Thomas Gaj

8.8k total citations · 2 hit papers
48 papers, 6.3k citations indexed

About

Thomas Gaj is a scholar working on Molecular Biology, Genetics and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Thomas Gaj has authored 48 papers receiving a total of 6.3k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Molecular Biology, 18 papers in Genetics and 5 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Thomas Gaj's work include CRISPR and Genetic Engineering (40 papers), RNA Interference and Gene Delivery (11 papers) and Virus-based gene therapy research (10 papers). Thomas Gaj is often cited by papers focused on CRISPR and Genetic Engineering (40 papers), RNA Interference and Gene Delivery (11 papers) and Virus-based gene therapy research (10 papers). Thomas Gaj collaborates with scholars based in United States, China and Portugal. Thomas Gaj's co-authors include Carlos F. Barbas, Charles A. Gersbach, David V. Schaffer, Shannon J. Sirk, Jia Liu, Jing Guo, David S. Ojala, Andrew C. Mercer, M. Alejandra Zeballos C. and Prajit Limsirichai and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Nucleic Acids Research.

In The Last Decade

Thomas Gaj

48 papers receiving 6.2k citations

Hit Papers

ZFN, TALEN, and CRISPR/Ca... 2013 2026 2017 2021 2013 2016 500 1000 1.5k 2.0k 2.5k

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Thomas Gaj 5.1k 1.6k 756 708 471 48 6.3k
Albert W. Cheng 11.4k 2.2× 3.0k 1.8× 489 0.6× 684 1.0× 261 0.6× 51 13.0k
Maximilian Haeussler 5.1k 1.0× 1.2k 0.7× 321 0.4× 736 1.0× 281 0.6× 45 6.3k
Alexandro E. Trevino 5.4k 1.1× 1.1k 0.7× 327 0.4× 609 0.9× 86 0.2× 30 6.0k
Yinqing Li 6.1k 1.2× 1.2k 0.7× 325 0.4× 629 0.9× 91 0.2× 53 7.0k
Meelad M. Dawlaty 5.3k 1.0× 1.4k 0.9× 307 0.4× 272 0.4× 112 0.2× 39 5.9k
Randall J. Platt 3.5k 0.7× 798 0.5× 430 0.6× 258 0.4× 137 0.3× 40 4.5k
Jing-Ruey Joanna Yeh 4.8k 0.9× 1.1k 0.7× 351 0.5× 457 0.6× 89 0.2× 48 5.9k
Morgan L. Maeder 10.3k 2.0× 2.5k 1.6× 387 0.5× 1.4k 1.9× 122 0.3× 41 11.4k
Chikdu Shivalila 4.5k 0.9× 1.5k 0.9× 245 0.3× 352 0.5× 92 0.2× 14 5.0k
Deepak Reyon 10.3k 2.0× 2.4k 1.5× 466 0.6× 1.6k 2.2× 104 0.2× 40 11.8k

Countries citing papers authored by Thomas Gaj

Since Specialization
Citations

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

Fields of papers citing papers by Thomas Gaj

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas Gaj. 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 Thomas Gaj. The network helps show where Thomas Gaj may publish in the future.

Co-authorship network of co-authors of Thomas Gaj

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Gaj. A scholar is included among the top collaborators of Thomas Gaj 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 Thomas Gaj. Thomas Gaj 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.
Winter, Jackson, Wendy S. Woods, Michael Gapinske, et al.. (2024). SPLICER: a highly efficient base editing toolbox that enables in vivo therapeutic exon skipping. Nature Communications. 15(1). 10354–10354. 3 indexed citations
2.
Gapinske, Michael, Alexandra K. Brooks, Wendy S. Woods, et al.. (2020). Treatment of a Mouse Model of ALS by In Vivo Base Editing. Molecular Therapy. 28(4). 1177–1189. 162 indexed citations
3.
Ekman, Freja K., David S. Ojala, Maroof M. Adil, et al.. (2019). CRISPR-Cas9-Mediated Genome Editing Increases Lifespan and Improves Motor Deficits in a Huntington’s Disease Mouse Model. Molecular Therapy — Nucleic Acids. 17. 829–839. 106 indexed citations
4.
Xu, Min, et al.. (2018). A Hypothalamic Switch for REM and Non-REM Sleep. Neuron. 97(5). 1168–1176.e4. 103 indexed citations
5.
Adil, Maroof M., Thomas Gaj, Antara Rao, et al.. (2018). hPSC-Derived Striatal Cells Generated Using a Scalable 3D Hydrogel Promote Recovery in a Huntington Disease Mouse Model. Stem Cell Reports. 10(5). 1481–1491. 39 indexed citations
6.
Gaj, Thomas, et al.. (2016). Reactivation of Latent HIV-1 Expression by Engineered TALE Transcription Factors. PLoS ONE. 11(3). e0150037–e0150037. 11 indexed citations
7.
Tervo, D. Gowanlock R., Sarada Viswanathan, Thomas Gaj, et al.. (2016). A Designer AAV Variant Permits Efficient Retrograde Access to Projection Neurons. Neuron. 92(2). 372–382. 830 indexed citations breakdown →
8.
Gaj, Thomas, et al.. (2016). Genome-Editing Technologies: Principles and Applications. Cold Spring Harbor Perspectives in Biology. 8(12). a023754–a023754. 241 indexed citations
9.
Gaj, Thomas, et al.. (2015). Redesigning Recombinase Specificity for Safe Harbor Sites in the Human Genome. PLoS ONE. 10(9). e0139123–e0139123. 9 indexed citations
10.
Gaj, Thomas, et al.. (2014). Protein Delivery Using Cys 2 –His 2 Zinc-Finger Domains. ACS Chemical Biology. 9(8). 1662–1667. 48 indexed citations
11.
Gersbach, Charles A., Thomas Gaj, & Carlos F. Barbas. (2014). Comparing Genome Editing Technologies. Genetic Engineering & Biotechnology News. 34(5). 1, 32–34. 1 indexed citations
12.
Liu, Jia, Thomas Gaj, James T Patterson, Shannon J. Sirk, & Carlos F. Barbas. (2014). Cell-Penetrating Peptide-Mediated Delivery of TALEN Proteins via Bioconjugation for Genome Engineering. PLoS ONE. 9(1). e85755–e85755. 119 indexed citations
13.
Gaj, Thomas & Carlos F. Barbas. (2014). Genome Engineering with Custom Recombinases. Methods in enzymology on CD-ROM/Methods in enzymology. 546. 79–91. 4 indexed citations
14.
Gaj, Thomas, Charles A. Gersbach, & Carlos F. Barbas. (2013). ZFN, TALEN, and CRISPR/Cas-based methods for genome engineering. Trends in biotechnology. 31(7). 397–405. 2659 indexed citations breakdown →
15.
Gaj, Thomas, Jing Guo, Yoshio Kato, Shannon J. Sirk, & Carlos F. Barbas. (2012). Targeted gene knockout by direct delivery of zinc-finger nuclease proteins. Nature Methods. 9(8). 805–807. 232 indexed citations
16.
Mercer, Andrew C., Thomas Gaj, Roberta Fuller, & Carlos F. Barbas. (2012). Chimeric TALE recombinases with programmable DNA sequence specificity. Nucleic Acids Research. 40(21). 11163–11172. 99 indexed citations
17.
Gersbach, Charles A., Thomas Gaj, Russell M. Gordley, & Carlos F. Barbas. (2010). Directed evolution of recombinase specificity by split gene reassembly. Nucleic Acids Research. 38(12). 4198–4206. 39 indexed citations
18.
Guo, Jing, Thomas Gaj, & Carlos F. Barbas. (2010). Directed Evolution of an Enhanced and Highly Efficient FokI Cleavage Domain for Zinc Finger Nucleases. Journal of Molecular Biology. 400(1). 96–107. 158 indexed citations
19.
Gaj, Thomas, et al.. (2007). The AviD-tag, a NeutrAvidin/avidin specific peptide affinity tag for the immobilization and purification of recombinant proteins. Protein Expression and Purification. 56(1). 54–61. 17 indexed citations
20.
Gaj, Thomas, et al.. (2006). Highly Selective Cyclic Peptide Ligands for NeutrAvidin and Avidin Identified by Phage Display. Chemical Biology & Drug Design. 68(1). 3–10. 42 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026