Deepak Reyon

18.1k total citations · 6 hit papers
40 papers, 11.8k citations indexed

About

Deepak Reyon is a scholar working on Molecular Biology, Cell Biology and Plant Science. According to data from OpenAlex, Deepak Reyon has authored 40 papers receiving a total of 11.8k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Molecular Biology, 7 papers in Cell Biology and 7 papers in Plant Science. Recurrent topics in Deepak Reyon's work include CRISPR and Genetic Engineering (24 papers), Advanced biosensing and bioanalysis techniques (17 papers) and RNA Interference and Gene Delivery (8 papers). Deepak Reyon is often cited by papers focused on CRISPR and Genetic Engineering (24 papers), Advanced biosensing and bioanalysis techniques (17 papers) and RNA Interference and Gene Delivery (8 papers). Deepak Reyon collaborates with scholars based in United States, France and United Kingdom. Deepak Reyon's co-authors include J. Keith Joung, Jeffry D. Sander, Yanfang Fu, Morgan L. Maeder, Cyd Khayter, Shengdar Q. Tsai, Vincent Cascio, Jing-Ruey Joanna Yeh, Randall T. Peterson and Woong Y. Hwang and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Deepak Reyon

39 papers receiving 11.6k citations

Hit Papers

High-frequency off-target mutagenesis induced by CRISPR-C... 2012 2026 2016 2021 2013 2013 2014 2012 2014 500 1000 1.5k 2.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells
    2013 2.5k citations Yanfang Fu, Cyd Khayter et al. Nature Biotechnology profile →
  2. Efficient genome editing in zebrafish using a CRISPR-Cas system
    2013 2.2k citations Woong Y. Hwang, Yanfang Fu et al. Nature Biotechnology profile →
  3. Improving CRISPR-Cas nuclease specificity using truncated guide RNAs
    2014 1.5k citations Yanfang Fu, Jeffry D. Sander et al. Nature Biotechnology profile →
  4. FLASH assembly of TALENs for high-throughput genome editing
    2012 928 citations Deepak Reyon, Shengdar Q. Tsai et al. Nature Biotechnology profile →
  5. Dimeric CRISPR RNA-guided FokI nucleases for highly specific genome editing
    2014 749 citations Shengdar Q. Tsai, Nicolas Wyvekens et al. Nature Biotechnology profile →
  6. Toddler: An Embryonic Signal That Promotes Cell Movement via Apelin Receptors
    2014 484 citations Andrea Pauli, Megan L. Norris et al. Science profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Deepak Reyon United States 28 10.3k 2.4k 1.6k 937 840 40 11.8k
Morgan L. Maeder United States 27 10.3k 1.0× 2.5k 1.1× 1.4k 0.9× 866 0.9× 816 1.0× 41 11.4k
Jeffry D. Sander United States 31 12.6k 1.2× 3.1k 1.3× 2.0k 1.3× 1.0k 1.1× 926 1.1× 41 14.2k
Shengdar Q. Tsai United States 37 12.6k 1.2× 3.1k 1.3× 1.4k 0.9× 1.4k 1.5× 663 0.8× 65 14.1k
Wenyan Jiang China 19 13.7k 1.3× 3.3k 1.4× 1.6k 1.0× 1.0k 1.1× 482 0.6× 39 15.5k
Ophir Shalem United States 21 11.6k 1.1× 2.3k 1.0× 935 0.6× 822 0.9× 585 0.7× 42 13.1k
Naomi Habib United States 20 15.6k 1.5× 3.2k 1.3× 1.6k 1.0× 894 1.0× 592 0.7× 34 18.0k
Yanfang Fu United States 16 7.3k 0.7× 1.7k 0.7× 736 0.5× 708 0.8× 677 0.8× 19 8.1k
Marc Güell Spain 20 8.8k 0.9× 2.3k 1.0× 981 0.6× 586 0.6× 310 0.4× 37 10.5k
Fyodor D. Urnov United States 39 11.6k 1.1× 3.6k 1.5× 1.4k 0.9× 668 0.7× 605 0.7× 84 13.4k
Silvana Konermann United States 18 15.2k 1.5× 2.7k 1.1× 1.7k 1.1× 1.5k 1.6× 337 0.4× 26 16.3k

Countries citing papers authored by Deepak Reyon

Since Specialization
Citations

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

Fields of papers citing papers by Deepak Reyon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Deepak Reyon. 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 Deepak Reyon. The network helps show where Deepak Reyon may publish in the future.

Co-authorship network of co-authors of Deepak Reyon

This figure shows the co-authorship network connecting the top 25 collaborators of Deepak Reyon. A scholar is included among the top collaborators of Deepak Reyon 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 Deepak Reyon. Deepak Reyon 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.
Sproule, Thomas J., Robert Y Wilpan, Benjamin E. Low, et al.. (2023). Functional analysis of Collagen 17a1: A genetic modifier of junctional epidermolysis bullosa in mice. PLoS ONE. 18(10). e0292456–e0292456. 3 indexed citations
2.
Weichert‐Leahey, Nina, Hui Shi, Ting Tao, et al.. (2023). Genetic predisposition to neuroblastoma results from a regulatory polymorphism that promotes the adrenergic cell state. Journal of Clinical Investigation. 133(10). 8 indexed citations
3.
Hu, Zhilian, Yang Liu, Deepak Reyon, et al.. (2020). Disruption of the kringle 1 domain of prothrombin leads to late onset mortality in zebrafish. Scientific Reports. 10(1). 4049–4049. 10 indexed citations
4.
Johnson, Bryce G., Lan Dang, Graham Marsh, et al.. (2017). Uromodulin p.Cys147Trp mutation drives kidney disease by activating ER stress and apoptosis. Journal of Clinical Investigation. 127(11). 3954–3969. 46 indexed citations
5.
Liao, Jing, Rahul Karnik, Hongcang Gu, et al.. (2015). Targeted disruption of DNMT1, DNMT3A and DNMT3B in human embryonic stem cells. Nature Genetics. 47(5). 469–478. 357 indexed citations
6.
Hwang, Woong Y., et al.. (2015). Targeted Mutagenesis in Zebrafish Using CRISPR RNA-Guided Nucleases. Methods in molecular biology. 1311. 317–334. 16 indexed citations
7.
Pauli, Andrea, Megan L. Norris, Eivind Valen, et al.. (2014). Toddler: An Embryonic Signal That Promotes Cell Movement via Apelin Receptors. Science. 343(6172). 1248636–1248636. 484 indexed citations breakdown →
8.
Chellappa, Vasant, Carlos Donado, Deepak Reyon, et al.. (2014). IκB Kinase β (IKBKB) Mutations in Lymphomas That Constitutively Activate Canonical Nuclear Factor κB (NFκB) Signaling. Journal of Biological Chemistry. 289(39). 26960–26972. 17 indexed citations
9.
Fu, Yanfang, Deepak Reyon, & J. Keith Joung. (2014). Targeted Genome Editing in Human Cells Using CRISPR/Cas Nucleases and Truncated Guide RNAs. Methods in enzymology on CD-ROM/Methods in enzymology. 546. 21–45. 35 indexed citations
10.
Tsai, Shengdar Q., Nicolas Wyvekens, Cyd Khayter, et al.. (2014). Dimeric CRISPR RNA-guided FokI nucleases for highly specific genome editing. Nature Biotechnology. 32(6). 569–576. 749 indexed citations breakdown →
11.
Fu, Yanfang, Jeffry D. Sander, Deepak Reyon, Vincent Cascio, & J. Keith Joung. (2014). Improving CRISPR-Cas nuclease specificity using truncated guide RNAs. Nature Biotechnology. 32(3). 279–284. 1536 indexed citations breakdown →
12.
Hwang, Woong Y., Yanfang Fu, Deepak Reyon, et al.. (2013). Heritable and Precise Zebrafish Genome Editing Using a CRISPR-Cas System. PLoS ONE. 8(7). e68708–e68708. 278 indexed citations
13.
Fu, Yanfang, Cyd Khayter, Morgan L. Maeder, et al.. (2013). High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells. Nature Biotechnology. 31(9). 822–826. 2476 indexed citations breakdown →
14.
Sander, Jeffry D., Cherie L. Ramirez, Samantha J Linder, et al.. (2013). In silico abstraction of zinc finger nuclease cleavage profiles reveals an expanded landscape of off-target sites. Nucleic Acids Research. 41(19). e181–e181. 44 indexed citations
15.
Cade, Lindsay, Deepak Reyon, Woong Y. Hwang, et al.. (2012). Highly efficient generation of heritable zebrafish gene mutations using homo- and heterodimeric TALENs. Nucleic Acids Research. 40(16). 8001–8010. 194 indexed citations
16.
Reyon, Deepak, et al.. (2012). FLASH assembly of TALENs for high-throughput genome editing. Nature Biotechnology. 30(5). 460–465. 928 indexed citations breakdown →
17.
Reyon, Deepak, Jeffry D. Sander, Feng Zhang, et al.. (2011). ZFNGenome: A comprehensive resource for locating zinc finger nuclease target sites in model organisms. BMC Genomics. 12(1). 83–83. 31 indexed citations
18.
Sander, Jeffry D., Morgan L. Maeder, Deepak Reyon, et al.. (2010). ZiFiT (Zinc Finger Targeter): an updated zinc finger engineering tool. Nucleic Acids Research. 38(Web Server). W462–W468. 269 indexed citations
19.
Sander, Jeffry D., Deepak Reyon, Morgan L. Maeder, et al.. (2010). Predicting success of oligomerized pool engineering (OPEN) for zinc finger target site sequences. BMC Bioinformatics. 11(1). 543–543. 17 indexed citations
20.
Su, Chih‐Chia, Feng Yang, Feng Long, et al.. (2009). Crystal Structure of the Membrane Fusion Protein CusB from Escherichia coli. Journal of Molecular Biology. 393(2). 342–355. 92 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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