Rebecca M. Terns

11.7k total citations · 2 hit papers
64 papers, 7.2k citations indexed

About

Rebecca M. Terns is a scholar working on Molecular Biology, Physiology and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Rebecca M. Terns has authored 64 papers receiving a total of 7.2k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Molecular Biology, 14 papers in Physiology and 7 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Rebecca M. Terns's work include RNA and protein synthesis mechanisms (37 papers), CRISPR and Genetic Engineering (26 papers) and RNA modifications and cancer (24 papers). Rebecca M. Terns is often cited by papers focused on RNA and protein synthesis mechanisms (37 papers), CRISPR and Genetic Engineering (26 papers) and RNA modifications and cancer (24 papers). Rebecca M. Terns collaborates with scholars based in United States, France and Switzerland. Rebecca M. Terns's co-authors include Michael P. Terns, Hong Li, Caryn Hale, A. Gregory Matera, Brenton R. Graveley, Sara Olson, Jason Carte, Ruiying Wang, Peng Zhao and Lance Wells and has published in prestigious journals such as Science, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Rebecca M. Terns

64 papers receiving 7.1k citations

Hit Papers

RNA-Guided RNA Cleavage by a CRISPR RNA-Cas Protein Complex 2007 2026 2013 2019 2009 2007 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rebecca M. Terns United States 43 6.6k 1.2k 960 755 552 64 7.2k
Michael P. Terns United States 49 7.7k 1.2× 1.2k 1.0× 1.1k 1.1× 831 1.1× 645 1.2× 99 8.2k
Gavin J. Knott United States 21 3.5k 0.5× 56 0.0× 504 0.5× 206 0.3× 193 0.3× 36 3.9k
Michèle P. Calos United States 40 5.6k 0.8× 82 0.1× 3.0k 3.1× 791 1.0× 376 0.7× 94 6.9k
Jonathan H. LeBowitz United States 29 2.7k 0.4× 321 0.3× 1.1k 1.1× 235 0.3× 999 1.8× 50 4.2k
James K. Nuñez United States 12 3.3k 0.5× 34 0.0× 620 0.6× 175 0.2× 244 0.4× 16 3.6k
Elisabetta Ullu United States 34 3.3k 0.5× 219 0.2× 336 0.3× 136 0.2× 2.4k 4.3× 84 4.9k
Atze T. Das Netherlands 30 2.8k 0.4× 58 0.0× 627 0.7× 150 0.2× 405 0.7× 113 3.8k
Simon Lillico United Kingdom 28 2.4k 0.4× 83 0.1× 2.0k 2.1× 67 0.1× 215 0.4× 66 3.2k
Giedrius Gasiūnas Lithuania 22 4.3k 0.6× 22 0.0× 993 1.0× 381 0.5× 270 0.5× 25 4.5k
Nicholas R. Pannunzio United States 14 2.0k 0.3× 113 0.1× 350 0.4× 471 0.6× 187 0.3× 26 2.5k

Countries citing papers authored by Rebecca M. Terns

Since Specialization
Citations

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

Fields of papers citing papers by Rebecca M. Terns

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rebecca M. Terns

This figure shows the co-authorship network connecting the top 25 collaborators of Rebecca M. Terns. A scholar is included among the top collaborators of Rebecca M. Terns 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 Rebecca M. Terns. Rebecca M. Terns 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.
Ichikawa, H. Travis, et al.. (2017). Programmable type III-A CRISPR-Cas DNA targeting modules. PLoS ONE. 12(4). e0176221–e0176221. 28 indexed citations
2.
Elmore, Joshua R., et al.. (2016). Bipartite recognition of target RNAs activates DNA cleavage by the Type III-B CRISPR–Cas system. Genes & Development. 30(4). 447–459. 177 indexed citations
3.
Terns, Rebecca M., et al.. (2015). Sequences spanning the leader-repeat junction mediate CRISPR adaptation to phage in Streptococcus thermophilus. Nucleic Acids Research. 43(3). 1749–1758. 87 indexed citations
4.
Elmore, Joshua R., Yuusuke Yokooji, Takaaki Sato, et al.. (2013). Programmable plasmid interference by the CRISPR-Cas system in Thermococcus kodakarensis . RNA Biology. 10(5). 828–840. 31 indexed citations
5.
Hale, Caryn, Sonali Majumdar, Joshua R. Elmore, et al.. (2012). Essential Features and Rational Design of CRISPR RNAs that Function with the Cas RAMP Module Complex to Cleave RNAs. Molecular Cell. 45(3). 292–302. 230 indexed citations
6.
Wang, Ruiying, et al.. (2011). Interaction of the Cas6 Riboendonuclease with CRISPR RNAs: Recognition and Cleavage. Structure. 19(2). 257–264. 130 indexed citations
7.
Terns, Michael P. & Rebecca M. Terns. (2011). CRISPR-based adaptive immune systems. Current Opinion in Microbiology. 14(3). 321–327. 332 indexed citations
8.
Xue, Song, Ruiying Wang, Fangping Yang, et al.. (2010). Structural Basis for Substrate Placement by an Archaeal Box C/D Ribonucleoprotein Particle. Molecular Cell. 39(6). 939–949. 48 indexed citations
9.
Wacheul, Ludivine, Marc Thiry, Adam C. Berger, et al.. (2008). Identification of Genes That Function in the Biogenesis and Localization of Small Nucleolar RNAs in Saccharomyces cerevisiae. Molecular and Cellular Biology. 28(11). 3686–3699. 17 indexed citations
10.
Hale, Caryn, et al.. (2008). Prokaryotic silencing (psi)RNAs inPyrococcus furiosus. RNA. 14(12). 2572–2579. 185 indexed citations
11.
Zhang, Yanming, Hongzhi Li, Howard Robinson, et al.. (2007). Alternative Conformations of the Archaeal Nop56/58-Fibrillarin Complex Imply Flexibility in Box C/D RNPs. Journal of Molecular Biology. 371(5). 1141–1150. 32 indexed citations
12.
Liang, Bo, Song Xue, Rebecca M. Terns, Michael P. Terns, & Hong Li. (2007). Substrate RNA positioning in the archaeal H/ACA ribonucleoprotein complex. Nature Structural & Molecular Biology. 14(12). 1189–1195. 51 indexed citations
13.
Cristofari, Gaël, Patrick Reichenbach, Katarzyna Sikora, et al.. (2007). Human Telomerase RNA Accumulation in Cajal Bodies Facilitates Telomerase Recruitment to Telomeres and Telomere Elongation. Molecular Cell. 27(6). 882–889. 142 indexed citations
14.
Supakorndej, Teerawit, et al.. (2005). Cell Cycle-regulated Trafficking of Human Telomerase to Telomeres. Molecular Biology of the Cell. 17(2). 955–965. 234 indexed citations
15.
Zhu, Yusheng, et al.. (2003). Telomerase RNA Accumulates in Cajal Bodies in Human Cancer Cells. Molecular Biology of the Cell. 15(1). 81–90. 158 indexed citations
16.
Li, Zhu‐Hong, et al.. (2002). Archaeal Guide RNAs Function in rRNA Modification in the Eukaryotic Nucleus. Current Biology. 12(3). 199–203. 19 indexed citations
17.
18.
Narayanan, Aarthi, et al.. (1999). Nuclear Retention Elements of U3 Small Nucleolar RNA. Molecular and Cellular Biology. 19(12). 8412–8421. 35 indexed citations
19.
Narayanan, Aarthi, et al.. (1999). Role of the Box C/D Motif in Localization of Small Nucleolar RNAs to Coiled Bodies and Nucleoli. Molecular Biology of the Cell. 10(7). 2131–2147. 122 indexed citations
20.
Gendreau, Steven, Ivan P. Moskowitz, Rebecca M. Terns, & Joel H. Rothman. (1994). The Potential to Differentiate Epidermis Is Unequally Distributed in the AB Lineage during Early Embryonic Development in C. elegans. Developmental Biology. 166(2). 770–781. 30 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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