2021
Xenopus as a platform for discovery of genes relevant to human disease
Kostiuk V, Khokha MK. Xenopus as a platform for discovery of genes relevant to human disease. Current Topics In Developmental Biology 2021, 145: 277-312. PMID: 34074532, PMCID: PMC8734201, DOI: 10.1016/bs.ctdb.2021.03.005.Peer-Reviewed Original ResearchConceptsCandidate genesHuman diseasesDiscovery of genesScreen candidate genesMultiple candidate genesPatient genomic dataHuman genomePatient phenotypesDisease pathogenesisGenomic dataComplex phenotypesFate mapGene knockoutKnockdown strategyPatient mutationsBirth defectsXenopusFunctional studiesGenesPhenotypeCongenital birthAbnormal developmentCongenital heart diseaseCause of deathBetter diagnostic methods
2019
Single-cell connectomic analysis of adult mammalian lungs
Raredon MSB, Adams TS, Suhail Y, Schupp JC, Poli S, Neumark N, Leiby KL, Greaney AM, Yuan Y, Horien C, Linderman G, Engler AJ, Boffa DJ, Kluger Y, Rosas IO, Levchenko A, Kaminski N, Niklason LE. Single-cell connectomic analysis of adult mammalian lungs. Science Advances 2019, 5: eaaw3851. PMID: 31840053, PMCID: PMC6892628, DOI: 10.1126/sciadv.aaw3851.Peer-Reviewed Original ResearchConceptsTissue homeostasisMammalian lungSingle-cell RNA sequencing techniquesAdult mammalian lungRNA sequencing techniquesCell-cell interactionsSequencing techniquesKey pathwaysAlveolar type IFunctional roleCell typesCell populationsRegenerative medicineHomeostatic mechanismsHomeostasisFine architectureFunctional lung tissueIncomplete understandingMajor roleType ITissueRegulationPathwayAlveolar cell populationsDistal lung
2015
Tissue-based map of the human proteome
Uhlén M, Fagerberg L, Hallström B, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, Olsson I, Edlund K, Lundberg E, Navani S, Szigyarto C, Odeberg J, Djureinovic D, Takanen J, Hober S, Alm T, Edqvist P, Berling H, Tegel H, Mulder J, Rockberg J, Nilsson P, Schwenk J, Hamsten M, von Feilitzen K, Forsberg M, Persson L, Johansson F, Zwahlen M, von Heijne G, Nielsen J, Pontén F. Tissue-based map of the human proteome. Science 2015, 347: 1260419. PMID: 25613900, DOI: 10.1126/science.1260419.Peer-Reviewed Original ResearchConceptsPutative protein-coding genesHuman tissue proteomesProtein-coding genesInteractive web-based databaseIntegrated omics approachDifferent tissuesGlobal expression patternsSingle-cell levelMembrane proteomeProteome variationDruggable proteomeOmics approachesHuman proteomeHuman secretomeMolecular detailsIndividual proteinsQuantitative transcriptomicsCancer proteomeTissue proteomeProteomeExpression patternsHuman biologyMetabolic functionsTissue microarray-based immunohistochemistryMajor tissues
2014
The contribution of de novo coding mutations to autism spectrum disorder
Iossifov I, O’Roak B, Sanders SJ, Ronemus M, Krumm N, Levy D, Stessman HA, Witherspoon KT, Vives L, Patterson KE, Smith JD, Paeper B, Nickerson DA, Dea J, Dong S, Gonzalez LE, Mandell JD, Mane SM, Murtha MT, Sullivan CA, Walker MF, Waqar Z, Wei L, Willsey AJ, Yamrom B, Lee YH, Grabowska E, Dalkic E, Wang Z, Marks S, Andrews P, Leotta A, Kendall J, Hakker I, Rosenbaum J, Ma B, Rodgers L, Troge J, Narzisi G, Yoon S, Schatz MC, Ye K, McCombie WR, Shendure J, Eichler EE, State MW, Wigler M. The contribution of de novo coding mutations to autism spectrum disorder. Nature 2014, 515: 216-221. PMID: 25363768, PMCID: PMC4313871, DOI: 10.1038/nature13908.Peer-Reviewed Original ResearchConceptsLGD mutationsMissense mutationsWild-type alleleChromatin modifiersGene-disrupting mutationsGenetic architectureCopy number variantsDe novo mutationsDe novo missense mutationsWhole-exome sequencingHuman diseasesGenesNovo missense mutationNumber variantsLikely gene-disrupting mutationsMutationsDe novoNovo mutationsExome sequencingSimplex familiesTargetFMRPPowerful toolSimilar numberSequencingThe impact of exposure-biased sampling designs on detection of gene–environment interactions in case–control studies with potential exposure misclassification
Stenzel S, Ahn J, Boonstra P, Gruber S, Mukherjee B. The impact of exposure-biased sampling designs on detection of gene–environment interactions in case–control studies with potential exposure misclassification. European Journal Of Epidemiology 2014, 30: 413-423. PMID: 24894824, PMCID: PMC4256150, DOI: 10.1007/s10654-014-9908-1.Peer-Reviewed Original ResearchConceptsG-E interactionsExposure informationDetection of gene-environment interactionsPrevalence of exposureGene-environment interactionsSampling designCase-control studyRandom selection of subjectsPerformance of sampling designsCase-onlyExposure prevalenceJoint testExposure misclassificationCase-controlRare exposuresMarginal associationSelection of subjectsType I errorEmpirical simulation studyIdeal sampling schemesJoint effectsPrevalenceRandom selectionG-EMisclassification
2013
High-resolution Xist binding maps reveal two-step spreading during X-chromosome inactivation
Simon MD, Pinter SF, Fang R, Sarma K, Rutenberg-Schoenberg M, Bowman SK, Kesner BA, Maier VK, Kingston RE, Lee JT. High-resolution Xist binding maps reveal two-step spreading during X-chromosome inactivation. Nature 2013, 504: 465-469. PMID: 24162848, PMCID: PMC3904790, DOI: 10.1038/nature12719.Peer-Reviewed Original ResearchNew Gene Evolution: Little Did We Know
Long M, VanKuren NW, Chen S, Vibranovski MD. New Gene Evolution: Little Did We Know. Annual Review Of Genetics 2013, 47: 307-333. PMID: 24050177, PMCID: PMC4281893, DOI: 10.1146/annurev-genet-111212-133301.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsBrainDrosophila melanogasterEvolution, MolecularForecastingGene DosageGene DuplicationGene Expression RegulationGene Regulatory NetworksGene Transfer, HorizontalGenesGenes, InsectGenes, PlantGenetic StructuresHumansMammalsModels, GeneticPhenotypePhylogenyRecombination, GeneticRNA, UntranslatedSelection, GeneticSex CharacteristicsTranscription, GeneticConceptsNew gene originationNew genesGene originationGenetic systemNew gene evolutionNew gene structuresGene evolutionPhenotypic evolutionGene structureDifferent organismsBiological diversityPhenotypic functionsGenesGenomeOriginationEvolutionDecades of effortOrganismsDiversityComprehensive pictureCritical componentNew genes as drivers of phenotypic evolution
Chen S, Krinsky BH, Long M. New genes as drivers of phenotypic evolution. Nature Reviews Genetics 2013, 14: 645-660. PMID: 23949544, PMCID: PMC4236023, DOI: 10.1038/nrg3521.Peer-Reviewed Original ResearchConceptsNew genesPhenotypic evolutionLineage-specific essential genesBiological processesUnexpected genetic diversityEvolution of phenotypesFundamental biological processesGene evolutionGenetic circuitryDrosophila sppEssential genesField of geneticsGenetic diversityGenetic systemBiological diversityMolecular explanationGenesIndispensable roleKey PointsThe studySpeciesDiversityEssential componentEvolutionMammalsCentral importanceQuantitative set analysis for gene expression: a method to quantify gene set differential expression including gene-gene correlations
Yaari G, Bolen CR, Thakar J, Kleinstein SH. Quantitative set analysis for gene expression: a method to quantify gene set differential expression including gene-gene correlations. Nucleic Acids Research 2013, 41: e170-e170. PMID: 23921631, PMCID: PMC3794608, DOI: 10.1093/nar/gkt660.Peer-Reviewed Original ResearchThe zebrafish reference genome sequence and its relationship to the human genome
Howe K, Clark M, Torroja C, Torrance J, Berthelot C, Muffato M, Collins J, Humphray S, McLaren K, Matthews L, McLaren S, Sealy I, Caccamo M, Churcher C, Scott C, Barrett J, Koch R, Rauch G, White S, Chow W, Kilian B, Quintais L, Guerra-Assunção J, Zhou Y, Gu Y, Yen J, Vogel J, Eyre T, Redmond S, Banerjee R, Chi J, Fu B, Langley E, Maguire S, Laird G, Lloyd D, Kenyon E, Donaldson S, Sehra H, Almeida-King J, Loveland J, Trevanion S, Jones M, Quail M, Willey D, Hunt A, Burton J, Sims S, McLay K, Plumb B, Davis J, Clee C, Oliver K, Clark R, Riddle C, Elliott D, Threadgold G, Harden G, Ware D, Begum S, Mortimore B, Kerry G, Heath P, Phillimore B, Tracey A, Corby N, Dunn M, Johnson C, Wood J, Clark S, Pelan S, Griffiths G, Smith M, Glithero R, Howden P, Barker N, Lloyd C, Stevens C, Harley J, Holt K, Panagiotidis G, Lovell J, Beasley H, Henderson C, Gordon D, Auger K, Wright D, Collins J, Raisen C, Dyer L, Leung K, Robertson L, Ambridge K, Leongamornlert D, McGuire S, Gilderthorp R, Griffiths C, Manthravadi D, Nichol S, Barker G, Whitehead S, Kay M, Brown J, Murnane C, Gray E, Humphries M, Sycamore N, Barker D, Saunders D, Wallis J, Babbage A, Hammond S, Mashreghi-Mohammadi M, Barr L, Martin S, Wray P, Ellington A, Matthews N, Ellwood M, Woodmansey R, Clark G, Cooper J, Tromans A, Grafham D, Skuce C, Pandian R, Andrews R, Harrison E, Kimberley A, Garnett J, Fosker N, Hall R, Garner P, Kelly D, Bird C, Palmer S, Gehring I, Berger A, Dooley C, Ersan-Ürün Z, Eser C, Geiger H, Geisler M, Karotki L, Kirn A, Konantz J, Konantz M, Oberländer M, Rudolph-Geiger S, Teucke M, Lanz C, Raddatz G, Osoegawa K, Zhu B, Rapp A, Widaa S, Langford C, Yang F, Schuster S, Carter N, Harrow J, Ning Z, Herrero J, Searle S, Enright A, Geisler R, Plasterk R, Lee C, Westerfield M, de Jong P, Zon L, Postlethwait J, Nüsslein-Volhard C, Hubbard T, Crollius H, Rogers J, Stemple D. The zebrafish reference genome sequence and its relationship to the human genome. Nature 2013, 496: 498-503. PMID: 23594743, PMCID: PMC3703927, DOI: 10.1038/nature12111.Peer-Reviewed Original ResearchConceptsHigh-quality sequence assembliesHuman protein-coding genesProtein-coding genesReference genome sequenceKey genomic featuresHuman reference genomeZebrafish genomeZebrafish orthologueReference genomeGenome sequenceHuman genomeSequence assemblyGenomic featuresLarge genesGenomeGenesOrthologuesVertebratesSequenceAssembly
2012
Genetic Association Test for Multiple Traits at Gene Level
Guo X, Liu Z, Wang X, Zhang H. Genetic Association Test for Multiple Traits at Gene Level. Genetic Epidemiology 2012, 37: 122-129. PMID: 23032486, PMCID: PMC3524409, DOI: 10.1002/gepi.21688.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesMultiple traitsGene levelSingle nucleotide polymorphismsGenetic association testsCommon genesAssociation studiesAssociation TestNucleotide polymorphismsTraitsStudy of AddictionComplex diseasesBiological mechanismsDisease of interestAssociation informationGenesGeneticsSuch studiesStrong evidencePolymorphismPrevious findingsLevelsConvergence of Genome‐Wide Association and Candidate Gene Studies for Alcoholism
Olfson E, Bierut LJ. Convergence of Genome‐Wide Association and Candidate Gene Studies for Alcoholism. Alcohol Clinical And Experimental Research 2012, 36: 2086-2094. PMID: 22978509, PMCID: PMC3521088, DOI: 10.1111/j.1530-0277.2012.01843.x.Peer-Reviewed Original ResearchMutation profiling identifies numerous rare drug targets and distinct mutation patterns in different clinical subtypes of breast cancers
Santarpia L, Qi Y, Stemke-Hale K, Wang B, Young EJ, Booser DJ, Holmes FA, O’Shaughnessy J, Hellerstedt B, Pippen J, Vidaurre T, Gomez H, Valero V, Hortobagyi GN, Symmans WF, Bottai G, Di Leo A, Gonzalez-Angulo AM, Pusztai L. Mutation profiling identifies numerous rare drug targets and distinct mutation patterns in different clinical subtypes of breast cancers. Breast Cancer Research And Treatment 2012, 134: 333-343. PMID: 22538770, PMCID: PMC3885980, DOI: 10.1007/s10549-012-2035-3.Peer-Reviewed Original ResearchConceptsTriple-negative breast cancerBreast cancer subtypesBreast cancerPIK3CA mutationsCancer subtypesEstrogen receptor-positive cancersBreast cancer molecular subtypesMajor breast cancer subtypesSingle needle biopsyProspective clinical trialsReceptor-positive cancersDifferent breast cancer subtypesDifferent clinical subtypesNegative breast cancerCancer molecular subtypesFine-needle aspirationMutation patternsClinical subtypesClinical trialsNeedle biopsyMolecular subtypesNeedle aspirationInvestigational drugsStage IFBXW7 mutations
2011
Evolving Genomic Approaches to Idiopathic Pulmonary Fibrosis: Moving Beyond Genes
Kass DJ, Kaminski N. Evolving Genomic Approaches to Idiopathic Pulmonary Fibrosis: Moving Beyond Genes. Clinical And Translational Science 2011, 4: 372-379. PMID: 22029812, PMCID: PMC3229869, DOI: 10.1111/j.1752-8062.2011.00287.x.Peer-Reviewed Original ResearchConceptsGenomic technologiesIdiopathic pulmonary fibrosisPathogenesis of diseaseGenomic discoveriesSystems biology approachGene expression profilingContribution of microRNAsMost human diseasesGenomic approachesGene networksBiology approachHuman genomeEpigenetic researchGenomic dataExpression profilingHuman diseasesPulmonary fibrosisDiagnosis of IPFGenesLarge collaborative studiesProgressive scarringPeripheral bloodClinical severityLung parenchymaTherapeutic goals
2007
The road to modularity
Wagner GP, Pavlicev M, Cheverud JM. The road to modularity. Nature Reviews Genetics 2007, 8: 921-931. PMID: 18007649, DOI: 10.1038/nrg2267.Peer-Reviewed Original ResearchConceptsProtein-protein interaction networkMolecular systems biologyPatterns of evolutionGene duplicationGene regulationEvolutionary biologyNeutral mutationsBiological organizationQuantitative traitsInteraction networksSystems biologyStructure of macromoleculesEnvironmental conditionsBiologyDuplicationTraitsMutationsRegulationSelectionModularityMacromoleculesGene symbol disambiguation using knowledge-based profiles
Xu H, Fan J, Hripcsak G, Mendonça E, Markatou M, Friedman C. Gene symbol disambiguation using knowledge-based profiles. Bioinformatics 2007, 23: 1015-1022. PMID: 17314123, DOI: 10.1093/bioinformatics/btm056.Peer-Reviewed Original ResearchConceptsKnowledge sourcesSimilarity scoresInformation retrieval methodsGene symbol disambiguationText mining systemKnowledge-based profilesTesting data setsBiomedical entitiesBiomedical domainMEDLINE abstractsHigh similarity scoresRetrieval methodAmbiguous genesEntrez GeneGene symbolsDisambiguation taskTesting setAutomatic correspondence of tags and genes (ACTG): a tool for the analysis of SAGE, MPSS and SBS data
Galante P, Trimarchi J, Cepko C, de Souza S, Ohno-Machado L, Kuo W. Automatic correspondence of tags and genes (ACTG): a tool for the analysis of SAGE, MPSS and SBS data. Bioinformatics 2007, 23: 903-905. PMID: 17277333, DOI: 10.1093/bioinformatics/btm023.Peer-Reviewed Original Research
2006
Phenotype-genotype association grid: a convenient method for summarizing multiple association analyses
Levy D, DePalma S, Benjamin E, O'Donnell C, Parise H, Hirschhorn J, Vasan R, Izumo S, Larson M. Phenotype-genotype association grid: a convenient method for summarizing multiple association analyses. BMC Genomic Data 2006, 7: 30. PMID: 16716207, PMCID: PMC1526453, DOI: 10.1186/1471-2156-7-30.Peer-Reviewed Original ResearchConceptsMultiple association analysisLarge-scale genetic studiesIndividual SNP associationsHTML pagesWeb ToolkitInteractive explorationOracle databaseLarge-scale association studiesWeb serverTcl scriptsMouse clicksGene-environment associationVast amountGenetic lociGenotypic dataNumerous phenotypesAssociation studiesGenetic studiesAssociation analysisSNP associationsMultiple phenotypesSingle SNPIdentification of patternsIndividual cellsGrid display
2004
Microarray analysis of in vitro pericyte differentiation reveals an angiogenic program of gene expression
Kale S, Hanai J, Chan B, Karihaloo A, Grotendorst G, Cantley L, Sukhatme VP. Microarray analysis of in vitro pericyte differentiation reveals an angiogenic program of gene expression. The FASEB Journal 2004, 19: 1-30. PMID: 15579670, DOI: 10.1096/fj.04-1604fje.Peer-Reviewed Original ResearchConceptsPericytes/vascular smooth muscle cellsVascular smooth muscle cellsHuman umbilical vein ECsGene expressionMicroarray analysisPericyte differentiationNormal blood vessel developmentAngiogenic programGene expression changesBlood vessel developmentEndothelial cellsEPH receptor A2Human umbilical vein endothelial cellsCoculture systemUmbilical vein endothelial cellsExpression changesCell differentiationVascular developmentVein endothelial cellsVessel developmentGenesMature vasculatureFunctional significanceHB-EGFIntegrin alpha5Deciphering gene expression profiles generated from DNA microarrays and their applications in oral medicine
Kuo W, Whipple M, Epstein J, Jenssen T, Santos G, Ohno-Machado L, Sonis S. Deciphering gene expression profiles generated from DNA microarrays and their applications in oral medicine. Oral Surgery Oral Medicine Oral Pathology And Oral Radiology 2004, 97: 584-591. PMID: 15153870, DOI: 10.1016/j.tripleo.2003.11.016.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsGene expression profilesTranscriptional mappingDNA microarraysExpression profilesGenome-wide monitoringThousands of genesApplication of microarraysTypical microarray experimentTranscription levelsBiological processesGenetic changesMicroarray technologyMicroarray experimentsDiseased cellsMicroarrayRelative expressionDisease etiologyNew therapeutic toolsWidespread hopeCellsGenesNew biomarkersTherapeutic toolExpressionDiscovery
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply