2024
Single-cell genomics and regulatory networks for 388 human brains
Emani P, Liu J, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee C, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken T, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard J, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman G, Huang A, Jiang Y, Jin T, Jorstad N, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran J, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan A, Riesenmy T, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini K, Wamsley B, Wang G, Xia Y, Xiao S, Yang A, Zheng S, Gandal M, Lee D, Lein E, Roussos P, Sestan N, Weng Z, White K, Won H, Girgenti M, Zhang J, Wang D, Geschwind D, Gerstein M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Berretta S, Bharadwaj R, Bhattacharya A, Brennand K, Capauto D, Champagne F, Chatzinakos C, Chen H, Cheng L, Chess A, Chien J, Clement A, Collado-Torres L, Cooper G, Crawford G, Dai R, Daskalakis N, Davila-Velderrain J, Deep-Soboslay A, Deng C, DiPietro C, Dracheva S, Drusinsky S, Duong D, Eagles N, Edelstein J, Galani K, Girdhar K, Goes F, Greenleaf W, Guo H, Guo Q, Hadas Y, Hallmayer J, Han X, Haroutunian V, He C, Hicks S, Ho M, Ho L, Huang Y, Huuki-Myers L, Hyde T, Iatrou A, Inoue F, Jajoo A, Jiang L, Jin P, Jops C, Jourdon A, Kellis M, Kleinman J, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Li J, Li M, Lin X, Liu S, Liu C, Loupe J, Lu D, Ma L, Mariani J, Martinowich K, Maynard K, Myers R, Micallef C, Mikhailova T, Ming G, Mohammadi S, Monte E, Montgomery K, Mukamel E, Nairn A, Nemeroff C, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Peters M, Pinto D, Pochareddy S, Pollard K, Pollen A, Przytycki P, Purmann C, Qin Z, Qu P, Raj T, Reach S, Reimonn T, Ressler K, Ross D, Rozowsky J, Ruth M, Ruzicka W, Sanders S, Schneider J, Scuderi S, Sebra R, Seyfried N, Shao Z, Shieh A, Shin J, Skarica M, Snijders C, Song H, State M, Stein J, Steyert M, Subburaju S, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Vo D, Voloudakis G, Wang T, Wang S, Wang Y, Wei Y, Weimer A, Weinberger D, Wen C, Whalen S, Willsey A, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Yap C, Zeng B, Zhang P, Zhang C, Zhang B, Zhang Y, Ziffra R, Zeier Z, Zintel T. Single-cell genomics and regulatory networks for 388 human brains. Science 2024, 384: eadi5199. PMID: 38781369, PMCID: PMC11365579, DOI: 10.1126/science.adi5199.Peer-Reviewed Original ResearchConceptsSingle-cell genomicsSingle-cell expression quantitative trait locusExpression quantitative trait lociDrug targetsQuantitative trait lociPopulation-level variationSingle-cell expressionCell typesDisease-risk genesTrait lociGene familyRegulatory networksGene expressionCell-typeMultiomics datasetsSingle-nucleiGenomeGenesCellular changesHeterogeneous tissuesExpressionCellsChromatinLociMultiomics
2023
More than bad luck: Cancer and aging are linked to replication-driven changes to the epigenome
Minteer C, Thrush K, Gonzalez J, Niimi P, Rozenblit M, Rozowsky J, Liu J, Frank M, McCabe T, Sehgal R, Higgins-Chen A, Hofstatter E, Pusztai L, Beckman K, Gerstein M, Levine M. More than bad luck: Cancer and aging are linked to replication-driven changes to the epigenome. Science Advances 2023, 9: eadf4163. PMID: 37467337, PMCID: PMC10355820, DOI: 10.1126/sciadv.adf4163.Peer-Reviewed Original ResearchConceptsStem cell divisionImmortalized human cellsTissue-specific cancer riskTumorigenic stateCell divisionDNA methylationEpigenetic changesAge-related accumulationHuman cellsMultiple tissuesSomatic mutationsClinical tissuesTissue differencesEpigenomeCellsTissueNormal tissuesMethylationMutationsReplicationNormal breast tissueSignaturesVitroAccumulationDivision
2021
Cross-platform transcriptomic profiling of the response to recombinant human erythropoietin
Wang G, Kitaoka T, Crawford A, Mao Q, Hesketh A, Guppy FM, Ash GI, Liu J, Gerstein MB, Pitsiladis YP. Cross-platform transcriptomic profiling of the response to recombinant human erythropoietin. Scientific Reports 2021, 11: 21705. PMID: 34737331, PMCID: PMC8568984, DOI: 10.1038/s41598-021-00608-9.Peer-Reviewed Original ResearchConceptsRNA-seqDifferential gene expressionPathway enrichment analysisRNA biologyTranscriptomic profilingTarget genesEnrichment analysisGene expressionEPO biologyMicroarray platformGene correlateCross-platform comparisonGenesBiologyImmune regulationHuman erythropoietinTissue protectionProfilingRegulationErythropoietinRecombinant human erythropoietinExpressionImportant toolErythropoiesisOxygen transportBayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions
Liu J, Spakowicz DJ, Ash GI, Hoyd R, Ahluwalia R, Zhang A, Lou S, Lee D, Zhang J, Presley C, Greene A, Stults-Kolehmainen M, Nally LM, Baker JS, Fucito LM, Weinzimer SA, Papachristos AV, Gerstein M. Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions. PLOS Computational Biology 2021, 17: e1009303. PMID: 34424894, PMCID: PMC8412351, DOI: 10.1371/journal.pcbi.1009303.Peer-Reviewed Original Research
2020
STARRPeaker: uniform processing and accurate identification of STARR-seq active regions
Lee D, Shi M, Moran J, Wall M, Zhang J, Liu J, Fitzgerald D, Kyono Y, Ma L, White KP, Gerstein M. STARRPeaker: uniform processing and accurate identification of STARR-seq active regions. Genome Biology 2020, 21: 298. PMID: 33292397, PMCID: PMC7722316, DOI: 10.1186/s13059-020-02194-x.Peer-Reviewed Original ResearchNIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis
Zhang J, Liu J, McGillivray P, Yi C, Lochovsky L, Lee D, Gerstein M. NIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis. BMC Bioinformatics 2020, 21: 474. PMID: 33092526, PMCID: PMC7580035, DOI: 10.1186/s12859-020-03758-1.Peer-Reviewed Original ResearchConceptsDNase I hypersensitive sitesMutation rate heterogeneityDNA elementsCancer whole genome sequencesMutational hotspotsMutation burden analysisFunctional genomics dataNon-coding regionsGene regulatory networksWhole Genomes (PCAWG) projectWhole genome sequencesBackground mutation rateBurden analysisChromatin organizationReplication timingGenome sequenceRegulatory networksTranscription factorsHypersensitive sitesGenomic featuresRate heterogeneityGenome ProjectGenomic dataIntegrative methodGamma-Poisson mixture modelRADAR: annotation and prioritization of variants in the post-transcriptional regulome of RNA-binding proteins
Zhang J, Liu J, Lee D, Feng JJ, Lochovsky L, Lou S, Rutenberg-Schoenberg M, Gerstein M. RADAR: annotation and prioritization of variants in the post-transcriptional regulome of RNA-binding proteins. Genome Biology 2020, 21: 151. PMID: 32727537, PMCID: PMC7391703, DOI: 10.1186/s13059-020-01979-4.Peer-Reviewed Original ResearchConceptsTissue-specific inputsPost-transcriptional regulationDisease-specific variantsPrioritization of variantsVariant prioritization methodsTranscriptional regulationRNA structureBinding sitesRNAProteinPrioritization methodRegulationKey roleVariantsRegulomeGenomeSplicingGermlineExonsOverall impact scoreMotifConservationAnnotationDysregulationAn integrative ENCODE resource for cancer genomics
Zhang J, Lee D, Dhiman V, Jiang P, Xu J, McGillivray P, Yang H, Liu J, Meyerson W, Clarke D, Gu M, Li S, Lou S, Xu J, Lochovsky L, Ung M, Ma L, Yu S, Cao Q, Harmanci A, Yan KK, Sethi A, Gürsoy G, Schoenberg MR, Rozowsky J, Warrell J, Emani P, Yang YT, Galeev T, Kong X, Liu S, Li X, Krishnan J, Feng Y, Rivera-Mulia JC, Adrian J, Broach JR, Bolt M, Moran J, Fitzgerald D, Dileep V, Liu T, Mei S, Sasaki T, Trevilla-Garcia C, Wang S, Wang Y, Zang C, Wang D, Klein RJ, Snyder M, Gilbert DM, Yip K, Cheng C, Yue F, Liu XS, White KP, Gerstein M. An integrative ENCODE resource for cancer genomics. Nature Communications 2020, 11: 3696. PMID: 32728046, PMCID: PMC7391744, DOI: 10.1038/s41467-020-14743-w.Peer-Reviewed Original ResearchConceptsCell typesFunctional genomics datasetsEffect of MycStem-like stateNetwork-based annotationUncharacterized RBPsOncogenic TFSTARR-seqOncogene knockdownTranscription factorsGenomic datasetsOncogenic transformationGenome interpretationUniversal annotationCancer genomicsDifferential expressionSiRNA knockdownLuciferase assayTargeted validationRegulatorTumor transitionCustom annotationsAnnotationKnockdownCoherent workflowExpanded encyclopaedias of DNA elements in the human and mouse genomes
Moore J, Purcaro M, Pratt H, Epstein C, Shoresh N, Adrian J, Kawli T, Davis C, Dobin A, Kaul R, Halow J, Van Nostrand E, Freese P, Gorkin D, Shen Y, He Y, Mackiewicz M, Pauli-Behn F, Williams B, Mortazavi A, Keller C, Zhang X, Elhajjajy S, Huey J, Dickel D, Snetkova V, Wei X, Wang X, Rivera-Mulia J, Rozowsky J, Zhang J, Chhetri S, Zhang J, Victorsen A, White K, Visel A, Yeo G, Burge C, Lécuyer E, Gilbert D, Dekker J, Rinn J, Mendenhall E, Ecker J, Kellis M, Klein R, Noble W, Kundaje A, Guigó R, Farnham P, Cherry J, Myers R, Ren B, Graveley B, Gerstein M, Pennacchio L, Snyder M, Bernstein B, Wold B, Hardison R, Gingeras T, Stamatoyannopoulos J, Weng Z. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 2020, 583: 699-710. PMID: 32728249, PMCID: PMC7410828, DOI: 10.1038/s41586-020-2493-4.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsChromatinDatabases, GeneticDeoxyribonuclease IDNADNA FootprintingDNA MethylationDNA Replication TimingGenomeGenome, HumanGenomicsHistonesHumansMiceMice, TransgenicMolecular Sequence AnnotationRegistriesRegulatory Sequences, Nucleic AcidRNA-Binding ProteinsTranscription, GeneticTransposasesConceptsMouse genomeCandidate cis-regulatory elementsCis-regulatory elementsDNA Elements (ENCODE) projectMouse fetal developmentChromatin structureGene regulationRespective genomesCellular contextDNA elementsDNA methylationENCODE dataTranscription factorsRNA transcriptionWeb-based serverGenomeExpansive resourceRNAEncyclopediaProteinFetal developmentChromatinTranscriptionHumansMethylationDiNeR: a Differential graphical model for analysis of co-regulation Network Rewiring
Zhang J, Liu J, Lee D, Lou S, Chen Z, Gürsoy G, Gerstein M. DiNeR: a Differential graphical model for analysis of co-regulation Network Rewiring. BMC Bioinformatics 2020, 21: 281. PMID: 32615918, PMCID: PMC7333332, DOI: 10.1186/s12859-020-03605-3.Peer-Reviewed Original ResearchConceptsCo-regulation networkCo-regulatory networkNetwork rewiringDisease regulatorsGenome-wide binding profilesGM12878 cell lineRNA polymerase IITumor suppressor BRCA1Transcription factor bindsChIP-seq dataDifferential graphical modelsBinding profileComplete binding profilesKey TFsPolymerase IIHub regulatorsPhenotypic variationFactor bindsGene expressionExpression changesCancerous stateRisk genesRegulatorCell linesCoordinated mannerTopicNet: a framework for measuring transcriptional regulatory network change
Lou S, Li T, Kong X, Zhang J, Liu J, Lee D, Gerstein M. TopicNet: a framework for measuring transcriptional regulatory network change. Bioinformatics 2020, 36: i474-i481. PMID: 32657410, PMCID: PMC7355251, DOI: 10.1093/bioinformatics/btaa403.Peer-Reviewed Original ResearchConceptsRegulatory network changesTranscription factorsCellular statesDifferent regulatory programsCollection of genesDifferent cellular statesParticular cellular stateParticular transcription factorsRegulatory network connectivityKey transcription factorGene expression dataChromatin immunoprecipitationRegulatory networksCell statesExpression dataRegulatory programsHuman cellsDifferential survivalDiverse groupSupplementary dataDynamic changesLoss of targetImmunoprecipitationGenesActivity differences