Fred W. Perrino

5.7k total citations
75 papers, 3.8k citations indexed

About

Fred W. Perrino is a scholar working on Molecular Biology, Immunology and Genetics. According to data from OpenAlex, Fred W. Perrino has authored 75 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 62 papers in Molecular Biology, 27 papers in Immunology and 10 papers in Genetics. Recurrent topics in Fred W. Perrino's work include DNA Repair Mechanisms (30 papers), interferon and immune responses (25 papers) and DNA and Nucleic Acid Chemistry (23 papers). Fred W. Perrino is often cited by papers focused on DNA Repair Mechanisms (30 papers), interferon and immune responses (25 papers) and DNA and Nucleic Acid Chemistry (23 papers). Fred W. Perrino collaborates with scholars based in United States, Spain and United Kingdom. Fred W. Perrino's co-authors include Thomas Hollis, Dan J. Mazur, Scott Harvey, Lawrence A. Loeb, Judy Lieberman, Dipanjan Chowdhury, Jason M. Grayson, Holly Miller, Linda J. Sandell and B D Preston and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Fred W. Perrino

74 papers receiving 3.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fred W. Perrino United States 33 2.6k 1.4k 489 392 389 75 3.8k
Avi Ashkenazi United States 26 2.3k 0.9× 2.4k 1.7× 322 0.7× 465 1.2× 197 0.5× 37 4.3k
Dinah S. Singer United States 43 3.1k 1.2× 2.3k 1.6× 160 0.3× 344 0.9× 627 1.6× 143 5.7k
Keith C. Deen United States 20 1.7k 0.7× 1.5k 1.0× 498 1.0× 1.0k 2.6× 193 0.5× 37 3.6k
Jens Dhein Germany 13 2.6k 1.0× 2.5k 1.8× 249 0.5× 756 1.9× 213 0.5× 24 4.8k
Mounira K. Chelbi‐Alix France 38 3.5k 1.4× 1.9k 1.4× 551 1.1× 588 1.5× 859 2.2× 83 5.3k
Dorian Bevec Austria 32 1.4k 0.6× 1.1k 0.7× 247 0.5× 442 1.1× 195 0.5× 62 2.9k
Rosa Ana Lacalle Spain 30 2.1k 0.8× 1.2k 0.8× 188 0.4× 442 1.1× 192 0.5× 40 3.9k
Michael A. Norcross United States 33 1.1k 0.4× 1.9k 1.3× 504 1.0× 1.2k 2.9× 207 0.5× 59 3.7k
Harold C. Smith United States 40 3.4k 1.3× 571 0.4× 493 1.0× 770 2.0× 384 1.0× 131 4.7k
Maurizio Zanetti United States 36 1.5k 0.6× 2.4k 1.6× 138 0.3× 139 0.4× 299 0.8× 158 4.1k

Countries citing papers authored by Fred W. Perrino

Since Specialization
Citations

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

Fields of papers citing papers by Fred W. Perrino

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fred W. Perrino. 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 Fred W. Perrino. The network helps show where Fred W. Perrino may publish in the future.

Co-authorship network of co-authors of Fred W. Perrino

This figure shows the co-authorship network connecting the top 25 collaborators of Fred W. Perrino. A scholar is included among the top collaborators of Fred W. Perrino 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 Fred W. Perrino. Fred W. Perrino 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.
Severino, Mariasavina, Claudio Moratti, Rosario Pascarella, et al.. (2022). Genotype-Phenotype Correlation and Functional Insights for Two Monoallelic TREX1 Missense Variants Affecting the Catalytic Core. Genes. 13(7). 1179–1179.
2.
Perrino, Fred W., et al.. (2019). Measuring TREX1 and TREX2 exonuclease activities. Methods in enzymology on CD-ROM/Methods in enzymology. 625. 109–133. 12 indexed citations
3.
Harvey, Scott, Mingyong Liu, Rajkumar Venkatadri, et al.. (2019). T Cells Produce IFN-α in the TREX1 D18N Model of Lupus-like Autoimmunity. The Journal of Immunology. 204(2). 348–359. 19 indexed citations
4.
Sakai, Tomomi, Takuya Miyazaki, Dong-Mi Shin, et al.. (2017). DNase-active TREX1 frame-shift mutants induce serologic autoimmunity in mice. Journal of Autoimmunity. 81. 13–23. 30 indexed citations
5.
Hollis, Thomas, et al.. (2011). Functional Consequences of the RNase H2A Subunit Mutations That Cause Aicardi-Goutières Syndrome. Journal of Biological Chemistry. 286(19). 16984–16991. 30 indexed citations
6.
Harvey, Scott, et al.. (2011). The TREX1 Exonuclease R114H Mutation in Aicardi-Goutières Syndrome and Lupus Reveals Dimeric Structure Requirements for DNA Degradation Activity. Journal of Biological Chemistry. 286(46). 40246–40254. 39 indexed citations
7.
Casteel, Darren E., Shunhui Zhuang, Ying Zeng, et al.. (2009). A DNA Polymerase-α·Primase Cofactor with Homology to Replication Protein A-32 Regulates DNA Replication in Mammalian Cells. Journal of Biological Chemistry. 284(9). 5807–5818. 124 indexed citations
8.
Perrino, Fred W., et al.. (2009). DNA binding induces active site conformational change in the human TREX2 3'-exonuclease. Nucleic Acids Research. 37(7). 2411–2417. 12 indexed citations
9.
Perrino, Fred W., et al.. (2008). Cooperative DNA Binding and Communication across the Dimer Interface in the TREX2 3′ → 5′-Exonuclease. Journal of Biological Chemistry. 283(31). 21441–21452. 16 indexed citations
10.
Pence, Matthew G., et al.. (2008). Lesion Bypass of N2-Ethylguanine by Human DNA Polymerase ι. Journal of Biological Chemistry. 284(3). 1732–1740. 62 indexed citations
11.
Choudhury, Sumana, et al.. (2007). The Crystal Structure of TREX1 Explains the 3′ Nucleotide Specificity and Reveals a Polyproline II Helix for Protein Partnering. Journal of Biological Chemistry. 282(14). 10537–10543. 90 indexed citations
12.
Kirby, Thomas W., Scott Harvey, Eugene F. DeRose, et al.. (2006). Structure of the Escherichia coli DNA Polymerase III ϵ-HOT Proofreading Complex. Journal of Biological Chemistry. 281(50). 38466–38471. 28 indexed citations
13.
Wang, Xueying, et al.. (2006). Mutagenesis by exocyclic alkylamino purine adducts in Escherichia coli. Mutation research. Fundamental and molecular mechanisms of mutagenesis. 599(1-2). 1–10. 17 indexed citations
14.
Harrigan, Jeanine A., Jinshui Fan, Jamil Momand, et al.. (2006). WRN exonuclease activity is blocked by DNA termini harboring 3′ obstructive groups. Mechanisms of Ageing and Development. 128(3). 259–266. 28 indexed citations
15.
Perrino, Fred W., et al.. (2004). Dysfunctional proofreading in the Escherichia coli DNA polymerase III core. Biochemical Journal. 384(2). 337–348. 10 indexed citations
16.
Mazur, Dan J. & Fred W. Perrino. (2001). Structure and Expression of the TREX1 and TREX2 3′→5′ Exonuclease Genes. Journal of Biological Chemistry. 276(18). 14718–14727. 63 indexed citations
17.
Mazur, Dan J. & Fred W. Perrino. (2001). Excision of 3′ Termini by the Trex1 and TREX2 3′→5′ Exonucleases. Journal of Biological Chemistry. 276(20). 17022–17029. 130 indexed citations
18.
Perrino, Fred W., Dan J. Mazur, Heather Ward, & Scott Harvey. (1999). Exonucleases and the incorporation of aranucleotides into DNA. Cell Biochemistry and Biophysics. 30(3). 331–352. 28 indexed citations
19.
Naryzhny, Stanislav, et al.. (1993). Proof‐reading 3′→5′ exonucleases isolated from rat liver nuclei. European Journal of Biochemistry. 217(2). 493–500. 22 indexed citations
20.

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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