw9285.Peer-Reviewed Original ResearchMeSH KeywordsBacteriaBacterial Physiological PhenomenaBacteroidetesBeta vulgarisBiodiversityChitinasesDisease ResistanceEndophytesFlavobacteriumGenes, BacterialGenome, BacterialMetagenomeMicrobiotaMutagenesis, Site-DirectedPeptide SynthasesPlant DiseasesPlant RootsPolyketide SynthasesRhizoctoniaSoil MicrobiologyConceptsEndophytic root microbiomeNonribosomal peptide synthetasesGene clusterPolyketide synthaseRoot microbiomeBiosynthetic gene clusterInfection of plant rootsFungal root diseasesSite-directed mutagenesisPromote plant growthGenome reconstructionPeptide synthetasesRoot endosphereFunctional traitsNRPS-PKSFunctional diversityChitinase geneEndophytic consortiaFungal infectionsDisease suppressionRoot diseasePlant growthNetwork inferenceDisease-suppressive functionsPlant roots
2018
Meta-analysis of the lung microbiota in pulmonary tuberculosis
Hong B, Paulson J, Stine O, Weinstock G, Cervantes J. Meta-analysis of the lung microbiota in pulmonary tuberculosis. Tuberculosis 2018, 109: 102-108. PMID: 29559113, DOI: 10.1016/j.tube.2018.02.006.Peer-Reviewed Original ResearchConceptsSpecies signatureNext generation sequencing dataLung microbiotaGeneration sequencing dataClustering of microbiotaMycobacterium tuberculosisCaulobacter henriciiLung microbiota compositionSequence dataBody nichesR. mucilaginosaMicrobiota compositionSputum microbiotaActinomyces graevenitziiBioinformatics analysisMicrobiotaRothia mucilaginosaTB patientsHealthy controlsTB casesAssociated with pulmonary TBHaemophilus parahaemolyticusMeta-analysisLower respiratory tractBody sites
2017
Simplified and representative bacterial community of maize roots
Niu B, Paulson J, Zheng X, Kolter R. Simplified and representative bacterial community of maize roots. Proceedings Of The National Academy Of Sciences Of The United States Of America 2017, 114: e2450-e2459. PMID: 28275097, PMCID: PMC5373366, DOI: 10.1073/pnas.1616148114.Peer-Reviewed Original ResearchConceptsCommunity assemblyBacterial communitiesDynamics of community assemblyComposition of plant microbiomesBacterial interspecies interactionsSynthetic bacterial communityPlant-associated microbesCulture-dependent methodsRoot microbiome assemblyHost-mediated selectionFusarium verticillioides</i>Maize rootsRoot microbiotaMicrobiome assemblyPlant microbiomeRoot microbiomePlant hostsHost genotypeKeystone speciesDominant phylaAbiotic factorsInterspecies interactionsModel ecosystemsMicrobiomeMaize seedlingsMentholation affects the cigarette microbiota by selecting for bacteria resistant to harsh environmental conditions and selecting against potential bacterial pathogens
Chopyk J, Chattopadhyay S, Kulkarni P, Claye E, Babik K, Reid M, Smyth E, Hittle L, Paulson J, Cruz-Cano R, Pop M, Buehler S, Clark P, Sapkota A, Mongodin E. Mentholation affects the cigarette microbiota by selecting for bacteria resistant to harsh environmental conditions and selecting against potential bacterial pathogens. Microbiome 2017, 5: 22. PMID: 28202080, PMCID: PMC5312438, DOI: 10.1186/s40168-017-0235-0.Peer-Reviewed Original ResearchConceptsQuantitative Insights Into Microbial EcologyResistant to harsh environmental conditionsBacterial pathogensHarsh environmental conditionsV3-V4 hypervariable regionIllumina MiSeq platformHuman bacterial pathogensBacterial community profilesBacterial community richnessPotential bacterial pathogensEnvironmental conditionsRRNA geneMiSeq platformAbundant generaBacterial microbiotaBacterial communitiesMicrobial ecologyGenomic DNACommunity richnessBacterial compositionHypervariable regionPCR amplificationCommunity profilesPseudomonas putidaLysis protocol
2016
Longitudinal analysis of the lung microbiota of cynomolgous macaques during long-term SHIV infection
Morris A, Paulson J, Talukder H, Tipton L, Kling H, Cui L, Fitch A, Pop M, Norris K, Ghedin E. Longitudinal analysis of the lung microbiota of cynomolgous macaques during long-term SHIV infection. Microbiome 2016, 4: 38. PMID: 27391224, PMCID: PMC4939015, DOI: 10.1186/s40168-016-0183-0.Peer-Reviewed Original ResearchConceptsDevelopment of obstructive lung diseaseObstructive lung diseaseNon-human primate modelLung microbiomeSHIV infectionLung microbiotaRibosomal RNALung diseasePrimate modelNon-obstructive groupSHIV-infected animalsResponse to immunosuppressionBronchoalveolar lavage fluid samplesCynomolgous macaquesDevelopment of obstructive diseaseLavage fluid samplesMultiple other speciesSIV-HIVOral anaerobesOral bacteriaTropheryma whippleiObstructive diseaseCommunity compositionBacterial communitiesDisease onsetPrivacy-preserving microbiome analysis using secure computation
Wagner J, Paulson J, Wang X, Bhattacharjee B, Corrada Bravo H. Privacy-preserving microbiome analysis using secure computation. Bioinformatics 2016, 32: 1873-1879. PMID: 26873931, PMCID: PMC4908319, DOI: 10.1093/bioinformatics/btw073.Peer-Reviewed Original ResearchConceptsDNA of micro-organismsMicrobiome research communityPrivacy concernsSensitive attributesResearch participant dataFeature countsSharing dataSupplementary dataMetagenomic analysisResearch datasetsMicrobiome sequencingSequencing studiesMicrobial DNAHuman DNAResearch communityMicrobiome analysisMicrobiomeDNAAnalysis toolsDatasetComputerMicro-organismsBioinformaticsImplementationIndividual collections
2014
Reply to: "A fair comparison"
Paulson J, Bravo H, Pop M. Reply to: "A fair comparison". Nature Methods 2014, 11: 359-360. PMID: 24681718, DOI: 10.1038/nmeth.2898.Peer-Reviewed Original ResearchDiarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition
Pop M, Walker A, Paulson J, Lindsay B, Antonio M, Hossain M, Oundo J, Tamboura B, Mai V, Astrovskaya I, Bravo H, Rance R, Stares M, Levine M, Panchalingam S, Kotloff K, Ikumapayi U, Ebruke C, Adeyemi M, Ahmed D, Ahmed F, Alam M, Amin R, Siddiqui S, Ochieng J, Ouma E, Juma J, Mailu E, Omore R, Morris J, Breiman R, Saha D, Parkhill J, Nataro J, Stine O. Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition. Genome Biology 2014, 15: r76. PMID: 24995464, PMCID: PMC4072981, DOI: 10.1186/gb-2014-15-6-r76.Peer-Reviewed Original ResearchConceptsMicrobiota compositionAnaerobic lineagesRRNA gene sequencesGut microbiota compositionLevels of PrevotellaDiarrhea-causing pathogensSevere diarrheal diseaseIntestinal microbiota compositionFecal microbiota compositionGene sequencesDiarrheal pathogensDiarrhea pathogensMolecular techniquesPathogensDiarrhea-free controlsGranulicatella speciesEscherichia/ShigellaDiarrheal diseaseLineagesMSD casesYears of ageBackgroundDiarrheal diseasesYoung childrenAssociated with MSDLow-income countries