The DOI matches its Crossref record; 140 of the 143 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
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
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
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
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
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
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 22,093
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
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
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
Research works printed but not linked: 3
- 10.1093/bioinformatics/btx194. · DOI · Paper page
- 10.1101/2020.04.28.066498 · DOI
- Shrikumar, A. , Greenside, P. & Kundaje, A . Learning Important Features Through Propagating Activation Differences. arXiv:1704.02685 [cs] (2019). · arXiv
Cited by
Sorted by citations
Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly
Li, H., Janssens, J., Waegeneer, M. D., Kolluru, S. S., Davie, K., Gardeux, V., Saelens, W., David, F. P. A., Brbić, M., Spanier, K. I., et al. (2022) · Science · doi:10.1126/science.abk2432 · Cited by 826
Self-citationSCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
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
Self-citationGene regulatory network inference in the era of single-cell multi-omics
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 507
de Almeida, B. P., Reiter, F., Pagani, M., & Stark, A. (2022) · Nature Genetics · doi:10.1038/s41588-022-01048-5 · Cited by 283
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
Self-citationGoshisht, M. K. (2024) · ACS Omega · doi:10.1021/acsomega.3c05913 · Cited by 173
scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks
Yuan, H., & Kelley, D. R. (2022) · Nature Methods · doi:10.1038/s41592-022-01562-8 · Cited by 133
Aging Fly Cell Atlas identifies exhaustive aging features at cellular resolution
Lu, T., Brbić, M., Park, Y.-J., Jackson, T., Chen, J., Kolluru, S. S., Qi, Y., Katheder, N. S., Cai, X. T., Lee, S., et al. (2023) · Science · doi:10.1126/science.adg0934 · Cited by 120
Hold out the genome: a roadmap to solving the cis-regulatory code
de Boer, C. G., & Taipale, J. (2024) · Nature · doi:10.1038/s41586-023-06661-w · Cited by 107
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 101
Principles and challenges of modeling temporal and spatial omics data
Velten, B., & Stegle, O. (2023) · Nature Methods · doi:10.1038/s41592-023-01992-y · Cited by 96
Deep flanking sequence engineering for efficient promoter design using DeepSEED
Zhang, P., Wang, H., Xu, H., Wei, L., Liu, L., Hu, Z., & Wang, X. (2023) · Nature Communications · doi:10.1038/s41467-023-41899-y · Cited by 94
Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo
de Almeida, B. P., Schaub, C., Pagani, M., Secchia, S., Furlong, E. E. M., & Stark, A. (2024) · Nature · doi:10.1038/s41586-023-06905-9 · Cited by 87
Bravo González-Blas, C., Matetovici, I., Hillen, H., Taskiran, I. I., Vandepoel, R., Christiaens, V., Sansores-García, L., Verboven, E., Hulselmans, G., Poovathingal, S., et al. (2024) · Nature Cell Biology · doi:10.1038/s41556-023-01316-4 · Cited by 85
Self-citationCurrent challenges in understanding the role of enhancers in disease
Zaugg, J. B., Sahlén, P., Andersson, R., Alberich-Jordà, M., de Laat, W. L., Deplancke, B., Ferrer, J. P., Mandrup, S., Natoli, G., Plewczyński, D., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00896-3 · Cited by 86
Chromosome-level organization of the regulatory genome in the Drosophila nervous system
Mohana, G., Dorier, J., Li, X., Mouginot, M., Smith, R. C., Malek, H., Leleu, M., Rodríguez, D., Khadka, J., Rosa, P., et al. (2023) · Cell · doi:10.1016/j.cell.2023.07.008 · Cited by 81
Predicting gene expression from DNA sequence using deep learning models
Barbadilla-Martínez, L., Klaassen, N. H. M., van Steensel, B., & de Ridder, J. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00841-2 · Cited by 89
Cell type diversity in a developing octopus brain
Styfhals, R., Zolotarov, G., Hulselmans, G. J., Spanier, K. I., Poovathingal, S. K., Elagoz, A. M., Winter, S. D., Deryckere, A., Rajewsky, N., Ponte, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35198-1 · Cited by 76
Self-citationZheng, P., Zhang, N., Ren, D., Yu, C., Zhao, B., & Zhang, Y. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101057 · Cited by 71
Modeling fragment counts improves single-cell ATAC-seq analysis
Martens, L. D., Fischer, D. S., Yépez, V. A., Theis, F. J., & Gagneur, J. (2023) · Nature Methods · doi:10.1038/s41592-023-02112-6 · Cited by 66
Decoding gene regulation in the mouse embryo using single-cell multi-omicsPreprint
Argelaguet, R., Lohoff, T., Li, J., Nakhuda, A., Drage, D., Krueger, F., Velten, L., Clark, S. J., & Reik, W. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.15.496239 · Cited by 64
Applications of deep learning in understanding gene regulation
Li, Z., Gao, E., Zhou, J., Han, W., Xu, X., & Gao, X. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100384 · Cited by 73
From time-series transcriptomics to gene regulatory networks: A review on inference methods
Marku, M., & Pancaldi, V. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011254 · Cited by 72
Advances and applications in single-cell and spatial genomics
Wang, J., Ye, F., Chai, H., Jiang, Y., Wang, T., Ran, X., Xia, Q., Xu, Z., Fu, Y., Zhang, G., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-024-2770-x · Cited by 70
Evaluating deep learning for predicting epigenomic profiles
Toneyan, S., Tang, Z., & Koo, P. K. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00570-9 · Cited by 64
Chromatin accessibility during human first-trimester neurodevelopment
Mannens, C. C. A., Hu, L., Lönnerberg, P., Schipper, M., Reagor, C. C., Li, X., He, X., Barker, R. A., Sundström, E., Posthuma, D., et al. (2024) · Nature · doi:10.1038/s41586-024-07234-1 · Cited by 69
GRaNIE and GRaNPA: inference and evaluation of enhancer‐mediated gene regulatory networks
Kamal, A., Arnold, C., Claringbould, A., Moussa, R., Servaas, N. H., Kholmatov, M. M., Daga, N., Nogina, D., Müller‐Dott, S., Reyes‐Palomares, A., et al. (2023) · Molecular Systems Biology · doi:10.15252/msb.202311627 · Cited by 69
Big data and deep learning for RNA biology
Hwang, H., Jeon, H., Yeo, N., & Baek, D. (2024) · Experimental & Molecular Medicine · doi:10.1038/s12276-024-01243-w · Cited by 64
Coordinated control of neuronal differentiation and wiring by sustained transcription factors
Özel, M. N., Gibbs, C. S., Holguera, I., Soliman, M., Bonneau, R., & Desplan, C. (2022) · Science · doi:10.1126/science.add1884 · Cited by 63
MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer
Wang, X., Duan, M., Li, J., Ma, A., Xin, G., Xu, D. H., Li, Z., Liu, B., & Ma, Q. (2024) · Nature Communications · doi:10.1038/s41467-023-44570-8 · Cited by 53
Effects of hunger on neuronal histone modifications slow aging in Drosophila
Weaver, K. J., Holt, R. A., Henry, E., Lyu, Y., & Pletcher, S. D. (2023) · Science · doi:10.1126/science.ade1662 · Cited by 61
Gliotransmission of D-serine promotes thirst-directed behaviors in Drosophila
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
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 45
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
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 51
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 38
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 42
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
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 39
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 35
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 26
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 28
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
Self-citationThomas, H., & Buecker, C. (2023) · BioEssays · doi:10.1002/bies.202300044 · Cited by 28
Hopkins, B. R., Barmina, O. Y., & Kopp, A. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002148 · Cited by 24
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 27
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
Neiro, J., Sridhar, D., Dattani, A., & Aboobaker, A. A. (2022) · eLife · doi:10.7554/elife.79675 · Cited by 22
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
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 21
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 29
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 22
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 24
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 22
Self-citationHuman neuron subtype programming via single-cell transcriptome-coupled patterning screens
Lin, H., Janssens, J., Eisinger, B., 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 27
Self-citationJacobs, J., Pagani, M., Wenzl, C., & Stark, A. (2023) · Science · doi:10.1126/science.adf6149 · Cited by 20
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
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 19
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 21
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
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 22
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 17
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
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 12
Katı, N., & Uçar, F. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118029 · Cited by 14
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
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
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 18
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 13
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
Salvatore, M., Horlacher, M., Marsico, A., Winther, O., & Andersson, R. (2023) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqad026 · Cited by 13
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
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
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
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 14
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
Lee, Y. S., Huang, N., Samuels, T. J., & Davis, I. (2025) · Science Advances · doi:10.1126/sciadv.adr6682 · Cited by 13
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 13
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
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
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 13
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 18
Self-citationKeegan, S. E., Haskins, J., Simmonds, A. J., & Hughes, S. C. (2023) · Development · doi:10.1242/dev.201484 · Cited by 10
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
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
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 12
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
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
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
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
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
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 9
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 12
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 9
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
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
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., Seçilmiş, D., Ziegenhain, C., & Llorens-Bobadilla, E. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02131-w · Cited by 10
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
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
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 6
Metadata
- 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
- 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
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