Jonathan M. Geisinger

566 total citations
11 papers, 354 citations indexed

About

Jonathan M. Geisinger is a scholar working on Molecular Biology, Genetics and Infectious Diseases. According to data from OpenAlex, Jonathan M. Geisinger has authored 11 papers receiving a total of 354 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 3 papers in Genetics and 0 papers in Infectious Diseases. Recurrent topics in Jonathan M. Geisinger's work include CRISPR and Genetic Engineering (6 papers), Pluripotent Stem Cells Research (4 papers) and RNA regulation and disease (3 papers). Jonathan M. Geisinger is often cited by papers focused on CRISPR and Genetic Engineering (6 papers), Pluripotent Stem Cells Research (4 papers) and RNA regulation and disease (3 papers). Jonathan M. Geisinger collaborates with scholars based in United States, China and Russia. Jonathan M. Geisinger's co-authors include Michèle P. Calos, Tim Stearns, Laura Spector, Fuqiang Geng, William P. Tansey, Masafumi Muratani, Simone E Salghetti, Jonathan K. Pritchard, Gokul Ramaswami and Jin Billy Li and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Jonathan M. Geisinger

10 papers receiving 350 citations

Peers

Jonathan M. Geisinger

GENET

IMMUN

EPIDE

Tetsuji Moriyama Japan

David Cano-Rodríguez Netherlands

Angus Yiu-Fai Lee United States

Siyou Tan United States

Rakesh S. Laishram India

Suneetha Nunna Germany

Madhurima Singh United States

M. Felicia Basilicata Germany

Hiroyuki Hozumi Japan

Kameron Kooshesh United States

Tetsuji Moriyama Japan

Jonathan M. Geisinger

236 ×0.8

53 ×0.7

GENET

23 ×0.6

IMMUN

18 ×0.8

15 ×0.8

EPIDE

Citations per year, relative to Jonathan M. Geisinger Jonathan M. Geisinger (= 1×) peers Tetsuji Moriyama

Countries citing papers authored by Jonathan M. Geisinger

Since Specialization

Citations

This map shows the geographic impact of Jonathan M. Geisinger'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 M. Geisinger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan M. Geisinger more than expected).

Fields of papers citing papers by Jonathan M. Geisinger

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan M. Geisinger. 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 M. Geisinger. The network helps show where Jonathan M. Geisinger may publish in the future.

Co-authorship network of co-authors of Jonathan M. Geisinger

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan M. Geisinger. A scholar is included among the top collaborators of Jonathan M. Geisinger 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 M. Geisinger. Jonathan M. Geisinger is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

Vogel, Paul, Inga Jarmoskaite, Jonathan M. Geisinger, et al.. (2025). Stereo-random oligonucleotides enable efficient recruitment of ADAR in vitro and in vivo. Nature Communications. 16(1). 8849–8849.

Sun, Tao, Qin Li, Jonathan M. Geisinger, et al.. (2025). ADAR1 editing is necessary for only a small subset of cytosolic dsRNAs to evade MDA5-mediated autoimmunity. Nature Genetics. 57(12). 3101–3111. 2 indexed citations

Li, Qin, Michael J. Gloudemans, Jonathan M. Geisinger, et al.. (2022). RNA editing underlies genetic risk of common inflammatory diseases. Nature. 608(7923). 569–577. 95 indexed citations

Geisinger, Jonathan M. & Tim Stearns. (2021). Assaying Cell Cycle Progression via Flow Cytometry in CRISPR/Cas9-Treated Cells. Methods in molecular biology. 2329. 195–204. 1 indexed citations

Geisinger, Jonathan M. & Tim Stearns. (2020). CRISPR/Cas9 treatment causes extended TP53-dependent cell cycle arrest in human cells. Nucleic Acids Research. 48(16). 9067–9081. 29 indexed citations

Geisinger, Jonathan M., et al.. (2016). In vivo blunt-end cloning through CRISPR/Cas9-facilitated non-homologous end-joining. Nucleic Acids Research. 44(8). e76–e76. 69 indexed citations

Geisinger, Jonathan M. & Michèle P. Calos. (2014). Using Phage Integrases in a Site-Specific Dual Integrase Cassette Exchange Strategy. Methods in molecular biology. 1239. 29–38. 3 indexed citations

Zhao, Chunli, Christopher R.R. Bjornson, Christopher L. Chavez, et al.. (2014). Recombinase-Mediated Reprogramming and Dystrophin Gene Addition in mdx Mouse Induced Pluripotent Stem Cells. PLoS ONE. 9(4). e96279–e96279. 27 indexed citations

Buchner, David A., Jonathan M. Geisinger, Lindsay C. Burrage, et al.. (2011). Deep congenic analysis identifies many strong, context-dependent QTLs, one of which, Slc35b4 , regulates obesity and glucose homeostasis. Genome Research. 21(7). 1065–1073. 44 indexed citations

10.

Karow, Marisa, Christopher L. Chavez, Jonathan M. Geisinger, et al.. (2011). Site-Specific Recombinase Strategy to Create Induced Pluripotent Stem Cells Efficiently with Plasmid DNA. Stem Cells. 29(11). 1696–1704. 31 indexed citations

11.

Geng, Fuqiang, et al.. (2008). Modulation of RNA polymerase II subunit composition by ubiquitylation. Proceedings of the National Academy of Sciences. 105(50). 19649–19654. 53 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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