2024
All the sites we cannot see: Sources and mitigation of false negatives in RNA modification studies
Oberdoerffer S, Gilbert W. All the sites we cannot see: Sources and mitigation of false negatives in RNA modification studies. Nature Reviews Molecular Cell Biology 2024, 26: 237-248. PMID: 39433914, DOI: 10.1038/s41580-024-00784-2.Peer-Reviewed Original ResearchRNA modificationsRNA-modifying enzymesTranscriptome-wide mappingRNA modification mappingPost-transcriptional controlModified sitesSequencing depthRNA functionRNA targetsModification mappingRNAModification studiesProfiling studiesEnzymeModification sequenceNeurodevelopmental disordersTechnical artifactsFalse negativesSitesSequenceHuman healthFalse positivesTransparent reportingGiants among Cnidaria: Large Nuclear Genomes and Rearranged Mitochondrial Genomes in Siphonophores
Ahuja N, Cao X, Schultz D, Picciani N, Lord A, Shao S, Jia K, Burdick D, Haddock S, Li Y, Dunn C. Giants among Cnidaria: Large Nuclear Genomes and Rearranged Mitochondrial Genomes in Siphonophores. Genome Biology And Evolution 2024, 16: evae048. PMID: 38502059, PMCID: PMC10980510, DOI: 10.1093/gbe/evae048.Peer-Reviewed Original ResearchConceptsK-mer spectraMitochondrial genomeGenomic diversityK-mersNuclear genomeEstimate nuclear genome sizesMitochondrial gene orderNuclear genome sizeGenome assembly projectsRearranged mitochondrial genomesK-mer countingGenome skimmingGene orderGenome sizeRead coverageSequencing depthPhylogenetic samplingIllumina sequencingCnidarian speciesGenomeAssembly projectsSiphonophoresZooplankton communityAbundant predatorsSpecies
2023
Evaluation of zero counts to better understand the discrepancies between bulk and single-cell RNA-Seq platforms
Zyla J, Papiez A, Zhao J, Qu R, Li X, Kluger Y, Polanska J, Hatzis C, Pusztai L, Marczyk M. Evaluation of zero counts to better understand the discrepancies between bulk and single-cell RNA-Seq platforms. Computational And Structural Biotechnology Journal 2023, 21: 4663-4674. PMID: 37841335, PMCID: PMC10568495, DOI: 10.1016/j.csbj.2023.09.035.Peer-Reviewed Original ResearchSingle-cell RNA-seq platformsSingle-cell RNA sequencingBulk RNA-seq dataRNA-seq platformsNumber of transcriptsLow-expression genesRNA-seq dataSingle-cell dataExpression levelsLow sequencing depthDiscordant genesRNA sequencingSequencing technologiesExpression shiftsPathway levelBiological pathwaysGene levelSequencing depthTranscriptomic platformsGenesIndividual cellsSingle cellsRNA integrityPathwayCells
2022
Benchmarking automated cell type annotation tools for single-cell ATAC-seq data
Wang Y, Sun X, Zhao H. Benchmarking automated cell type annotation tools for single-cell ATAC-seq data. Frontiers In Genetics 2022, 13: 1063233. PMID: 36583014, PMCID: PMC9792779, DOI: 10.3389/fgene.2022.1063233.Peer-Reviewed Original ResearchCell type annotationScATAC-seq dataScRNA-seq dataScATAC-seqCell typesSingle-cell ATAC-seq dataAvailable single-cell datasetsRegulatory genomic regionsScRNA-seq data setsSingle-cell datasetsATAC-seq dataNovel cell typesSimilar cell typesSeurat v3Genomic regionsSequencing depthComplex tissuesDeep annotationAnnotationCellular compositionHuman tissuesType annotationsAnnotation toolAnnotation methodLabel transfer
2021
Fast alignment and preprocessing of chromatin profiles with Chromap
Zhang H, Song L, Wang X, Cheng H, Wang C, Meyer C, Liu T, Tang M, Aluru S, Yue F, Liu X, Li H. Fast alignment and preprocessing of chromatin profiles with Chromap. Nature Communications 2021, 12: 6566. PMID: 34772935, PMCID: PMC8589834, DOI: 10.1038/s41467-021-26865-w.Peer-Reviewed Original Research
2019
Phylogeny-based tumor subclone identification using a Bayesian feature allocation model
Zeng L, Warren J, Zhao H. Phylogeny-based tumor subclone identification using a Bayesian feature allocation model. The Annals Of Applied Statistics 2019, 13: 1212-1241. DOI: 10.1214/18-aoas1223.Peer-Reviewed Original ResearchCopy number variationsCourse of evolutionSubgroup of cellsWhole-genome sequencing samplesTumor subclonesBayesian feature allocation modelPhylogenetic structureDifferent genetic alterationsPhylogeny structureSequencing depthFeature allocation modelNumber variationsSequencing samplesTree sizeDistinct genotypesGenetic alterationsSubclonesResult of competitionTumor progressionBayesian modelEstimation accuracySifADrug resistanceCellsSimulation study
2014
A Targeted Next‐Generation Sequencing Assay Detects a High Frequency of Therapeutically Targetable Alterations in Primary and Metastatic Breast Cancers: Implications for Clinical Practice
Vasan N, Yelensky R, Wang K, Moulder S, Dzimitrowicz H, Avritscher R, Wang B, Wu Y, Cronin MT, Palmer G, Symmans WF, Miller VA, Stephens P, Pusztai L. A Targeted Next‐Generation Sequencing Assay Detects a High Frequency of Therapeutically Targetable Alterations in Primary and Metastatic Breast Cancers: Implications for Clinical Practice. The Oncologist 2014, 19: 453-458. PMID: 24710307, PMCID: PMC4012963, DOI: 10.1634/theoncologist.2013-0377.Peer-Reviewed Original ResearchConceptsBreast cancerGenomic alterationsV-akt murine thymoma viral oncogene homolog 1Stage IV cancerMetastatic breast cancerActionable genomic alterationsPotential treatment optionOncogene homolog 1Primary tumor biopsiesCancer-related genesClinical Laboratory Improvement AmendmentsDependent kinasesMedian sequencing depthGene fusionsSequencing depthBase substitutionsHER2 mutationsHomolog 1Actionable alterationsTargetable alterationsTreatment optionsClinical trialsHER2 amplificationMetastatic cancerTumor biopsiesIntegrating B Cell Lineage Information into Statistical Tests for Detecting Selection in Ig Sequences
Uduman M, Shlomchik MJ, Vigneault F, Church GM, Kleinstein SH. Integrating B Cell Lineage Information into Statistical Tests for Detecting Selection in Ig Sequences. The Journal Of Immunology 2014, 192: 867-874. PMID: 24376267, PMCID: PMC4363135, DOI: 10.4049/jimmunol.1301551.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsAntibody AffinityAntibody DiversityB-Lymphocyte SubsetsCell LineageClonal Selection, Antigen-MediatedComputer SimulationConfounding Factors, EpidemiologicGene Rearrangement, B-LymphocyteGenes, ImmunoglobulinHumansMiceModels, ImmunologicalModels, StatisticalROC CurveSequence Analysis, DNASomatic Hypermutation, ImmunoglobulinVDJ ExonsConceptsLineage treesHigh-throughput sequencing technologyLineage tree shapesCell lineage informationIg sequencesRatio of replacementTree-shape analysisStatistical frameworkSequence-based methodsBinomial statistical analysisExperimental data setsIndicators of selectionSequencing technologiesLineage informationSequencing depthNumber of generationsData setsHybrid methodVivo selectionSilent mutationsTree shapeStatistical testsSequenceShape analysisMutations
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