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Joint profiling of chromatin accessibility and gene expression in thousands of single cells
Open accessAbstract
Single-cell chromatin and RNA analysis: Single-cell analyses have begun to provide insight into the differences among and within the individual cells that make up a tissue or organism. However, technological barriers owing to the small amount of material present in each single cell have prevented parallel analyses. Cao et al. present sci-CAR, a pooled barcode method that jointly analyzes both the RNA transcripts and chromatin profiles of single cells. By applying sci-CAR to lung adenocarcinoma cells and mouse kidney tissue, the authors demonstrate precision in assessing expression and genome accessibility at a genome-wide scale. The approach provides an improvement over bulk analysis, which can be confounded by differing cellular subgroups. Science , this issue p. 1380
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“Milo may also be applicable to single-cell assays other than scRNA-seq, including multi-omic assays [35–39].”
From Differential abundance testing on single-cell data using k-nearest neighbor graphs (2022) · cites this paper as [36]
In the citing paper
“Furthermore, multimodal single-cell approaches are swiftly gaining traction within hepatology, allowing ‘combination readouts’ from the same cell, for example simultaneous acquisition of transcriptomic and epigenomic information, again maximising our ability to identify the key therapeutic targets in pathological cells.165,166”
From Understanding the cellular interactome of non-alcoholic fatty liver disease (2022) · cites this paper as [165]
In the citing paper
“Novel therapeutics developed for one fibrotic disorder may be applicable to a wide range of fibrotic diseases due to shared pathways across organs that are uncovered by this work.”
From Fibrosis: from mechanisms to medicines (2020) · cites this paper as [182]
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“The cardinal identity of a cell is probably already instilled at the postmitotic stage during development9,61,62,63,64, and its subsequent phenotypic differentiation and expression are regulated by extrinsic signals such as cell interactions and neural activity.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [62]
In the citing paper
“To achieve higher throughput in single-cell analysis, Sci-CAR-seq combined single-cell combinatorial indexing55 with ATAC-seq to provide deep insights into chromatin states jointly with gene expression in over 11,200 nuclei56.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [56]
In the citing paper
“Single-cell multiomics allows uncovering of the mechanisms of gene regulation across different omics layers6–9 but lacks spatial information, which is crucial to understanding cellular function in tissue.”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [8]
In the citing paper
“Single-cell measurements of transcriptional states, chromatin accessibility, and DNA methylation, have also provided a better understanding of the heterogeneity that exists in such regulatory mechanisms during hematopoiesis (Buenrostro et al., 2018; Cao et al., 2018; Ludwig et al., 2019b; Ulirsch et al., 2019).”
From Unraveling Hematopoiesis through the Lens of Genomics (2020) · cites this paper as Cao et al., 2018
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Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
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
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
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
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
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
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Items 701–800 of 948
Morgans, W. G., Sharrocks, A. D., & Iqbal, M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.25.559293 · Cited by 2
Adelus, M. L., Ding, J., Tran, B. T., Conklin, A. C., Golebiewski, A. K., Stolze, L. K., Whalen, M. B., Cusanovich, D. A., & Romanoski, C. E. (2023) · eLife · doi:10.7554/elife.91729.1 · Cited by 2
Self-citationWeinand, K., Langan, E. M., Curtis, M. L., & Raychaudhuri, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.02.646871 · Cited by 2
Hypoxia-Induced Metabolic Reprogramming and Markings of Cell Fate in Concentric Arterial HypertrophyPreprint
de Almeida, L. F., Smith, J. P., Yamaguchi, M., Medrano, S., Martini, A. G., Matsuoka, D., Juśkiewicz, Z. J., Isakson, B. E., Yamaguchi, H., Trevisan-Silva, D., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.09.663881 · Cited by 2
Li, Y., Tan, Y. J., Chen, R., Zhou, X., Chen, Y., Huo, Y., Guo, X., Tiwari, A., Wen, Z., Zhong, X. Y., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.11.693589 · Cited by 2
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Adelus, M. L., Ding, J., Tran, B. T., Conklin, A. C., Golebiewski, A. K., Stolze, L. K., Whalen, M. B., Cusanovich, D. A., & Romanoski, C. E. (2024) · eLife · doi:10.7554/elife.91729.3 · Cited by 1
Self-citationZhou, M., Zhang, H., Bai, Z., Mann‐Krzisnik, D., Wang, F., & Li, Y. (2024) · STAR Protocols · doi:10.1016/j.xpro.2024.103066 · Cited by 1
Unlocking cross-modal interplay of single-cell joint profiling with CellMATE
Wang, Q., Zhang, B., Guo, Y., Gong, L., Li, E., & Yang, J. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae582 · Cited by 2
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Kang, B., Lee, H., & Roh, T. (2025) · Genomics & Informatics · doi:10.1186/s44342-025-00037-4 · Cited by 2
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Casmey, K., Zimmermann, M., Xie, Y., Codeluppi-Arrowsmith, S. A., & Turecki, G. (2025) · Genes · doi:10.3390/genes16121394 · Cited by 3
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Liao, A., Zhang, Z., Sziráki, A., Abdulraouf, A., Rehman, A., Xu, Z., Lu, Z.-Y., Jiang, W., Arya, A., Lee, J., et al. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.101101 · Cited by 1
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Kandror, E. K., Wang, A., Carriere, M., Peterson, A., Liao, W., Tjärnberg, A., Fung, J. H., Mahbubani, K. T. A., Loper, J., Pangburn, W., et al. (2026) · Neuron · doi:10.1016/j.neuron.2025.12.035 · Cited by 2
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Naqing, F., Yuan, Q., & Duren, Z. (2026) · Genome biology · doi:10.1186/s13059-026-04071-5 · Cited by 1
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Xi, X., Li, C., Wei, L., & Zhang, X. (2026) · Trends in Genetics · doi:10.1016/j.tig.2026.05.005 · Cited by 1
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Uhlig, M., Schumacher, D., Marx, G., Seikrit, C., Schmidt-Ott, K. M., Saritas, T., & Kramann, R. (2026) · Nature Reviews Nephrology · doi:10.1038/s41581-026-01107-8 · Cited by 1
de Groot, D. H., Leonardo, S. X. M., Pachkov, M. V., & van Nimwegen, E. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03220-2 · Cited by 5
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Tran, B., Pham, T. N., Huynh, T. V., & Hoang, K. (2023) · International Conference on Multimedia Analysis and Pattern Recognition (MAPR) · doi:10.1109/mapr59823.2023.10289050 · Cited by 1
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Role of histone acylation and related technical developments
Yan, H., & Zhang, H. (2023) · Scientia Sinica Vitae · doi:10.1360/ssv-2023-0226 · Cited by 1
Zhang, Y., Hu, L., Ren, G., Zeng, Y., Zhao, X., & Zhong, C. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1271879 · Cited by 1
High-Quality Brain and Bone Marrow Nuclei Preparation for Single Nuclei Multiome Assays
Moraes-Cabé, C., Novault, S., Choi, A. J., Seffer, V., Barrio, L., Libri, V., & Hasan, M. (2023) · Journal of Visualized Experiments · doi:10.3791/65715 · Cited by 1
UMINT-FS: UMINT-guided Feature Selection for multi-omics datasets
Maitra, C., Seal, D. B., Das, V., Vorobeychik, Y., & De, R. K. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385731 · Cited by 1
Single-cell multiomics to advance cell therapy
Goss, K. L., & Horwitz, E. M. (2024) · Cytotherapy · doi:10.1016/j.jcyt.2024.10.009 · Cited by 1
Kazmi, A., Gill, R., Restrepo, P., & Ji, A. L. (2025) · Seminars in Immunology · doi:10.1016/j.smim.2025.101958 · Cited by 1
MOADE: a multimodal autoencoder for dissociating bulk multi-omics data
Sun, J., Malik, A. A., Lin, T., Bratton, A., Pan, Y., Smith, K. S., Onar‐Thomas, A., Robinson, G. W., Zhang, W., Northcott, P. A., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03805-1 · Cited by 1
PETScan: score-based genome-wide association analysis of RNA-Seq and ATAC-Seq data
Hao, Y. B., Kafri, T., & Zou, F. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btaf672 · Cited by 1
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Chen, X. X., & Xuan, J. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.88799 · Cited by 1
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Cotovio, J. P., Fernandes, T. G., Diogo, M. M., & Cabral, J. M. S. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816221-7.00001-x · Cited by 1
Closing the gap: a roadmap to single‐cell regulatory genomics
Carnesecchi, J., & Lohmann, I. (2020) · Molecular Systems Biology · doi:10.15252/msb.20209497
Chen, M., Xu, C., Xu, Z., He, W., Zhang, H., Su, J., & Song, Q. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105999 · Cited by 1
Leveraging Genetic Variants for Rapid and Robust Upstream Analysis of Massive Sequence Data
Zhang, F. (2019) · Deep Blue (University of Michigan) · Cited by 1
Single-Cell Manifold Preserving Feature Selection (SCMER)Preprint
Chen, K., Liang, S., Mohanty, V., Dou, J., Miao, Q., Huang, Y., Müftüoğlu, M., Ding, L., & Peng, W. (2020) · Research Square · doi:10.21203/rs.3.rs-119885/v1 · Cited by 1
Hu, J., Zhong, Y., & Shang, X. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.06.438733 · Cited by 1
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Woessner, R., Borodovsky, A., Sachsenmeier, K. F., Scheuplein, F., Wilkinson, R. W., Ye, C., & Dovedi, S. J. (2022) · Wiley eBooks · doi:10.1002/9781119535331.ch1 · Cited by 1
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Xiong, H., Wang, Q., Li, C. C., & He, A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.04.535478 · Cited by 1
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Self-citationYan, C., He, Z., Ye, L., Zheng, S., Lin, N., Li, G., & Li, W. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.27.678942 · Cited by 1
Karri, K., Chang, T.-Y., Pyatkov, M., Gandhi, S., Siggers, T., & Waxman, D. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.31.685903 · Cited by 1
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Owiredu, E., Denslow, A. J., Chen, S., Mousseau, B. J., Foote, J. B., Yang, G., Peel, J. N., Burnham, R., Browning, K., Scharer, C. D., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.06.704376 · Cited by 1
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Chen, K. Y., Lopez, R., Eraslan, B., Hata, M., Takeshima, Y., Makino, K., Kibayashi, T., Ichiyama, K., Biton, A., Huetter, J.-C., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.23.727396 · Cited by 1
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Chen, Y., Jung, S., Choi, Y., & Yoon, E. (2020) · Springer eBooks · doi:10.1007/978-981-10-4857-9_42-1
Li, R., Grimm, S. A., & Wade, P. A. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3802023
Xu, Y., & Strick, A. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.12320
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Bilichak, A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-85679-9.00013-1
Miyashiro, K., & Eberwine, J. H. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823577-5.00034-9
Harigaya, Y., Zhang, Z., Zhang, H., Zang, C., Zhang, N. R., & Jiang, Y. (2021) · Research Square · doi:10.21203/rs.3.rs-930184/v1
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Bouwers, E. G. (2011) · PUB – Publications at Bielefeld University (Bielefeld University)
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Kuffler, L., Skelly, D. A., Czechanski, A., Fortin, H. J., Munger, S. C., Baker, C. L., Reinholdt, L. G., & Carter, G. W. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.07.479436
McKinsey, T. A., & Bagchi, R. A. (2020) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.736864418.793573105
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Tiwari, V. K., & Lavin, D. P. (2021) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.739024475.793583149
Yu, T., Zong, Y., Wang, Y., Wang, X., & Li, Y. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.28.493838
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Xu, Y. (2022) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/159232
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李, 萌. (2022) · Advances in Clinical Medicine · doi:10.12677/acm.2022.126742
Liakh, I., Durnyak, B., & Babichev, S. A. (2021) · Printing and Publishing · doi:10.32403/0554-4866-2021-2-82-92-103
Dharmaratne, M., Fard, A. T., & Mar, J. C. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4162072
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Nanda, A. S., Wu, K., Kasinathan, S., Ostrowski, M. S., Clugston, A. S., Satpathy, A. T., Sweet-Cordero, A., Goodarzi, H., & Ramani, V. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.07.502893
Self-citationTrajectory inference in single cell data: A systematic literature review
Emu, I. J., & Ahmed, S. (2022) · Systematic Literature Review and Meta-Analysis Journal · doi:10.54480/slrm.v3i3.46
de Vries, D. H. (2022) · University of Groningen research database (University of Groningen / Centre for Information Technology) · doi:10.33612/diss.255266474
Model-Based Clustering of Single-Cell Omics Data
Wang, X., Hu, H., & Chen, W. (2022) · Springer handbooks of computational statistics · doi:10.1007/978-3-662-65902-1_5
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Lin, Z. (2022) · Springer handbooks of computational statistics · doi:10.1007/978-3-662-65902-1_3
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Patnaik, P. K., Barlit, H., Shoushtari, S. I., & Labunskyy, V. M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-823679-6.00016-3
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Zhu, C., Wang, Z., & Ren, B. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-2899-7_10
Wang, L., Nie, R., Cai, Y., Wang, A., Zhang, H., Zhang, J., & Cai, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.13.532376
Adelus, M. L., Ding, J., Tran, B. T., Conklin, A. C., Golebiewski, A. K., Stolze, L. K., Whalen, M. B., Cusanovich, D. A., & Romanoski, C. E. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.03.535495
Self-citationTools for Kidney Tissue Analysis
Melk, A. (2023) · Springer eBooks · doi:10.1007/978-3-031-11665-0_5
Epigenomic profiling at genome scale: from assays and analysis to clinical insights
Ennis, S., Digby, B., & Broin, P. Ó. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91367-6.00001-5
Metadata
- Authors
- Cao, J., Cusanovich, D. A., Ramani, V., Aghamirzaie, D., Pliner, H. A., Hill, A. J., Daza, R. M., McFaline-Figueroa, J. L., Packer, J. S., Christiansen, L., Steemers, F. J., Adey, A. C., Trapnell, C., & Shendure, J.
- Affiliations
- University of Washington (US)
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Illumina (United States) (US)
- Oregon Health & Science University (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Cusanovich, Darren A.
- Ramani, Vijay
- Trapnell, Cole
- Shendure, Jay
- ORCID
- Venue
- Science
- Published
- 2018-09-28
- Volume
- 361
- Issue
- 6409
- Pages
- 1380-1385
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC6571013
- OpenAlex
- W2889236955
- Cited by
- 938
- References
- 42
- Citations by year
- 2026: 102 · 2025: 109 · 2024: 113 · 2023: 132 · 2022: 125 · 2021: 156 · 2020: 117 · 2019: 74 · 2018: 9
- FWCI
- 31.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (DGE-1256082)
- National Institutes of Health (DP1HG007811, R01HG006283, DP2 HD088158, R35GM124704)
- Paul G. Allen Frontiers Foundation
- Howard Hughes Medical Institute
- W. M. Keck Foundation
- Dale. F. Frey Award for Breakthrough Scientists
- Alfred P. Sloan Foundation Research Fellowship
- National Heart, Lung, and Blood Institute (T32HL007828)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- chromatin accessibility, single-cell transcriptomics, combinatorial indexing, dexamethasone treatment, mouse kidney, cis-regulatory elements, gene expression regulation, single-cell multi-omics
- MeSH terms
- A549 Cells, Animals, Chromatin, Dexamethasone, Gene Expression Regulation, Humans, Kidney, Transcription, Genetic, Gene Expression Profiling, Genomics, NIH 3T3 Cells, Regulatory Elements, Transcriptional, Mice, HEK293 Cells, Single-Cell Analysis
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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.
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