Decoding gene regulation in the fly brain
Open accessPreprint — published as 10.1038/s41586-021-04262-z · Paper page
Abstract
Summary: The Drosophila brain is a work horse in neuroscience. Single-cell transcriptome analysis 1–5, 3D morphological classification 6 , and detailed EM mapping of the connectome 7–10 have revealed an immense diversity of neuronal and glial cell types that underlie the wide array of functional and behavioral traits in the fruit fly. The identities of these cell types are controlled by – still unknown – gene regulatory networks (GRNs), involving combinations of transcription factors that bind to genomic enhancers to regulate their target genes. To characterize the GRN for each cell type in the Drosophila brain, we profiled chromatin accessibility of 240,919 single cells spanning nine developmental timepoints, and integrated this data with single-cell transcriptomes. We identify more than 95,000 regulatory regions that are used in different neuronal cell types, of which around 70,000 are linked to specific developmental trajectories, involving neurogenesis, reprogramming and maturation. For 40 cell types, their uniquely accessible regions could be associated with their expressed transcription factors and downstream target genes, through a combination of motif discovery, network inference techniques, and deep learning. We illustrate how these “enhancer-GRNs” can be used to reveal enhancer architectures leading to a better understanding of neuronal regulatory diversity. Finally, our atlas of regulatory elements can be used to design genetic driver lines for specific cell types at specific timepoints, facilitating the characterization of brain cell types and the manipulation of brain function.
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Methods for inferring enhancer-driven GRNs that integrate enhancer sequence, chromatin accessibility and gene expression found that accuracy to predict active enhancers increases by approximately 10% compared to using accessibility alone.
“methods for inferring enhancer-driven GRNs (eGRNs) that integrate enhancer sequence, chromatin accessibility and gene expression22–24 found that the accuracy to predict active enhancers increases by ~10% compared to using accessibility alone25.”
From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as [25]
Extended Data Fig. 10 (p. 27) identifies accessibility and correlation with nearby gene expression as key features for predicting GFP activity. The paper also describes using enhancer sequence in its eGRN analysis, but the passages in our copy do not establish an approximately 10% accuracy gain over accessibility alone. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041 · see citing works
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888 · see citing works
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430 · see citing works
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2649-2 · Cited by 23,826 · see citing works
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,089 · see citing works
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 · see citing works
Harris, C., Millman, K., van der Walt, S., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N., et al. (2020) · RePEc: Research Papers in Economics · Cited by 17,844 · see citing works
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 · see citing works
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works
Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488 · see citing works
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2019) · Apollo - University of Cambridge Repository · doi:10.17863/cam.64134 · Cited by 10,460 · see citing works
Targeted gene expression as a means of altering cell fates and generating dominant phenotypes
Brand, A. H., & Perrimon, N. (1993) · Development · doi:10.1242/dev.118.2.401 · Cited by 9,510 · see citing works
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881 · see citing works
deepTools2: a next generation web server for deep-sequencing data analysis
Ramírez, F., Ryan, D. P., Grüning, B., Bhardwaj, V., Kilpert, F., Richter, A. S., Heyne, S., Dündar, F., & Manke, T. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw257 · Cited by 8,096 · see citing works
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314 · see citing works
Statsmodels: Econometric and Statistical Modeling with Python
Seabold, S., & Perktold, J. (2010) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-92bf1922-011 · Cited by 6,686 · see citing works
Scenic: single-cell regulatory network inference and clustering.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520 · see citing works
Self-citationBuenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013) · Nature Methods · doi:10.1038/nmeth.2688 · Cited by 6,422 · see citing works
Reversed graph embedding resolves complex single-cell trajectories
Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070 · see citing works
The single-cell transcriptional landscape of mammalian organogenesis
Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490 · see citing works
An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues
Corces, M. R., Trevino, A. E., Hamilton, E. G., Greenside, P. G., Sinnott-Armstrong, N. A., Vesuna, S., Satpathy, A. T., Rubin, A. J., Montine, K. S., Wu, B., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4396 · Cited by 2,496 · see citing works
Single-cell chromatin accessibility reveals principles of regulatory variation
Buenrostro, J. D., Wu, B., Litzenburger, U. M., Ruff, D., Gonzales, M. L., Snyder, M. P., Chang, H. Y., & Greenleaf, W. J. (2015) · Nature · doi:10.1038/nature14590 · Cited by 2,308 · see citing works
CUT&Tag for efficient epigenomic profiling of small samples and single cells
Kaya-Okur, H. S., Wu, S. J., Codomo, C. A., Pledger, E. S., Bryson, T. D., Henikoff, J. G., Ahmad, K., & Henikoff, S. (2019) · Nature Communications · doi:10.1038/s41467-019-09982-5 · Cited by 2,187 · see citing works
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012776 · Cited by 2,131 · see citing works
The ENCODE Blacklist: Identification of Problematic Regions of the Genome
Amemiya, H. M., Kundaje, A., & Boyle, A. P. (2019) · Scientific Reports · doi:10.1038/s41598-019-45839-z · Cited by 1,831 · see citing works
The Encyclopedia of DNA elements (ENCODE): data portal update
Davis, C. A., Hitz, B. C., Sloan, C. A., Chan, E. T., Davidson, J. M., Gabdank, I., Hilton, J. A., Jain, K., Baymuradov, U. K., Narayanan, A. K., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1081 · Cited by 1,717 · see citing works
The Drosophila melanogaster Genetic Reference Panel
Mackay, T., Richards, S., Stone, E. A., Barbadilla, A., Ayroles, J., Zhu, D., Casillas, S., Han, Y., Magwire, M., Cridland, J., et al. (2012) · Nature · doi:10.1038/nature10811 · Cited by 1,686 · see citing works
Induction of Ectopic Eyes by Targeted Expression of the <i>eyeless</i> Gene in <i>Drosophila</i>
Halder, G., Callaerts, P., & Gehring, W. J. (1995) · Science · doi:10.1126/science.7892602 · Cited by 1,573 · see citing works
A scalable SCENIC workflow for single-cell gene regulatory network analysis
Van de Sande, B., Flerin, C., Davie, K., De Waegeneer, M., Hulselmans, G., Aibar, S., Seurinck, R., Saelens, W., Cannoodt, R., Rouchon, Q., et al. (2020) · Nature Protocols · doi:10.1038/s41596-020-0336-2 · Cited by 1,494 · see citing works
Self-citationA GAL4-Driver Line Resource for Drosophila Neurobiology
Jenett, A., Rubin, G. M., Ngo, T.-T. B., Shepherd, D., Murphy, C., Dionne, H., Pfeiffer, B. D., Cavallaro, A., Hall, D., Jeter, J., et al. (2012) · Cell Reports · doi:10.1016/j.celrep.2012.09.011 · Cited by 1,449 · see citing works
ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis
Granja, J. M., Corces, M. R., Pierce, S. E., Bagdatli, S. T., Choudhry, H., Chang, H. Y., & Greenleaf, W. J. (2021) · Nature Genetics · doi:10.1038/s41588-021-00790-6 · Cited by 1,392 · see citing works
Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
Kang, H. M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., Wan, E., Wong, S., Byrnes, L., Lanata, C. M., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4042 · Cited by 1,339 · see citing works
Chromatin 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 · see citing works
A connectome and analysis of the adult Drosophila central brain
Scheffer, L. K., Xu, C. S., Januszewski, M., Lu, Z., Takemura, S.-Y., Hayworth, K. J., Huang, G. B., Shinomiya, K., Maitlin-Shepard, J., Berg, S., et al. (2020) · eLife · doi:10.7554/elife.57443 · Cited by 1,128 · see citing works
A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster
Zheng, Z., Lauritzen, J. S., Perlman, E., Robinson, C. G., Nichols, M., Milkie, D., Torrens, O., Price, J., Fisher, C. B., Sharifi, N., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.019 · Cited by 1,050 · see citing works
Simulation physiologischer Regelkreise mit der objektorientierten Modellbibliothek “HumanLib”
Brunberg, A., Spillner, J. W., Autschbach, R., & Abel, D. (2011) · at - Automatisierungstechnik · doi:10.1524/auto.2011.0951 · Cited by 1,008 · see citing works
Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain
Lake, B. B., Chen, S., Sos, B. C., Fan, J., Kaeser, G. E., Yung, Y. C., Duong, T. E., Gao, D., Chun, J., Kharchenko, P. V., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4038 · Cited by 983 · see citing works
Quang, D. X., & Xie, X. S. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw226 · Cited by 840 · see citing works
A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain
Davie, K., Janssens, J., Koldere, D., De Waegeneer, M., Pech, U., Kreft, Ł., Aibar, S., Makhzami, S., Christiaens, V., Bravo González-Blas, C., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.057 · Cited by 840 · see citing works
Self-citationHigh-throughput sequencing of the transcriptome and chromatin accessibility in the same cell
Chen, S., Lake, B. B., & Zhang, K. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0290-0 · Cited by 810 · see citing works
A human cell atlas of fetal gene expression
Cao, J., O’Day, D. R., Pliner, H. A., Kingsley, P. D., Deng, M., Daza, R. M., Zager, M. A., Aldinger, K. A., Blecher-Gonen, R., Zhang, F., et al. (2020) · Science · doi:10.1126/science.aba7721 · Cited by 764 · see citing works
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 · see citing works
Gene regulatory network inference: Data integration in dynamic models—A review
Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., & Guthke, R. (2009) · Biosystems · doi:10.1016/j.biosystems.2008.12.004 · Cited by 755 · see citing works
Selection of DNA binding sites by regulatory proteins
Berg, O. G., & von Hippel, P. H. (1987) · Journal of Molecular Biology · doi:10.1016/0022-2836(87)90354-8 · Cited by 754 · see citing works
Base-resolution models of transcription-factor binding reveal soft motif syntax
Avsec, Ž., Weilert, M., Shrikumar, A., Krueger, S., Alexandari, A., Dalal, K., Fropf, R., McAnany, C., Gagneur, J., Kundaje, A., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00782-6 · Cited by 610 · see citing works
FlyBase at 25: looking to the future
Gramates, L. S., Marygold, S. J., dos Santos, G., Urbano, J.-M., Antonazzo, G., Matthews, B. B., Rey, A. J., Tabone, C. J., Crosby, M. A., Emmert, D. B., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1016 · Cited by 535 · see citing works
PHAST and RPHAST: phylogenetic analysis with space/time models
Hubisz, M. J., Pollard, K. S., & Siepel, A. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbq072 · Cited by 490 · see citing works
cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data
Bravo González-Blas, C., Minnoye, L., Papasokrati, D., Aibar, S., Hulselmans, G., Christiaens, V., Davie, K., Wouters, J., & Aerts, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0367-1 · Cited by 456 · see citing works
Self-citationIdentification of transcription factor binding sites using ATAC-seq
Li, Z., Schulz, M. H., Look, T., Begemann, M., Zenke, M., & Costa, I. G. (2019) · Genome biology · doi:10.1186/s13059-019-1642-2 · Cited by 436 · see citing works
A human cell atlas of fetal chromatin accessibility
Domcke, S., Hill, A. J., Daza, R. M., Cao, J., O’Day, D. R., Pliner, H. A., Aldinger, K. A., Pokholok, D., Zhang, F., Milbank, J. H., et al. (2020) · Science · doi:10.1126/science.aba7612 · Cited by 375 · see citing works
The cis-regulatory dynamics of embryonic development at single-cell resolution
Cusanovich, D. A., Reddington, J. P., Garfield, D. A., Daza, R. M., Aghamirzaie, D., Marco-Ferreres, R., Pliner, H. A., Christiansen, L., Qiu, X., Steemers, F. J., et al. (2018) · Nature · doi:10.1038/nature25981 · Cited by 363 · see citing works
An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome
Zhu, C., Yu, M., Huang, H., Juric, I., Abnousi, A., Hu, R., Lucero, J., Behrens, M. M., Hu, M., & Ren, B. (2019) · Nature Structural & Molecular Biology · doi:10.1038/s41594-019-0323-x · Cited by 344 · see citing works
Preissl, S., Fang, R., Huang, H., Zhao, Y., Raviram, R., Gorkin, D. U., Zhang, Y., Sos, B. C., Afzal, V., Dickel, D. E., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0079-3 · Cited by 332 · see citing works
Cluster-Buster: finding dense clusters of motifs in DNA sequences
Frith, M. C. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg540 · Cited by 326 · see citing works
Cellular diversity in the Drosophila midbrain revealed by single-cell transcriptomics
Croset, V., Treiber, C. D., & Waddell, S. (2018) · eLife · doi:10.7554/elife.34550 · Cited by 299 · see citing works
Costa, M., Manton, J. D., Ostrovsky, A. D., Prohaska, S., & Jefferis, G. S. X. E. (2016) · Neuron · doi:10.1016/j.neuron.2016.06.012 · Cited by 294 · see citing works
Temporal Patterning in the Drosophila CNS
Doe, C. Q. (2017) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-111315-125210 · Cited by 270 · see citing works
A genetic, genomic, and computational resource for exploring neural circuit function
Davis, F. P., Nern, A., Picard, S., Reiser, M. B., Rubin, G. M., Eddy, S. R., & Henry, G. L. (2020) · eLife · doi:10.7554/elife.50901 · Cited by 268 · see citing works
Southall, T. D., Gold, K. S., Egger, B., Davidson, C. M., Caygill, E. E., Marshall, O. J., & Brand, A. H. (2013) · Developmental Cell · doi:10.1016/j.devcel.2013.05.020 · Cited by 257 · see citing works
Classifying Drosophila Olfactory Projection Neuron Subtypes by Single-Cell RNA Sequencing
Li, H., Horns, F., Wu, B., Xie, Q., Li, J., Li, T., Luginbuhl, D. J., Quake, S. R., & Luo, L. (2017) · Cell · doi:10.1016/j.cell.2017.10.019 · Cited by 256 · see citing works
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 · see citing works
Direct and concentration-dependent regulation of the proneural gene Neurogenin2 by Pax6
Scardigli, R., Bäumer, N., Gruss, P., Guillemot, F., & Roux, I. L. (2003) · Development · doi:10.1242/dev.00539 · Cited by 239 · see citing works
Neuronal diversity and convergence in a visual system developmental atlas
Özel, M. N., Simon, F., Jafari, S., Holguera, I., Chen, Y.-C., Benhra, N., El-Danaf, R. N., Kapuralin, K., Malin, J. A., Konstantinides, N., et al. (2021) · Nature · doi:10.1038/s41586-020-2879-3 · Cited by 237 · see citing works
Shrikumar, A., Tian, K., Avsec, Ž., Shcherbina, A., Banerjee, A., Sharmin, M., Nair, S., & Kundaje, A. (2018) · arXiv · doi:10.48550/arxiv.1811.00416 · Cited by 229 · see citing works
Phenotypic convergence: distinct transcription factors regulate common terminal features
Konstantinides, N., Kapuralin, K., Fadil, C., Barboza, L., Satija, R., & Desplan, C. (2018) · Cell · doi:10.1016/j.cell.2018.05.021 · Cited by 226 · see citing works
Wiring Economy and Volume Exclusion Determine Neuronal Placement in the Drosophila Brain
Rivera-Alba, M., Vitaladevuni, S. N., Mishchenko, Y., Lu, Z., Takemura, S.-Y., Scheffer, L., Meinertzhagen, I. A., Chklovskii, D. B., & de Polavieja, G. G. (2011) · Current Biology · doi:10.1016/j.cub.2011.10.022 · Cited by 223 · see citing works
Single-cell transcriptomics unveils gene regulatory network plasticity
Iacono, G., Massoni-Badosa, R., & Heyn, H. (2019) · Genome Biology · doi:10.1186/s13059-019-1713-4 · Cited by 218 · see citing works
Low-Affinity Binding Sites and the Transcription Factor Specificity Paradox in Eukaryotes
Kribelbauer, J. F., Rastogi, C., Bussemaker, H. J., & Mann, R. S. (2019) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-100617-062719 · Cited by 216 · see citing works
Kudron, M. M., Victorsen, A., Gevirtzman, L., Hillier, L., Fisher, W. W., Vafeados, D. K., Kirkey, M., Hammonds, A. S., Gersch, J., Ammouri, H., et al. (2017) · Genetics · doi:10.1534/genetics.117.300657 · Cited by 216 · see citing works
Herrmann, C., de Sande, B. V., Potier, D., & Aerts, S. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks543 · Cited by 202 · see citing works
Self-citationi-cisTarget 2015 update: generalized cis-regulatory enrichment analysis in human, mouse and fly
Imrichová, H., Hulselmans, G., Kalender Atak, Z., Potier, D., & Aerts, S. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv395 · Cited by 202 · see citing works
Self-citationMapping the Neural Substrates of Behavior
Robie, A. A., Hirokawa, J., Edwards, A. W., Umayam, L. A., Lee, A., Phillips, M. L., Card, G. M., Korff, W. L., Rubin, G. M., Simpson, J. H., et al. (2017) · Cell · doi:10.1016/j.cell.2017.06.032 · Cited by 199 · see citing works
Ig Superfamily Ligand and Receptor Pairs Expressed in Synaptic Partners in Drosophila
Tan, L., Zhang, K. X., Pecot, M. Y., Nagarkar-Jaiswal, S., Lee, P.-T., Takemura, S.-Y., McEwen, J. M., Nern, A., Xu, S., Tadros, W., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.021 · Cited by 180 · see citing works
A single-cell transcriptomic atlas of the adult Drosophila ventral nerve cord
Allen, A. M., Neville, M. C., Birtles, S., Croset, V., Treiber, C. D., Waddell, S., & Goodwin, S. F. (2020) · eLife · doi:10.7554/elife.54074 · Cited by 177 · see citing works
Jackson, C. A., Castro, D. M., Saldi, G.-A., Bonneau, R., & Gresham, D. (2020) · eLife · doi:10.7554/elife.51254 · Cited by 174 · see citing works
Wang, Q., Sun, Q., Czajkowsky, D. M., & Shao, Z. (2018) · Nature Communications · doi:10.1038/s41467-017-02526-9 · Cited by 166 · see citing works
Harmston, N., Ing‐Simmons, E., Tan, G., Perry, M., Merkenschlager, M., & Lenhard, B. (2017) · Nature Communications · doi:10.1038/s41467-017-00524-5 · Cited by 166 · see citing works
Transcriptional Programs of Circuit Assembly in the Drosophila Visual System
Kurmangaliyev, Y. Z., Yoo, J., Valdes-Aleman, J., Sanfilippo, P., & Zipursky, S. L. (2020) · Neuron · doi:10.1016/j.neuron.2020.10.006 · Cited by 166 · see citing works
damidseq_pipeline: an automated pipeline for processing DamID sequencing datasets
Marshall, O. J., & Brand, A. H. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv386 · Cited by 162 · see citing works
Koemans, T. S., Kleefstra, T., Chubak, M. C., Stone, M. H., Reijnders, M. R. F., de Munnik, S., Willemsen, M. H., Fencková, M., Schrander-Stumpel, C. T. R. M., Bok, L. A., et al. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006864 · Cited by 159 · see citing works
Marshall, O. J., Southall, T. D., Cheetham, S. W., & Brand, A. H. (2016) · Nature Protocols · doi:10.1038/nprot.2016.084 · Cited by 157 · see citing works
Boll, W., & Noll, M. (2002) · Development · doi:10.1242/dev.00157 · Cited by 156 · see citing works
From Lineage to Wiring Specificity
Komiyama, T., Johnson, W. A., Luo, L., & Jefferis, G. S. X. E. (2003) · Cell · doi:10.1016/s0092-8674(03)00030-8 · Cited by 155 · see citing works
A genetic pathway composed of Sox14 and Mical governs severing of dendrites during pruning
Kirilly, D., Gu, Y., Huang, Y., Wu, Z., Bashirullah, A., Low, B. C., Kolodkin, A. L., Wang, H., & Yu, F. (2009) · Nature Neuroscience · doi:10.1038/nn.2415 · Cited by 155 · see citing works
Jacobs, J., Atkins, M., Davie, K., Imrichova, H., Romanelli, L., Christiaens, V., Hulselmans, G., Potier, D., Wouters, J., Taskiran, I. I., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0140-x · Cited by 155 · see citing works
Self-citationIntegration of temporal and spatial patterning generates neural diversity
Erclik, T., Li, X., Courgeon, M., Bertet, C., Chen, Z., Baumert, R., Ng, J., Koo, C., Arain, U., Behnia, R., et al. (2017) · Nature · doi:10.1038/nature20794 · Cited by 153 · see citing works
Single cell transcriptome atlas of the Drosophila larval brain
Avalos, C. B., Maier, G. L., Bruggmann, R., & Sprecher, S. G. (2019) · eLife · doi:10.7554/elife.50354 · Cited by 153 · see citing works
A Connectome of the Adult Drosophila Central BrainPreprint
Xu, C. S., Januszewski, M., Lu, Z., Takemura, S.-Y., Hayworth, K. J., Huang, G. B., Shinomiya, K., Maitin-Shepard, J., Ackerman, D., Berg, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.21.911859 · Cited by 150 · see citing works
A complete temporal transcription factor series in the fly visual system
Konstantinides, N., Holguera, I., Rossi, A. M., Escobar, A., Dudragne, L., Chen, Y., Tran, T. N., Jaimes, A. M. M., Özel, M. N., Simon, F., et al. (2022) · Nature · doi:10.1038/s41586-022-04564-w · Cited by 147 · see citing works
SWI/SNF complex prevents lineage reversion and induces temporal patterning in neural stem cells.
Eroglu, E., Burkard, T., Jiang, Y.-R., Saini, N., Homem, C. C. F., Reichert, H., & Knoblich, J. A. (2014) · Cell · doi:10.1016/j.cell.2014.01.053 · Cited by 140 · see citing works
Direct neuronal reprogramming: learning from and for development
Masserdotti, G., Gascón, S., & Götz, M. (2016) · Development · doi:10.1242/dev.092163 · Cited by 130 · see citing works
Crocker, J., Noon, E. P., & Stern, D. L. (2016) · Current topics in developmental biology/Current Topics in Developmental Biology · doi:10.1016/bs.ctdb.2015.11.018 · Cited by 122 · see citing works
Chotard, C., Leung, W., & Salecker, I. (2005) · Neuron · doi:10.1016/j.neuron.2005.09.019 · Cited by 122 · see citing works
Cross-species analysis of enhancer logic using deep learning
Minnoye, L., Taskiran, I. I., Mauduit, D., Fazio, M., Van Aerschot, L., Hulselmans, G., Christiaens, V., Makhzami, S., Seltenhammer, M., Karras, P., et al. (2020) · Genome Research · doi:10.1101/gr.260844.120 · Cited by 116 · see citing works
Self-citationSamata, M., Alexiadis, A., Richard, G., Georgiev, P., Nuebler, J., Kulkarni, T., Renschler, G., Basilicata, M. F., Zenk, F. L., Shvedunova, M., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.026 · Cited by 97 · see citing works
Aerts, S., Quan, X., Claeys, A., Naval-Sánchez, M., Tate, P., Yan, J., & Hassan, B. A. (2010) · PLoS Biology · doi:10.1371/journal.pbio.1000435 · Cited by 92 · see citing works
Self-citationChromatin modification of Notch targets in olfactory receptor neuron diversification
Endo, K., Karim, M. R., Taniguchi, H., Krejčı́, A., Kinameri, E., Siebert, M., Ito, K., Bray, S. J., & Moore, A. W. (2011) · Nature Neuroscience · doi:10.1038/nn.2998 · Cited by 90 · see citing works
Schulz, R. A., Chromey, C., Lu, M. F., Zhao, B., & Olson, E. N. (1996) · PubMed · Cited by 89 · see citing works
Bravo González‐Blas, C., Quan, X., Duran‐Romaña, R., Taskiran, I. I., Koldere, D., Davie, K., Christiaens, V., Makhzami, S., Hulselmans, G., de Waegeneer, M., et al. (2020) · Molecular Systems Biology · doi:10.15252/msb.20209438 · Cited by 88 · see citing works
Self-citationAlyagor, I., Berkun, V., Keren‐Shaul, H., Marmor‐Kollet, N., David, E., Mayseless, O., Issman-Zecharya, N., Amit, I., & Schuldiner, O. (2018) · Developmental Cell · doi:10.1016/j.devcel.2018.09.013 · Cited by 79 · see citing works
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Southall, T. D., Davidson, C. M., Miller, C., Carr, A. R., & Brand, A. H. (2014) · Developmental Cell · doi:10.1016/j.devcel.2014.01.030 · Cited by 75 · see citing works
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Apitz, H., & Salecker, I. (2014) · Journal of Neurogenetics · doi:10.3109/01677063.2014.922558 · Cited by 74 · see citing works
Combgap contributes to recruitment of Polycomb group proteins in Drosophila
Ray, P., De, S., Mitra, A., Bezstarosti, K., Demmers, J., Pfeifer, K. E., & Kassis, J. A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1520926113 · Cited by 73 · see citing works
Modular transcriptional programs separately define axon and dendrite connectivity
Kurmangaliyev, Y. Z., Yoo, J., LoCascio, S. A., & Zipursky, S. L. (2019) · eLife · doi:10.7554/elife.50822 · Cited by 69 · see citing works
Rop, F. V. D., Ismail, J. N., González‐Blas, C. B., Hulselmans, G. J., Flerin, C., Janssens, J., Theunis, K., Christiaens, V., Wouters, J., Marcassa, G., et al. (2022) · eLife · doi:10.7554/elife.73971 · Cited by 68 · see citing works
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Médioni, C., Ramialison, M., Ephrussi, A., & Besse, F. (2014) · Current Biology · doi:10.1016/j.cub.2014.02.038 · Cited by 67 · see citing works
Ravenscroft, T. A., Janssens, J., Lee, P.-T., Tepe, B., Marcogliese, P. C., Makhzami, S., Holmes, T. C., Aerts, S., & Bellen, H. J. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.0142-20.2020 · Cited by 56 · see citing works
Self-citationChen, J., Li, K., Zhu, J., & Chen, W. (2016) · Proceedings of the VLDB Endowment · doi:10.14778/2977797.2977801 · Cited by 56 · see citing works
Shih, M.-F. M., Davis, F. P., Henry, G. L., & Dubnau, J. (2018) · G3 Genes Genomes Genetics · doi:10.1534/g3.118.200726 · Cited by 55 · see citing works
Yang, J., Ramos, E., & Corces, V. G. (2012) · Genome Research · doi:10.1101/gr.142125.112 · Cited by 52 · see citing works
Interpretation of allele-specific chromatin accessibility using cell state–aware deep learning
Atak, Z. K., Taskiran, I. I., Demeulemeester, J., Flerin, C., Mauduit, D., Minnoye, L., Hulselmans, G., Christiaens, V., Ghanem, G.-E., Wouters, J., et al. (2021) · Genome Research · doi:10.1101/gr.260851.120 · Cited by 48 · see citing works
Self-citationSchertel, C., Albarca-Aguilera, M., Rockel-Bauer, C., Kelley, N. W., Bischof, J., Hens, K., van Nimwegen, E., Basler, K., & Deplancke, B. (2015) · Genome Research · doi:10.1101/gr.181305.114 · Cited by 46 · see citing works
Koromila, T., Gao, F., Iwasaki, Y., He, P., Pachter, L. S., Gergen, J. P., & Stathopoulos, A. (2020) · eLife · doi:10.7554/elife.59610 · Cited by 46 · see citing works
Ngo, K., Andrade, I. V., & Hartenstein, V. (2017) · Developmental Biology · doi:10.1016/j.ydbio.2017.05.008 · Cited by 43 · see citing works
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Cheng, S., Ashley, J., Kurleto, J. D., Lobb-Rabe, M., Park, Y. J., Carrillo, R. A., & Özkan, E. (2019) · eLife · doi:10.7554/elife.41028 · Cited by 41 · see citing works
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Vijayakumar, J., Perrois, C., Heim, M., Bousset, L., Alberti, S., & Besse, F. (2019) · Nature Communications · doi:10.1038/s41467-019-10554-w · Cited by 41 · see citing works
Özdemir, A., Ma, L., White, K. P., & Stathopoulos, A. (2014) · Developmental Cell · doi:10.1016/j.devcel.2014.08.005 · Cited by 35 · see citing works
Single cell transcriptomic profiling of mouse pancreatic progenitors
Stanescu, D. E., Yu, R., Won, K., & Stoffers, D. A. (2016) · Physiological Genomics · doi:10.1152/physiolgenomics.00114.2016 · Cited by 35 · see citing works
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Donohoe, C. D., Csordás, G., Correia, A., Jindra, M., Klein, C., Habermann, B., & Uhlirova, M. (2018) · PLOS Genetics · doi:10.1371/journal.pgen.1007241 · Cited by 32 · see citing works
Naidu, V. G., Zhang, Y., Lowe, S., Ray, A., Zhu, H., & Li, X. (2020) · Developmental Biology · doi:10.1016/j.ydbio.2020.05.005 · Cited by 29 · see citing works
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Jusiak, B., Karandikar, U. C., Kwak, S., Wang, F., Wang, H., Chen, R., & Mardon, G. (2014) · PLoS ONE · doi:10.1371/journal.pone.0089695 · Cited by 28 · see citing works
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Piñeiro, C., Lopes, C. S., & Casares, F. (2014) · Development · doi:10.1242/dev.108670 · Cited by 24 · see citing works
Schilling, T., Ali, A. H., Leonhardt, A., Borst, A., & Pujol‐Martí, J. (2019) · Development · doi:10.1242/dev.169763 · Cited by 22 · see citing works
Yoo, S., Nair, S., Kim, H., Kim, Y., Lee, C., Lee, G., & Park, J. H. (2020) · Developmental Biology · doi:10.1016/j.ydbio.2020.02.008 · Cited by 22 · see citing works
Newcomb, S., Voutev, R., Jory, A., Delker, R. K., Slattery, M. G., & Mann, R. S. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007568 · Cited by 21 · see citing works
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Brás‐Pereira, C., Potier, D., Jacobs, J., Aerts, S., Casares, F., & Janody, F. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006204 · Cited by 19 · see citing works
Self-citationAvet‐Rochex, A., Maierbrugger, K. T., & Bateman, J. E. (2014) · Gene Expression Patterns · doi:10.1016/j.gep.2014.09.001 · Cited by 18 · see citing works
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Crittenden, J. R., Skoulakis, E. M. C., Goldstein, E. S., & Davis, R. L. (2018) · Biology Open · doi:10.1242/bio.035618 · Cited by 16 · see citing works
Magadi, S. S., Voutyraki, C., Anagnostopoulos, G., Zacharioudaki, E., Poutakidou, I. K., Efraimoglou, C., Stapountzi, M., Theodorou, V., Nikolaou, C., Koumbanakis, K. A., et al. (2020) · Development · doi:10.1242/dev.191544 · Cited by 16 · see citing works
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Cocanougher, B., Wittenbach, J. D., Long, X., Kohn, A. B., Norekian, T. P., Yan, J., Colonell, J. I., Masson, J., Truman, J. W., Cardona, A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/785931 · Cited by 14 · see citing works
Estacio‐Gómez, A., Hassan, A., Walmsley, E. L., Le, L. W., & Southall, T. D. (2020) · Biology Open · doi:10.1242/bio.052928 · Cited by 13 · see citing works
vib-singlecell-nf/vsn-pipelines: v0.14.0
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Self-citationAughey, G., Delandre, C., McMullen, J. P. D., Southall, T. D., & Marshall, O. J. (2020) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkaa005 · Cited by 11 · see citing works
Chromatin remodeling during the in vivo glial differentiation in early Drosophila embryos
Ye, Y., Gu, L., Chen, X., Shi, J., Zhang, X., & Jiang, C. (2016) · Scientific Reports · doi:10.1038/srep33422 · Cited by 10 · see citing works
Minocha, S., Boll, W., & Noll, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176002 · Cited by 10 · see citing works
Yeung, K., Wang, F., Li, Y., Wang, K., Mardon, G., & Chen, R. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky892 · Cited by 8 · see citing works
Cyclin G and the Polycomb Repressive complexes PRC1 and PR-DUB cooperate for developmental stability
Dardalhon-Cuménal, D., Deraze, J., Dupont, C., Ribeiro, V., Costes, A., Pouch, J., Crom, S. L., Thomassin, H., Debat, V., Randsholt, N. B., et al. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007498 · Cited by 8 · see citing works
Lee, J., Park, S., & Yoo, S. (2019) · IBRO Reports · doi:10.1016/j.ibror.2019.07.1048 · Cited by 1 · see citing works
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- (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted August 12, 2021. ; https://doi.org/10.1101/2021.08.11.454937doi: bioRxiv preprint https://doi.org/10.1101/2021.08.11.454937 · DOI
- (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted August 12, 2021. ; https://doi.org/10.1101/2021.08.11.454937doi: bioRxiv preprint https://doi.org/10.1101/2021.08.11.454937 · DOI
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- Expression Data Using Tree-Based Methods. PLOS ONE 5, e12776 (2010).
Cited by
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Bravo González-Blas, C., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01938-4 · Cited by 778 · see citing works
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Badia-i-Mompel, P., Wessels, L., Müller‐Dott, S., Trimbour, R., Flores, R. O. R., Argelaguet, R., & Sáez-Rodríguez, J. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00618-5 · Cited by 475 · see citing works
de Almeida, B. P., Reiter, F., Pagani, M., & Stark, A. (2022) · Nature Genetics · doi:10.1038/s41588-022-01048-5 · Cited by 283 · see citing works
Cell-type-directed design of synthetic enhancers
Taskiran, I. I., Spanier, K. I., Dickmänken, H., Kempynck, N., Pančíková, A., Ekşi, E. C., Hulselmans, G., Ismail, J. N., Theunis, K., Vandepoel, R., et al. (2024) · Nature · doi:10.1038/s41586-023-06936-2 · Cited by 175 · see citing works
Self-citationGoshisht, M. K. (2024) · ACS Omega · doi:10.1021/acsomega.3c05913 · Cited by 169 · see citing works
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Wang, M., Liu, Y., Sun, R., Liu, F.-T., Li, J., Long, Y., Zhang, J., Xie, X., Li, D., Wang, Y., et al. (2024) · Nature Genetics · doi:10.1038/s41588-023-01647-w · Cited by 98 · see citing works
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Park, A., Croset, V., Otto, N., Agarwal, D., Treiber, C. D., Meschi, E., Sims, D., & Waddell, S. (2022) · Current Biology · doi:10.1016/j.cub.2022.07.038 · Cited by 45 · see citing works
Direct androgen receptor control of sexually dimorphic gene expression in the mammalian kidney
Xiong, L., Liu, J., Han, S. Y., Koppitch, K. A., Guo, J., Rommelfanger, M. K., Miao, Z., Gao, F., Hallgrímsdóttir, I. B., Pachter, L. S., et al. (2023) · Developmental Cell · doi:10.1016/j.devcel.2023.08.010 · Cited by 43 · see citing works
Hormonal gatekeeping via the blood-brain barrier governs caste-specific behavior in ants
Ju, L., Glastad, K. M., Sheng, L., Gospočić, J., Kingwell, C. J., Davidson, S. M., Kocher, S. D., Bonasio, R., & Berger, S. L. (2023) · Cell · doi:10.1016/j.cell.2023.08.002 · Cited by 46 · see citing works
Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium
Hecker, N., Kempynck, N., Mauduit, D., Abaffyová, D., Vandepoel, R., Dieltiens, S., Borm, L. E., Sarropoulos, I., González‐Blas, C. B., Man, J. D., et al. (2025) · Science · doi:10.1126/science.adp3957 · Cited by 44 · see citing works
Self-citationTemporal regulation of neural diversity in Drosophila and vertebrates
El-Danaf, R. N., Rajesh, R., & Desplan, C. (2022) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2022.05.011 · Cited by 41 · see citing works
scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference
Li, Z., Nagai, J. S., Kuppe, C., Kramann, R., & Costa, I. G. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad003 · Cited by 40 · see citing works
Deep learning the cis-regulatory code of chromatin dynamics during cellular reprogrammingPreprint
Nair, S., Ameen, M., Sundaram, L., Pampari, A., Schreiber, J. M., Balsubramani, A., Wang, Y. X., Burns, D., Blau, H. M., Karakikes, I., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.04.560808 · Cited by 38 · see citing works
Annotating the genome at single-nucleotide resolution with DNA foundation models
de Almeida, B. P., Dalla-Torre, H., Richard, G., Blum, C. F., Hexemer, L., Gélard, M., Mendoza‐Revilla, J., Tang, Z., Marin, F. I., Emms, D., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02881-2 · Cited by 35 · see citing works
Designing DNA With Tunable Regulatory Activity Using Discrete DiffusionPreprint
Sarkar, A., Durán, A., Yu, Y., Lin, D.-W., Kang, Y., Somia, N., Mantilla, P., Zhou, J., Nagai, M., Tang, Z., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.23.595630 · Cited by 31 · see citing works
Generating neural diversity through spatial and temporal patterning
Sen, S. (2022) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2022.06.002 · Cited by 28 · see citing works
Tani, S., Okada, H., Onodera, S., Chijimatsu, R., Seki, M., Suzuki, Y., Xin, X., Rowe, D. W., Saito, T., Tanaka, S., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2023.112276 · Cited by 27 · see citing works
Shared enhancer gene regulatory networks between wound and oncogenic programs
Floc'hlay, S., Balaji, R., Stanković, D., Christiaens, V. M., Bravo González-Blas, C., De Winter, S., Hulselmans, G. J., De Waegeneer, M., Quan, X., Koldere, D., et al. (2023) · eLife · doi:10.7554/elife.81173 · Cited by 27 · see citing works
Self-citationThomas, H., & Buecker, C. (2023) · BioEssays · doi:10.1002/bies.202300044 · Cited by 27 · see citing works
Hopkins, B. R., Barmina, O. Y., & Kopp, A. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002148 · Cited by 23 · see citing works
Enhancers display constrained sequence flexibility and context-specific modulation of motif functionPreprint
Reiter, F., de Almeida, B. P., & Stark, A. (2023) · Genome Research · doi:10.1101/gr.277246.122 · Cited by 24 · see citing works
Stochastic neuro-fuzzy system implemented in memristor crossbar arrays
Shi, T., Zhang, H., Cui, S., Liu, J., Gu, Z., Wang, Z., Yan, X., & Liu, Q. (2024) · Science Advances · doi:10.1126/sciadv.adl3135 · Cited by 22 · see citing works
Neiro, J., Sridhar, D., Dattani, A., & Aboobaker, A. A. (2022) · eLife · doi:10.7554/elife.79675 · Cited by 20 · see citing works
Naqvi, S., Kim, S., Tabatabaee, S., Pampari, A., Kundaje, A., Pritchard, J. K., & Wysocka, J. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100780 · Cited by 18 · see citing works
Modelling and design of transcriptional enhancers
Winter, S. D., Konstantakos, V., & Aerts, S. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00280-y · Cited by 20 · see citing works
Self-citationCREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species
Kempynck, N., Winter, S. D., Blaauw, C. H., Konstantakos, V., Ekşi, E. C., Dieltiens, S., Abaffyová, D., Bercier, V., Taskiran, I. I., Hulselmans, G. J., et al. (2026) · Nature Methods · doi:10.1038/s41592-026-03057-2 · Cited by 24 · see citing works
Self-citationMICA: a multi-omics method to predict gene regulatory networks in early human embryos
Alanis‐Lobato, G., Bartlett, T. E., Huang, Q., Simon, C. S., McCarthy, A., Elder, K. T., Snell, P., Christie, L., & Niakan, K. K. (2023) · Life Science Alliance · doi:10.26508/lsa.202302415 · Cited by 21 · see citing works
Predictive analyses of regulatory sequences with EUGENe
Klie, A., Laub, D., Talwar, J. V., Stites, H., Jores, T., Solvason, J. J., Farley, E. K., & Carter, H. K. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00544-w · Cited by 23 · see citing works
Evaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortex
Johansen, N., Kempynck, N., Zemke, N. R., Somasundaram, S., Winter, S. D., Hooper, M. J., Dwivedi, D., Lohia, R., Wehbe, F., Li, B., et al. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100879 · Cited by 19 · see citing works
Self-citationHuman neuron subtype programming via single-cell transcriptome-coupled patterning screens
Lin, H., Janssens, J., Eisinger, B. N., Hornauer, P., Kroell, A.-S. H., Santel, M., Pascual-Garcia, M., Okamoto, R., Karava, K., He, Z., et al. (2025) · Science · doi:10.1126/science.adn6121 · Cited by 19 · see citing works
Self-citationJacobs, J., Pagani, M., Wenzl, C., & Stark, A. (2023) · Science · doi:10.1126/science.adf6149 · Cited by 19 · see citing works
Canalizing cell fate by transcriptional repression
Lim, B., Domsch, K., Mall, M., & Lohmann, I. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00014-z · Cited by 19 · see citing works
Fundamental and practical approaches for single-cell ATAC-seq analysis
Shi, P., Nie, Y., Yang, J., Zhang, W., Tang, Z., & Xu, J. (2022) · aBIOTECH · doi:10.1007/s42994-022-00082-5 · Cited by 18 · see citing works
Clock-dependent chromatin accessibility rhythms regulate circadian transcription
Yuan, Y., Chen, Q., Brovkina, M. V., Clowney, E. J., & Yadlapalli, S. (2024) · PLoS Genetics · doi:10.1371/journal.pgen.1011278 · Cited by 18 · see citing works
Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data
Li, Y., Ma, A., Wang, Y., Guo, Q., Wang, C., Chen, S., Fu, H., Liu, B., & Ma, Q. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae369 · Cited by 18 · see citing works
The Biodiversity Cell Atlas: mapping the tree of life at cellular resolution
Sebé-Pedrós, A., Tanay, A., Lawniczak, M., Arendt, D., Aerts, S., Archibald, J. M., Arnone, M. I., Blaxter, M., Cleves, P. A., Coelho, S. M., et al. (2025) · Nature · doi:10.1038/s41586-025-09312-4 · Cited by 16 · see citing works
Self-citationMulet‐Lazaro, R., van Herk, S., Nuetzel, M., Sijs‐Szabó, A., Díaz, N., Kelly, K., Erpelinck-Verschueren, C. A. J., Schwarzfischer, L., Stanewsky, H., Ackermann, U., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49811-y · Cited by 16 · see citing works
Hormone-controlled changes in the differentiation state of post-mitotic neurons
Lai, Y.-W., Miyares, R. L., Liu, L., Chu, S.-Y., Lee, T., & Yu, H. (2022) · Current Biology · doi:10.1016/j.cub.2022.04.027 · Cited by 16 · see citing works
Temporal control of neuronal wiring
Jain, S., & Zipursky, S. L. (2022) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2022.05.012 · Cited by 15 · see citing works
Katı, N., & Uçar, F. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118029 · Cited by 14 · see citing works
The serotonin receptor 5-HT2A modulates lifespan and protein feeding in Drosophila melanogaster
Munneke, A. S., Chakraborty, T. S., Porter, S. S., Gendron, C. M., & Pletcher, S. D. (2022) · Frontiers in Aging · doi:10.3389/fragi.2022.1068455 · Cited by 14 · see citing works
Single-cell transcriptomics dissecting the development and evolution of nervous system in insects
Liu, W., & Li, Q. (2024) · Current Opinion in Insect Science · doi:10.1016/j.cois.2024.101201 · Cited by 14 · see citing works
Perspective on recent developments and challenges in regulatory and systems genomics
Zeitlinger, J., Roy, S., Ay, F., Mathelier, A., Medina-Rivera, A., Mahony, S., Sinha, S. K., & Ernst, J. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf106 · Cited by 14 · see citing works
NuRD‐independent Mi‐2 activity represses ectopic gene expression during neuronal maturation
Aughey, G., Forsberg, E., Grimes, K., Zhang, S., & Southall, T. D. (2023) · EMBO Reports · doi:10.15252/embr.202255362 · Cited by 14 · see citing works
Angiogenesis goes computational – The future way forward to discover new angiogenic targets?
Subramanian, A., Zakeri, P., Mousa, M., Alnaqbi, H., Alshamsi, F. Y., Bettoni, L., Damiani, E., AlSafar, H. S., Saeys, Y., & Carmeliet, P. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.09.019 · Cited by 12 · see citing works
Salvatore, M., Horlacher, M., Marsico, A., Winther, O., & Andersson, R. (2023) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqad026 · Cited by 13 · see citing works
Deep learning applications advance plant genomics research
Fan, W., Guo, Z., Wang, X., Zhang, L., Liu, Y., Cai, C., Zhang, K., & Cheng, F. (2025) · Horticultural Plant Journal · doi:10.1016/j.hpj.2025.08.004 · Cited by 13 · see citing works
Kamal, A., Arnold, C., Claringbould, A., Moussa, R., Servaas, N. H., Kholmatov, M., Daga, N., Nogina, D., Mueller-Dott, S., Reyes-Palomares, A., et al. (2021) · doi:10.1101/2021.12.18.473290 · Cited by 12 · see citing works
Multiplex profiling of developmental enhancers with quantitative, single-cell expression reportersPreprint
Lalanne, J.-B., Regalado, S. G., Domcke, S., Calderon, D., Martin, B., Li, T., Suiter, C. C., Lee, C., Trapnell, C., & Shendure, J. (2022) · bioRxiv · doi:10.1101/2022.12.10.519236 · Cited by 14 · see citing works
Dissecting the regulatory logic of specification and differentiation during vertebrate embryogenesisPreprint
Liu, J., Castillo-Hair, S. M., Du, L. Y., Wang, Y., Carte, A. N., Colomer-Rosell, M., Yin, C., Seelig, G., & Schier, A. F. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.27.609971 · Cited by 13 · see citing works
Zhang, G., Fu, Y., Yang, L., Ye, F., Zhang, P., Zhang, S., Ma, L., Li, J., Wu, H., Han, X., et al. (2024) · Developmental Cell · doi:10.1016/j.devcel.2024.01.015 · Cited by 11 · see citing works
Lee, Y. S., Huang, N., Samuels, T. J., & Davis, I. (2025) · Science Advances · doi:10.1126/sciadv.adr6682 · Cited by 11 · see citing works
The evolution of gene regulation in mammalian cerebellum development
Sarropoulos, I., Sepp, M., Yamada, T., Schäfer, P. S. L., Trost, N., Schmidt, J., Schneider, C., Drummer, C., Mißbach, S., Taskiran, I. I., et al. (2026) · Science · doi:10.1126/science.adw9154 · Cited by 10 · see citing works
Self-citationSpatiotemporal Control of Neuronal Remodeling by Cell Adhesion Molecules: Insights From Drosophila
Meltzer, H., & Schuldiner, O. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.897706 · Cited by 11 · see citing works
Lola-I is a promoter pioneer factor that establishes de novo Pol II pausing during development
Ramalingam, V., Yu, X., Slaughter, B. D., Unruh, J. R., Brennan, K., Onyshchenko, A., Lange, J. J., Natarajan, M., Buck, M. J., & Zeitlinger, J. (2023) · Nature Communications · doi:10.1038/s41467-023-41408-1 · Cited by 11 · see citing works
Mapping Cell Atlases at the Single‐Cell Level
Ye, F., Wang, J., Li, J., Mei, Y., & Guo, G. (2023) · Advanced Science · doi:10.1002/advs.202305449 · Cited by 11 · see citing works
Pech, U., Janssens, J., Schoovaerts, N., Kuenen, S., Calatayud, C., Gallego, S. F., Makhzami, S., Hulselmans, G. J., Poovathingal, S. K., Davie, K., et al. (2024) · eLife · doi:10.1101/2023.03.11.532176 · Cited by 11 · see citing works
Self-citationKeegan, S. E., Haskins, J., Simmonds, A. J., & Hughes, S. C. (2023) · Development · doi:10.1242/dev.201484 · Cited by 10 · see citing works
Encoding of hunger by the neuronal epigenome slows aging in DrosophilaPreprint
Weaver, K. J., Holt, R., Henry, E., & Pletcher, S. D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.21.501022 · Cited by 10 · see citing works
Dissecting regulatory syntax in human development with scalable multiomics and deep learningPreprint
Liu, B. B., Jessa, S., Kim, S. H., Ng, Y. T., Higashino, S. I., Marinov, G. K., Chen, D. C., Parks, B. E., Li, L., Nguyen, T. C., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.30.651381 · Cited by 10 · see citing works
A high-resolution atlas of the brain predicts lineage and birth order underlying neuronal identity
Allen, A. M., Neville, M. C., Nojima, T., Alejevski, F., Agarwal, D., Sims, D., & Goodwin, S. F. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.101103 · Cited by 9 · see citing works
The industrial genomic revolution: A new era in neuroimmunology
Shalita, R., & Amit, I. (2022) · Neuron · doi:10.1016/j.neuron.2022.09.030 · Cited by 9 · see citing works
The evolution and mutational robustness of chromatin accessibility in Drosophila
Khodursky, S., Zheng, E. B., Svetec, N., Durkin, S. M., Benjamin, S., Gadau, A., Wu, X., & Zhao, L. (2023) · Genome biology · doi:10.1186/s13059-023-03079-5 · Cited by 7 · see citing works
Chu, S.-Y., Lai, Y.-W., Hsu, T., Lu, T.-M., & Yu, H. (2024) · Developmental Biology · doi:10.1016/j.ydbio.2024.06.010 · Cited by 8 · see citing works
Pech, U., Janssens, J., Schoovaerts, N., Kuenen, S., Makhzami, S., Hulselmans, G. J., Poovathingal, S. K., Bademosi, A. T., Swerts, J., Vilain, S., et al. (2024) · eLife · doi:10.7554/elife.98348.1 · Cited by 8 · see citing works
Self-citationInterpreting regulatory mechanisms of Hippo signaling through a deep learning sequence model
Dalal, K., McAnany, C. E., Weilert, M., McKinney, M. C., Krueger, S., & Zeitlinger, J. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100821 · Cited by 7 · see citing works
Shen, D., Vincent, A. J., Udine, E., Buhidma, Y., Anoar, S., Tsintzas, E., Maeland, M. E., Xu, D., Carcolé, M., Osumi-Sutherland, D., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115459 · Cited by 7 · see citing works
Differential neuronal survival defines a novel axis of sexual dimorphism in the Drosophila brain
Allen, A. M., Neville, M. C., Nojima, T., Alejevski, F., & Goodwin, S. F. (2026) · Cell Genomics · doi:10.1016/j.xgen.2025.101125 · Cited by 7 · see citing works
SCALA: A complete solution for multimodal analysis of single-cell Next Generation Sequencing data
Tzaferis, C., Karatzas, E., Baltoumas, F. A., Pavlopoulos, G. A., Kollias, G. A., & Konstantopoulos, D. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.10.032 · Cited by 7 · see citing works
Özel, M. N., Gibbs, C. S., Holguera, I., Soliman, M., Bonneau, R., & Desplan, C. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.01.490216 · Cited by 7 · see citing works
Dynamics of chromatin accessibility during human first-trimester neurodevelopmentPreprint
Mannens, C. C. A., Hu, L., Lönnerberg, P., Schipper, M., Reagor, C. C., Li, X., He, X., Barker, R. A., Sundström, E., Danielle Posthuma, B., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.18.553878 · Cited by 7 · see citing works
The regulatory code of injury-responsive enhancers enables precision cell-state targeting in the CNS
Zamboni, M., Martínez-Martín, A., Rydholm, G., Häneke, T., Almeida, L. P., Seçi̇lmi̇ş, D., Ziegenhain, C., & Llorens-Bobadilla, E. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02131-w · Cited by 6 · see citing works
Deal, S. L., Bei, D., Gibson, S., Delgado-Seo, H., Fujita, Y., Wilwayco, K., Seto, E., Sehgal, A., & Yamamoto, S. (2025) · iScience · doi:10.1016/j.isci.2025.114388 · Cited by 6 · see citing works
Enhancer grammar of liver cell types and hepatocyte zonation states
González-Blas, C. B., Matetovici, I., Hillen, H., Taskiran, I. I., Vandepoel, R., Christiaens, V., Sansores-García, L., Verboven, E., Hulselmans, G., Poovathingal, S., et al. (2022) · doi:10.1101/2022.12.08.519575 · Cited by 5 · see citing works
Self-citationRegulatory logic of neuronal differentiation in the Drosophila visual system
Treese, M., Chen, Y., Tyree, A., Coyne, R., Lake, C. M., Tabi, O. B., Rajesh, R., Chen, Y.-C. D., Hassan, H., Li, H., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2614314123 · Cited by 5 · see citing works
- Authors
- Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Spanier, K. I., Bravo González-Blas, C., Quan, X. J., Papasokrati, D., Hulselmans, G., Makhzami, S., Waegeneer, M. D., Christiaens, V., & Aerts, S.
- Affiliations
- VIB-KU Leuven Center for Brain & Disease Research (BE)
- Corresponding author
- Aerts, Stein
- ORCID
- Venue
- Nature
- Publisher
- openRxiv
- Published
- 2021-08-12
- Volume
- 601
- Issue
- 7894
- Pages
- 630-636
- ISSN
- 0028-0836 (ISSN-L)
- OpenAlex
- W4205345182
- Cited by
- 170
- References
- 143
- Citations by year
- 2026: 22 · 2025: 36 · 2024: 35 · 2023: 40 · 2022: 35 · 2021: 1 · 2013: 1
- FWCI
- 12.15
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Lirias (submittedVersion, other-oa)
- Funders
- Wellcome Trust (207467/Z/17/Z)
- Medical Research Council (MR/L018802/1, MR/L018802/2)
- Biotechnology and Biological Sciences Research Council (BB/W001004/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- Drosophila brain, gene regulatory networks, enhancers, chromatin accessibility, single-cell transcriptomics, transcription factors, transcription factor binding sites, neuronal cell types, neurogenesis, developmental trajectories
- MeSH terms
- Animals, Brain, Drosophila, Gene Expression Regulation, Transcription Factors, Gene Expression Regulation, Developmental, Gene Regulatory Networks
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