Kosuke Yusa

11.1k total citations · 3 hit papers
66 papers, 5.1k citations indexed

About

Kosuke Yusa is a scholar working on Molecular Biology, Genetics and Plant Science. According to data from OpenAlex, Kosuke Yusa has authored 66 papers receiving a total of 5.1k indexed citations (citations by other indexed papers that have themselves been cited), including 59 papers in Molecular Biology, 11 papers in Genetics and 9 papers in Plant Science. Recurrent topics in Kosuke Yusa's work include CRISPR and Genetic Engineering (46 papers), Pluripotent Stem Cells Research (23 papers) and RNA Interference and Gene Delivery (11 papers). Kosuke Yusa is often cited by papers focused on CRISPR and Genetic Engineering (46 papers), Pluripotent Stem Cells Research (23 papers) and RNA Interference and Gene Delivery (11 papers). Kosuke Yusa collaborates with scholars based in United Kingdom, Japan and United States. Kosuke Yusa's co-authors include Allan Bradley, Junji Takeda, Yilong Li, Hiroko Koike-Yusa, Martin Del Castillo Velasco‐Herrera, Meng Amy Li, Roland Rad, Nancy L. Craig, Liqin Zhou and Kyoji Horie and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Kosuke Yusa

65 papers receiving 5.0k citations

Hit Papers

Genome-wide recessive genetic screening in mammalian cell... 2011 2026 2016 2021 2013 2011 2011 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kosuke Yusa United Kingdom 35 4.4k 1.1k 502 442 330 66 5.1k
Luhan Yang United States 6 7.3k 1.6× 1.7k 1.5× 804 1.6× 394 0.9× 276 0.8× 8 8.0k
Alexandro E. Trevino United States 13 5.4k 1.2× 1.1k 0.9× 609 1.2× 375 0.8× 238 0.7× 30 6.0k
G. Grant Welstead United States 15 4.6k 1.0× 905 0.8× 241 0.5× 458 1.0× 712 2.2× 22 5.6k
Xi Shi China 14 4.4k 1.0× 685 0.6× 186 0.4× 678 1.5× 495 1.5× 30 5.3k
Peter D. Rathjen Australia 30 3.3k 0.8× 510 0.5× 472 0.9× 374 0.8× 405 1.2× 76 4.1k
Patrick J. Paddison United States 31 5.1k 1.2× 1.1k 1.0× 222 0.4× 472 1.1× 430 1.3× 66 6.0k
Mudra Hegde United States 14 4.5k 1.0× 732 0.6× 462 0.9× 498 1.1× 365 1.1× 18 5.1k
David E. Paschon United States 16 3.3k 0.7× 1.2k 1.0× 347 0.7× 309 0.7× 122 0.4× 27 3.8k
Vishal Thapar United States 14 3.4k 0.8× 541 0.5× 296 0.6× 607 1.4× 375 1.1× 23 4.2k
Alexander Brehm Germany 31 5.1k 1.1× 911 0.8× 480 1.0× 1.2k 2.8× 356 1.1× 56 5.9k

Countries citing papers authored by Kosuke Yusa

Since Specialization
Citations

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

Fields of papers citing papers by Kosuke Yusa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kosuke Yusa

This figure shows the co-authorship network connecting the top 25 collaborators of Kosuke Yusa. A scholar is included among the top collaborators of Kosuke Yusa 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 Kosuke Yusa. Kosuke Yusa 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.
Fukuoka, Makoto, Takahiro Kodama, Kazuhiro Murai, et al.. (2024). Genome‐wide loss‐of‐function genetic screen identifies INSIG2 as the vulnerability of hepatitis B virus‐integrated hepatoma cells. Cancer Science. 115(3). 859–870. 2 indexed citations
3.
Tarumoto, Yusuke, Yusuke Seto, Yasuhiro Kojima, et al.. (2023). RENGE infers gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations. Communications Biology. 6(1). 1290–1290. 6 indexed citations
4.
Yoshihara, Masahito, Meryam Beniazza, James Ashmore, et al.. (2023). B1 SINE-binding ZFP266 impedes mouse iPSC generation through suppression of chromatin opening mediated by reprogramming factors. Nature Communications. 14(1). 488–488. 12 indexed citations
5.
Aoki, Kazunari, Yusuke Tarumoto, Gohei Nishibuchi, et al.. (2023). Canonical BAF complex regulates the oncogenic program in human T-cell acute lymphoblastic leukemia. Blood. 143(7). 604–618. 1 indexed citations
6.
Collier, Amanda J., Katarzyna Tilgner, Claudia I. Semprich, et al.. (2022). Genome-wide screening identifies Polycomb repressive complex 1.3 as an essential regulator of human naïve pluripotent cell reprogramming. Science Advances. 8(12). eabk0013–eabk0013. 13 indexed citations
7.
Gu, Muxin, Étienne De Braekeleer, Malgorzata Gozdecka, et al.. (2020). KAT7 is a genetic vulnerability of acute myeloid leukemias driven by MLL rearrangements. Leukemia. 35(4). 1012–1022. 33 indexed citations
8.
Thompson, Oliver, Ferdinand von Meyenn, Zoë Hewitt, et al.. (2020). Low rates of mutation in clinical grade human pluripotent stem cells under different culture conditions. Nature Communications. 11(1). 1528–1528. 82 indexed citations
9.
Yu, Jason & Kosuke Yusa. (2019). Genome-wide CRISPR-Cas9 screening in mammalian cells. Methods. 164-165. 29–35. 50 indexed citations
10.
Gonçalves, Emanuel, Fiona M. Behan, Sandra Louzada, et al.. (2019). Structural rearrangements generate cell-specific, gene-independent CRISPR-Cas9 loss of fitness effects. Genome biology. 20(1). 27–27. 32 indexed citations
11.
Sharma, Sumana, S. Josefin Bartholdson, Amalie C. M. Couch, Kosuke Yusa, & Gavin J. Wright. (2018). Genome-scale identification of cellular pathways required for cell surface recognition. Genome Research. 28(9). 1372–1382. 26 indexed citations
12.
Lu, Quinn, et al.. (2017). Applications of CRISPR genome editing technology in drug target identification and validation. Expert Opinion on Drug Discovery. 12(6). 541–552. 12 indexed citations
13.
Ong, Swee Hoe, Yilong Li, Hiroko Koike-Yusa, & Kosuke Yusa. (2017). Optimised metrics for CRISPR-KO screens with second-generation gRNA libraries. Scientific Reports. 7(1). 7384–7384. 28 indexed citations
14.
Yusa, Kosuke, Kyoji Horie, Masahiro Tokunaga, et al.. (2013). Enhancement of microhomology-mediated genomic rearrangements by transient loss of mouse Bloom syndrome helicase. Genome Research. 23(9). 1462–1473. 13 indexed citations
15.
Koike-Yusa, Hiroko, et al.. (2013). Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nature Biotechnology. 32(3). 267–273. 766 indexed citations breakdown →
16.
Yusa, Kosuke, Liqin Zhou, Meng Amy Li, Allan Bradley, & Nancy L. Craig. (2011). A hyperactive piggyBac transposase for mammalian applications. Proceedings of the National Academy of Sciences. 108(4). 1531–1536. 524 indexed citations breakdown →
17.
Yusa, Kosuke, Roland Rad, Junji Takeda, & Allan Bradley. (2009). Generation of transgene-free induced pluripotent mouse stem cells by the piggyBac transposon. Nature Methods. 6(5). 363–369. 467 indexed citations
18.
Ikeda, Ryuji, Chikara Kokubu, Kosuke Yusa, et al.. (2006). Sleeping Beauty Transposase Has an Affinity for Heterochromatin Conformation. Molecular and Cellular Biology. 27(5). 1665–1676. 43 indexed citations
19.
Akiyama, Koichi, Kosuke Yusa, Hideharu Hashimoto, et al.. (2006). Rad54 is dispensable for the ALT pathway. Genes to Cells. 11(11). 1305–1315. 7 indexed citations
20.
Keng, Vincent W., Kojiro Yae, Tomoko Hayakawa, et al.. (2005). Region-specific saturation germline mutagenesis in mice using the Sleeping Beauty transposon system. Nature Methods. 2(10). 763–769. 100 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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