The DOI matches its Crossref record; 75 of the 78 references it cites were found in a registry or an index.
Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution
Open accessPublished version of a preprint
Abstract
Genetic perturbations of cortical development can lead to neurodevelopmental disease, including autism spectrum disorder (ASD). To identify genomic regions crucial to corticogenesis, we mapped the activity of gene-regulatory elements generating a single-cell atlas of gene expression and chromatin accessibility both independently and jointly. This revealed waves of gene regulation by key transcription factors (TFs) across a nearly continuous differentiation trajectory, distinguished the expression programs of glial lineages, and identified lineage-determining TFs that exhibited strong correlation between linked gene-regulatory elements and expression levels. These highly connected genes adopted an active chromatin state in early differentiating cells, consistent with lineage commitment. Base-pair-resolution neural network models identified strong cell-type-specific enrichment of noncoding mutations predicted to be disruptive in a cohort of ASD individuals and identified frequently disrupted TF binding sites. This approach illustrates how cell-type-specific mapping can provide insights into the programs governing human development and disease.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Many ASD risk genes associated with transcriptional regulation and chromatin remodelling are crucial for cortical development21,22.”
From Single-cell brain organoid screening identifies developmental defects in autism (2023) · cites this paper as [22]
In the citing paper
“This module was previously identified as crucial for oligodendrocyte differentiation in the developing human cortex22, highlighting the involvement of the oligodendrocytes in ASD pathophysiology.”
From Single-cell brain organoid screening identifies developmental defects in autism (2023) · cites this paper as [22]
In the citing paper
“Single-cell multiomics allows uncovering of the mechanisms of gene regulation across different omics layers6–9 but lacks spatial information, which is crucial to understanding cellular function in tissue.”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [9]
Citations
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
The mutational constraint spectrum quantified from variation in 141,456 humans
Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., Collins, R. L., Laricchia, K. M., Ganna, A., Birnbaum, D. P., et al. (2020) · Nature · doi:10.1038/s41586-020-2308-7 · Cited by 9,434
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101
McGinnis, C. S., Murrow, L. M., & Gartner, Z. J. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.003 · Cited by 4,521
Master Transcription Factors and Mediator Establish Super-Enhancers at Key Cell Identity Genes
Whyte, W. A., Orlando, D. A., Hnisz, D., Abraham, B. J., Lin, C. Y., Kagey, M. H., Rahl, P. B., Lee, T. I., & Young, R. A. (2013) · Cell · doi:10.1016/j.cell.2013.03.035 · Cited by 4,143
La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lönnerberg, P., Furlan, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0414-6 · Cited by 4,118
Generalizing RNA velocity to transient cell states through dynamical modeling
Bergen, V., Lange, M., Peidli, S., Wolf, F. A., & Theis, F. J. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0591-3 · Cited by 2,951
Learning Important Features Through Propagating Activation Differences
Shrikumar, A., Greenside, P., & Kundaje, A. (2017) · arXiv · doi:10.48550/arxiv.1704.02685 · Cited by 2,761
Self-citationThe contribution of de novo coding mutations to autism spectrum disorder
Iossifov, I., O’Roak, B. J., Sanders, S. J., Ronemus, M., Krumm, N., Levy, D., Stessman, H. A., Witherspoon, K. T., Vives, L., Patterson, K. E., et al. (2014) · Nature · doi:10.1038/nature13908 · Cited by 2,585
Predicting effects of noncoding variants with deep learning–based sequence model
Zhou, J., & Troyanskaya, O. G. (2015) · Nature Methods · doi:10.1038/nmeth.3547 · Cited by 2,338
Zhang, Y., Sloan, S. A., Clarke, L. E., Caneda, C., Plaza, C. A., Blumenthal, P. D., Vogel, H., Steinberg, G. K., Edwards, M. S. B., Li, G., et al. (2016) · Neuron · doi:10.1016/j.neuron.2015.11.013 · Cited by 2,295
Improved scoring of functional groups from gene expression data by decorrelating GO graph structure
Alexa, A., Rahnenführer, J., & Lengauer, T. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl140 · Cited by 2,194
Spatio-temporal transcriptome of the human brain
Kang, H. J., Kawasawa, Y. I., Cheng, F., Zhu, Y., Xu, X., Li, M., Sousa, A. M. M., Pletikos, M., Meyer, K. A., Sedmak, G., et al. (2011) · Nature · doi:10.1038/nature10523 · Cited by 2,084
Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.061 · Cited by 1,829
Chromatin accessibility and the regulatory epigenome
Klemm, S. L., Shipony, Z., & Greenleaf, W. J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-018-0089-8 · Cited by 1,793
Self-citationConserved cell types with divergent features in human versus mouse cortex
Hodge, R. D., Bakken, T. E., Miller, J. A., Smith, K. A., Barkan, E. R., Graybuck, L. T., Close, J. L., Long, B., Johansen, N., Penn, O., et al. (2019) · Nature · doi:10.1038/s41586-019-1506-7 · Cited by 1,775
chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data
Schep, A. N., Wu, B., Buenrostro, J. D., & Greenleaf, W. J. (2017) · Nature Methods · doi:10.1038/nmeth.4401 · Cited by 1,680
Self-citationUniquely Hominid Features of Adult Human Astrocytes
Oberheim, N. A., Takano, T., Han, X., He, W., Lin, J. H. C., Wang, F., Xu, Q., Wyatt, J. D., Pilcher, W., Ojemann, J. G., et al. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.4707-08.2009 · Cited by 1,488
Khan, A., Fornés, O., Stigliani, A., Gheorghe, M., Castro-Mondragon, J. A., van der Lee, R., Bessy, A., Chèneby, J., Kulkarni, S. R., Tan, G., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1126 · Cited by 1,457
Neurogenic radial glia in the outer subventricular zone of human neocortex
Hansen, D. V., Lui, J. H., Parker, P. R. L., & Kriegstein, A. R. (2010) · Nature · doi:10.1038/nature08845 · Cited by 1,388
Development and Evolution of the Human Neocortex
Lui, J. H., Hansen, D. V., & Kriegstein, A. R. (2011) · Cell · doi:10.1016/j.cell.2011.06.030 · Cited by 1,342
Transcriptional landscape of the prenatal human brain
Miller, J. A., Ding, S.-L., Sunkin, S. M., Smith, K. A., Ng, L., Szafer, A., Ebbert, A., Riley, Z. L., Royall, J. J., Aiona, K., et al. (2014) · Nature · doi:10.1038/nature13185 · Cited by 1,325
Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder
Gandal, M. J., Zhang, P., Hadjimichael, E., Walker, R. L., Chen, C., Liu, S., Won, H., van Bakel, H., Varghese, M., Wang, Y., et al. (2018) · Science · doi:10.1126/science.aat8127 · Cited by 1,247
The chromatin accessibility landscape of primary human cancers
Corces, M. R., Granja, J. M., Shams, S., Louie, B. H., Seoane, J. A., Zhou, W., Silva, T. C., Groeneveld, C., Wong, C. K., Cho, S. W., et al. (2018) · Science · doi:10.1126/science.aav1898 · Cited by 1,230
Self-citationChromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin
Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.056 · Cited by 1,146
The bHLH Transcription Factors OLIG2 and OLIG1 Couple Neuronal and Glial Subtype Specification
Zhou, Q., & Anderson, D. J. (2002) · Cell · doi:10.1016/s0092-8674(02)00677-3 · Cited by 1,059
Kelley, D. R., Snoek, J., & Rinn, J. L. (2016) · Genome Research · doi:10.1101/gr.200535.115 · Cited by 1,048
Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism
Parikshak, N. N., Luo, R., Zhang, A., Won, H., Lowe, J. K., Chandran, V., Horvath, S., & Geschwind, D. H. (2013) · Cell · doi:10.1016/j.cell.2013.10.031 · Cited by 1,039
The origin and specification of cortical interneurons
Wonders, C. P., & Anderson, S. A. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn1954 · Cited by 980
Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex
Nowakowski, T. J., Bhaduri, A., Pollen, A. A., Alvarado, B., Mostajo-Radji, M. A., Di Lullo, E., Haeussler, M., Sandoval-Espinosa, C., Liu, S. J., Velmeshev, D., et al. (2017) · Science · doi:10.1126/science.aap8809 · Cited by 964
Willsey, A. J., Sanders, S. J., Li, M., Dong, S., Tebbenkamp, A. T., Muhle, R. A., Reilly, S. K., Lin, L., Fertuzinhos, S., Miller, J. A., et al. (2013) · Cell · doi:10.1016/j.cell.2013.10.020 · Cited by 934
Molecular Identity of Human Outer Radial Glia during Cortical Development
Pollen, A. A., Nowakowski, T. J., Chen, J., Retallack, H., Sandoval-Espinosa, C., Nicholas, C. R., Shuga, J., Liu, S. J., Oldham, M. C., Diaz, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.004 · Cited by 908
Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis
Farrell, J. A., Wang, Y., Riesenfeld, S. J., Shekhar, K., Regev, A., & Schier, A. F. (2018) · Science · doi:10.1126/science.aar3131 · Cited by 876
SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs)
Abrahams, B. S., Arking, D. E., Campbell, D. B., Mefford, H. C., Morrow, E. M., Weiss, L. A., Menashe, I., Wadkins, T., Banerjee-Basu, S., & Packer, A. (2013) · Molecular Autism · doi:10.1186/2040-2392-4-36 · Cited by 873
Molecular logic of neocortical projection neuron specification, development and diversity
Greig, L. C., Woodworth, M. B., Galazo, M. J., Padmanabhan, H., & Macklis, J. D. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3586 · Cited by 863
OSVZ progenitors of human and ferret neocortex are epithelial-like and expand by integrin signaling
Fietz, S. A., Kelava, I., Vogt, J., Wilsch-Bräuninger, M., Stenzel, D., Fish, J. L., Corbeil, D., Riehn, A., Distler, W., Nitsch, R., et al. (2010) · Nature Neuroscience · doi:10.1038/nn.2553 · Cited by 830
The Cellular and Molecular Landscapes of the Developing Human Central Nervous System
Silbereis, J. C., Pochareddy, S., Zhu, Y., Li, M., & Sestan, N. (2016) · Neuron · doi:10.1016/j.neuron.2015.12.008 · Cited by 818
Integrative functional genomic analysis of human brain development and neuropsychiatric risks
Li, M., Santpere, G., Imamura Kawasawa, Y., Evgrafov, O. V., Gulden, F. O., Pochareddy, S., Sunkin, S. M., Li, Z., Shin, Y., Zhu, Y., et al. (2018) · Science · doi:10.1126/science.aat7615 · Cited by 810
Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data
Pliner, H. A., Packer, J. S., McFaline-Figueroa, J. L., Cusanovich, D. A., Daza, R. M., Aghamirzaie, D., Srivatsan, S., Qiu, X., Jackson, D., Minkina, A., et al. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.06.044 · Cited by 789
A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility
Cusanovich, D. A., Hill, A. J., Aghamirzaie, D., Daza, R. M., Pliner, H. A., Berletch, J. B., Filippova, G. N., Huang, X., Christiansen, L., DeWitt, W. S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.052 · Cited by 759
Buenrostro, J. D., Corces, M. R., Lareau, C. A., Wu, B., Schep, A. N., Aryee, M. J., Majeti, R., Chang, H. Y., & Greenleaf, W. J. (2018) · Cell · doi:10.1016/j.cell.2018.03.074 · Cited by 670
Self-citationParker, S. C. J., Stitzel, M. L., Taylor, D. L., Orozco, J. M., Erdos, M. R., Akiyama, J. A., van Bueren, K. L., Chines, P. S., Narisu, N., Program, N. C. S., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1317023110 · Cited by 665
Bentsen, M. S., Goymann, P., Schultheis, H., Klee, K., Petrova, A., Wiegandt, R., Fust, A., Preussner, J., Kuenne, C. T., Braun, T. W., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18035-1 · Cited by 649
Cell stress in cortical organoids impairs molecular subtype specification
Bhaduri, A., Andrews, M. G., Mancia Leon, W., Jung, D., Shin, D., Allen, D., Jung, D., Schmunk, G., Haeussler, M., Salma, J., et al. (2020) · Nature · doi:10.1038/s41586-020-1962-0 · Cited by 624
Sloan, S. A., Darmanis, S., Huber, N., Khan, T. A., Birey, F., Caneda, C., Reimer, R., Quake, S. R., Barres, B. A., & Paşca, S. P. (2017) · Neuron · doi:10.1016/j.neuron.2017.07.035 · Cited by 593
Self-citationSequential regulatory activity prediction across chromosomes with convolutional neural networks
Kelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y., & Snoek, J. (2018) · Genome Research · doi:10.1101/gr.227819.117 · Cited by 537
A Single-Cell Transcriptomic Atlas of Human Neocortical Development during Mid-gestation
Polioudakis, D., de la Torre-Ubieta, L., Langerman, J., Elkins, A. G., Shi, X., Stein, J. L., Vuong, C. K., Nichterwitz, S., Gevorgian, M., Opland, C. K., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.06.011 · Cited by 513
Oscillatory Control of Factors Determining Multipotency and Fate in Mouse Neural Progenitors
Imayoshi, I., Isomura, A., Harima, Y., Kawaguchi, K., Kori, H., Miyachi, H., Fujiwara, T., Ishidate, F., & Kageyama, R. (2013) · Science · doi:10.1126/science.1242366 · Cited by 509
Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia
Granja, J. M., Klemm, S., McGinnis, L. M., Kathiria, A. S., Mezger, A., Corces, M. R., Parks, B., Gars, E., Liedtke, M., Zheng, G. X. Y., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0332-7 · Cited by 498
Self-citationLOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor
Sheffield, N. C., & Bock, C. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv612 · Cited by 456
Human astrocytes: structure and functions in the healthy brain
Vasile, F., Dossi, E., & Rouach, N. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1383-5 · Cited by 428
New insights into the development of the human cerebral cortex
Molnár, Z., Clowry, G. J., Šestan, N., Alzu’bi, A., Bakken, T. E., Hevner, R. F., Hüppi, P. S., Kostović, I., RAKIC, P. T., Anton, E. S., et al. (2019) · Journal of Anatomy · doi:10.1111/joa.13055 · Cited by 426
Constructing the cerebral cortex: Neurogenesis and fate determination
McConnell, S. K. (1995) · Neuron · doi:10.1016/0896-6273(95)90168-x · Cited by 416
Single-cell epigenomics: Recording the past and predicting the future
Kelsey, G., Stegle, O., & Reik, W. (2017) · Science · doi:10.1126/science.aan6826 · Cited by 415
Global reference mapping of human transcription factor footprints
Vierstra, J., Lazar, J., Sandstrom, R., Halow, J., Lee, K., Bates, D., Diegel, M., Dunn, D., Neri, F., Haugen, E., et al. (2020) · Nature · doi:10.1038/s41586-020-2528-x · Cited by 386
Nowakowski, T. J., Pollen, A. A., Sandoval-Espinosa, C., & Kriegstein, A. R. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.005 · Cited by 362
Developmental Fate and Cellular Maturity Encoded in Human Regulatory DNA Landscapes
Stergachis, A. B., Neph, S. J., Reynolds, A. P., Humbert, R., Miller, B., Paige, S. L., Vernot, B., Cheng, J. B., Thurman, R. E., Sandstrom, R. S., et al. (2013) · Cell · doi:10.1016/j.cell.2013.07.020 · Cited by 343
Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder
An, J.-Y., Lin, K., Zhu, L., Werling, D. M., Dong, S., Brand, H., Wang, H. Z., Zhao, X., Schwartz, G. B., Collins, R. L., et al. (2018) · Science · doi:10.1126/science.aat6576 · Cited by 316
Transcriptome and epigenome landscape of human cortical development modeled in organoids
Amiri, A., Coppola, G., Scuderi, S., Wu, F., Roychowdhury, T., Liu, F., Pochareddy, S., Shin, Y., Safi, A., Song, L., et al. (2018) · Science · doi:10.1126/science.aat6720 · Cited by 308
Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk
Zhou, J., Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0420-0 · Cited by 295
Jacquet, B. V., Salinas-Mondragon, R., Liang, H.-X., Therit, B., Buie, J. D., Dykstra, M., Campbell, K., Ostrowski, L., Brody, S., & Ghashghaei, H. T. (2009) · Development · doi:10.1242/dev.041129 · Cited by 277
Astrocytes as a Source for Extracellular Matrix Molecules and Cytokines
Wiese, S., Karus, M., & Faissner, A. (2012) · Frontiers in Pharmacology · doi:10.3389/fphar.2012.00120 · Cited by 262
Chromatin accessibility dynamics in a model of human forebrain development
Trevino, A. E., Sinnott-Armstrong, N., Andersen, J., Yoon, S.-J., Huber, N., Pritchard, J. K., Chang, H. Y., Greenleaf, W. J., & Pașca, S. P. (2020) · Science · doi:10.1126/science.aay1645 · Cited by 251
Self-citationFixed single-cell transcriptomic characterization of human radial glial diversity
Thomsen, E. R., Mich, J. K., Yao, Z., Hodge, R. D., Doyle, A. M., Jang, S., Shehata, S. I., Nelson, A. M., Shapovalova, N. V., Levi, B. P., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3629 · Cited by 237
Rubenstein, J. L. R. (2010) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.2010.02307.x · Cited by 184
Cell-type-specific 3D epigenomes in the developing human cortex
Song, M., Pebworth, M.-P., Yang, X., Abnousi, A., Fan, C., Wen, J., Rosen, J. D., Choudhary, M. N. K., Cui, X., Jones, I. R., et al. (2020) · Nature · doi:10.1038/s41586-020-2825-4 · Cited by 145
Ramanathan, S., Woodroffe, A., Flodman, P. L., Mays, L. Z., Hanouni, M., Modahl, C., Steinberg-Epstein, R., Bocian, M. E., Spence, M. A., & Smith, M. (2004) · BMC Medical Genetics · doi:10.1186/1471-2350-5-10 · Cited by 88
Expression and Localization of Myelin Basic Protein in Oligodendrocytes and Transfected Fibroblasts
Barbarese, E., Barry, C. T., Chou, C. J., Goldstein, D. J., Nakos, G. A., Hyde‐DeRuyscher, R., Scheld, K., & Carson, J. H. (1988) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.1988.tb01153.x · Cited by 70
Li, Z., Cogswell, M., Hixson, K. M., Brooks‐Kayal, A. R., & Russek, S. J. (2018) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2018.00285 · Cited by 36
Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data
Tjärnberg, A., Mahmood, O., Jackson, C. A., Saldi, G.-A., Cho, K., Christiaen, L., & Bonneau, R. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008569 · Cited by 33
SA75LARGE-SCALE TRANSCRIPTOME-WIDE CHARACTERIZATION OF ASD, SCHIZOPHRENIA, AND BIPOLAR DISORDER
Gandal, M. J., Zhang, P., Walker, R. L., Won, H., Hadjimichael, E., van Bakel, H., Haney, J. R., Jaffe, A. E., White, K. P., Peters, M. A., et al. (2019) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2018.08.297 · Cited by 33
Flagged first, then by citations
3 of the 78 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Avsec, 2020, Deep learning at base-resolution reveals cis-regulatory motif syntax, bioRxiv
Cited by
Sorted by citations
A single-cell atlas of chromatin accessibility in the human genome
Zhang, K., Hocker, J. D., Miller, M., Hou, X., Chiou, J., Poirion, O. B., Qiu, Y., Li, Y. E., Gaulton, K. J., Wang, A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.10.024 · Cited by 502
Maturation and circuit integration of transplanted human cortical organoids
Revah, O., Gore, F., Kelley, K. W., Andersen, J., Sakai, N., Chen, X., Li, M.-Y., Birey, F., Yang, X., Saw, N. L., et al. (2022) · Nature · doi:10.1038/s41586-022-05277-w · Cited by 504
Self-citationSpatial epigenome–transcriptome co-profiling of mammalian tissues
Zhang, D., Deng, Y., Kukanja, P., Agirre, E., Bartosovic, M., Dong, M., Ma, C., Ma, S., Su, G., Bao, S., et al. (2023) · Nature · doi:10.1038/s41586-023-05795-1 · Cited by 483
AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model
Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2025) · doi:10.1101/2025.06.25.661532 · Cited by 395
Advancing regulatory variant effect prediction with AlphaGenome
Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2026) · Nature · doi:10.1038/s41586-025-10014-0 · Cited by 373
Metadata
- Authors
- Trevino, A. E., Müller, F., Andersen, J., Sundaram, L., Kathiria, A., Shcherbina, A., Farh, K., Chang, H. Y., Pașca, A. M., Kundaje, A., Pașca, S. P., & Greenleaf, W. J.
- Affiliations
- Stanford University (US)
- Saarland University (DE)
- Stanford Medicine (US)
- Illumina (United States) (US)
- Howard Hughes Medical Institute (US)
- Chan Zuckerberg Biohub San Francisco (US)
- Corresponding author
- Pașca, Sergiu P.
- Greenleaf, William J.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2021-09
- Volume
- 184
- Issue
- 19
- Pages
- 5053-5069.e23
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W3194509722
- Cited by
- 478
- References
- 78
- Citations by year
- 2026: 76 · 2025: 99 · 2024: 110 · 2023: 99 · 2022: 75 · 2021: 17
- FWCI
- 24.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 380 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, other-oa)
- PubMed (publishedVersion)
- Funders
- Leland Stanford Junior University
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Keywords
- cortical development, chromatin accessibility, gene regulatory elements, single-cell atlas, transcription factors, lineage commitment, autism spectrum disorder, non-coding mutations, transcription factor binding sites, neural network model, neurodevelopmental disorders
- MeSH terms
- Deep Learning, Astrocytes, Cell Differentiation, Cerebral Cortex, Chromatin, Glutamates, Humans, Mutation, Neurons, Regulatory Sequences, Nucleic Acid, Cluster Analysis, Fuzzy Logic, Gene Expression Regulation, Developmental, Cell Lineage, Epigenesis, Genetic, Single-Cell Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
