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
2018
Histopathological Image QTL Discovery of Immune Infiltration Variants
Barry J, Fagny M, Paulson J, Aerts H, Platig J, Quackenbush J. Histopathological Image QTL Discovery of Immune Infiltration Variants. IScience 2018, 5: 80-89. PMID: 30240647, PMCID: PMC6123851, DOI: 10.1016/j.isci.2018.07.001.Peer-Reviewed Original ResearchGenotype-Tissue ExpressionQuantitative traitsQuantitative trait lociGenotype-phenotype associationsVariant discoveryThyroid pathology imagesGenomic dataGenomic networksAssociation studiesTrait lociQTL dataQuantitative imaging biomarkersTissue length scalesMolecular readoutsImaging biomarkersClinical metadataGenomeImmune cell infiltrationQuantitative imaging featuresTXNDC5Hypothyroid datasetTraitsCell infiltrationVariantsMedical records
2017
Exploring regulation in tissues with eQTL networks
Fagny M, Paulson J, Kuijjer M, Sonawane A, Chen C, Lopes-Ramos C, Glass K, Quackenbush J, Platig J. Exploring regulation in tissues with eQTL networks. Proceedings Of The National Academy Of Sciences Of The United States Of America 2017, 114: e7841-e7850. PMID: 28851834, PMCID: PMC5604022, DOI: 10.1073/pnas.1707375114.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociEQTL networksGenetic variantsCoherent biological processesImpact of genetic variantsActive chromatin regionsQuantitative trait lociTissue-specific processesGene expression levelsChromatin regionsGroup genesTrait lociComplex phenotypesRegulatory potentialGenotype to phenotype mappingPhenotype mappingBiological processesRegulatory functionsExpression levelsGenesRegulatory impactVariantsLociBipartite networksPhenotype