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Supplementary Information (762K, pdf)

Supplementary Notes 1–6.

Supplementary Tables (29M, xlsx)

Supplementary Tables 1–6.

Acknowledgements

Computing was performed at the Vlaams Supercomputer Center. This work was funded by the following grants to S. Aerts: ERC Consolidator Grant (724226_cis-CONTROL), ERC Proof of Concept (963884), Special Research Fund (BOF) KU Leuven (grants C14/18/092 and C14/22/125), Foundation Against Cancer (2020-062), EOS (G0I2722N/40007513) and FWO (grants G0B5619N and G094121N); PhD fellowships from the FWO to C.B.G.-B. (11F1519N) and S.D.W. (1191323N) and postdoctoral fellowships from FWO to N.H. (1273822N) and Stichting tegen Kanker (Foundation Against Cancer) to J.W. (2019-100). We thank members of various groups that make curated position weight matrices publicly available, including T. Hughes (cisbp), M. Bulyk (Uniprobe), A. Mathelier (Jaspar), V. Makeev (Hocomoco) and many others, listed in Supplementary Table 3 . We thank Resolve Biosciences, especially J. Aerts, for performing the Molecular Cartography experiments in the mouse cortex; and Janssen Pharmaceutica, VIB Tech Watch and the VIB single-cell accelerator for help and funding for generating the mouse cortex data. We thank D. Daaboul for proofreading the manuscript.

Extended data

Author contributions

C.B.G.-B., S.D.W. and S. Aerts conceived the study. C.B.G.-B. developed pycisTopic, C.B.G.-B. and S.D.W. co-developed pycisTarget and the SCENIC+ modules and workflow and G.H. developed the code to generate custom cisTarget databases. C.B.G.-B. and G.H. made the SCENIC+ motif collection. C.B.G.-B. and S.D.W. performed the computational analyses, with the assistance of G.H., N.H. and S. Aibar. I.M. and S.P. generated the mouse cortex multiome data and J.W. performed the single-cell ATAC-seq experiments on the melanoma cell lines, with the assistance of V.C. C.B.G.-B., S.D.W. and S. Aerts wrote the manuscript.

Peer review

Peer review information

Nature Methods thanks Ivan Costa, Zizhen Yao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Lin Tang, in collaboration with the Nature Methods team.

Data availability

Data generated in this manuscript, namely scATAC-seq in melanoma cell lines, 10x multiome in the mouse cortex and scATAC-seq in the Drosophila eye disc, are available in GEO under accession code {"type":"entrez-geo","attrs":{"text":"GSE210749","term_id":"210749"}} GSE210749 . GRCh38.86 genome annotation used in this study is available at https://ftp.ensembl.org/pub/release-86/gtf/homo_sapiens/Homo_sapiens.GRCh38.86.chr.gtf.gz. The GRCh38 genome index used in this study is available at https://cf.10xgenomics.com/supp/cell-arc/refdata-cellranger-arc-GRCh38-2020-A-2.0.0.tar.gz . The mm10 genome index used in this study is available at https://cf.10xgenomics.com/supp/cell-arc/refdata-cellranger-arc-mm10-2020-A-2.0.0.tar.gz . Data from ENCODE deeply profiled cell lines were downloaded from https://www.encodeproject.org/ , including bulk RNA-seq and ATAC-seq for eight cell lines, namely MCF7 ( ENCFF136ANW and ENCFF772EFK , for RNA-seq and ATAC-seq, respectively), HepG2 ( ENCFF660EXG and ENCFF239RGZ ), PC3 ( ENCFF874CFD and ENCFF516GDK ), GM12878 ( ENCFF626GVO and ENCFF415FEC ), K562 ( ENCFF833WFD and ENCFF512VEZ ), Panc1 ( ENCFF602HCV and ENCFF836WDC ), IMR90 ( ENCFF027FUC and ENCFF848XMR ) and HCT116 ( ENCFF766TYC and ENCFF724QHH ); and Hi-C data on five of the cell lines (IMR90 ( ENCFF685BLG ), GM12878 ( ENCFF053VBX ), HCT116 ( ENCFF750AOC ), HepG2 ( ENCFF020DPP ) and K562 ( ENCFF080DPJ )). STARR-seq data were downloaded from ENCODE ( ENCFF045TVA (K562), ENCFF047LDJ (HepG2), ENCFF428KHI (HCT116), ENCFF826BPU (MCF7)). ChIP-seq bigWig and summit bed files were downloaded from ENCODE using the following accession numbers: ENCFF702MTT and ENCSR000BHD for PAX5; ENCFF107LDM and ENCSR000BGU for EBF1; ENCFF803HIP and ENCFF934JFA for POU2F2 for bigWig and summit bed files respectively. The bulk RNA-seq experiments upon perturbation in these cell lines and ChIP-seq datasets are described in Supplementary Tables 1 and 4 , respectively. The 10x multiome data on PBMCs were downloaded from the 10x website. scRNA-seq data of baseline MM-lines and bulk RNA-seq data after SOX10 knockdown were downloaded from GEO ( {"type":"entrez-geo","attrs":{"text":"GSE134432","term_id":"134432"}} GSE134432 ). MITF, SOX10 and TFAP2A ChIP-seq data were downloaded from GEO ( {"type":"entrez-geo","attrs":{"text":"GSE61965","term_id":"61965"}} GSE61965 (MITF and SOX10) and {"type":"entrez-geo","attrs":{"text":"GSE67555","term_id":"67555"}} GSE67555 (TFAP2A)). SNARE-seq2 data on the human cortex were downloaded from Bakken et al. 60 scATAC-seq and scRNA-seq data from the Drosophila eye-antennal disc were downloaded from GEO ( {"type":"entrez-geo","attrs":{"text":"GSE115476","term_id":"115476"}} GSE115476 ). The 10x Visium data and 10x single-cell multiome data from the human cerebellum were downloaded from the 10x website. All analyses can be explored in SCope ( https://scope.aertslab.org/#/scenic-v2 ) and UCSC in the following sessions: PBMCs ( https://genome-euro.ucsc.edu/s/Seppe%20De%20Winter/scenicplus_pbmc ), ENCODE cell lines ( https://genome.ucsc.edu/s/cbravo/SCENIC%2B_DPCL ), melanoma ( http://genome-euro.ucsc.edu/s/Seppe%20De%20Winter/scenicplus_mix_melanoma ), mouse and human cortex ( https://genome-euro.ucsc.edu/s/cbravo/SCENIC%2B_Cortex ), eye-antennal disc ( http://genome.ucsc.edu/s/cbravo/SCENIC%2B_EAD ) and human cerebellum ( https://genome-euro.ucsc.edu/s/cbravo/SCENIC%2B_cerebellum ). The SCENIC+ motif collection is available at https://resources.aertslab.org/cistarget/motif_collections .

Code availability

pycisTopic is available at https://github.com/aertslab/pycisTopic and deposited in Zenodo at 10.5281/zenodo.7857024. pycisTarget is available at https://github.com/aertslab/pycistarget and deposited in Zenodo at 10.5281/zenodo.7857022. SCENIC+ is available at https://github.com/aertslab/scenicplus and deposited in Zenodo at 10.5281/zenodo.7857017. Detailed tutorials and documentation on the SCENIC+ workflow are available at scenicplus.readthedocs.io and tutorials on pycisTopic and pycisTarget (within the SCENIC+ workflow and as standalone packages) are available at pycisTopic.readthedocs.io and pycistarget.readthedocs.io , respectively. Code to generate custom cisTarget databases is available at https://github.com/aertslab/create_cisTarget_databases . Our implementation of Cluster-Buster is available at https://github.com/ghuls/cluster-buster/tree/change_f4_output . Notebooks to reproduce the analyses presented in this manuscript are available at https://github.com/aertslab/scenicplus_analyses .

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Carmen Bravo González-Blas, Seppe De Winter.

Extended data

is available for this paper at 10.1038/s41592-023-01938-4.

Supplementary information

The online version contains supplementary material available at 10.1038/s41592-023-01938-4.

References

1. Davidson EH, et al. A genomic regulatory network for development. Science. 2002; 295 :1669–1678. doi: 10.1126/science.1069883. [ PubMed ] [ CrossRef ] [ Google Scholar ]
2. Janssens J, et al. Decoding gene regulation in the fly brain. Nature. 2022; 601 :630–636. doi: 10.1038/s41586-021-04262-z. [ PubMed ] [ CrossRef ] [ Google Scholar ]
3. Long HK, Prescott SL, Wysocka J. Ever-changing landscapes: transcriptional enhancers in development and evolution. Cell. 2016; 167 :1170–1187. doi: 10.1016/j.cell.2016.09.018. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
4. Erwin DH. The origin of animal body plans: a view from fossil evidence and the regulatory genome. Development. 2020; 147 :dev182899. doi: 10.1242/dev.182899. [ PubMed ] [ CrossRef ] [ Google Scholar ]
5. Rickels R, Shilatifard A. Enhancer logic and mechanics in development and disease. Trends Cell Biol. 2018; 28 :608–630. doi: 10.1016/j.tcb.2018.04.003. [ PubMed ] [ CrossRef ] [ Google Scholar ]
6. Bartosovic M, Kabbe M, Castelo-Branco G. Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nat. Biotechnol. 2021; 39 :825–835. doi: 10.1038/s41587-021-00869-9. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
7. Bartosovic M, Castelo-Branco G. Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag. Nat. Biotechnol. 2022 doi: 10.1038/s41587-022-01535-4. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
8. Stuart T, et al. Nanobody-tethered transposition enables multifactorial chromatin profiling at single-cell resolution. Nat. Biotechnol. 2022 doi: 10.1038/s41587-022-01588-5. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
9. van Steensel B, Delrow J, Henikoff S. Chromatin profiling using targeted DNA adenine methyltransferase. Nat. Genet. 2001; 27 :304–308. doi: 10.1038/85871. [ PubMed ] [ CrossRef ] [ Google Scholar ]
10. Tang JLY, et al. NanoDam identifies Homeobrain (ARX) and Scarecrow (NKX2.1) as conserved temporal factors in the Drosophila central brain and visual system. Dev. Cell. 2022; 57 :1193–1207.e7. doi: 10.1016/j.devcel.2022.04.008. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
11. Aibar S, et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods. 2017; 14 :1083–1086. doi: 10.1038/nmeth.4463. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
12. Van de Sande B, et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat. Protoc. 2020; 15 :2247–2276. doi: 10.1038/s41596-020-0336-2. [ PubMed ] [ CrossRef ] [ Google Scholar ]
13. Fulco, C. P. et al. Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51 , 1664–1669 (2019). [ PMC free article ] [ PubMed ]
14. Bravo González‐Blas C, et al. Identification of genomic enhancers through spatial integration of single‐cell transcriptomics and epigenomics. Mol. Syst. Biol. 2020; 16 :e9438. doi: 10.15252/msb.20209438. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
15. Argelaguet, R. et al. Decoding gene regulation in the mouse embryo using single-cell multi-omics. Preprint at bioRxiv 10.1101/2022.06.15.496239 (2022).
16. Bravo González-Blas C, et al. cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nat. Methods. 2019; 16 :397–400. doi: 10.1038/s41592-019-0367-1. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
17. Minnoye L, et al. Cross-species analysis of enhancer logic using deep learning. Genome Res. 2020; 30 :1815–1834. doi: 10.1101/gr.260844.120. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
18. Mauduit D, et al. Analysis of long and short enhancers in melanoma cell states. eLife. 2021; 10 :e71735. doi: 10.7554/eLife.71735. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
19. Janky R, et al. iRegulon: from a gene list to a gene regulatory network using large motif and track collections. PLoS Comput. Biol. 2014; 10 :e1003731. doi: 10.1371/journal.pcbi.1003731. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
20. Imrichová H, Hulselmans G, Kalender Atak Z, Potier D, Aerts S. i-cisTarget 2015 update: generalized cis-regulatory enrichment analysis in human, mouse and fly. Nucleic Acids Res. 2015; 43 :W57–W64. doi: 10.1093/nar/gkv395. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
21. Verfaillie A, Imrichova H, Janky R, Aerts S. iRegulon and i‐cisTarget: reconstructing regulatory networks using motif and track enrichment. Curr. Protoc. Bioinforma. 2015; 52 :2.16.1–2.16.39. doi: 10.1002/0471250953.bi0216s52. [ PubMed ] [ CrossRef ] [ Google Scholar ]
22. Heinz S, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell. 2010; 38 :576–589. doi: 10.1016/j.molcel.2010.05.004. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
23. Moerman T, et al. GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks. Bioinformatics. 2019; 35 :2159–2161. doi: 10.1093/bioinformatics/bty916. [ PubMed ] [ CrossRef ] [ Google Scholar ]
24. Rothenberg EV. Transcriptional control of early T and B cell developmental choices. Annu. Rev. Immunol. 2014; 32 :283–321. doi: 10.1146/annurev-immunol-032712-100024. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
25. Hodson DJ, et al. Regulation of normal B-cell differentiation and malignant B-cell survival by OCT2. Proc. Natl Acad. Sci. USA. 2016; 113 :E2039–E2046. doi: 10.1073/pnas.1600557113. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
26. Wang D, Malarkannan S. Transcriptional regulation of natural killer cell development and functions. Cancers. 2020; 12 :1591. doi: 10.3390/cancers12061591. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
27. Chopin M, Allan RS, Belz GT. Transcriptional regulation of dendritic cell diversity. Front. Immunol. 2012; 3 :26. doi: 10.3389/fimmu.2012.00026. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
28. Pundhir S, et al. Enhancer and transcription factor dynamics during myeloid differentiation reveal an early differentiation block in cebpa null progenitors. Cell Rep. 2018; 23 :2744–2757. doi: 10.1016/j.celrep.2018.05.012. [ PubMed ] [ CrossRef ] [ Google Scholar ]
29. The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012; 489 :57–74. doi: 10.1038/nature11247. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
30. Luo Y, et al. New developments on the Encyclopedia of DNA Elements (ENCODE) data portal. Nucleic Acids Res. 2020; 48 :D882–D889. doi: 10.1093/nar/gkz1062. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
31. Kamimoto K, et al. Dissecting cell identity via network inference and in silico gene perturbation. Nature. 2023; 614 :742–751. doi: 10.1038/s41586-022-05688-9. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
32. Fleck, J. S. et al. Inferring and perturbing cell fate regulomes in human brain organoids. Nature 10.1038/s41586-022-05279-8 (2022). [ PMC free article ] [ PubMed ]
33. Kartha VK, et al. Functional inference of gene regulation using single-cell multi-omics. Cell Genom. 2022; 2 :100166. doi: 10.1016/j.xgen.2022.100166. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
34. Kamal, A. et al. GRaNIE and GRaNPA: inference and evaluation of enhancer‐mediated gene regulatory networks. Mol. Syst. Biol . 10.15252/msb.202311627 (2023). [ PMC free article ] [ PubMed ]
35. Puig RR, Boddie P, Khan A, Castro-Mondragon JA, Mathelier A. UniBind: maps of high-confidence direct TF–DNA interactions across nine species. BMC Genom. 2021; 22 :482. doi: 10.1186/s12864-021-07760-6. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
36. Gheorghe M, et al. A map of direct TF–DNA interactions in the human genome. Nucleic Acids Res. 2019; 47 :e21. doi: 10.1093/nar/gky1210. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
37. Han GC, et al. Genome-wide organization of GATA1 and TAL1 determined at high resolution. Mol. Cell. Biol. 2015; 36 :157–172. doi: 10.1128/MCB.00806-15. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
38. Lemma RB, et al. Chromatin occupancy and target genes of the haematopoietic master transcription factor MYB. Sci. Rep. 2021; 11 :9008. doi: 10.1038/s41598-021-88516-w. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
39. Inoue A, et al. Elucidation of the role of LMO2 in human erythroid cells. Exp. Hematol. 2013; 41 :1062–1076. doi: 10.1016/j.exphem.2013.09.003. [ PubMed ] [ CrossRef ] [ Google Scholar ]
40. Smith RP, et al. Massively parallel decoding of mammalian regulatory sequences supports a flexible organizational model. Nat. Genet. 2013; 45 :1021–1028. doi: 10.1038/ng.2713. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
41. Holding AN, et al. VULCAN integrates ChIP-seq with patient-derived co-expression networks to identify GRHL2 as a key co-regulator of ERa at enhancers in breast cancer. Genome Biol. 2019; 20 :91. doi: 10.1186/s13059-019-1698-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
42. Avsec Ž, et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat. Methods. 2021; 18 :1196–1203. doi: 10.1038/s41592-021-01252-x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
43. The ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583 , 699–710 (2020). [ PMC free article ] [ PubMed ]
44. Granja JM, et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 2021; 53 :403–411. doi: 10.1038/s41588-021-00790-6. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
45. Stuart T, Srivastava A, Madad S, Lareau CA, Satija R. Single-cell chromatin state analysis with Signac. Nat. Methods. 2021; 18 :15. doi: 10.1038/s41592-021-01282-5. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
46. Aerts S, et al. Gene prioritization through genomic data fusion. Nat. Biotechnol. 2006; 24 :537–544. doi: 10.1038/nbt1203. [ PubMed ] [ CrossRef ] [ Google Scholar ]
47. Hoek KS, et al. Metastatic potential of melanomas defined by specific gene expression profiles with no BRAF signature. Pigment Cell Res. 2006; 19 :290–302. doi: 10.1111/j.1600-0749.2006.00322.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
48. Wouters J, et al. Robust gene expression programs underlie recurrent cell states and phenotype switching in melanoma. Nat. Cell Biol. 2020; 22 :986–998. doi: 10.1038/s41556-020-0547-3. [ PubMed ] [ CrossRef ] [ Google Scholar ]
49. Pratapa A, Jalihal AP, Law JN, Bharadwaj A, Murali TM. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat. Methods. 2020; 17 :147–154. doi: 10.1038/s41592-019-0690-6. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
50. Verfaillie A, et al. Decoding the regulatory landscape of melanoma reveals TEADS as regulators of the invasive cell state. Nat. Commun. 2015; 6 :6683. doi: 10.1038/ncomms7683. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
51. Caramel J, et al. A switch in the expression of embryonic EMT-inducers drives the development of malignant melanoma. Cancer Cell. 2013; 24 :466–480. doi: 10.1016/j.ccr.2013.08.018. [ PubMed ] [ CrossRef ] [ Google Scholar ]
52. Hoek KS, Goding CR. Cancer stem cells versus phenotype-switching in melanoma: phenotype-switching in melanoma. Pigment Cell Melanoma Res. 2010; 23 :746–759. doi: 10.1111/j.1755-148X.2010.00757.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
53. Yang H, Fu J, Yao L, Hou A, Xue X. Runx3 is a key modulator during the epithelial-mesenchymal transition of alveolar type II cells in animal models of BPD. Int. J. Mol. Med. 2017; 40 :1466–1476. doi: 10.3892/ijmm.2017.3135. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
54. Dilshat R, et al. MITF reprograms the extracellular matrix and focal adhesion in melanoma. eLife. 2021; 10 :e63093. doi: 10.7554/eLife.63093. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
55. Zhang, P., Sun, Y. & Ma, L. ZEB1: at the crossroads of epithelial-mesenchymal transition, metastasis and therapy resistance. Cell Cycle 14 , 481–487 (2015). [ PMC free article ] [ PubMed ]
56. Tiwari N, et al. Sox4 is a master regulator of epithelial-mesenchymal transition by controlling Ezh2 expression and epigenetic reprogramming. Cancer Cell. 2013; 23 :768–783. doi: 10.1016/j.ccr.2013.04.020. [ PubMed ] [ CrossRef ] [ Google Scholar ]
57. Meng F, Li J, Yang X, Yuan X, Tang X. Role of Smad3 signaling in the epithelial‑mesenchymal transition of the lens epithelium following injury. Int. J. Mol. Med. 2018 doi: 10.3892/ijmm.2018.3662. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
58. Tasic B, et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature. 2018; 563 :72–78. doi: 10.1038/s41586-018-0654-5. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
59. BRAIN Initiative Cell Census Network (BICCN). A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 598 , 86–102 (2021). [ PMC free article ] [ PubMed ]
60. Bakken TE, et al. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature. 2021; 598 :111–119. doi: 10.1038/s41586-021-03465-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
61. Stergachis AB, et al. Conservation of trans-acting circuitry during mammalian regulatory evolution. Nature. 2014; 515 :365–370. doi: 10.1038/nature13972. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
62. Wittstatt J, Reiprich S, Küspert M. Crazy little thing called sox—new insights in oligodendroglial sox protein function. Int. J. Mol. Sci. 2019; 20 :2713. doi: 10.3390/ijms20112713. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
63. Wang, J. et al. Paired related homeobox protein 1 regulates quiescence in human oligodendrocyte progenitors. Cell Rep . 10.1016/j.celrep.2018.11.068 (2018). [ PMC free article ] [ PubMed ]
64. La Manno G, et al. RNA velocity of single cells. Nature. 2018; 560 :494–498. doi: 10.1038/s41586-018-0414-6. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
65. Bergen V, Lange M, Peidli S, Wolf FA, Theis FJ. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 2020; 38 :1408–1414. doi: 10.1038/s41587-020-0591-3. [ PubMed ] [ CrossRef ] [ Google Scholar ]
66. Li C, Virgilio MC, Collins KL, Welch JD. Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction. Nat. Biotechnol. 2022 doi: 10.1038/s41587-022-01476-y. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
67. Ma S, et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell. 2020; 183 :1103–1116. doi: 10.1016/j.cell.2020.09.056. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
68. Isogai E, Okumura K, Saito M, Tokunaga Y, Wakabayashi Y. Meis1 plays roles in cortical development through regulation of cellular proliferative capacity in the embryonic cerebrum. Biomed. Res. 2022; 43 :91–97. doi: 10.2220/biomedres.43.91. [ PubMed ] [ CrossRef ] [ Google Scholar ]
69. Wang, C.-W. & Sun, Y. H. Segregation of eye and antenna fates maintained by mutual antagonism in Drosophila . Development 139 , 3413–3421 (2012). [ PubMed ]
70. Zaugg JB, et al. Current challenges in understanding the role of enhancers in disease. Nat. Struct. Mol. Biol. 2022; 29 :1148–1158. doi: 10.1038/s41594-022-00896-3. [ PubMed ] [ CrossRef ] [ Google Scholar ]
71. Tarashansky AJ, et al. Mapping single-cell atlases throughout Metazoa unravels cell type evolution. eLife. 2021; 10 :e66747. doi: 10.7554/eLife.66747. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
72. Bakken TE, et al. Single-cell and single-nucleus RNA-seq uncovers shared and distinct axes of variation in dorsal LGN neurons in mice, non-human primates, and humans. eLife. 2021; 10 :e64875. doi: 10.7554/eLife.64875. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
73. Sebé-Pedrós A, et al. Cnidarian cell type diversity and regulation revealed by whole-organism single-cell RNA-seq. Cell. 2018; 173 :1520–1534. doi: 10.1016/j.cell.2018.05.019. [ PubMed ] [ CrossRef ] [ Google Scholar ]
74. Schmidt D, et al. Five-vertebrate ChIP-seq reveals the evolutionary dynamics of transcription factor binding. Science. 2010; 328 :1036–1040. doi: 10.1126/science.1186176. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
75. Arendt D, et al. The origin and evolution of cell types. Nat. Rev. Genet. 2016; 17 :744–757. doi: 10.1038/nrg.2016.127. [ PubMed ] [ CrossRef ] [ Google Scholar ]
76. Alexander JM, et al. Live-cell imaging reveals enhancer-dependent Sox2 transcription in the absence of enhancer proximity. eLife. 2019; 8 :e41769. doi: 10.7554/eLife.41769. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
77. Xiao JY, Hafner A, Boettiger AN. How subtle changes in 3D structure can create large changes in transcription. eLife. 2021; 10 :e64320. doi: 10.7554/eLife.64320. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
78. Zuin J, et al. Nonlinear control of transcription through enhancer–promoter interactions. Nature. 2022; 604 :571–577. doi: 10.1038/s41586-022-04570-y. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
79. Hafner A, Boettiger A. The spatial organization of transcriptional control. Nat. Rev. Genet. 2023; 24 :53–68. doi: 10.1038/s41576-022-00526-0. [ PubMed ] [ CrossRef ] [ Google Scholar ]
80. Novakovsky G, Dexter N, Libbrecht MW, Wasserman WW, Mostafavi S. Obtaining genetics insights from deep learning via explainable artificial intelligence. Nat. Rev. Genet. 2023; 24 :125–137. doi: 10.1038/s41576-022-00532-2. [ PubMed ] [ CrossRef ] [ Google Scholar ]
81. Zhang Y, et al. Model-based analysis of ChIP-seq (MACS) Genome Biol. 2008; 9 :R137. doi: 10.1186/gb-2008-9-9-r137. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
82. Corces MR, et al. The chromatin accessibility landscape of primary human cancers. Science. 2018; 362 :eaav1898. doi: 10.1126/science.aav1898. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
83. Chang, J. lda: Collapsed Gibbs Sampling Methods for Topic Models . https://cran.r-project.org/web/packages/lda/index.html (2015).
84. McCallum, A. K. MALLET: MAchine Learning for LanguagE Toolkit. https://mimno.github.io/Mallet/ (2002).
85. Arun, R., Suresh, V., Veni Madhavan, C. E. & Narasimha Murthy, M. N. On finding the natural number of topics with Latent Dirichlet Allocation: some observations. In Advances in Knowledge Discovery and Data Mining (eds Zaki, M. J. et al.) 391–402 (Springer, 2010).
86. Cao, J., Xia, T., Li, J., Zhang, Y. & Tang, S. A density-based method for adaptive LDA model selection. Neurocomputing 72 , 1775–1781 (2009).
87. Frith MC. Cluster-Buster: finding dense clusters of motifs in DNA sequences. Nucleic Acids Res. 2003; 31 :3666–3668. doi: 10.1093/nar/gkg540. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
88. Hopfensitz M, et al. Multiscale binarization of gene expression data for reconstructing Boolean networks. IEEE/ACM Trans. Comput. Biol. Bioinform. 2012; 9 :487–498. doi: 10.1109/TCBB.2011.62. [ PubMed ] [ CrossRef ] [ Google Scholar ]
89. Suo S, et al. Revealing the critical regulators of cell identity in the mouse cell atlas. Cell Rep. 2018; 25 :1436–1445. doi: 10.1016/j.celrep.2018.10.045. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
90. Gupta S, Stamatoyannopoulos JA, Bailey TL, Noble W. Quantifying similarity between motifs. Genome Biol. 2007; 8 :R24. doi: 10.1186/gb-2007-8-2-r24. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
91. Stuart T, et al. Comprehensive integration of single-cell data. Cell. 2019; 177 :1888–1902. doi: 10.1016/j.cell.2019.05.031. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
92. Mahony S, Benos PV. STAMP: a web tool for exploring DNA-binding motif similarities. Nucleic Acids Res. 2007; 35 :W253–W258. doi: 10.1093/nar/gkm272. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
93. Griffiths TL, Steyvers M. Finding scientific topics. Proc. Natl Acad. Sci. USA. 2004; 101 :5228–5235. doi: 10.1073/pnas.0307752101. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
94. Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018; 19 :15. doi: 10.1186/s13059-017-1382-0. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
95. Wolock SL, Lopez R, Klein AM. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 2019; 8 :281–291. doi: 10.1016/j.cels.2018.11.005. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
96. Mimno, D., Wallach, H. M., Talley, E., Leenders, M. & McCallum, A. Optimizing semantic coherence in topic models. In Proc. 2011 Conference on Empirical Methods in Natural Language Processing 262–272 (Association for Computational Linguistics, 2011).
97. Lambert SA, et al. The human transcription factors. Cell. 2018; 172 :650–665. doi: 10.1016/j.cell.2018.01.029. [ PubMed ] [ CrossRef ] [ Google Scholar ]
98. Durand NC, et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 2016; 3 :95–98. doi: 10.1016/j.cels.2016.07.002. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
99. Pliner HA, et al. Cicero predicts cis-regulatory DNA Interactions from single-cell chromatin accessibility data. Mol. Cell. 2018; 71 :858–871. doi: 10.1016/j.molcel.2018.06.044. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
100. Bruse, N. & van Heeringen, S. J. GimmeMotifs: an analysis framework for transcription factor motif analysis. Preprint at bioRxiv 10.1101/474403 (2018).
101. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014; 15 :550. doi: 10.1186/s13059-014-0550-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
102. De Rop FV, et al. Hydrop enables droplet-based single-cell ATAC-seq and single-cell RNA-seq using dissolvable hydrogel beads. eLife. 2022; 11 :e73971. doi: 10.7554/eLife.73971. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
103. Frankish A, et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 2019; 47 :D766–D773. doi: 10.1093/nar/gky955. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
104. Marçais G, Kingsford C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics. 2011; 27 :764–770. doi: 10.1093/bioinformatics/btr011. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
105. Gans JD, Wolinsky M. Improved assay-dependent searching of nucleic acid sequence databases. Nucleic Acids Res. 2008; 36 :e74. doi: 10.1093/nar/gkn301. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
106. Rodriguez JM, et al. APPRIS 2017: principal isoforms for multiple gene sets. Nucleic Acids Res. 2018; 46 :D213–D217. doi: 10.1093/nar/gkx997. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

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