David T. Long

2.0k total citations
27 papers, 1.5k citations indexed

About

David T. Long is a scholar working on Molecular Biology, Oncology and Genetics. According to data from OpenAlex, David T. Long has authored 27 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 5 papers in Oncology and 5 papers in Genetics. Recurrent topics in David T. Long's work include DNA Repair Mechanisms (15 papers), DNA and Nucleic Acid Chemistry (6 papers) and Protein Degradation and Inhibitors (5 papers). David T. Long is often cited by papers focused on DNA Repair Mechanisms (15 papers), DNA and Nucleic Acid Chemistry (6 papers) and Protein Degradation and Inhibitors (5 papers). David T. Long collaborates with scholars based in United States, Germany and Netherlands. David T. Long's co-authors include Johannes C. Walter, Markus Räschle, Vladimir Joukov, Orlando D. Schärer, Puck Knipscheer, Kenneth N. Kreuzer, Rick A.C.M. Boonen, Angelo Guainazzi, Vladimir P. Bermudez and Jerard Hurwitz and has published in prestigious journals such as Science, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

David T. Long

26 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David T. Long United States 16 1.3k 244 234 211 191 27 1.5k
Oliver Limbo United States 18 1.6k 1.2× 437 1.8× 170 0.7× 184 0.9× 309 1.6× 24 1.8k
Bernard P. Duncker Canada 20 798 0.6× 130 0.5× 137 0.6× 206 1.0× 75 0.4× 42 1.1k
Molly C. Kottemann United States 9 762 0.6× 159 0.7× 158 0.7× 99 0.5× 129 0.7× 13 944
Ryan L. Ragland United States 13 1.0k 0.8× 524 2.1× 293 1.3× 150 0.7× 154 0.8× 21 1.3k
Cristina M. Montagna Argentina 12 483 0.4× 169 0.7× 103 0.4× 361 1.7× 141 0.7× 20 811
Rekha Rai United States 14 1.1k 0.8× 179 0.7× 106 0.5× 145 0.7× 125 0.7× 24 1.3k
Jia Zhou United States 13 829 0.6× 226 0.9× 83 0.4× 80 0.4× 99 0.5× 22 1.1k
Miguel G. Blanco Spain 13 1.3k 1.0× 163 0.7× 164 0.7× 333 1.6× 156 0.8× 28 1.4k
András Blastyák Hungary 10 914 0.7× 219 0.9× 77 0.3× 162 0.8× 207 1.1× 15 996
Françoise Lacroix France 14 1.4k 1.0× 562 2.3× 126 0.5× 978 4.6× 153 0.8× 20 1.7k

Countries citing papers authored by David T. Long

Since Specialization
Citations

This map shows the geographic impact of David T. Long'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 David T. Long with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David T. Long more than expected).

Fields of papers citing papers by David T. Long

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David T. Long. 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 David T. Long. The network helps show where David T. Long may publish in the future.

Co-authorship network of co-authors of David T. Long

This figure shows the co-authorship network connecting the top 25 collaborators of David T. Long. A scholar is included among the top collaborators of David T. Long 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 David T. Long. David T. Long 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.
Jiménez-Sáinz, Judit, et al.. (2025). The Development of ATM Inhibitors in Cancer Therapy. Targeted Oncology. 20(2). 281–297. 4 indexed citations
2.
Bowers, Robert R., Silvia G. Vaena, George Fullbright, et al.. (2024). MYC is Sufficient to Generate Mid-Life High-Grade Serous Ovarian and Uterine Serous Carcinomas in a p53-R270H Mouse Model. Cancer Research Communications. 4(9). 2525–2538. 4 indexed citations
3.
Bowers, Robert R., et al.. (2022). SWAN pathway-network identification of common aneuploidy-based oncogenic drivers. Nucleic Acids Research. 50(7). 3673–3692. 13 indexed citations
4.
Long, David T., et al.. (2022). Transcription suppression is mediated by the HDAC1–Sin3 complex in Xenopus nucleoplasmic extract. Journal of Biological Chemistry. 298(11). 102578–102578. 4 indexed citations
5.
Fullbright, George, et al.. (2022). BRD4 promotes resection and homology-directed repair of DNA double-strand breaks. Nature Communications. 13(1). 3016–3016. 28 indexed citations
6.
Fullbright, George, et al.. (2021). BRCA1-BARD1 regulates transcription through BRD4 in Xenopus nucleoplasmic extract. Nucleic Acids Research. 49(6). 3263–3273. 8 indexed citations
7.
Mohanty, Bidyut K., Breege V. Howley, Annamarie C. Dalton, et al.. (2021). Heterogeneous nuclear ribonucleoprotein E1 binds polycytosine DNA and monitors genome integrity. Life Science Alliance. 4(9). e202000995–e202000995. 8 indexed citations
8.
Long, David T., et al.. (2019). Chromatin Immunoprecipitation (ChIP) of Plasmid-Bound Proteins in Xenopus Egg Extracts. Methods in molecular biology. 1999. 173–184. 4 indexed citations
9.
Yeh, Elizabeth S., et al.. (2019). Uncoupling of p97 ATPase activity has a dominant negative effect on protein extraction. Scientific Reports. 9(1). 10329–10329. 5 indexed citations
10.
Long, David T., et al.. (2019). Cell-free transcription in Xenopus egg extract. Journal of Biological Chemistry. 294(51). 19645–19654. 7 indexed citations
11.
Long, David T., et al.. (2018). The evolving role of DNA inter-strand crosslinks in chemotherapy. Current Opinion in Pharmacology. 41. 20–26. 48 indexed citations
12.
Räschle, Markus, Godelieve Smeenk, Yasuyoshi Oka, et al.. (2015). Proteomics reveals dynamic assembly of repair complexes during bypass of DNA cross-links. Science. 348(6234). 1253671–1253671. 170 indexed citations
13.
Boonen, Rick A.C.M., et al.. (2014). XPF-ERCC1 Acts in Unhooking DNA Interstrand Crosslinks in Cooperation with FANCD2 and FANCP/SLX4. Molecular Cell. 54(3). 460–471. 239 indexed citations
14.
Long, David T., Vladimir Joukov, Magda Budzowska, & Johannes C. Walter. (2014). BRCA1 Promotes Unloading of the CMG Helicase from a Stalled DNA Replication Fork. Molecular Cell. 56(1). 174–185. 93 indexed citations
15.
Enoiu, Milica, et al.. (2012). Construction of Plasmids Containing Site-Specific DNA Interstrand Cross-Links for Biochemical and Cell Biological Studies. Methods in molecular biology. 920. 203–219. 25 indexed citations
16.
Fu, Yu, Hasan Yardimci, David T. Long, et al.. (2011). Selective Bypass of a Lagging Strand Roadblock by the Eukaryotic Replicative DNA Helicase. Cell. 146(6). 931–941. 289 indexed citations
17.
Long, David T. & Kenneth N. Kreuzer. (2009). Fork regression is an active helicase‐driven pathway in bacteriophage T4. EMBO Reports. 10(4). 394–399. 25 indexed citations
18.
Long, David T. & Kenneth N. Kreuzer. (2008). Regression supports two mechanisms of fork processing in phage T4. Proceedings of the National Academy of Sciences. 105(19). 6852–6857. 26 indexed citations
19.
Webb, Michael R., Jody L. Plank, David T. Long, Tao‐shih Hsieh, & Kenneth N. Kreuzer. (2007). The Phage T4 Protein UvsW Drives Holliday Junction Branch Migration. Journal of Biological Chemistry. 282(47). 34401–34411. 24 indexed citations
20.
Haack, Sheridan K., Lisa R. Fogarty, Elizabeth Wheeler Alm, et al.. (2004). Spatial and temporal changes in microbial community structure associated with recharge‐influenced chemical gradients in a contaminated aquifer. Environmental Microbiology. 6(5). 438–448. 64 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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