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
Genomic data resources of the Brain Somatic Mosaicism Network for neuropsychiatric diseases
Garrison M, Jang Y, Bae T, Cherskov A, Emery S, Fasching L, Jones A, Moldovan J, Molitor C, Pochareddy S, Peters M, Shin J, Wang Y, Yang X, Akbarian S, Chess A, Gage F, Gleeson J, Kidd J, McConnell M, Mills R, Moran J, Park P, Sestan N, Urban A, Vaccarino F, Walsh C, Weinberger D, Wheelan S, Abyzov A. Genomic data resources of the Brain Somatic Mosaicism Network for neuropsychiatric diseases. Scientific Data 2023, 10: 813. PMID: 37985666, PMCID: PMC10662356, DOI: 10.1038/s41597-023-02645-7.Peer-Reviewed Original Research
2022
Somatic genomic mosaicism in the brain during aging: Scratching the surface
Bae T, Wang Y, Vaccarino F, Abyzov A. Somatic genomic mosaicism in the brain during aging: Scratching the surface. Clinical And Translational Medicine 2022, 12: e1138. PMID: 36495113, PMCID: PMC9736788, DOI: 10.1002/ctm2.1138.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus Statements
2021
CNVpytor: a tool for copy number variation detection and analysis from read depth and allele imbalance in whole-genome sequencing
Suvakov M, Panda A, Diesh C, Holmes I, Abyzov A. CNVpytor: a tool for copy number variation detection and analysis from read depth and allele imbalance in whole-genome sequencing. GigaScience 2021, 10: giab074. PMID: 34817058, PMCID: PMC8612020, DOI: 10.1093/gigascience/giab074.Peer-Reviewed Original ResearchComprehensive identification of somatic nucleotide variants in human brain tissue
Wang Y, Bae T, Thorpe J, Sherman MA, Jones AG, Cho S, Daily K, Dou Y, Ganz J, Galor A, Lobon I, Pattni R, Rosenbluh C, Tomasi S, Tomasini L, Yang X, Zhou B, Akbarian S, Ball LL, Bizzotto S, Emery SB, Doan R, Fasching L, Jang Y, Juan D, Lizano E, Luquette LJ, Moldovan JB, Narurkar R, Oetjens MT, Rodin RE, Sekar S, Shin JH, Soriano E, Straub RE, Zhou W, Chess A, Gleeson JG, Marquès-Bonet T, Park PJ, Peters MA, Pevsner J, Walsh CA, Weinberger DR, Vaccarino F, Moran J, Urban A, Kidd J, Mills R, Abyzov A. Comprehensive identification of somatic nucleotide variants in human brain tissue. Genome Biology 2021, 22: 92. PMID: 33781308, PMCID: PMC8006362, DOI: 10.1186/s13059-021-02285-3.Peer-Reviewed Original ResearchConceptsSomatic SNVsSomatic single nucleotide variantsWhole-genome sequencing dataSequencing dataBulk DNA samplesCell lineage treesSomatic mosaicismSingle nucleotide variantsLineage treesSomatic nucleotide variantsCellular processesDNA replicationHuman genomeSomatic tissuesDNA repairNucleotide variantsComprehensive identificationDNA samplesMosaic variantsNon-cancerous tissuesDNASingle individualMultiple replicatesHuman brain tissueVariants
2019
Haplotype-resolved and integrated genome analysis of the cancer cell line HepG2
Zhou B, Ho S, Greer S, Spies N, Bell J, Zhang X, Zhu X, Arthur J, Byeon S, Pattni R, Saha I, Huang Y, Song G, Perrin D, Wong W, Ji H, Abyzov A, Urban A. Haplotype-resolved and integrated genome analysis of the cancer cell line HepG2. Nucleic Acids Research 2019, 47: 3846-3861. PMID: 30864654, PMCID: PMC6486628, DOI: 10.1093/nar/gkz169.Peer-Reviewed Original ResearchConceptsGenome sequenceStructural variantsGenomic structural featuresSomatic genomic rearrangementsFunctional genomics dataAllele-specific expressionEntire chromosome armsIntegrated genome analysisCRISPR/Cas9Cell linesMain cell linesGenome structureEpigenomic characteristicsChromosome armsGenome analysisDNA methylationGenome characteristicsRetrotransposon insertionChromosomal segmentsGenomic rearrangementsGenomic dataRegulatory complexityCell line HepG2Copy numberLoss of heterozygosity
2018
Molecular characterization of colorectal adenomas with and without malignancy reveals distinguishing genome, transcriptome and methylome alterations
Druliner B, Wang P, Bae T, Baheti S, Slettedahl S, Mahoney D, Vasmatzis N, Xu H, Kim M, Bockol M, O’Brien D, Grill D, Warner N, Munoz-Gomez M, Kossick K, Johnson R, Mouchli M, Felmlee-Devine D, Washechek-Aletto J, Smyrk T, Oberg A, Wang J, Chia N, Abyzov A, Ahlquist D, Boardman L. Molecular characterization of colorectal adenomas with and without malignancy reveals distinguishing genome, transcriptome and methylome alterations. Scientific Reports 2018, 8: 3161. PMID: 29453410, PMCID: PMC5816667, DOI: 10.1038/s41598-018-21525-4.Peer-Reviewed Original ResearchConceptsColorectal cancerManagement of polypsPolyp patientsPolyp groupMalignant polypsPrecursor lesionsColorectal adenomasResidual polypPolyp tissuesPolyp sizePolypsCancerAltered expressionMethylome alterationsPatientsWhole-genome sequencingTissueMore mutationsAlterationsExpression changesSignificant expression changesMolecular determinantsMethylation alterationsMolecular distinctionMolecular characterization
2017
Comprehensive performance comparison of high-resolution array platforms for genome-wide Copy Number Variation (CNV) analysis in humans
Haraksingh RR, Abyzov A, Urban AE. Comprehensive performance comparison of high-resolution array platforms for genome-wide Copy Number Variation (CNV) analysis in humans. BMC Genomics 2017, 18: 321. PMID: 28438122, PMCID: PMC5402652, DOI: 10.1186/s12864-017-3658-x.Peer-Reviewed Original ResearchConceptsCopy number variants
2016
A uniform survey of allele-specific binding and expression over 1000-Genomes-Project individuals
Chen J, Rozowsky J, Galeev TR, Harmanci A, Kitchen R, Bedford J, Abyzov A, Kong Y, Regan L, Gerstein M. A uniform survey of allele-specific binding and expression over 1000-Genomes-Project individuals. Nature Communications 2016, 7: 11101. PMID: 27089393, PMCID: PMC4837449, DOI: 10.1038/ncomms11101.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsBinding SitesChromosome MappingComputational BiologyDatabases, GeneticGene ExpressionGene FrequencyGenome, HumanGenomicsGenotypeHigh-Throughput Nucleotide SequencingHuman Genome ProjectHumansInternetMolecular Sequence AnnotationPolymorphism, Single NucleotidePrecision MedicineConceptsSingle nucleotide variantsAllele-specific bindingFunctional genomics data setsAllele-specific behaviorLarge-scale sequencingGenomic data setsAllelic imbalanceNumber of readsChIP-seqRNA-seqGenome ProjectMaternal chromosomesNucleotide variantsPersonal genomesMapping biasAllelic variantsVariant catalogMultiple individualsFunctional effectsProject individualsBindingExpressionVariantsGenomeChromosomes
2015
Understanding genome structural variations
Abyzov A, Li S, Gerstein MB. Understanding genome structural variations. Oncotarget 2015, 7: 7370-7371. PMID: 26657727, PMCID: PMC4884923, DOI: 10.18632/oncotarget.6485.Peer-Reviewed Original ResearchAn integrated map of structural variation in 2,504 human genomes
Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, Zhang Y, Ye K, Jun G, Hsi-Yang Fritz M, Konkel MK, Malhotra A, Stütz AM, Shi X, Paolo Casale F, Chen J, Hormozdiari F, Dayama G, Chen K, Malig M, Chaisson MJP, Walter K, Meiers S, Kashin S, Garrison E, Auton A, Lam HYK, Jasmine Mu X, Alkan C, Antaki D, Bae T, Cerveira E, Chines P, Chong Z, Clarke L, Dal E, Ding L, Emery S, Fan X, Gujral M, Kahveci F, Kidd JM, Kong Y, Lameijer EW, McCarthy S, Flicek P, Gibbs RA, Marth G, Mason CE, Menelaou A, Muzny DM, Nelson BJ, Noor A, Parrish NF, Pendleton M, Quitadamo A, Raeder B, Schadt EE, Romanovitch M, Schlattl A, Sebra R, Shabalin AA, Untergasser A, Walker JA, Wang M, Yu F, Zhang C, Zhang J, Zheng-Bradley X, Zhou W, Zichner T, Sebat J, Batzer MA, McCarroll SA, Mills R, Gerstein M, Bashir A, Stegle O, Devine S, Lee C, Eichler E, Korbel J. An integrated map of structural variation in 2,504 human genomes. Nature 2015, 526: 75-81. PMID: 26432246, PMCID: PMC4617611, DOI: 10.1038/nature15394.Peer-Reviewed Original ResearchMeSH KeywordsAmino Acid SequenceGenetic Predisposition to DiseaseGenetic VariationGenetics, MedicalGenetics, PopulationGenome, HumanGenome-Wide Association StudyGenomicsGenotypeHaplotypesHomozygoteHumansMolecular Sequence DataMutation RatePhysical Chromosome MappingPolymorphism, Single NucleotideQuantitative Trait LociSequence Analysis, DNASequence DeletionConceptsStructural variantsHuman genomeExpression quantitative trait lociGenome-wide association studiesIndividual mutational eventsQuantitative trait lociComplex structural variantsHomozygous gene knockoutsDNA sequencing dataLoci subjectTrait lociHuman genesGene knockoutIntegrated mapSequencing dataAssociation studiesMutational eventsHaplotype blocksVariant classesFunctional impactPopulation stratificationGenomeNumerous diseasesHuman populationStructural variations
2014
VarSim: a high-fidelity simulation and validation framework for high-throughput genome sequencing with cancer applications
Mu JC, Mohiyuddin M, Li J, Bani Asadi N, Gerstein MB, Abyzov A, Wong WH, Lam HY. VarSim: a high-fidelity simulation and validation framework for high-throughput genome sequencing with cancer applications. Bioinformatics 2014, 31: 1469-1471. PMID: 25524895, PMCID: PMC4410653, DOI: 10.1093/bioinformatics/btu828.Peer-Reviewed Original ResearchConceptsMap data structureCompute frameworkGraphical reportsData structureParallel computationValidation frameworkRead alignmentSupplementary dataValidation toolReal dataHigh-fidelity simulationHigh-throughput genome sequencingDetailed statisticsFrameworkValidation resultsPythonInformationJavaSimulatorSupplementary informationComputationBioinformaticsRealistic modelCodeImplementation
2013
Integrative Annotation of Variants from 1092 Humans: Application to Cancer Genomics
Khurana E, Fu Y, Colonna V, Mu XJ, Kang HM, Lappalainen T, Sboner A, Lochovsky L, Chen J, Harmanci A, Das J, Abyzov A, Balasubramanian S, Beal K, Chakravarty D, Challis D, Chen Y, Clarke D, Clarke L, Cunningham F, Evani US, Flicek P, Fragoza R, Garrison E, Gibbs R, Gümüş ZH, Herrero J, Kitabayashi N, Kong Y, Lage K, Liluashvili V, Lipkin SM, MacArthur DG, Marth G, Muzny D, Pers TH, Ritchie GRS, Rosenfeld JA, Sisu C, Wei X, Wilson M, Xue Y, Yu F, Consortium 1, Dermitzakis ET, Yu H, Rubin MA, Tyler-Smith C, Gerstein M. Integrative Annotation of Variants from 1092 Humans: Application to Cancer Genomics. Science 2013, 342: 1235587. PMID: 24092746, PMCID: PMC3947637, DOI: 10.1126/science.1235587.Peer-Reviewed Original Research
2012
Architecture of the human regulatory network derived from ENCODE data
Gerstein MB, Kundaje A, Hariharan M, Landt SG, Yan KK, Cheng C, Mu XJ, Khurana E, Rozowsky J, Alexander R, Min R, Alves P, Abyzov A, Addleman N, Bhardwaj N, Boyle AP, Cayting P, Charos A, Chen DZ, Cheng Y, Clarke D, Eastman C, Euskirchen G, Frietze S, Fu Y, Gertz J, Grubert F, Harmanci A, Jain P, Kasowski M, Lacroute P, Leng J, Lian J, Monahan H, O’Geen H, Ouyang Z, Partridge EC, Patacsil D, Pauli F, Raha D, Ramirez L, Reddy TE, Reed B, Shi M, Slifer T, Wang J, Wu L, Yang X, Yip KY, Zilberman-Schapira G, Batzoglou S, Sidow A, Farnham PJ, Myers RM, Weissman SM, Snyder M. Architecture of the human regulatory network derived from ENCODE data. Nature 2012, 489: 91-100. PMID: 22955619, PMCID: PMC4154057, DOI: 10.1038/nature11245.Peer-Reviewed Original ResearchMeSH KeywordsAllelesCell LineDNAEncyclopedias as TopicGATA1 Transcription FactorGene Expression ProfilingGene Regulatory NetworksGenome, HumanGenomicsHumansK562 CellsMolecular Sequence AnnotationOrgan SpecificityPhosphorylationPolymorphism, Single NucleotideProtein Interaction MapsRegulatory Sequences, Nucleic AcidRNA, UntranslatedSelection, GeneticTranscription FactorsTranscription Initiation SiteConceptsTranscription factorsRegulatory networksHuman transcriptional regulatory networkHuman regulatory networkSpecific genomic locationsTranscription-related factorsState of genesTranscriptional regulatory networksAllele-specific activityPersonal genome sequencesGenomic locationStrong selectionGenome sequenceENCODE dataGenomic informationInformation-flow bottlenecksRegulatory informationConnected network componentsCombinatorial fashionInfluences expressionHuman biologyBinding informationNetwork motifsCo-associationGenes
2011
Mapping copy number variation by population-scale genome sequencing
Mills RE, Walter K, Stewart C, Handsaker RE, Chen K, Alkan C, Abyzov A, Yoon SC, Ye K, Cheetham RK, Chinwalla A, Conrad DF, Fu Y, Grubert F, Hajirasouliha I, Hormozdiari F, Iakoucheva LM, Iqbal Z, Kang S, Kidd JM, Konkel MK, Korn J, Khurana E, Kural D, Lam HY, Leng J, Li R, Li Y, Lin CY, Luo R, Mu XJ, Nemesh J, Peckham HE, Rausch T, Scally A, Shi X, Stromberg MP, Stütz AM, Urban AE, Walker JA, Wu J, Zhang Y, Zhang ZD, Batzer MA, Ding L, Marth GT, McVean G, Sebat J, Snyder M, Wang J, Ye K, Eichler EE, Gerstein MB, Hurles ME, Lee C, McCarroll SA, Korbel JO. Mapping copy number variation by population-scale genome sequencing. Nature 2011, 470: 59-65. PMID: 21293372, PMCID: PMC3077050, DOI: 10.1038/nature09708.Peer-Reviewed Original ResearchConceptsMost structural variantsStructural variantsSequencing-based association studiesUnbalanced structural variantsGenomic structural variantsFunctional impactDNA sequencing dataSV hotspotsSV discoveryHuman genomeNucleotide resolutionGene disruptionAdditional structural variantsHigh-frequency deletionSequencing dataGenome sequencingAssociation studiesTandem duplicationNumber variationsGene deletionPartial gene deletionsDeletionCommon mechanismForm of variationSize spectra
2009
PEMer: a computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data
Korbel JO, Abyzov A, Mu XJ, Carriero N, Cayting P, Zhang Z, Snyder M, Gerstein MB. PEMer: a computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data. Genome Biology 2009, 10: r23. PMID: 19236709, PMCID: PMC2688268, DOI: 10.1186/gb-2009-10-2-r23.Peer-Reviewed Original Research
2008
MSB: A mean-shift-based approach for the analysis of structural variation in the genome
Wang LY, Abyzov A, Korbel JO, Snyder M, Gerstein M. MSB: A mean-shift-based approach for the analysis of structural variation in the genome. Genome Research 2008, 19: 106-117. PMID: 19037015, PMCID: PMC2612956, DOI: 10.1101/gr.080069.108.Peer-Reviewed Original ResearchConceptsProbability density functionNumber of segmentsGood parameter initializationLikelihood functionArray CGH experimentsKernel-based approachUnderlying distributionModel parametersParameter initializationParticular assumptionsNonparametric methodsExpectation maximizationComputational methodsConvergenceGlobal criterionLocal gradients