2021
Quantifying the effect of experimental perturbations at single-cell resolution
Burkhardt DB, Stanley JS, Tong A, Perdigoto AL, Gigante SA, Herold KC, Wolf G, Giraldez AJ, van Dijk D, Krishnaswamy S. Quantifying the effect of experimental perturbations at single-cell resolution. Nature Biotechnology 2021, 39: 619-629. PMID: 33558698, PMCID: PMC8122059, DOI: 10.1038/s41587-020-00803-5.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsCluster AnalysisComputational BiologyComputer SimulationHumansLikelihood FunctionsSequence Analysis, RNASingle-Cell AnalysisTranscriptomeConceptsSingle-cell RNA sequencing datasetsClusters of cellsRNA sequencing datasetsSingle-cell resolutionSingle-cell levelTranscriptomic spaceSequencing datasetsExperimental perturbationsCell populationsGene signatureVertex frequencyDiscrete regionsCellsEffects of perturbationsMultiple conditionsPerturbation responseClustersPopulationPerturbationsLikelihood estimatesGraph signal processing
2014
Probing the effect of promoters on noise in gene expression using thousands of designed sequences
Sharon E, van Dijk D, Kalma Y, Keren L, Manor O, Yakhini Z, Segal E. Probing the effect of promoters on noise in gene expression using thousands of designed sequences. Genome Research 2014, 24: 1698-1706. PMID: 25030889, PMCID: PMC4199362, DOI: 10.1101/gr.168773.113.Peer-Reviewed Original ResearchConceptsExpression noiseGene expressionMean expression levelTranscription factorsPromoter sequencesExpression levelsGene expression noisePromoter DNA sequencesMore transcription factorsNucleosome-disfavoring sequencesHigher expression noiseDifferent promoter sequencesNonspecific DNA bindingOne-dimensional slidingCellular functionsHigh-throughput methodNative promoterDNA sequencesDNA bindingSynthetic promotersPromoterIdentical cellsTarget siteSequence