Charles C. Query

4.9k total citations · 2 hit papers
38 papers, 4.0k citations indexed

About

Charles C. Query is a scholar working on Molecular Biology, Cardiology and Cardiovascular Medicine and Immunology. According to data from OpenAlex, Charles C. Query has authored 38 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Molecular Biology, 4 papers in Cardiology and Cardiovascular Medicine and 2 papers in Immunology. Recurrent topics in Charles C. Query's work include RNA Research and Splicing (35 papers), RNA and protein synthesis mechanisms (33 papers) and RNA modifications and cancer (24 papers). Charles C. Query is often cited by papers focused on RNA Research and Splicing (35 papers), RNA and protein synthesis mechanisms (33 papers) and RNA modifications and cancer (24 papers). Charles C. Query collaborates with scholars based in United States, China and Poland. Charles C. Query's co-authors include Jack D. Keene, Daniel J. Kenan, Maria M. Konarska, Melissa J. Moore, Rex C. Bentley, Phillip A. Sharp, Yong‐Zhen Xu, Duncan J. Smith, P A Sharp and Alberto Moldón and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Charles C. Query

37 papers receiving 4.0k citations

Hit Papers

RNA recognition: towards identifying determinants of spec... 1989 2026 2001 2013 1991 1989 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Charles C. Query United States 28 3.6k 322 275 217 208 38 4.0k
Reinhard Lührmann Germany 35 4.0k 1.1× 201 0.6× 220 0.8× 201 0.9× 180 0.9× 65 4.3k
Klaus Hartmuth Germany 32 3.3k 0.9× 160 0.5× 239 0.9× 132 0.6× 159 0.8× 38 3.7k
Berthold Kastner Germany 40 4.5k 1.2× 548 1.7× 181 0.7× 178 0.8× 193 0.9× 63 5.3k
Richard J Maraia United States 47 4.9k 1.3× 274 0.9× 328 1.2× 354 1.6× 582 2.8× 112 5.4k
Fabio Cobianchi Italy 25 2.1k 0.6× 97 0.3× 224 0.8× 189 0.9× 189 0.9× 42 2.3k
Stephen P. Goff United States 26 1.7k 0.5× 402 1.2× 63 0.2× 546 2.5× 154 0.7× 41 2.8k
Harold E Varmus United States 18 2.4k 0.7× 507 1.6× 147 0.5× 680 3.1× 279 1.3× 25 3.7k
T Nakagawa United States 15 1.4k 0.4× 917 2.8× 129 0.5× 201 0.9× 61 0.3× 18 2.6k
Violaine Moreau France 28 1.7k 0.5× 229 0.7× 151 0.5× 444 2.0× 137 0.7× 59 3.2k
Adrian R. Krainer United States 29 4.2k 1.1× 269 0.8× 266 1.0× 242 1.1× 140 0.7× 48 4.6k

Countries citing papers authored by Charles C. Query

Since Specialization
Citations

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

Fields of papers citing papers by Charles C. Query

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Charles C. Query. 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 Charles C. Query. The network helps show where Charles C. Query may publish in the future.

Co-authorship network of co-authors of Charles C. Query

This figure shows the co-authorship network connecting the top 25 collaborators of Charles C. Query. A scholar is included among the top collaborators of Charles C. Query 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 Charles C. Query. Charles C. Query 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.
DeAngelo, Joseph D., Maxim I. Maron, Jacob S. Roth, et al.. (2025). Productive mRNA chromatin escape is promoted by PRMT5 activity. Molecular Cell. 85(21). 4016–4031.e9. 1 indexed citations
2.
Query, Charles C., et al.. (2025). Finding and recycling stalled spliceosomes. Nature Structural & Molecular Biology. 32(5). 775–776.
3.
Tang, Qing, Jing Wang, Andrea Yuste, et al.. (2016). SF3B1/Hsh155 HEAT motif mutations affect interaction with the spliceosomal ATPase Prp5, resulting in altered branch site selectivity in pre-mRNA splicing. Genes & Development. 30(24). 2710–2723. 70 indexed citations
4.
Chen, Weijun, Hennady P. Shulha, Jing Yan, et al.. (2014). Endogenous U2·U5·U6 snRNA complexes in S. pombe are intron lariat spliceosomes. RNA. 20(3). 308–320. 35 indexed citations
5.
Yang, Fei, Xiuye Wang, Yujie Fan, et al.. (2013). Splicing proofreading at 5′ splice sites by ATPase Prp28p. Nucleic Acids Research. 41(8). 4660–4670. 47 indexed citations
6.
Smith, Duncan J., Maria M. Konarska, & Charles C. Query. (2009). Insights into Branch Nucleophile Positioning and Activation from an Orthogonal Pre-mRNA Splicing System in Yeast. Molecular Cell. 34(3). 333–343. 35 indexed citations
7.
Smith, Duncan J., Charles C. Query, & Maria M. Konarska. (2007). trans-Splicing to Spliceosomal U2 snRNA Suggests Disruption of Branch Site-U2 Pairing during Pre-mRNA Splicing. Molecular Cell. 26(6). 883–890. 17 indexed citations
8.
Liu, Li, Charles C. Query, & Maria M. Konarska. (2007). Opposing classes of prp8 alleles modulate the transition between the catalytic steps of pre-mRNA splicing. Nature Structural & Molecular Biology. 14(6). 519–526. 72 indexed citations
9.
Xu, Yong‐Zhen & Charles C. Query. (2007). Competition between the ATPase Prp5 and Branch Region-U2 snRNA Pairing Modulates the Fidelity of Spliceosome Assembly. Molecular Cell. 28(5). 838–849. 105 indexed citations
10.
Konarska, Maria M., Josep Vilardell, & Charles C. Query. (2006). Repositioning of the Reaction Intermediate within the Catalytic Center of the Spliceosome. Molecular Cell. 21(4). 543–553. 88 indexed citations
11.
Wang, Chen, Charles C. Query, & U. Thomas Meier. (2002). Immunopurified Small Nucleolar Ribonucleoprotein Particles Pseudouridylate rRNA Independently of Their Association with Phosphorylated Nopp140. Molecular and Cellular Biology. 22(24). 8457–8466. 59 indexed citations
12.
Query, Charles C.. (2002). A Glimpse of the Catalytic Core of a Group II Intron. Structure. 10(4). 444–446. 2 indexed citations
13.
Query, Charles C., et al.. (2001). The ATP requirement for U2 snRNP addition is linked to the pre-mRNA region 5′ to the branch site. RNA. 7(9). 1298–1309. 17 indexed citations
14.
Moore, Melissa J. & Charles C. Query. (2000). [7] Joining of RNAs by splinted ligation. Methods in enzymology on CD-ROM/Methods in enzymology. 317. 109–123. 138 indexed citations
15.
Moore, Melissa J., Charles C. Query, & Phillip A. Sharp. (1993). 13 Splicing of Precursors to mRNA by the Spliceosome. Cold Spring Harbor Monograph Archive. 24. 303–357. 264 indexed citations
16.
Keene, Jack D. & Charles C. Query. (1991). Nuclear RNA-binding Proteins. Progress in nucleic acid research and molecular biology. 41. 179–202. 62 indexed citations
17.
Kenan, Daniel J., Charles C. Query, & Jack D. Keene. (1991). RNA recognition: towards identifying determinants of specificity. Trends in Biochemical Sciences. 16(6). 214–220. 661 indexed citations breakdown →
18.
Query, Charles C., Rex C. Bentley, & Jack D. Keene. (1989). A common RNA recognition motif identified within a defined U1 RNA binding domain of the 70K U1 snRNP protein. Cell. 57(1). 89–101. 543 indexed citations breakdown →
19.
Query, Charles C., Rex C. Bentley, & Jack D. Keene. (1989). A specific 31-nucleotide domain of U1 RNA directly interacts with the 70K small nuclear ribonucleoprotein component.. Molecular and Cellular Biology. 9(11). 4872–4881. 65 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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