Joshua R. Elmore

2.0k total citations
17 papers, 1.3k citations indexed

About

Joshua R. Elmore is a scholar working on Molecular Biology, Biomedical Engineering and Genetics. According to data from OpenAlex, Joshua R. Elmore has authored 17 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 6 papers in Biomedical Engineering and 3 papers in Genetics. Recurrent topics in Joshua R. Elmore's work include CRISPR and Genetic Engineering (7 papers), Biofuel production and bioconversion (6 papers) and Microbial Metabolic Engineering and Bioproduction (6 papers). Joshua R. Elmore is often cited by papers focused on CRISPR and Genetic Engineering (7 papers), Biofuel production and bioconversion (6 papers) and Microbial Metabolic Engineering and Bioproduction (6 papers). Joshua R. Elmore collaborates with scholars based in United States, France and Türkiye. Joshua R. Elmore's co-authors include Adam M. Guss, Rebecca M. Terns, Michael P. Terns, Gregg T. Beckham, Davinia Salvachúa, Hong Li, Nancy Ramia, Jay D. Huenemann, Gara N. Dexter and George Peabody and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Genes & Development.

In The Last Decade

Joshua R. Elmore

17 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Joshua R. Elmore United States 14 950 363 195 177 152 17 1.3k
Dahe Zhao China 19 680 0.7× 110 0.3× 346 1.8× 141 0.8× 193 1.3× 45 1.1k
N. Nandhagopal India 12 531 0.6× 639 1.8× 143 0.7× 73 0.4× 50 0.3× 20 1.2k
Beatriz S. Méndez Argentina 20 726 0.8× 235 0.6× 400 2.1× 207 1.2× 273 1.8× 52 1.3k
Jacob A. Englaender United States 9 582 0.6× 115 0.3× 146 0.7× 103 0.6× 153 1.0× 10 857
Steven Slater United States 18 1.1k 1.2× 251 0.7× 704 3.6× 475 2.7× 301 2.0× 22 1.7k
Calvin A. Henard United States 23 703 0.7× 332 0.9× 43 0.2× 110 0.6× 50 0.3× 34 1.3k
Leonilde M. Moreira Portugal 21 597 0.6× 152 0.4× 53 0.3× 99 0.6× 55 0.4× 46 1.5k
Stevens M. Brumbley Australia 19 739 0.8× 434 1.2× 183 0.9× 51 0.3× 77 0.5× 50 1.4k
Changwei Zhang China 21 764 0.8× 113 0.3× 60 0.3× 107 0.6× 38 0.3× 92 1.7k
Corinne Vander Wauven Belgium 17 516 0.5× 67 0.2× 159 0.8× 206 1.2× 142 0.9× 26 976

Countries citing papers authored by Joshua R. Elmore

Since Specialization
Citations

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

Fields of papers citing papers by Joshua R. Elmore

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joshua R. Elmore. 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 Joshua R. Elmore. The network helps show where Joshua R. Elmore may publish in the future.

Co-authorship network of co-authors of Joshua R. Elmore

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

All Works

17 of 17 papers shown
1.
Anderson, Lindsey, Yasuhiro Oda, William Nelson, et al.. (2024). Profiling sorghum-microbe interactions with a specialized photoaffinity probe identifies key sorgoleone binders in Acinetobacter pittii. Applied and Environmental Microbiology. 90(10). e0102624–e0102624. 1 indexed citations
2.
Fonseca-García, Citlali, Joshua R. Elmore, Ryan McClure, et al.. (2024). Defined synthetic microbial communities colonize and benefit field-grown sorghum. The ISME Journal. 18(1). 10 indexed citations
3.
Elmore, Joshua R., Gara N. Dexter, Jay D. Huenemann, et al.. (2023). High-throughput genetic engineering of nonmodel and undomesticated bacteria via iterative site-specific genome integration. Science Advances. 9(10). eade1285–eade1285. 46 indexed citations
4.
Elmore, Joshua R., et al.. (2022). Engineering Citrobacter freundii using CRISPR/Cas9 system. Journal of Microbiological Methods. 200. 106533–106533. 4 indexed citations
5.
Elmore, Joshua R., Gara N. Dexter, Davinia Salvachúa, et al.. (2021). Production of itaconic acid from alkali pretreated lignin by dynamic two stage bioconversion. Nature Communications. 12(1). 2261–2261. 87 indexed citations
6.
Werner, Allison Z., Thomas D. Mand, Isabel Pardo, et al.. (2021). Tandem chemical deconstruction and biological upcycling of poly(ethylene terephthalate) to β-ketoadipic acid by Pseudomonas putida KT2440. Metabolic Engineering. 67. 250–261. 130 indexed citations
7.
Wilton, Rosemarie, Yuqian Gao, Nathalie Munoz Munoz, et al.. (2020). Evaluation of chromosomal insertion loci in the Pseudomonas putida KT2440 genome for predictable biosystems design. Metabolic Engineering Communications. 11. e00139–e00139. 23 indexed citations
8.
Elmore, Joshua R., Gara N. Dexter, Davinia Salvachúa, et al.. (2020). Engineered Pseudomonas putida simultaneously catabolizes five major components of corn stover lignocellulose: Glucose, xylose, arabinose, p-coumaric acid, and acetic acid. Metabolic Engineering. 62. 62–71. 86 indexed citations
9.
Bentley, Gayle J., Niju Narayanan, Ramesh K. Jha, et al.. (2020). Engineering glucose metabolism for enhanced muconic acid production in Pseudomonas putida KT2440. Metabolic Engineering. 59. 64–75. 90 indexed citations
10.
Peabody, George, et al.. (2019). Engineered Pseudomonas putida KT2440 co-utilizes galactose and glucose. Biotechnology for Biofuels. 12(1). 295–295. 18 indexed citations
11.
Salvachúa, Davinia, Thomas Rydzak, Brenna A. Black, et al.. (2019). Metabolic engineering of Pseudomonas putida for increased polyhydroxyalkanoate production from lignin. Microbial Biotechnology. 13(1). 290–298. 162 indexed citations
12.
Elmore, Joshua R., et al.. (2017). Development of a high efficiency integration system and promoter library for rapid modification of Pseudomonas putida KT2440. Metabolic Engineering Communications. 5. 1–8. 89 indexed citations
13.
Elmore, Joshua R., et al.. (2016). Bipartite recognition of target RNAs activates DNA cleavage by the Type III-B CRISPR–Cas system. Genes & Development. 30(4). 447–459. 177 indexed citations
14.
Elmore, Joshua R., et al.. (2015). DNA targeting by the type I-G and type I-A CRISPR–Cas systems ofPyrococcus furiosus. Nucleic Acids Research. 43(21). gkv1140–gkv1140. 34 indexed citations
15.
Ramia, Nancy, Michael Spilman, Li Tang, et al.. (2014). Essential Structural and Functional Roles of the Cmr4 Subunit in RNA Cleavage by the Cmr CRISPR-Cas Complex. Cell Reports. 9(5). 1610–1617. 51 indexed citations
16.
Elmore, Joshua R., Yuusuke Yokooji, Takaaki Sato, et al.. (2013). Programmable plasmid interference by the CRISPR-Cas system in Thermococcus kodakarensis . RNA Biology. 10(5). 828–840. 31 indexed citations
17.
Hale, Caryn, Sonali Majumdar, Joshua R. Elmore, et al.. (2012). Essential Features and Rational Design of CRISPR RNAs that Function with the Cas RAMP Module Complex to Cleave RNAs. Molecular Cell. 45(3). 292–302. 230 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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