Jonathan R. Chubb

4.6k total citations · 1 hit paper
47 papers, 3.1k citations indexed

About

Jonathan R. Chubb is a scholar working on Molecular Biology, Cell Biology and Biophysics. According to data from OpenAlex, Jonathan R. Chubb has authored 47 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Molecular Biology, 15 papers in Cell Biology and 10 papers in Biophysics. Recurrent topics in Jonathan R. Chubb's work include Gene Regulatory Network Analysis (14 papers), Cellular Mechanics and Interactions (13 papers) and Genomics and Chromatin Dynamics (12 papers). Jonathan R. Chubb is often cited by papers focused on Gene Regulatory Network Analysis (14 papers), Cellular Mechanics and Interactions (13 papers) and Genomics and Chromatin Dynamics (12 papers). Jonathan R. Chubb collaborates with scholars based in United Kingdom, United States and Germany. Jonathan R. Chubb's co-authors include Wendy A. Bickmore, Shelagh Boyle, Paul Perry, Robert H. Singer, Shailesh M. Shenoy, Tatjana Trcek, Edward Tunnacliffe, Adam Corrigan, Tetsuya Muramoto and Robert H. Insall and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Jonathan R. Chubb

44 papers receiving 3.1k citations

Hit Papers

Transcriptional Pulsing of a Developmental Gene 2006 2026 2012 2019 2006 100 200 300 400 500

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jonathan R. Chubb United Kingdom 25 2.8k 439 426 385 261 47 3.1k
Tobias Knoch Germany 21 1.8k 0.7× 322 0.7× 350 0.8× 376 1.0× 162 0.6× 50 2.4k
Janet Iwasa United States 18 1.8k 0.6× 550 1.3× 275 0.6× 217 0.6× 114 0.4× 47 2.4k
Daniel Zenklusen United States 31 4.1k 1.5× 238 0.5× 334 0.8× 183 0.5× 282 1.1× 50 4.4k
Anders S. Hansen United States 28 3.9k 1.4× 162 0.4× 327 0.8× 672 1.7× 329 1.3× 49 4.4k
Stefano Di Talia United States 24 1.9k 0.7× 703 1.6× 204 0.5× 221 0.6× 128 0.5× 51 2.4k
Gaku Mizuguchi United States 21 3.4k 1.2× 289 0.7× 194 0.5× 831 2.2× 273 1.0× 27 3.9k
Jean-Karim Hèriché Germany 26 1.8k 0.6× 411 0.9× 187 0.4× 173 0.4× 278 1.1× 42 2.2k
Satoru Uzawa United States 18 2.2k 0.8× 860 2.0× 170 0.4× 671 1.7× 211 0.8× 22 2.5k
Jonathan W. Jarvik United States 24 1.5k 0.6× 698 1.6× 328 0.8× 107 0.3× 373 1.4× 52 2.1k
Sébastien Huet France 28 2.1k 0.8× 536 1.2× 159 0.4× 320 0.8× 215 0.8× 74 2.8k

Countries citing papers authored by Jonathan R. Chubb

Since Specialization
Citations

This map shows the geographic impact of Jonathan R. Chubb's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jonathan R. Chubb with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan R. Chubb more than expected).

Fields of papers citing papers by Jonathan R. Chubb

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan R. Chubb. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jonathan R. Chubb. The network helps show where Jonathan R. Chubb may publish in the future.

Co-authorship network of co-authors of Jonathan R. Chubb

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan R. Chubb. A scholar is included among the top collaborators of Jonathan R. Chubb based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jonathan R. Chubb. Jonathan R. Chubb is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Walker, Benjamin J., et al.. (2025). Pattern formation along signaling gradients driven by active droplet behavior of cell swarms. Proceedings of the National Academy of Sciences. 122(21). e2419152122–e2419152122.
2.
Gaboriau, David C. A., Arlo Sheridan, Tchern Lenn, et al.. (2024). Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimaging. Nature Methods. 21(2). 322–330. 16 indexed citations
3.
Chubb, Jonathan R., et al.. (2024). Single Cell Transcriptome Analysis During Development in Dictyostelium. Methods in molecular biology. 2814. 223–245.
4.
Manhart, Angelika, et al.. (2023). Controlling periodic long-range signalling to drive a morphogenetic transition. eLife. 12. 7 indexed citations
5.
Lenn, Tchern, et al.. (2023). Collective signalling drives rapid jumping between cell states. Development. 150(23). 6 indexed citations
6.
Williams, Robin S. B., Jonathan R. Chubb, Robert H. Insall, et al.. (2021). Moving the Research Forward: The Best of British Biology Using the Tractable Model System Dictyostelium discoideum. Cells. 10(11). 3036–3036. 7 indexed citations
7.
Paschke, Peggy, David A. Knecht, Thomas D. Williams, et al.. (2019). Genetic Engineering of <em>Dictyostelium discoideum</em> Cells Based on Selection and Growth on Bacteria. Journal of Visualized Experiments. 2 indexed citations
8.
Lenn, Tchern, et al.. (2019). Transition state dynamics during a stochastic fate choice. Development. 146(12). 19 indexed citations
9.
Paschke, Peggy, David A. Knecht, Thomas D. Williams, et al.. (2019). Genetic Engineering of <em>Dictyostelium discoideum</em> Cells Based on Selection and Growth on Bacteria. Journal of Visualized Experiments. 6 indexed citations
10.
Paschke, Peggy, David A. Knecht, Augustinas Silale, et al.. (2018). Rapid and efficient genetic engineering of both wild type and axenic strains of Dictyostelium discoideum. PLoS ONE. 13(5). e0196809–e0196809. 57 indexed citations
11.
Miermont, Agnès, et al.. (2017). Generation of Single-Cell Transcript Variability by Repression. Current Biology. 27(12). 1811–1817.e3. 34 indexed citations
12.
Chubb, Jonathan R., et al.. (2016). Developmental accumulation of inorganic polyphosphate affects germination and energetic metabolism in Dictyostelium discoideum. Proceedings of the National Academy of Sciences. 113(4). 996–1001. 43 indexed citations
13.
Corrigan, Adam & Jonathan R. Chubb. (2014). Regulation of Transcriptional Bursting by a Naturally Oscillating Signal. Current Biology. 24(2). 205–211. 48 indexed citations
14.
Stevense, Michelle, Jonathan R. Chubb, & Tetsuya Muramoto. (2011). Nuclear organization and transcriptional dynamics in Dictyostelium. Development Growth & Differentiation. 53(4). 576–586. 22 indexed citations
15.
Muramoto, Tetsuya, et al.. (2010). Methylation of H3K4 Is Required for Inheritance of Active Transcriptional States. Current Biology. 20(5). 397–406. 144 indexed citations
16.
Finlan, Lee, Duncan Sproul, Shelagh Boyle, et al.. (2008). Recruitment to the Nuclear Periphery Can Alter Expression of Genes in Human Cells. PLoS Genetics. 4(3). e1000039–e1000039. 448 indexed citations
17.
Chubb, Jonathan R., Gareth Bloomfield, Qikai Xu, et al.. (2006). Developmental timing in Dictyostelium is regulated by the Set1 histone methyltransferase. Developmental Biology. 292(2). 519–532. 33 indexed citations
18.
Chubb, Jonathan R., Tatjana Trcek, Shailesh M. Shenoy, & Robert H. Singer. (2006). Transcriptional Pulsing of a Developmental Gene. Current Biology. 16(10). 1018–1025. 542 indexed citations breakdown →
19.
Gilchrist, Susan, et al.. (2004). The Radial Positioning of Chromatin Is Not Inherited through Mitosis but Is Established De Novo in Early G1. Current Biology. 14(2). 166–172. 142 indexed citations
20.
Chubb, Jonathan R. & Robert H. Insall. (2001). Dictyostelium: an ideal organism for genetic dissection of Ras signalling networks. Biochimica et Biophysica Acta (BBA) - General Subjects. 1525(3). 262–271. 25 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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