Gaëtan Benoit

594 total citations · 1 hit paper
7 papers, 176 citations indexed

About

Gaëtan Benoit is a scholar working on Molecular Biology, Artificial Intelligence and Plant Science. According to data from OpenAlex, Gaëtan Benoit has authored 7 papers receiving a total of 176 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 3 papers in Artificial Intelligence and 2 papers in Plant Science. Recurrent topics in Gaëtan Benoit's work include Genomics and Phylogenetic Studies (7 papers), Gene expression and cancer classification (3 papers) and Algorithms and Data Compression (3 papers). Gaëtan Benoit is often cited by papers focused on Genomics and Phylogenetic Studies (7 papers), Gene expression and cancer classification (3 papers) and Algorithms and Data Compression (3 papers). Gaëtan Benoit collaborates with scholars based in France, United Kingdom and Austria. Gaëtan Benoit's co-authors include Claire Lemaitre, Erwan Drézen, Dominique Lavenier, Mahendra Mariadassou, Sophie Schbath, Pierre Peterlongo, Robert James, Guillaume Rizk, Adam M. Phillippy and Sébastien Raguideau and has published in prestigious journals such as Nature Communications, Nature Biotechnology and Bioinformatics.

In The Last Decade

Gaëtan Benoit

5 papers receiving 175 citations

Hit Papers

align trajectories

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.

High-quality metagenome assembly from long accurate reads with metaMDBG

2024 47 citations Gaëtan Benoit, Sébastien Raguideau et al. Nature Biotechnology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Gaëtan Benoit France	4	138	53	50	27	13	7	176
Rosangela Canino-Koning United States	4	183 1.3×	69 1.3×	69 1.4×	38 1.4×	26 2.0×	8	253
David Pellow Israel	8	165 1.2×	50 0.9×	83 1.7×	19 0.7×	11 0.8×	12	252
Antoine Limasset France	8	294 2.1×	61 1.2×	100 2.0×	13 0.5×	69 5.3×	14	335
Martin D. Muggli United States	5	132 1.0×	23 0.4×	47 0.9×	9 0.3×	41 3.2×	10	165
Peter Belmann Germany	6	267 1.9×	145 2.7×	11 0.2×	11 0.4×	29 2.2×	10	329
Hannes Hauswedell Germany	3	91 0.7×	22 0.4×	32 0.6×	8 0.3×	12 0.9×	4	112
Zhenmiao Zhang Hong Kong	7	143 1.0×	83 1.6×	4 0.1×	7 0.3×	20 1.5×	10	192
Hirokazu Chiba Japan	7	143 1.0×	38 0.7×	23 0.5×		15 1.2×	17	175
Md Al Amin Bangladesh	8	246 1.8×	68 1.3×	25 0.5×	19 0.7×	5 0.4×	21	347
Marius Welzel Germany	9	187 1.4×	31 0.6×	51 1.0×	20 0.7×		13	216

Countries citing papers authored by Gaëtan Benoit

Since Specialization

Citations

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

Fields of papers citing papers by Gaëtan Benoit

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gaëtan Benoit

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

All Works

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7 of 7 papers shown

1.

Benoit, Gaëtan, et al.. (2026). High-quality metagenome assembly from nanopore reads with nanoMDBG. Nature Communications.

2.

Benoit, Gaëtan, Sébastien Raguideau, Robert James, et al.. (2024). High-quality metagenome assembly from long accurate reads with metaMDBG. Nature Biotechnology. 42(9). 1378–1383. 47 indexed citations breakdown →

3.

Benoit, Gaëtan, Mahendra Mariadassou, Stéphane Robin, et al.. (2019). SimkaMin: fast and resource frugal de novo comparative metagenomics. Bioinformatics. 36(4). 1275–1276. 9 indexed citations

4.

Benoit, Gaëtan, Pierre Peterlongo, Mahendra Mariadassou, et al.. (2016). Multiple comparative metagenomics using multiset k -mer counting. PeerJ Computer Science. 2. e94–e94. 63 indexed citations

5.

Benoit, Gaëtan. (2015). Simka: fast kmer-based method for estimating the similarity between numerous metagenomic datasets. SPIRE - Sciences Po Institutional REpository. 1 indexed citations

6.

Benoit, Gaëtan, Claire Lemaitre, Dominique Lavenier, et al.. (2015). Reference-free compression of high throughput sequencing data with a probabilistic de Bruijn graph. BMC Bioinformatics. 16(1). 288–288. 56 indexed citations

7.

Benoit, Gaëtan, Claire Lemaitre, & Pierre Peterlongo. (2015). Fast kmer-based method for estimating the similarity between numerous metagenomic datasets. Faculty of 1000 Research Ltd. 4.

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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