Megan E. Garber

769 total citations
13 papers, 535 citations indexed

About

Megan E. Garber is a scholar working on Molecular Biology, Genetics and Biomedical Engineering. According to data from OpenAlex, Megan E. Garber has authored 13 papers receiving a total of 535 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 4 papers in Genetics and 4 papers in Biomedical Engineering. Recurrent topics in Megan E. Garber's work include Microbial Metabolic Engineering and Bioproduction (4 papers), Bacterial Genetics and Biotechnology (4 papers) and CRISPR and Genetic Engineering (3 papers). Megan E. Garber is often cited by papers focused on Microbial Metabolic Engineering and Bioproduction (4 papers), Bacterial Genetics and Biotechnology (4 papers) and CRISPR and Genetic Engineering (3 papers). Megan E. Garber collaborates with scholars based in United States, Denmark and Spain. Megan E. Garber's co-authors include Aindrila Mukhopadhyay, Jay D. Keasling, Fabienne Venet, Alfred Ayala, Xin Huang, Chun‐Shiang Chung, Guillaume Monneret, Nathan J. Hillson, Maren Wehrs and Leo d’Espaux and has published in prestigious journals such as Nucleic Acids Research, Scientific Reports and Genetics.

In The Last Decade

Megan E. Garber

12 papers receiving 530 citations

Peers

Megan E. Garber
Yanbo Kou China
Jiayu Chen China
Lin Hu China
Thunnicha Ondee Thailand
Qunying Lin China
Xingyu Rong China
Xiaoqin Han China
Eun‐Hye Hong South Korea
Zhihui Ren China
Bridget Graves United States
Yanbo Kou China
Megan E. Garber
Citations per year, relative to Megan E. Garber Megan E. Garber (= 1×) peers Yanbo Kou

Countries citing papers authored by Megan E. Garber

Since Specialization
Citations

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

Fields of papers citing papers by Megan E. Garber

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Megan E. Garber. 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 Megan E. Garber. The network helps show where Megan E. Garber may publish in the future.

Co-authorship network of co-authors of Megan E. Garber

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

All Works

13 of 13 papers shown
1.
Chen, Yan, Jennifer Gin, Emine Akyüz Turumtay, et al.. (2025). Biosensor-driven strain engineering reveals key cellular processes for maximizing isoprenol production in Pseudomonas putida. Science Advances. 11(43). eady2677–eady2677. 1 indexed citations
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Garber, Megan E., et al.. (2022). Pseudomonas response regulators produced in an E. coli heterologous expression host exhibit host-derived post-translational phosphorylation. Scientific Reports. 12(1). 10336–10336. 3 indexed citations
5.
Iwai, Kosuke, Maren Wehrs, Megan E. Garber, et al.. (2022). Scalable and automated CRISPR-based strain engineering using droplet microfluidics. Microsystems & Nanoengineering. 8(1). 31–31. 26 indexed citations
6.
Chiniquy, Jennifer, Megan E. Garber, Aindrila Mukhopadhyay, & Nathan J. Hillson. (2020). Fluorescent amplification for next generation sequencing (FA-NGS) library preparation. BMC Genomics. 21(1). 85–85. 5 indexed citations
7.
Rajeev, Lara, Megan E. Garber, & Aindrila Mukhopadhyay. (2020). Tools to map target genes of bacterial two‐component system response regulators. Environmental Microbiology Reports. 12(3). 267–276. 14 indexed citations
8.
Thompson, Mitchell G., Allison N. Pearson, Jesus F. Barajas, et al.. (2019). Identification, Characterization, and Application of a Highly Sensitive Lactam Biosensor from Pseudomonas putida. ACS Synthetic Biology. 9(1). 53–62. 28 indexed citations
9.
Rajeev, Lara, Megan E. Garber, Grant M. Zane, et al.. (2018). A new family of transcriptional regulators of tungstoenzymes and molybdate/tungstate transport. Environmental Microbiology. 21(2). 784–799. 7 indexed citations
10.
Garber, Megan E., Lara Rajeev, Alexey E. Kazakov, et al.. (2018). Multiple signaling systems target a core set of transition metal homeostasis genes using similar binding motifs. Molecular Microbiology. 107(6). 704–717. 14 indexed citations
11.
Apel, Amanda Reider, Leo d’Espaux, Maren Wehrs, et al.. (2016). A Cas9-based toolkit to program gene expression in Saccharomyces cerevisiae. Nucleic Acids Research. 45(1). 496–508. 219 indexed citations
12.
Haushalter, Robert W., Won-Cheol Kim, Theodore A. Chavkin, et al.. (2014). Production of anteiso-branched fatty acids in Escherichia coli; next generation biofuels with improved cold-flow properties. Metabolic Engineering. 26. 111–118. 50 indexed citations
13.
Venet, Fabienne, Chun‐Shiang Chung, Guillaume Monneret, et al.. (2007). Regulatory T cell populations in sepsis and trauma. Journal of Leukocyte Biology. 83(3). 523–535. 167 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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