Charles Plessy

13.6k total citations
51 papers, 1.6k citations indexed

About

Charles Plessy is a scholar working on Molecular Biology, Hepatology and Biomedical Engineering. According to data from OpenAlex, Charles Plessy has authored 51 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Molecular Biology, 6 papers in Hepatology and 6 papers in Biomedical Engineering. Recurrent topics in Charles Plessy's work include RNA and protein synthesis mechanisms (9 papers), Pluripotent Stem Cells Research (9 papers) and CRISPR and Genetic Engineering (7 papers). Charles Plessy is often cited by papers focused on RNA and protein synthesis mechanisms (9 papers), Pluripotent Stem Cells Research (9 papers) and CRISPR and Genetic Engineering (7 papers). Charles Plessy collaborates with scholars based in Japan, France and United Kingdom. Charles Plessy's co-authors include Piero Carninci, Uwe Strähle, Patrick Blader, Stefano Gustincich, T Gingeras, Philippe Batut, Alexander Dobin, Dejan Lazarević, Roberto Simone and Sachi Kato and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Charles Plessy

49 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Charles Plessy Japan	21	1.1k	234	202	165	141	51	1.6k
Rory Kirchner United States	21	994 0.9×	152 0.6×	181 0.9×	309 1.9×	253 1.8×	36	1.8k
Shirley Horn‐Saban Israel	12	1.6k 1.4×	329 1.4×	117 0.6×	168 1.0×	383 2.7×	18	2.1k
Sachiko Kamakura Japan	16	1.2k 1.1×	164 0.7×	338 1.7×	173 1.0×	113 0.8×	33	1.7k
Niels Vandamme Belgium	17	1.0k 0.9×	241 1.0×	175 0.9×	102 0.6×	72 0.5×	37	2.0k
Gangcai Xie China	16	1.1k 1.0×	253 1.1×	41 0.2×	148 0.9×	210 1.5×	33	1.6k
Michelle Wu United States	16	1.2k 1.1×	142 0.6×	598 3.0×	105 0.6×	162 1.1×	20	2.5k
Corinne Blugeon France	22	1.1k 1.0×	73 0.3×	83 0.4×	199 1.2×	97 0.7×	46	1.6k
Benno Jungblut Germany	22	1.3k 1.2×	127 0.5×	651 3.2×	215 1.3×	227 1.6×	30	2.3k
Monique Frain France	22	1.6k 1.4×	147 0.6×	256 1.3×	318 1.9×	536 3.8×	36	2.4k

Countries citing papers authored by Charles Plessy

Since Specialization

Citations

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

Fields of papers citing papers by Charles Plessy

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Charles Plessy

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Torres‐Aguila, Nuria P., Michael J. Mansfield, Charles Plessy, et al.. (2024). Less, but More: New Insights From Appendicularians on Chordate Fgf Evolution and the Divergence of Tunicate Lifestyles. Molecular Biology and Evolution. 42(1). 1 indexed citations

Bhat, Deepak, et al.. (2022). Speed variations of bacterial replisomes. eLife. 11. 9 indexed citations

Luginbühl, Joachim, Tsukasa Kouno, Rei Nakano, et al.. (2021). Decoding Neuronal Diversification by Multiplexed Single-cell RNA-Seq. Stem Cell Reports. 16(4). 810–824. 9 indexed citations

Mansfield, Michael J., et al.. (2021). Telomere-to-telomere assembly of the genome of an individual Oikopleura dioica from Okinawa using Nanopore-based sequencing. BMC Genomics. 22(1). 222–222. 22 indexed citations

Poulain, Stéphane, Ophélie Arnaud, Sachi Kato, et al.. (2020). Machine-driven parameter screen of biochemical reactions. Nucleic Acids Research. 48(7). e37–e37. 1 indexed citations

Danoy, Mathieu, Stéphane Poulain, Yuta Koui, et al.. (2020). Transcriptome profiling of hiPSC-derived LSECs with nanoCAGE. Molecular Omics. 16(2). 138–146. 11 indexed citations

Danoy, Mathieu, Yannick Tauran, Stéphane Poulain, et al.. (2020). Analysis of hiPSCs differentiation toward hepatocyte-like cells upon extended exposition to oncostatin. Differentiation. 114. 36–48. 5 indexed citations

Taguchi, Ayumi, Kazunori Nagasaka, Charles Plessy, et al.. (2020). Use of Cap Analysis Gene Expression to detect human papillomavirus promoter activity patterns at different disease stages. Scientific Reports. 10(1). 17991–17991. 4 indexed citations

Abugessaisa, Imad, Shuhei Noguchi, Michael E. Böttcher, et al.. (2017). SCPortalen: human and mouse single-cell centric database. Nucleic Acids Research. 46(D1). D781–D787. 39 indexed citations

10.

Bertin, Nicolas, Mickaël Mendez, Akira Hasegawa, et al.. (2017). Linking FANTOM5 CAGE peaks to annotations with CAGEscan. Scientific Data. 4(1). 170147–170147. 11 indexed citations

11.

Arnaud, Ophélie, Sachi Kato, Stéphane Poulain, & Charles Plessy. (2016). Targeted Reduction of Highly Abundant Transcripts Using Pseudo-Random Primers. BioTechniques. 60(4). 169–174. 18 indexed citations

12.

Kratz, Anton, Pascal Béguin, Megumi Kaneko, et al.. (2014). Digital expression profiling of the compartmentalized translatome of Purkinje neurons. Genome Research. 24(8). 1396–1410. 42 indexed citations

13.

Batut, Philippe, Alexander Dobin, Charles Plessy, Piero Carninci, & T Gingeras. (2012). High-fidelity promoter profiling reveals widespread alternative promoter usage and transposon-driven developmental gene expression. Genome Research. 23(1). 169–180. 144 indexed citations

14.

Plessy, Charles, et al.. (2012). Population transcriptomics with single‐cell resolution: A new field made possible by microfluidics. BioEssays. 35(2). 131–140. 14 indexed citations

15.

Plessy, Charles, Giovanni Pascarella, Nicolas Bertin, et al.. (2011). Promoter architecture of mouse olfactory receptor genes. Genome Research. 22(3). 486–497. 48 indexed citations

16.

Möller, Steffen, Alan Williams, Katherine Wolstencroft, et al.. (2010). Community-driven computational biology with Debian Linux. BMC Bioinformatics. 11(S12). S5–S5. 47 indexed citations

17.

Gustincich, Stefano, Albin Sandelin, Charles Plessy, et al.. (2006). The complexity of the mammalian transcriptome. The Journal of Physiology. 575(2). 321–332. 78 indexed citations

18.

Blader, Patrick, Chen Sok Lam, Sepand Rastegar, et al.. (2004). Conserved and acquired features of neurogenin1 regulation. Development. 131(22). 5627–5637. 58 indexed citations

19.

Dickmeis, Thomas, Charles Plessy, Sepand Rastegar, et al.. (2004). Expression Profiling and Comparative Genomics Identify a Conserved Regulatory Region Controlling Midline Expression in the Zebrafish Embryo. Genome Research. 14(2). 228–238. 30 indexed citations

20.

Dickmeis, Thomas, Sepand Rastegar, Pia Aanstad, et al.. (2001). Expression of brain subtype creatine kinase in the zebrafish embryo. Mechanisms of Development. 109(2). 409–412. 17 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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