2022
Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity
Iturria-Medina Y, Adewale Q, Khan A, Ducharme S, Rosa-Neto P, O'Donnell K, Petyuk V, Gauthier S, De Jager P, Breitner J, Bennett D. Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity. Science Advances 2022, 8: eabo6764. PMID: 36399579, PMCID: PMC9674284, DOI: 10.1126/sciadv.abo6764.Peer-Reviewed Original ResearchConceptsMolecular taxonomy of ADAlzheimer's diseaseMolecular dataAlzheimer's disease progressionGenetic variationDNA methylationPattern of alterationsAD dementia progressionAD variantsCell typesPostmortem brainsHeterogeneous disorderBiological domainSpatial pattern of alterationAlzheimerClinical heterogeneityTaxonomyAD subtypesMetabolomic profilesSpatial patternsMolecular indicesAdvanced machine learning analysisDisease progressionRNADNA
2019
Biological embedding of experience: A primer on epigenetics
Aristizabal MJ, Anreiter I, Halldorsdottir T, Odgers CL, McDade TW, Goldenberg A, Mostafavi S, Kobor MS, Binder EB, Sokolowski MB, O'Donnell KJ. Biological embedding of experience: A primer on epigenetics. Proceedings Of The National Academy Of Sciences Of The United States Of America 2019, 117: 23261-23269. PMID: 31624126, PMCID: PMC7519272, DOI: 10.1073/pnas.1820838116.BooksConceptsEpigenetic mechanismsSpecific epigenetic mechanismsEpigenetic landscapeEpigenome editingDNA sequencesBiological embeddingGene expressionBiological processesCell typesComparative animalHuman longitudinal studiesMolecular profilingPotential roleCorrelative dataEpigenomeGenomeRecent advancesEpigeneticsEditingProfilingCausal dataPrimersMechanismSequenceExpression
2015
funtooNorm: an R package for normalization of DNA methylation data when there are multiple cell or tissue types
Oros Klein K, Grinek S, Bernatsky S, Bouchard L, Ciampi A, Colmegna I, Fortin JP, Gao L, Hivert MF, Hudson M, Kobor MS, Labbe A, MacIsaac JL, Meaney MJ, Morin AM, O'Donnell KJ, Pastinen T, Van Ijzendoorn MH, Voisin G, Greenwood CM. funtooNorm: an R package for normalization of DNA methylation data when there are multiple cell or tissue types. Bioinformatics 2015, 32: 593-595. PMID: 26500152, PMCID: PMC4743629, DOI: 10.1093/bioinformatics/btv615.Peer-Reviewed Original ResearchConceptsMethylation patternsIllumina Infinium Human Methylation450 BeadChipDNA methylation patternsBenefits of cellsDNA methylation dataTissue typesR packageInter-tissue variabilityMethylation dataChromosome XCell typesNew R packageNormalization of dataMore cellsCellsMultiple cellsCross-validated errorBeadChipBioconductor