Paul A. DaRosa

905 total citations
10 papers, 621 citations indexed

About

Paul A. DaRosa is a scholar working on Molecular Biology, Oncology and Cell Biology. According to data from OpenAlex, Paul A. DaRosa has authored 10 papers receiving a total of 621 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 4 papers in Oncology and 2 papers in Cell Biology. Recurrent topics in Paul A. DaRosa's work include Ubiquitin and proteasome pathways (5 papers), Nuclear Structure and Function (2 papers) and RNA modifications and cancer (2 papers). Paul A. DaRosa is often cited by papers focused on Ubiquitin and proteasome pathways (5 papers), Nuclear Structure and Function (2 papers) and RNA modifications and cancer (2 papers). Paul A. DaRosa collaborates with scholars based in United States, United Kingdom and France. Paul A. DaRosa's co-authors include Rachel E. Klevit, Wenqing Xu, Jonathan N. Pruneda, Xiaomo Jiang, Zhizhi Wang, Feng Cong, Ron R. Kopito, Nicholas T. Ingolia, Ryan Muller and Christopher P. Walczak and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and The EMBO Journal.

In The Last Decade

Paul A. DaRosa

10 papers receiving 617 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Paul A. DaRosa United States	9	519	202	89	77	54	10	621
Christopher Bruhn Germany	10	411 0.8×	190 0.9×	106 1.2×	62 0.8×	35 0.6×	19	508
Michael Boettcher Germany	14	502 1.0×	147 0.7×	34 0.4×	79 1.0×	100 1.9×	27	673
Roberta Cariati Italy	13	209 0.4×	108 0.5×	76 0.9×	28 0.4×	49 0.9×	21	405
Karthikeyan Ponnienselvan United States	7	570 1.1×	142 0.7×	33 0.4×	137 1.8×	33 0.6×	7	600
Houqing Yu United States	9	468 0.9×	117 0.6×	170 1.9×	45 0.6×	154 2.9×	14	575
Daniel Stauffer United States	10	370 0.7×	89 0.4×	149 1.7×	133 1.7×	43 0.8×	10	539
Sneha Saxena India	11	532 1.0×	223 1.1×	46 0.5×	42 0.5×	34 0.6×	13	617
Leena Kuruvilla United States	10	248 0.5×	46 0.2×	76 0.9×	58 0.8×	82 1.5×	11	439
Bonnie L. Bertolaet United States	8	718 1.4×	133 0.7×	221 2.5×	89 1.2×	96 1.8×	9	765
Delphine Quénet United States	11	535 1.0×	225 1.1×	72 0.8×	56 0.7×	11 0.2×	13	631

Countries citing papers authored by Paul A. DaRosa

Since Specialization

Citations

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

Fields of papers citing papers by Paul A. DaRosa

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Paul A. DaRosa. 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 Paul A. DaRosa. The network helps show where Paul A. DaRosa may publish in the future.

Co-authorship network of co-authors of Paul A. DaRosa

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

All Works

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10 of 10 papers shown

Stepanyuk, Galina A., David F. Thieker, Kui K. Chan, et al.. (2024). Modulation of the pharmacokinetics of soluble ACE2 decoy receptors through glycosylation. Molecular Therapy — Methods & Clinical Development. 32(3). 101301–101301. 1 indexed citations

DaRosa, Paul A., João A. Paulo, Alban Ordureau, et al.. (2024). UFM1 E3 ligase promotes recycling of 60S ribosomal subunits from the ER. Nature. 627(8003). 445–452. 24 indexed citations

Peter, Joshua, Paul A. DaRosa, David Millrine, et al.. (2022). A non‐canonical scaffold‐type E3 ligase complex mediates protein UFMylation. The EMBO Journal. 41(21). e111015–e111015. 48 indexed citations

Walczak, Christopher P., Dara E. Leto, Lichao Zhang, et al.. (2019). Ribosomal protein RPL26 is the principal target of UFMylation. Proceedings of the National Academy of Sciences. 116(4). 1299–1308. 130 indexed citations

DaRosa, Paul A., Joseph S. Harrison, Alex Zelter, et al.. (2018). A Bifunctional Role for the UHRF1 UBL Domain in the Control of Hemi-methylated DNA-Dependent Histone Ubiquitylation. Molecular Cell. 72(4). 753–765.e6. 63 indexed citations

DaRosa, Paul A., Rachel E. Klevit, & Wenqing Xu. (2018). Structural basis for tankyrase‐RNF146 interaction reveals noncanonical tankyrase‐binding motifs. Protein Science. 27(6). 1057–1067. 25 indexed citations

Harrison, Joseph S., Evan M. Cornett, Dennis Goldfarb, et al.. (2016). Hemi-methylated DNA regulates DNA methylation inheritance through allosteric activation of H3 ubiquitylation by UHRF1. eLife. 5. 98 indexed citations

DaRosa, Paul A., Sergey Ovchinnikov, Wenqing Xu, & Rachel E. Klevit. (2016). Structural insights into SAM domain‐mediated tankyrase oligomerization. Protein Science. 25(9). 1744–1752. 23 indexed citations

Stewart, Mikaela D., et al.. (2016). Tuning BRCA1 and BARD1 activity to investigate RING ubiquitin ligase mechanisms. Protein Science. 26(3). 475–483. 26 indexed citations

10.

DaRosa, Paul A., Zhizhi Wang, Xiaomo Jiang, et al.. (2014). Allosteric activation of the RNF146 ubiquitin ligase by a poly(ADP-ribosyl)ation signal. Nature. 517(7533). 223–226. 183 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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