An atlas of gene regulatory elements in adult mouse cerebrum.

Animals Atlases as Topic Cerebrum / cytology Chromatin / chemistry Chromatin Assembly and Disassembly Gene Expression Regulation Genetic Predisposition to Disease / genetics Humans Male Mice Mice, Inbred C57BL Nervous System Diseases / genetics Neuroglia / classification Neurons / classification Regulatory Sequences, Nucleic Acid / genetics Sequence Analysis, DNA Single-Cell Analysis

Journal

Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462

Informations de publication

Date de publication:
10 2021
Historique:
received: 11 05 2020
accepted: 30 04 2021
entrez: 7 10 2021
pubmed: 8 10 2021
medline: 3 11 2021
Statut: ppublish

Résumé

The mammalian cerebrum performs high-level sensory perception, motor control and cognitive functions through highly specialized cortical and subcortical structures

Identifiants

DOI: 10.1038/s41586-021-03604-1 PMID: 34616068 PMC: PMC8494637
pubmed: 34616068
doi: 10.1038/s41586-021-03604-1
pii: 10.1038/s41586-021-03604-1
pmc: PMC8494637
doi:

Substances chimiques

Chromatin 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

129-136

Subventions

Organisme : NIMH NIH HHS
ID : U19 MH114830
Pays : United States
Organisme : NIMH NIH HHS
ID : F30 MH011483
Pays : United States
Organisme : Howard Hughes Medical Institute
Pays : United States
Organisme : NIH HHS
ID : S10 OD026929
Pays : United States
Organisme : NIMH NIH HHS
ID : U19 MH114831
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA014195
Pays : United States

Informations de copyright

© 2021. The Author(s).

Références

Paxinos, G. The Rat Nervous System 4th edn (2015).
Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 (2018).
pubmed: 30096314 pmcid: 6086934 doi: 10.1016/j.cell.2018.06.021
Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018).
pubmed: 30096299 pmcid: 6447408 doi: 10.1016/j.cell.2018.07.028
Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).
pubmed: 30382198 pmcid: 6456269 doi: 10.1038/s41586-018-0654-5
Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).
pubmed: 31435019 pmcid: 6919571 doi: 10.1038/s41586-019-1506-7
Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).
pubmed: 26727548 pmcid: 4985242 doi: 10.1038/nn.4216
Moffitt, J. R. et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018).
pubmed: 30385464 pmcid: 6482113 doi: 10.1126/science.aau5324
Eng, C. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019).
pubmed: 30911168 pmcid: 6544023 doi: 10.1038/s41586-019-1049-y
Herculano-Houzel, S., Mota, B. & Lent, R. Cellular scaling rules for rodent brains. Proc. Natl Acad. Sci. USA 103, 12138–12143 (2006).
pubmed: 16880386 pmcid: 1567708 doi: 10.1073/pnas.0604911103
Harris, K. D. & Shepherd, G. M. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170–181 (2015).
pubmed: 25622573 doi: 10.1038/nn.3917
Huang, Z. J. Toward a genetic dissection of cortical circuits in the mouse. Neuron 83, 1284–1302 (2014).
pubmed: 25233312 doi: 10.1016/j.neuron.2014.08.041
Douglas, R. J. & Martin, K. A. Neuronal circuits of the neocortex. Annu. Rev. Neurosci. 27, 419–451 (2004).
pubmed: 15217339 doi: 10.1146/annurev.neuro.27.070203.144152
Shlyueva, D., Stampfel, G. & Stark, A. Transcriptional enhancers: from properties to genome-wide predictions. Nat. Rev. Genet. 15, 272–286 (2014).
pubmed: 24614317 doi: 10.1038/nrg3682
Moore, J. E. et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699–710 (2020).
pubmed: 32728249 pmcid: 7410828 doi: 10.1038/s41586-020-2493-4
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
pubmed: 24097267 pmcid: 3959825 doi: 10.1038/nmeth.2688
Rivera, C. M. & Ren, B. Mapping human epigenomes. Cell 155, 39–55 (2013).
pubmed: 24074860 doi: 10.1016/j.cell.2013.09.011
Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).
pubmed: 25953818 pmcid: 4836442 doi: 10.1126/science.aab1601
Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).
pubmed: 26083756 pmcid: 4685948 doi: 10.1038/nature14590
Lareau, C. A. et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat. Biotechnol. 37, 916–924 (2019).
pubmed: 31235917 doi: 10.1038/s41587-019-0147-6
Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019).
pubmed: 31375813 pmcid: 7299161 doi: 10.1038/s41587-019-0206-z
Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).
pubmed: 29227469 doi: 10.1038/nbt.4038
Cusanovich, D. A. et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell 174, 1309–1324 (2018).
pubmed: 30078704 pmcid: 6158300 doi: 10.1016/j.cell.2018.06.052
Sinnamon, J. R. et al. The accessible chromatin landscape of the murine hippocampus at single-cell resolution. Genome Res. 29, 857–869 (2019).
pubmed: 30936163 pmcid: 6499306 doi: 10.1101/gr.243725.118
Thornton, C. A. et al. Spatially mapped single-cell chromatin accessibility. Nat. Commun. 12, 1274 (2021).
pubmed: 33627658 pmcid: 7904839 doi: 10.1038/s41467-021-21515-7
Preissl, S. et al. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat. Neurosci. 21, 432–439 (2018).
pubmed: 29434377 pmcid: 5862073 doi: 10.1038/s41593-018-0079-3
Fang, R. et al. Comprehensive analysis of single cell ATAC-seq data with SnapATAC. Nat. Commun. 12, 1337 (2021).
pubmed: 33637727 pmcid: 7910485 doi: 10.1038/s41467-021-21583-9
Graybuck, L. T. et al. Enhancer viruses for combinatorial cell-subclass-specific labeling. Neuron 109, 1449–1464.e13 (2021).
pubmed: 33789083 doi: 10.1016/j.neuron.2021.03.011 pmcid: 8610077
Wang, Q. et al. The Allen Mouse Brain common coordinate framework: a 3D reference atlas. Cell 181, 936–953.e20 (2020).
pubmed: 32386544 pmcid: 8152789 doi: 10.1016/j.cell.2020.04.007
Liu, H. et al. DNA methylation atlas of the mouse brain at single-cell resolution. Nature https://doi.org/10.1038/s41586-020-03182-8 (2020).
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
pubmed: 18798982 pmcid: 2592715 doi: 10.1186/gb-2008-9-9-r137
Shen, Y. et al. A map of the cis-regulatory sequences in the mouse genome. Nature 488, 116–120 (2012).
pubmed: 22763441 pmcid: 4041622 doi: 10.1038/nature11243
Yue, F. et al. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515, 355–364 (2014).
pubmed: 25409824 pmcid: 4266106 doi: 10.1038/nature13992
Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).
pubmed: 22373907 pmcid: 3577932 doi: 10.1038/nmeth.1906
Tatsumi, K. et al. Olig2-lineage astrocytes: a distinct subtype of astrocytes that differs from GFAP astrocytes. Front. Neuroanat. 12, 8 (2018).
pubmed: 29497365 pmcid: 5819569 doi: 10.3389/fnana.2018.00008
Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).
pubmed: 25700174 doi: 10.1126/science.aaa1934
Bayraktar, O. A. et al. Astrocyte layers in the mammalian cerebral cortex revealed by a single-cell in situ transcriptomic map. Nat. Neurosci. 23, 500–509 (2020).
pubmed: 32203496 pmcid: 7116562 doi: 10.1038/s41593-020-0602-1
Pliner, H. A. et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data. Mol. Cell 71, 858–871 (2018).
pubmed: 30078726 pmcid: 6582963 doi: 10.1016/j.molcel.2018.06.044
Harrow, J. et al. GENCODE: producing a reference annotation for ENCODE. Genome Biol. 7, S4 (2006).
pubmed: 16925838 pmcid: 1810553 doi: 10.1186/gb-2006-7-s1-s4
Ong, C. T. & Corces, V. G. CTCF: an architectural protein bridging genome topology and function. Nat. Rev. Genet. 15, 234–246 (2014).
pubmed: 24614316 pmcid: 4610363 doi: 10.1038/nrg3663
Hirayama, T., Tarusawa, E., Yoshimura, Y., Galjart, N. & Yagi, T. CTCF is required for neural development and stochastic expression of clustered Pcdh genes in neurons. Cell Rep. 2, 345–357 (2012).
pubmed: 22854024 doi: 10.1016/j.celrep.2012.06.014
Guo, Y. et al. CTCF/cohesin-mediated DNA looping is required for protocadherin α promoter choice. Proc. Natl Acad. Sci. USA 109, 21081–21086 (2012).
pubmed: 23204437 pmcid: 3529044 doi: 10.1073/pnas.1219280110
Ming, G. L. & Song, H. Adult neurogenesis in the mammalian brain: significant answers and significant questions. Neuron 70, 687–702 (2011).
pubmed: 21609825 pmcid: 3106107 doi: 10.1016/j.neuron.2011.05.001
Hsieh, J. Orchestrating transcriptional control of adult neurogenesis. Genes Dev. 26, 1010–1021 (2012).
pubmed: 22588716 pmcid: 3360557 doi: 10.1101/gad.187336.112
Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).
pubmed: 17151600 doi: 10.1038/nature05453
Zhang, K., Wang, M., Zhao, Y. & Wang, W. Taiji: System-level identification of key transcription factors reveals transcriptional waves in mouse embryonic development. Sci. Adv. 5, eaav3262 (2019).
pubmed: 30944857 pmcid: 6436936 doi: 10.1126/sciadv.aav3262
Sobhan, P. K. & Funa, K. TLX—its emerging role for neurogenesis in health and disease. Mol. Neurobiol. 54, 272–280 (2017).
pubmed: 26738856 doi: 10.1007/s12035-015-9608-1
Cooper-Kuhn, C. M. et al. Impaired adult neurogenesis in mice lacking the transcription factor E2F1. Mol. Cell. Neurosci. 21, 312–323 (2002).
pubmed: 12401450 doi: 10.1006/mcne.2002.1176
Visel, A., Minovitsky, S., Dubchak, I. & Pennacchio, L. A. VISTA Enhancer Browser—a database of tissue-specific human enhancers. Nucleic Acids Res. 35, D88–D92 (2007).
pubmed: 17130149 doi: 10.1093/nar/gkl822
Hu, W. H. et al. NIBP, a novel NIK and IKKβ-binding protein that enhances NFκB activation. J. Biol. Chem. 280, 29233–29241 (2005).
pubmed: 15951441 doi: 10.1074/jbc.M501670200
Claussnitzer, M. et al. A brief history of human disease genetics. Nature 577, 179–189 (2020).
pubmed: 31915397 pmcid: 7405896 doi: 10.1038/s41586-019-1879-7
Tyner, C. et al. The UCSC Genome Browser database: 2017 update. Nucleic Acids Res. 45 (D1), D626–D634 (2017).
pubmed: 27899642
Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
pubmed: 25642630 pmcid: 4495769 doi: 10.1038/ng.3211
Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).
pubmed: 29785013 pmcid: 6477180 doi: 10.1038/s41588-018-0129-5
Volkow, N. D. & Morales, M. The brain on drugs: from reward to addiction. Cell 162, 712–725 (2015).
pubmed: 26276628 doi: 10.1016/j.cell.2015.07.046
The BRAIN Initiative Cell Census Consortium. The BRAIN Initiative Cell Census Consortium: Lessons Learned toward Generating a Comprehensive Brain Cell Atlas. Neuron 96, 542–557 (2017).
doi: 10.1016/j.neuron.2017.10.007
NIH The BRAIN Initiative. BRAIN 2025 Report https://braininitiative.nih.gov/strategic-planning/brain-2025-report (2014).
Sullivan, P. F. & Geschwind, D. H. Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders. Cell 177, 162–183 (2019).
pubmed: 30901538 pmcid: 6432948 doi: 10.1016/j.cell.2019.01.015
McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: Uniform Manifold Approximation and Projection. J. Open Source Softw. 3, 861 (2018).
doi: 10.21105/joss.00861
Siepel, A. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15, 1034–1050 (2005).
pubmed: 16024819 pmcid: 1182216 doi: 10.1101/gr.3715005
Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).
pubmed: 21221095 pmcid: 3346182 doi: 10.1038/nbt.1754
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
pubmed: 20513432 pmcid: 2898526 doi: 10.1016/j.molcel.2010.05.004
Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).
pubmed: 28798132 pmcid: 5570439 doi: 10.1126/science.aan3351
Yao, Z. et al. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex. Nature https://doi.org/10.1038/s41586-021-03500-8 (2021).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
pubmed: 19451168 pmcid: 2705234 doi: 10.1093/bioinformatics/btp324
Ou, J. et al. ATACseqQC: a Bioconductor package for post-alignment quality assessment of ATAC-seq data. BMC Genomics 19, 169 (2018).
pubmed: 29490630 pmcid: 5831847 doi: 10.1186/s12864-018-4559-3
Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291 (2019).
pubmed: 30954476 pmcid: 6625319 doi: 10.1016/j.cels.2018.11.005
Benaglia, T., Chauveau, D., Hunter, D. R. & Young, D. S. mixtools: an R package for analyzing mixture models. 32, 29 (2009).
Bouneffouf, D. B. I. Theoretical analysis of the Minimum Sum of Squared Similarities sampling for Nyström-based spectral clustering. In 2016 Int. Joint Conf. Neural Networks (IJCNN) 3856–3862 (2016).
Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).
pubmed: 30914743 pmcid: 6435756 doi: 10.1038/s41598-019-41695-z
Suzuki, R. & Shimodaira, H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 22, 1540–1542 (2006).
pubmed: 16595560 doi: 10.1093/bioinformatics/btl117
Drost, H. Philentropy: information theory and distance quantification with R. J. Open Source Softw. 3, 765 (2018).
doi: 10.21105/joss.00765
Corces, M. R. et al. The chromatin accessibility landscape of primary human cancers. Science 362, eaav1898 (2018).
pubmed: 30361341 pmcid: 6408149 doi: 10.1126/science.aav1898
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
pubmed: 20110278 pmcid: 2832824 doi: 10.1093/bioinformatics/btq033
van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729 (2018).
pubmed: 29961576 pmcid: 6771278 doi: 10.1016/j.cell.2018.05.061
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
pubmed: 31178118 pmcid: 6687398 doi: 10.1016/j.cell.2019.05.031
Li, Y. E. et al. Identification of high-confidence RNA regulatory elements by combinatorial classification of RNA-protein binding sites. Genome Biol. 18, 169 (2017).
pubmed: 28886744 pmcid: 5591525 doi: 10.1186/s13059-017-1298-8
Fabian Pedregosa, G. V. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Hoyer, P. O. Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res. 5, 1457–1469 (2004).
Kim, H. & Park, H. Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis. Bioinformatics 23, 1495–1502 (2007).
pubmed: 17483501 doi: 10.1093/bioinformatics/btm134
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
pubmed: 24658644 pmcid: 4122333 doi: 10.1038/nbt.2859
Delignette-Muller, M. L. & Dutang, C. fitdistrplus: An R Package for Fitting Distributions. J. Stat. Software 64, 34 (2015).
doi: 10.18637/jss.v064.i04
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
pubmed: 20436461 pmcid: 4840234 doi: 10.1038/nbt.1630
Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).
pubmed: 23586463 pmcid: 3637064 doi: 10.1186/1471-2105-14-128
Barban, N. et al. Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat. Genet. 48, 1462–1472 (2016).
pubmed: 27798627 pmcid: 5695684 doi: 10.1038/ng.3698
Deary, V. et al. Genetic contributions to self-reported tiredness. Mol. Psychiatry 23, 609–620 (2018).
pubmed: 28194004 doi: 10.1038/mp.2017.5
de Lange, K. M. et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat. Genet. 49, 256–261 (2017).
pubmed: 28067908 pmcid: 5289481 doi: 10.1038/ng.3760
Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).
pubmed: 30478444 doi: 10.1038/s41588-018-0269-7
Ferreira, M. A. et al. Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology. Nat. Genet. 49, 1752–1757 (2017).
pubmed: 29083406 pmcid: 5989923 doi: 10.1038/ng.3985
Horikoshi, M. et al. Genome-wide associations for birth weight and correlations with adult disease. Nature 538, 248–252 (2016).
pubmed: 27680694 pmcid: 5164934 doi: 10.1038/nature19806
Hou, L. et al. Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder. Hum. Mol. Genet. 25, 3383–3394 (2016).
pubmed: 27329760 pmcid: 5179929 doi: 10.1093/hmg/ddw181
Jansen, P. R. et al. Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways. Nat. Genet. 51, 394–403 (2019).
pubmed: 30804565 doi: 10.1038/s41588-018-0333-3
Jones, S. E. et al. Genome-wide association analyses in 128,266 individuals identifies new morningness and sleep duration loci. PLoS Genet. 12, e1006125 (2016).
pubmed: 27494321 pmcid: 4975467 doi: 10.1371/journal.pgen.1006125
Luciano, M. et al. Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nat. Genet. 50, 6–11 (2018).
pubmed: 29255261 doi: 10.1038/s41588-017-0013-8
Nelson, C. P. et al. Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nat. Genet. 49, 1385–1391 (2017).
pubmed: 28714975 doi: 10.1038/ng.3913
Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).
pubmed: 24390342 doi: 10.1038/nature12873
Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).
pubmed: 27225129 pmcid: 4883595 doi: 10.1038/nature17671
Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
pmcid: 4112379 doi: 10.1038/nature13595
Day, F. R. et al. Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk. Nat. Genet. 49, 834–841 (2017).
pubmed: 28436984 pmcid: 5841952 doi: 10.1038/ng.3841
Zhou, W. et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335–1341 (2018).
pubmed: 30104761 pmcid: 6119127 doi: 10.1038/s41588-018-0184-y
Savage, J. E. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet. 50, 912–919 (2018).
pubmed: 29942086 pmcid: 6411041 doi: 10.1038/s41588-018-0152-6
van Rheenen, W. et al. Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis. Nat. Genet. 48, 1043–1048 (2016).
pubmed: 27455348 pmcid: 5556360 doi: 10.1038/ng.3622
Watson, H. J. et al. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nat. Genet. 51, 1207–1214 (2019).
pubmed: 31308545 pmcid: 6779477 doi: 10.1038/s41588-019-0439-2
Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).
pubmed: 30124842 pmcid: 6488973 doi: 10.1093/hmg/ddy271
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
doi: 10.1038/nature11247
ENCODE Project Consortium. A user’s guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol. 9, e1001046 (2011).
doi: 10.1371/journal.pbio.1001046
Büttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A. & Theis, F. J. A test metric for assessing single-cell RNA-seq batch correction. Nat. Methods 16, 43–49 (2019).
pubmed: 30573817 doi: 10.1038/s41592-018-0254-1
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
pubmed: 31740819 pmcid: 6884693 doi: 10.1038/s41592-019-0619-0
Gorkin, D. U. et al. An atlas of dynamic chromatin landscapes in mouse fetal development. Nature 583, 744–751 (2020).
pubmed: 32728240 pmcid: 7398618 doi: 10.1038/s41586-020-2093-3

Auteurs

Yang Eric Li (YE)

Ludwig Institute for Cancer Research, La Jolla, CA, USA.
ORCID: 0000-0001-6997-6018

Sebastian Preissl (S)

Department of Cellular and Molecular Medicine, Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA.
ORCID: 0000-0001-8971-5616

Xiaomeng Hou (X)

Department of Cellular and Molecular Medicine, Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA.

Ziyang Zhang (Z)

Ludwig Institute for Cancer Research, La Jolla, CA, USA.
ORCID: 0000-0001-5870-704X

Kai Zhang (K)

Ludwig Institute for Cancer Research, La Jolla, CA, USA.

Yunjiang Qiu (Y)

Ludwig Institute for Cancer Research, La Jolla, CA, USA.
ORCID: 0000-0002-0539-9714

Olivier B Poirion (OB)

Department of Cellular and Molecular Medicine, Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA.

Bin Li (B)

Ludwig Institute for Cancer Research, La Jolla, CA, USA.

Joshua Chiou (J)

Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, USA.
Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
ORCID: 0000-0002-4618-0647

Hanqing Liu (H)

Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.

Antonio Pinto-Duarte (A)

Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA.
ORCID: 0000-0002-2215-7653

Naoki Kubo (N)

Ludwig Institute for Cancer Research, La Jolla, CA, USA.

Xiaoyu Yang (X)

Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA.

Rongxin Fang (R)

Ludwig Institute for Cancer Research, La Jolla, CA, USA.

Xinxin Wang (X)

Department of Cellular and Molecular Medicine, Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA.
ORCID: 0000-0001-6393-2276

Jee Yun Han (JY)

Department of Cellular and Molecular Medicine, Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA.

Jacinta Lucero (J)

Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA.

Yiming Yan (Y)

Ludwig Institute for Cancer Research, La Jolla, CA, USA.

Michael Miller (M)

Department of Cellular and Molecular Medicine, Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA.

Samantha Kuan (S)

Ludwig Institute for Cancer Research, La Jolla, CA, USA.

David Gorkin (D)

Department of Cellular and Molecular Medicine, Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA.

Kyle J Gaulton (KJ)

Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
ORCID: 0000-0003-1318-7161

Yin Shen (Y)

Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA.
Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
ORCID: 0000-0001-9901-5613

Michael Nunn (M)

Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.

Eran A Mukamel (EA)

Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA.
ORCID: 0000-0003-3203-9535

M Margarita Behrens (MM)

Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA.
ORCID: 0000-0002-7168-8186

Joseph R Ecker (JR)

Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.
Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA.
ORCID: 0000-0001-5799-5895

Bing Ren (B)

Ludwig Institute for Cancer Research, La Jolla, CA, USA. biren@health.ucsd.edu.
Department of Cellular and Molecular Medicine, Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA. biren@health.ucsd.edu.
Institute of Genomic Medicine, Moores Cancer Center, School of Medicine, University of California San Diego, La Jolla, CA, USA. biren@health.ucsd.edu.
ORCID: 0000-0002-5435-1127

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › An atlas of gene regulatory elements in adult...
questionsmedicales.fr › Sciences de l'information › Sources d'information › Moyens de communication › Publications › Livres › Ouvrages de référence › Atlas comme sujet › An atlas of gene regulatory elements in adult...
questionsmedicales.fr › Système nerveux › Système nerveux central › Encéphale › Prosencéphale › Télencéphale › Cerveau › An atlas of gene regulatory elements in adult...
questionsmedicales.fr › Phénomènes génétiques › Structures génétiques › Structures de chromosome › Chromatine › An atlas of gene regulatory elements in adult...
questionsmedicales.fr › Phénomènes génétiques › Régulation de l'expression des gènes › Assemblage et désassemblage de la chromatine › An atlas of gene regulatory elements in adult...
questionsmedicales.fr › Phénomènes génétiques › Régulation de l'expression des gènes › An atlas of gene regulatory elements in adult...
questionsmedicales.fr › Phénomènes génétiques › Génotype › Prédisposition génétique à une maladie › An atlas of gene regulatory elements in adult...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › An atlas of gene regulatory elements in adult...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Rodentia › Muridae › Murinae › Souris › An atlas of gene regulatory elements in adult...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Rodentia › Muridae › Murinae › Souris › Lignées consanguines de souris › Souris de lignée C57BL › An atlas of gene regulatory elements in adult...
questionsmedicales.fr › Maladies du système nerveux › An atlas of gene regulatory elements in adult...
questionsmedicales.fr › Cellules › Névroglie › An atlas of gene regulatory elements in adult...
questionsmedicales.fr › Cellules › Neurones › An atlas of gene regulatory elements in adult...
questionsmedicales.fr › Phénomènes génétiques › Structures génétiques › Séquences d'acides nucléiques régulatrices › An atlas of gene regulatory elements in adult...
questionsmedicales.fr › Techniques d'investigation › Techniques génétiques › Analyse de séquence › Analyse de séquence d'ADN › An atlas of gene regulatory elements in adult...
questionsmedicales.fr › Techniques d'investigation › Techniques cytologiques › Analyse sur cellule unique › An atlas of gene regulatory elements in adult...