2024
Representing core gene expression activity relationships using the latent structure implicit in bayesian networks
Gao J, Gerstein M. Representing core gene expression activity relationships using the latent structure implicit in bayesian networks. Bioinformatics 2024, btae463. PMID: 39051682, DOI: 10.1093/bioinformatics/btae463.Peer-Reviewed Original ResearchTranscriptional regulatory networksGene regulatory networksCo-expression networkGene expression activityChIP-seqGene conservationCluster genesSupplementary dataRegulatory networksBiological networksClearer clusteringCo-expressionExpression activityBioinformaticsGenesBiomedical studiesConservationExpressionClustersMolLM: a unified language model for integrating biomedical text with 2D and 3D molecular representations
Tang X, Tran A, Tan J, Gerstein M. MolLM: a unified language model for integrating biomedical text with 2D and 3D molecular representations. Bioinformatics 2024, 40: i357-i368. PMID: 38940177, PMCID: PMC11256921, DOI: 10.1093/bioinformatics/btae260.Peer-Reviewed Original ResearchConceptsTransformer encoderDownstream tasksLanguage modelBiomedical textSelf-supervised pre-trainingExplicit 3D representationRepresentation improves performanceDeep learning modelsRepresentation of moleculesContrastive learningSupervisory signalExtract embeddingsRepresentation capabilityJoint representationBiomedical domainPre-trainingTextual dataLearning modelsMolecular representationsModel weightsJupyter NotebookStep-by-step guidanceEncodingProperty predictionStructural informationTranscriptional determinism and stochasticity contribute to the complexity of autism-associated SHANK family genes
Lu X, Ni P, Suarez-Meade P, Ma Y, Forrest E, Wang G, Wang Y, Quiñones-Hinojosa A, Gerstein M, Jiang Y. Transcriptional determinism and stochasticity contribute to the complexity of autism-associated SHANK family genes. Cell Reports 2024, 43: 114376. PMID: 38900637, DOI: 10.1016/j.celrep.2024.114376.Peer-Reviewed Original ResearchSHANK family genesFamily genesLong-read sequencingCDNA captureTranscript structureDeleterious variantsGenomic studiesAbundant mRNAsTranscriptional dysregulationStochastic transcriptionStudies of neuropsychiatric disordersCausative genesTranscriptional profilesTranscriptional determinantsTranscriptomePostmortem brain tissueAutism spectrum disorderShank3 transcriptsTranscriptionGenesGenomeSHANK3Neuropsychiatric disordersSpectrum disorderAutism modelThe single-cell opioid responses in the context of HIV (SCORCH) consortium
Ament S, Campbell R, Lobo M, Receveur J, Agrawal K, Borjabad A, Byrareddy S, Chang L, Clarke D, Emani P, Gabuzda D, Gaulton K, Giglio M, Giorgi F, Gok B, Guda C, Hadas E, Herb B, Hu W, Huttner A, Ishmam M, Jacobs M, Kelschenbach J, Kim D, Lee C, Liu S, Liu X, Madras B, Mahurkar A, Mash D, Mukamel E, Niu M, O’Connor R, Pagan C, Pang A, Pillai P, Repunte-Canonigo V, Ruzicka W, Stanley J, Tickle T, Tsai S, Wang A, Wills L, Wilson A, Wright S, Xu S, Yang J, Zand M, Zhang L, Zhang J, Akbarian S, Buch S, Cheng C, Corley M, Fox H, Gerstein M, Gummuluru S, Heiman M, Ho Y, Kellis M, Kenny P, Kluger Y, Milner T, Moore D, Morgello S, Ndhlovu L, Rana T, Sanna P, Satterlee J, Sestan N, Spector S, Spudich S, Tilgner H, Volsky D, White O, Williams D, Zeng H. The single-cell opioid responses in the context of HIV (SCORCH) consortium. Molecular Psychiatry 2024, 1-12. PMID: 38879719, DOI: 10.1038/s41380-024-02620-7.Peer-Reviewed Original ResearchContext of human immunodeficiency virusHuman immunodeficiency virusSubstance use disordersOpioid responseAnimal modelsEffects of substance use disordersOpioid pain medicationsPrevalence of co-morbid conditionsChronic pain syndromesStage of diseaseCell typesAffected cell typesCo-morbid conditionsPain syndromeImmunodeficiency virusPain medicationOpioid addictionIncreased riskRisk factorsHuman cohortsDrug addictionBrain tissue collectionBrain cell typesTissue collectionSingle-cell levelMassively parallel characterization of regulatory elements in the developing human cortex
Deng C, Whalen S, Steyert M, Ziffra R, Przytycki P, Inoue F, Pereira D, Capauto D, Norton S, Vaccarino F, Pollen A, Nowakowski T, Ahituv N, Pollard K, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Bendl J, Berretta S, Bharadwaj R, Bhattacharya A, Bicks L, Brennand K, Capauto D, Champagne F, Chatterjee T, Chatzinakos C, Chen Y, Chen H, Cheng Y, Cheng L, Chess A, Chien J, Chu Z, Clarke D, 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, Duan Z, Duong D, Dursun C, Eagles N, Edelstein J, Emani P, Fullard J, Galani K, Galeev T, Gandal M, Gaynor S, Gerstein M, Geschwind D, Girdhar K, Goes F, Greenleaf W, Grundman J, Guo H, Guo Q, Gupta C, Hadas Y, Hallmayer J, Han X, Haroutunian V, Hawken N, He C, Henry E, Hicks S, Ho M, Ho L, Hoffman G, Huang Y, Huuki-Myers L, Hwang A, Hyde T, Iatrou A, Inoue F, Jajoo A, Jensen M, Jiang L, Jin P, Jin T, Jops C, Jourdon A, Kawaguchi R, Kellis M, Khullar S, Kleinman J, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Lee C, Lee D, Li J, Li M, Lin X, Liu S, Liu J, Liu J, Liu C, Liu S, Lou S, Loupe J, Lu D, Ma S, Ma L, Margolis M, Mariani J, Martinowich K, Maynard K, Mazariegos S, Meng R, Myers R, Micallef C, Mikhailova T, Ming G, Mohammadi S, Monte E, Montgomery K, Moore J, Moran J, Mukamel E, Nairn A, Nemeroff C, Ni P, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Peters M, Phalke N, Pinto D, Pjanic M, Pochareddy S, Pollard K, Pollen A, Pratt H, Przytycki P, Purmann C, Qin Z, Qu P, Quintero D, Raj T, Rajagopalan A, Reach S, Reimonn T, Ressler K, Ross D, Roussos P, Rozowsky J, Ruth M, Ruzicka W, Sanders S, Schneider J, Scuderi S, Sebra R, Sestan N, Seyfried N, Shao Z, Shedd N, 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, Wamsley B, Wang T, Wang S, Wang D, Wang Y, Warrell J, Wei Y, Weimer A, Weinberger D, Wen C, Weng Z, Whalen S, White K, Willsey A, Won H, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Xu S, Yap C, Zeng B, Zhang P, Zhang C, Zhang B, Zhang J, Zhang Y, Zhou X, Ziffra R, Zeier Z, Zintel T. Massively parallel characterization of regulatory elements in the developing human cortex. Science 2024, 384: eadh0559. PMID: 38781390, DOI: 10.1126/science.adh0559.Peer-Reviewed Original ResearchConceptsGene regulatory elementsRegulatory elementsRegulation of enhancer activityCharacterization of regulatory elementsCis-regulatory activityNeuronal developmentPrimary cellsEnhanced activityGene regulationHuman neuronal developmentNucleotide changesEnhancer sequencesSequence basisUpstream regulatorComprehensive catalogHuman cellsDeveloping cortexSequenceVariantsOrganoidsCellsCerebral organoidsCortexHuman cortexNucleotideCross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain
Wen C, Margolis M, Dai R, Zhang P, Przytycki P, Vo D, Bhattacharya A, Matoba N, Tang M, Jiao C, Kim M, Tsai E, Hoh C, Aygün N, Walker R, Chatzinakos C, Clarke D, Pratt H, Peters M, Gerstein M, Daskalakis N, Weng Z, Jaffe A, Kleinman J, Hyde T, Weinberger D, Bray N, Sestan N, Geschwind D, Roeder K, Gusev A, Pasaniuc B, Stein J, Love M, Pollard K, Liu C, Gandal M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Bendl J, Berretta S, Bharadwaj R, Bicks L, Brennand K, Capauto D, Champagne F, Chatterjee T, Chatzinakos C, Chen Y, Chen H, Cheng Y, Cheng L, Chess A, Chien J, Chu Z, Clement A, Collado-Torres L, Cooper G, Crawford G, Davila-Velderrain J, Deep-Soboslay A, Deng C, DiPietro C, Dracheva S, Drusinsky S, Duan Z, Duong D, Dursun C, Eagles N, Edelstein J, Emani P, Fullard J, Galani K, Galeev T, Gaynor S, Girdhar K, Goes F, Greenleaf W, Grundman J, Guo H, Guo Q, Gupta C, Hadas Y, Hallmayer J, Han X, Haroutunian V, Hawken N, He C, Henry E, Hicks S, Ho M, Ho L, Hoffman G, Huang Y, Huuki-Myers L, Hwang A, Iatrou A, Inoue F, Jajoo A, Jensen M, Jiang L, Jin P, Jin T, Jops C, Jourdon A, Kawaguchi R, Kellis M, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Lee C, Lee D, Li J, Li M, Lin X, Liu S, Liu J, Liu J, Liu S, Lou S, Loupe J, Lu D, Ma S, Ma L, Mariani J, Martinowich K, Maynard K, Mazariegos S, Meng R, Myers R, Micallef C, Mikhailova T, Ming G, Mohammadi S, Monte E, Montgomery K, Moore J, Moran J, Mukamel E, Nairn A, Nemeroff C, Ni P, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Phalke N, Pinto D, Pjanic M, Pochareddy S, Pollen A, Purmann C, Qin Z, Qu P, Quintero D, Raj T, Rajagopalan A, Reach S, Reimonn T, Ressler K, Ross D, Roussos P, Rozowsky J, Ruth M, Ruzicka W, Sanders S, Schneider J, Scuderi S, Sebra R, Seyfried N, Shao Z, Shedd N, Shieh A, Shin J, Skarica M, Snijders C, Song H, State M, Steyert M, Subburaju S, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Voloudakis G, Wamsley B, Wang T, Wang S, Wang D, Wang Y, Warrell J, Wei Y, Weimer A, Whalen S, White K, Willsey A, Won H, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Xu S, Yap C, Zeng B, Zhang C, Zhang B, Zhang J, Zhang Y, Zhou X, Ziffra R, Zeier Z, Zintel T. Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain. Science 2024, 384: eadh0829. PMID: 38781368, DOI: 10.1126/science.adh0829.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGenome-wide association study lociSplicing quantitative trait lociQuantitative trait lociSplicing regulationCross-ancestryTrait lociAssociation studiesRegulatory elementsCellular contextHuman brainTranscriptome regulationCoexpression networkRisk genesAutism spectrum disorderGenesCellular heterogeneityComprehensive landscapeSpectrum disorderIsoformsSplicingIncreased cellular heterogeneityLociNeuronal maturationRegulationSingle-cell multi-cohort dissection of the schizophrenia transcriptome
Ruzicka W, Mohammadi S, Fullard J, Davila-Velderrain J, Subburaju S, Tso D, Hourihan M, Jiang S, Lee H, Bendl J, Voloudakis G, Haroutunian V, Hoffman G, Roussos P, Kellis M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Berretta S, Bharadwaj R, Bhattacharya A, Bicks L, Brennand K, Capauto D, Champagne F, Chatterjee T, Chatzinakos C, Chen Y, Chen H, Cheng Y, Cheng L, Chess A, Chien J, Chu Z, Clarke D, Clement A, Collado-Torres L, Cooper G, Crawford G, Dai R, Daskalakis N, Deep-Soboslay A, Deng C, DiPietro C, Dracheva S, Drusinsky S, Duan Z, Duong D, Dursun C, Eagles N, Edelstein J, Emani P, Galani K, Galeev T, Gandal M, Gaynor S, Gerstein M, Geschwind D, Girdhar K, Goes F, Greenleaf W, Grundman J, Guo H, Guo Q, Gupta C, Hadas Y, Hallmayer J, Han X, Hawken N, He C, Henry E, Hicks S, Ho M, Ho L, Huang Y, Huuki-Myers L, Hwang A, Hyde T, Iatrou A, Inoue F, Jajoo A, Jensen M, Jiang L, Jin P, Jin T, Jops C, Jourdon A, Kawaguchi R, Kleinman J, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Lee C, Lee D, Li J, Li M, Lin X, Liu S, Liu J, Liu J, Liu C, Liu S, Lou S, Loupe J, Lu D, Ma S, Ma L, Margolis M, Mariani J, Martinowich K, Maynard K, Mazariegos S, Meng R, Myers R, Micallef C, Mikhailova T, Ming G, Monte E, Montgomery K, Moore J, Moran J, Mukamel E, Nairn A, Nemeroff C, Ni P, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Peters M, Phalke N, Pinto D, Pjanic M, Pochareddy S, Pollard K, Pollen A, Pratt H, Przytycki P, Purmann C, Qin Z, Qu P, Quintero D, Raj T, Rajagopalan A, Reach S, Reimonn T, Ressler K, Ross D, Rozowsky J, Ruth M, Sanders S, Schneider J, Scuderi S, Sebra R, Sestan N, Seyfried N, Shao Z, Shedd N, Shieh A, Shin J, Skarica M, Snijders C, Song H, State M, Stein J, Steyert M, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Vo D, Wamsley B, Wang T, Wang S, Wang D, Wang Y, Warrell J, Wei Y, Weimer A, Weinberger D, Wen C, Weng Z, Whalen S, White K, Willsey A, Won H, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Xu S, Yap C, Zeng B, Zhang P, Zhang C, Zhang B, Zhang J, Zhang Y, Zhou X, Ziffra R, Zeier Z, Zintel T. Single-cell multi-cohort dissection of the schizophrenia transcriptome. Science 2024, 384: eadg5136. PMID: 38781388, DOI: 10.1126/science.adg5136.Peer-Reviewed Original ResearchConceptsGenetic risk factorsRisk factorsTranscriptional changesHeterogeneity of schizophreniaNeuronal cell statesSchizophrenia pathophysiologySingle-cell dissectionExcitatory neuronsEffective therapySchizophrenia transcriptomicsCortical cytoarchitectureSingle-cell atlasGenomic variantsCell groupsHuman prefrontal cortexMolecular pathwaysSchizophreniaTranscriptional alterationsTranscriptomic changesPrefrontal cortexCell statesAlterationsTherapyPathophysiologyDissectionSingle-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, 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 tissuesExpressionCellsChromatinLociMultiomicsA data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex
Huuki-Myers L, Spangler A, Eagles N, Montgomery K, Kwon S, Guo B, Grant-Peters M, Divecha H, Tippani M, Sriworarat C, Nguyen A, Ravichandran P, Tran M, Seyedian A, Hyde T, Kleinman J, Battle A, Page S, Ryten M, Hicks S, Martinowich K, Collado-Torres L, Maynard K, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Bendl J, Berretta S, Bharadwaj R, Bhattacharya A, Bicks L, Brennand K, Capauto D, Champagne F, Chatterjee T, Chatzinakos C, Chen Y, Chen H, Cheng Y, Cheng L, Chess A, Chien J, Chu Z, Clarke D, 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, Duan Z, Duong D, Dursun C, Eagles N, Edelstein J, Emani P, Fullard J, Galani K, Galeev T, Gandal M, Gaynor S, Gerstein M, Geschwind D, Girdhar K, Goes F, Greenleaf W, Grundman J, Guo H, Guo Q, Gupta C, Hadas Y, Hallmayer J, Han X, Haroutunian V, Hawken N, He C, Henry E, Hicks S, Ho M, Ho L, Hoffman G, Huang Y, Huuki-Myers L, Hwang A, Hyde T, Iatrou A, Inoue F, Jajoo A, Jensen M, Jiang L, Jin P, Jin T, Jops C, Jourdon A, Kawaguchi R, Kellis M, Kleinman J, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Lee C, Lee D, Li J, Li M, Lin X, Liu S, Liu J, Liu J, Liu C, Liu S, Lou S, Loupe J, Lu D, Ma S, Ma L, Margolis M, Mariani J, Martinowich K, Maynard K, Mazariegos S, Meng R, Myers R, Micallef C, Mikhailova T, Ming G, Mohammadi S, Monte E, Montgomery K, Moore J, Moran J, Mukamel E, Nairn A, Nemeroff C, Ni P, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Peters M, Phalke N, Pinto D, Pjanic M, Pochareddy S, Pollard K, Pollen A, Pratt H, Przytycki P, Purmann C, Qin Z, Qu P, Quintero D, Raj T, Rajagopalan A, Reach S, Reimonn T, Ressler K, Ross D, Roussos P, Rozowsky J, Ruth M, Ruzicka W, Sanders S, Schneider J, Scuderi S, Sebra R, Sestan N, Seyfried N, Shao Z, Shedd N, 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, Wamsley B, Wang T, Wang S, Wang D, Wang Y, Warrell J, Wei Y, Weimer A, Weinberger D, Wen C, Weng Z, Whalen S, White K, Willsey A, Won H, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Xu S, Yap C, Zeng B, Zhang P, Zhang C, Zhang B, Zhang J, Zhang Y, Zhou X, Ziffra R, Zeier Z, Zintel T. A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex. Science 2024, 384: eadh1938. PMID: 38781370, DOI: 10.1126/science.adh1938.Peer-Reviewed Original ResearchConceptsRNA sequencing dataCell type compositionGene expression platformSpatial transcriptomics technologiesAnterior-posterior axisCell-cell interactionsTranscriptome mapExpression platformHuman dorsolateral prefrontal cortexTranscriptomic technologiesSingle-cellCell typesPrefrontal cortexMolecular organizationDorsolateral prefrontal cortexHuman prefrontal cortexUsing a comprehensive atlas and predictive models to reveal the complexity and evolution of brain-active regulatory elements
Pratt H, Andrews G, Shedd N, Phalke N, Li T, Pampari A, Jensen M, Wen C, Consortium P, Gandal M, Geschwind D, Gerstein M, Moore J, Kundaje A, Colubri A, Weng Z. Using a comprehensive atlas and predictive models to reveal the complexity and evolution of brain-active regulatory elements. Science Advances 2024, 10: eadj4452. PMID: 38781344, PMCID: PMC11114231, DOI: 10.1126/sciadv.adj4452.Peer-Reviewed Original ResearchConceptsEpigenetic dataCell-type-specific gene regulationCis-regulatory elementsComprehensive atlasGenetic variants associated with psychiatric disordersLineage-specific transcription factorsBrain cell typesMammalian elementsPsychENCODE ConsortiumNoncoding regionsEvolutionary historyGene regulationRegulatory elementsSequence mutationsTranscription factorsSequence syntaxRegulatory informationPrimate-specific sequencesBinding sitesHuman traitsCell typesFunctional implicationsPsychiatric disordersSequenceFetal brain developmentLatent evolutionary signatures: a general framework for analysing music and cultural evolution
Warrell J, Salichos L, Gancz M, Gerstein M. Latent evolutionary signatures: a general framework for analysing music and cultural evolution. Journal Of The Royal Society Interface 2024, 21: 20230647. PMID: 38503341, PMCID: PMC10950459, DOI: 10.1098/rsif.2023.0647.Peer-Reviewed Original ResearchConceptsDomain of musicModeling musical styleCultural processes of changeChord transitionsMusical corporaMusical piecesMusical stylesGenre predictionPrinciples of organizationSongMusicCultural processesCultural evolutionProcess of changeRepresentationGenreDeep generative architecturePiecesStyleHarmonyGenerator architectureEvolutionary spaceLatent embeddingsLatent spaceVariational autoencoderLess-is-more: selecting transcription factor binding regions informative for motif inference
Xu J, Gao J, Ni P, Gerstein M. Less-is-more: selecting transcription factor binding regions informative for motif inference. Nucleic Acids Research 2024, 52: e20-e20. PMID: 38214231, PMCID: PMC10899791, DOI: 10.1093/nar/gkad1240.Peer-Reviewed Original ResearchConceptsChIP-seq signalsChIP-seqGenomic regionsMotif inferenceTranscription factorsTargeting motifTranscription factor binding regionsChIP-seq datasetsNon-specific interactionsC-scoreDNA motifsBinding regionMotifTranscriptionTF signalingAccurate inferenceStronger signalSignalDNARegionTargetInteraction
2023
Assessing and mitigating privacy risks of sparse, noisy genotypes by local alignment to haplotype databases
Emani P, Geradi M, Gürsoy G, Grasty M, Miranker A, Gerstein M. Assessing and mitigating privacy risks of sparse, noisy genotypes by local alignment to haplotype databases. Genome Research 2023, 33: 2156-2173. PMID: 38097386, PMCID: PMC10760520, DOI: 10.1101/gr.278322.123.Peer-Reviewed Original Research
2020
Data Sanitization to Reduce Private Information Leakage from Functional Genomics
Gürsoy G, Emani P, Brannon CM, Jolanki OA, Harmanci A, Strattan JS, Cherry JM, Miranker AD, Gerstein M. Data Sanitization to Reduce Private Information Leakage from Functional Genomics. Cell 2020, 183: 905-917.e16. PMID: 33186529, PMCID: PMC7672785, DOI: 10.1016/j.cell.2020.09.036.Peer-Reviewed Original ResearchConceptsFunctional genomicsSingle-cell RNA sequencingAccurate reference genomesFunctional genomics datasetsFunctional genomics experimentsOrganismal phenotypesGene regulationReference genomeNext-generation sequencingRaw readsGenomics experimentsRNA sequencingGenomic datasetsGenetic variantsGenomicsKnown individualsSequencingReadsEnvironmental samplesGenomeIlluminaPhenotypeGood statistical powerRegulationStatistical powerPassenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences
Kumar S, Warrell J, Li S, McGillivray PD, Meyerson W, Salichos L, Harmanci A, Martinez-Fundichely A, Chan CWY, Nielsen MM, Lochovsky L, Zhang Y, Li X, Lou S, Pedersen JS, Herrmann C, Getz G, Khurana E, Gerstein MB. Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences. Cell 2020, 180: 915-927.e16. PMID: 32084333, PMCID: PMC7210002, DOI: 10.1016/j.cell.2020.01.032.Peer-Reviewed Original Research
2016
The real cost of sequencing: scaling computation to keep pace with data generation
Muir P, Li S, Lou S, Wang D, Spakowicz DJ, Salichos L, Zhang J, Weinstock GM, Isaacs F, Rozowsky J, Gerstein M. The real cost of sequencing: scaling computation to keep pace with data generation. Genome Biology 2016, 17: 53. PMID: 27009100, PMCID: PMC4806511, DOI: 10.1186/s13059-016-0917-0.Peer-Reviewed Original ResearchTemporal Dynamics of Collaborative Networks in Large Scientific Consortia
Wang D, Yan KK, Rozowsky J, Pan E, Gerstein M. Temporal Dynamics of Collaborative Networks in Large Scientific Consortia. Trends In Genetics 2016, 32: 251-253. PMID: 27005445, DOI: 10.1016/j.tig.2016.02.006.Peer-Reviewed Original ResearchQuantification of private information leakage from phenotype-genotype data: linking attacks
Harmanci A, Gerstein M. Quantification of private information leakage from phenotype-genotype data: linking attacks. Nature Methods 2016, 13: 251-256. PMID: 26828419, PMCID: PMC4834871, DOI: 10.1038/nmeth.3746.Peer-Reviewed Original Research
2014
Comparative analysis of the transcriptome across distant species
Gerstein MB, Rozowsky J, Yan KK, Wang D, Cheng C, Brown JB, Davis CA, Hillier L, Sisu C, Li JJ, Pei B, Harmanci AO, Duff MO, Djebali S, Alexander RP, Alver BH, Auerbach R, Bell K, Bickel PJ, Boeck ME, Boley NP, Booth BW, Cherbas L, Cherbas P, Di C, Dobin A, Drenkow J, Ewing B, Fang G, Fastuca M, Feingold EA, Frankish A, Gao G, Good PJ, Guigó R, Hammonds A, Harrow J, Hoskins RA, Howald C, Hu L, Huang H, Hubbard TJ, Huynh C, Jha S, Kasper D, Kato M, Kaufman TC, Kitchen RR, Ladewig E, Lagarde J, Lai E, Leng J, Lu Z, MacCoss M, May G, McWhirter R, Merrihew G, Miller DM, Mortazavi A, Murad R, Oliver B, Olson S, Park PJ, Pazin MJ, Perrimon N, Pervouchine D, Reinke V, Reymond A, Robinson G, Samsonova A, Saunders GI, Schlesinger F, Sethi A, Slack FJ, Spencer WC, Stoiber MH, Strasbourger P, Tanzer A, Thompson OA, Wan KH, Wang G, Wang H, Watkins KL, Wen J, Wen K, Xue C, Yang L, Yip K, Zaleski C, Zhang Y, Zheng H, Brenner SE, Graveley BR, Celniker SE, Gingeras TR, Waterston R. Comparative analysis of the transcriptome across distant species. Nature 2014, 512: 445-448. PMID: 25164755, PMCID: PMC4155737, DOI: 10.1038/nature13424.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCaenorhabditis elegansChromatinCluster AnalysisDrosophila melanogasterGene Expression ProfilingGene Expression Regulation, DevelopmentalHistonesHumansLarvaModels, GeneticMolecular Sequence AnnotationPromoter Regions, GeneticPupaRNA, UntranslatedSequence Analysis, RNATranscriptomeComparative analysis of pseudogenes across three phyla
Sisu C, Pei B, Leng J, Frankish A, Zhang Y, Balasubramanian S, Harte R, Wang D, Rutenberg-Schoenberg M, Clark W, Diekhans M, Rozowsky J, Hubbard T, Harrow J, Gerstein MB. Comparative analysis of pseudogenes across three phyla. Proceedings Of The National Academy Of Sciences Of The United States Of America 2014, 111: 13361-13366. PMID: 25157146, PMCID: PMC4169933, DOI: 10.1073/pnas.1407293111.Peer-Reviewed Original ResearchConceptsGenome evolutionLarge effective population sizesNumerous duplication eventsProtein-coding genesNumber of pseudogenesEffective population sizePotential regulatory roleDuplication eventsSelective sweepsGene familyHuman pseudogenesPrimate lineageDifferent remodeling processesPseudogenesUpstream sequencesHigh deletion ratePromoter activityBiochemical activityRegulatory rolePopulation sizePartial activityGenomePhylaDeletion rateLineages