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 evidenceAccumulationInference
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
2020
microbiomeDASim: 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
microbiomeDASim: Simulating longitudinal differential abundance for microbiome data
Williams J, Bravo H, Tom J, Paulson J. microbiomeDASim: Simulating longitudinal differential abundance for microbiome data. F1000Research 2019, 8: 1769. DOI: 10.12688/f1000research.20660.1.Peer-Reviewed Original Research