2023
Assessing transcriptomic reidentification risks using discriminative sequence models
Sadhuka S, Fridman D, Berger B, Cho H. Assessing transcriptomic reidentification risks using discriminative sequence models. Genome Research 2023, 33: 1101-1112. PMID: 37541758, PMCID: PMC10538488, DOI: 10.1101/gr.277699.123.Peer-Reviewed Original ResearchMeSH KeywordsGene Expression ProfilingGenome-Wide Association StudyGenotypeHumansPolymorphism, Single NucleotideQuantitative Trait LociTranscriptomeConceptsExpression quantitative trait lociGene expression dataExpression dataQuantitative trait lociOmics data setsGene expression profilesTrait lociGenomic regionsGenetic variationGene expressionExpression profilesMolecular insightsLinkage disequilibriumFunctional impactGenotypesTranscriptomicsLociSame individualDisequilibriumSequenceExpressionPrevious studiesFull extentData sets
2021
Bayesian information sharing enhances detection of regulatory associations in rare cell types
Wu A, Peng J, Berger B, Cho H. Bayesian information sharing enhances detection of regulatory associations in rare cell types. Bioinformatics 2021, 37: i349-i357. PMID: 34252956, PMCID: PMC8275330, DOI: 10.1093/bioinformatics/btab269.Peer-Reviewed Original ResearchMeSH KeywordsBayes TheoremGene Expression ProfilingInformation DisseminationSequence Analysis, RNASingle-Cell AnalysisSoftwareConceptsScRNA-seq datasetsRegulatory associationsCell typesRegulatory networksCell type-specific gene regulatory networksCell-type specific gene regulationSingle-cell RNA sequencing technologyCell-type specific networksBenchmark scRNA-seq datasetsDiverse cellular contextsGene regulatory network inference methodRNA sequencing technologyGene regulatory networksRare cell typesSingle-cell datasetsSpecific cell typesNetwork inference methodsDynamic biological processesTranscriptional statesGene regulationCellular contextNetwork inference algorithmsComplex rewiringBiological processesGene associationsAssessing single-cell transcriptomic variability through density-preserving data visualization
Narayan A, Berger B, Cho H. Assessing single-cell transcriptomic variability through density-preserving data visualization. Nature Biotechnology 2021, 39: 765-774. PMID: 33462509, PMCID: PMC8195812, DOI: 10.1038/s41587-020-00801-7.Peer-Reviewed Original ResearchAlgorithmsData VisualizationGene Expression ProfilingHumansPrincipal Component AnalysisSingle-Cell AnalysisTranscriptome
2014
High-Resolution Transcriptome Analysis with Long-Read RNA Sequencing
Cho H, Davis J, Li X, Smith K, Battle A, Montgomery S. High-Resolution Transcriptome Analysis with Long-Read RNA Sequencing. PLOS ONE 2014, 9: e108095. PMID: 25251678, PMCID: PMC4176000, DOI: 10.1371/journal.pone.0108095.Peer-Reviewed Original ResearchMeSH KeywordsAllelesAlternative SplicingCell Line, TumorDNA, ComplementaryGene Expression ProfilingHigh-Throughput Nucleotide SequencingHumansRNASequence Analysis, RNATranscriptomeConceptsAllele-specific expressionTranscriptome analysisRNA sequencingAlternative splicing patternsCell line GM12878RNA-seq protocolsSequencing of cDNARNA-seq datasetsIndividual transcriptomesGenomic elementsAlternative splicingSplicing patternsAllelic expressionTranscript quantificationRead lengthMRNA transcriptsMapping biasSequencingExpressionTranscriptomeGM12878SplicingTechnical hurdlesCDNAGenes