Gene‐Wei Li

11.3k total citations · 4 hit papers
42 papers, 7.9k citations indexed

About

Gene‐Wei Li is a scholar working on Molecular Biology, Genetics and Ecology. According to data from OpenAlex, Gene‐Wei Li has authored 42 papers receiving a total of 7.9k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Molecular Biology, 24 papers in Genetics and 10 papers in Ecology. Recurrent topics in Gene‐Wei Li's work include RNA and protein synthesis mechanisms (29 papers), Bacterial Genetics and Biotechnology (24 papers) and Genomics and Phylogenetic Studies (12 papers). Gene‐Wei Li is often cited by papers focused on RNA and protein synthesis mechanisms (29 papers), Bacterial Genetics and Biotechnology (24 papers) and Genomics and Phylogenetic Studies (12 papers). Gene‐Wei Li collaborates with scholars based in United States, Germany and France. Gene‐Wei Li's co-authors include Jonathan S. Weissman, X. Sunney Xie, Johan Elf, Xiao Xie, Carol A. Gross, David H. Burkhardt, Paul Choi, Yuichi Taniguchi, Mohan Babu and Huiyi Chen and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Gene‐Wei Li

41 papers receiving 7.8k citations

Hit Papers

Quantifying E. coli Proteome and Transcriptome with Singl... 2007 2026 2013 2019 2010 2013 2014 2007 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gene‐Wei Li United States 25 6.9k 2.3k 853 718 464 42 7.9k
Harley H. McAdams United States 44 7.8k 1.1× 4.2k 1.8× 1.7k 2.0× 507 0.7× 510 1.1× 58 9.3k
Zemer Gitai United States 46 5.4k 0.8× 2.6k 1.1× 1.6k 1.9× 284 0.4× 632 1.4× 99 7.4k
Johan Paulsson United States 35 6.4k 0.9× 2.9k 1.2× 503 0.6× 861 1.2× 699 1.5× 57 7.4k
Christine Jacobs‐Wagner United States 44 4.8k 0.7× 3.4k 1.4× 1.8k 2.1× 563 0.8× 444 1.0× 89 7.1k
Achillefs N. Kapanidis United Kingdom 41 4.6k 0.7× 1.4k 0.6× 801 0.9× 1.9k 2.6× 567 1.2× 122 5.9k
Siyuan Wang China 26 4.3k 0.6× 736 0.3× 362 0.4× 785 1.1× 435 0.9× 116 5.5k
Mark C. Leake United Kingdom 37 3.6k 0.5× 1.1k 0.5× 507 0.6× 1.1k 1.6× 780 1.7× 128 5.3k
Jie Xiao United States 31 2.5k 0.4× 1.3k 0.6× 656 0.8× 620 0.9× 314 0.7× 116 3.9k
Jörg Langowski Germany 53 7.0k 1.0× 792 0.3× 614 0.7× 948 1.3× 730 1.6× 192 8.7k
Farren J. Isaacs United States 38 7.2k 1.0× 2.2k 0.9× 656 0.8× 161 0.2× 721 1.6× 63 8.2k

Countries citing papers authored by Gene‐Wei Li

Since Specialization
Citations

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

Fields of papers citing papers by Gene‐Wei Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gene‐Wei Li. 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 Gene‐Wei Li. The network helps show where Gene‐Wei Li may publish in the future.

Co-authorship network of co-authors of Gene‐Wei Li

This figure shows the co-authorship network connecting the top 25 collaborators of Gene‐Wei Li. A scholar is included among the top collaborators of Gene‐Wei Li 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 Gene‐Wei Li. Gene‐Wei Li 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.
Taggart, James, Jean‐Benoît Lalanne, Sylvain Durand, et al.. (2025). A high-resolution view of RNA endonuclease cleavage in Bacillus subtilis. Nucleic Acids Research. 53(3). 1 indexed citations
2.
Keating, Amy E., et al.. (2025). High-throughput discovery of inhibitory protein fragments with AlphaFold. Proceedings of the National Academy of Sciences. 122(6). e2322412122–e2322412122. 3 indexed citations
3.
Schaening-Burgos, Cassandra, et al.. (2024). RluA is the major mRNA pseudouridine synthase in Escherichia coli. PLoS Genetics. 20(9). e1011100–e1011100. 6 indexed citations
4.
Li, Gene‐Wei, et al.. (2024). How do bacteria tune transcription termination efficiency?. Current Opinion in Microbiology. 82. 102557–102557.
5.
Herzel, Lydia, et al.. (2022). Ubiquitous mRNA decay fragments in E. coli redefine the functional transcriptome. Nucleic Acids Research. 50(9). 5029–5046. 17 indexed citations
6.
Mori, Matteo, Zhongge Zhang, Amir Banaei‐Esfahani, et al.. (2021). From coarse to fine: the absolute Escherichia coli proteome under diverse growth conditions. Molecular Systems Biology. 17(5). e9536–e9536. 79 indexed citations
7.
Lalanne, Jean‐Benoît, et al.. (2021). Differential translation of mRNA isoforms transcribed with distinct sigma factors. RNA. 27(7). 791–804. 4 indexed citations
8.
Lalanne, Jean‐Benoît, et al.. (2020). Growth-Optimized Aminoacyl-tRNA Synthetase Levels Prevent Maximal tRNA Charging. Cell Systems. 11(2). 121–130.e6. 26 indexed citations
9.
Lalanne, Jean‐Benoît, et al.. (2020). Functionally uncoupled transcription–translation in Bacillus subtilis. Nature. 585(7823). 124–128. 111 indexed citations
10.
Taggart, James, Henrik Zauber, Matthias Selbach, Gene‐Wei Li, & Erik McShane. (2020). Keeping the Proportions of Protein Complex Components in Check. Cell Systems. 10(2). 125–132. 62 indexed citations
11.
Lalanne, Jean‐Benoît, et al.. (2018). Maturation of polycistronic mRNAs by the endoribonuclease RNase Y and its associated Y-complex inBacillus subtilis. Proceedings of the National Academy of Sciences. 115(24). E5585–E5594. 54 indexed citations
12.
Babina, Arianne M., et al.. (2018). Fitness advantages conferred by the L20-interacting RNAcis-regulator of ribosomal protein synthesis inBacillus subtilis. RNA. 24(9). 1133–1143. 6 indexed citations
13.
Burkhardt, David H., Silvi Rouskin, Yan Zhang, et al.. (2017). Operon mRNAs are organized into ORF-centric structures that predict translation efficiency. eLife. 6. 110 indexed citations
14.
Li, Gene‐Wei. (2015). How do bacteria tune translation efficiency?. Current Opinion in Microbiology. 24. 66–71. 54 indexed citations
15.
Li, Gene‐Wei, David H. Burkhardt, Carol A. Gross, & Jonathan S. Weissman. (2014). Quantifying Absolute Protein Synthesis Rates Reveals Principles Underlying Allocation of Cellular Resources. Cell. 157(3). 624–635. 929 indexed citations breakdown →
16.
Chen, Baohui, Luke A. Gilbert, Beth A. Cimini, et al.. (2013). Dynamic Imaging of Genomic Loci in Living Human Cells by an Optimized CRISPR/Cas System. Cell. 155(7). 1479–1491. 1422 indexed citations breakdown →
17.
Brandman, Onn, Jacob Stewart-Ornstein, Daisy Y.L. Wong, et al.. (2012). A Ribosome-Bound Quality Control Complex Triggers Degradation of Nascent Peptides and Signals Translation Stress. Cell. 151(5). 1042–1054. 485 indexed citations
18.
Chen, Chongyi, Wenqin Wang, Gene‐Wei Li, Xiaowei Zhuang, & X. Sunney Xie. (2012). Chromosome Organization by a Nucleoid-Associated Protein in Live Bacteria. Biophysical Journal. 102(3). 479a–479a. 7 indexed citations
19.
Wang, Wenqin, Gene‐Wei Li, Chongyi Chen, X. Sunney Xie, & Xiaowei Zhuang. (2011). Chromosome Organization by a Nucleoid-Associated Protein in Live Bacteria. Science. 333(6048). 1445–1449. 312 indexed citations
20.
Taniguchi, Yuichi, Paul Choi, Gene‐Wei Li, et al.. (2010). Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells. Science. 329(5991). 533–538. 1505 indexed citations breakdown →

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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