Suma Chakravarthy

2.8k total citations
34 papers, 2.1k citations indexed

About

Suma Chakravarthy is a scholar working on Plant Science, Molecular Biology and Biotechnology. According to data from OpenAlex, Suma Chakravarthy has authored 34 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Plant Science, 11 papers in Molecular Biology and 6 papers in Biotechnology. Recurrent topics in Suma Chakravarthy's work include Plant-Microbe Interactions and Immunity (24 papers), Plant Pathogenic Bacteria Studies (17 papers) and Legume Nitrogen Fixing Symbiosis (9 papers). Suma Chakravarthy is often cited by papers focused on Plant-Microbe Interactions and Immunity (24 papers), Plant Pathogenic Bacteria Studies (17 papers) and Legume Nitrogen Fixing Symbiosis (9 papers). Suma Chakravarthy collaborates with scholars based in United States, India and Canada. Suma Chakravarthy's co-authors include Gregory B. Martin, Alan Collmer, Xiaohua He, Yu Han, Mary C. Wildermuth, Caimei Yang, Yongqiang Gu, Ying-Tsu Loh, Mark D’Ascenzo and Robert P. Tuori and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The EMBO Journal and PLoS ONE.

In The Last Decade

Suma Chakravarthy

34 papers receiving 2.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Suma Chakravarthy United States 20 1.9k 744 172 148 71 34 2.1k
Ghanasyam Rallapalli United Kingdom 16 1.5k 0.8× 793 1.1× 108 0.6× 96 0.6× 52 0.7× 19 1.9k
Lennart Eschen‐Lippold Germany 28 2.0k 1.0× 885 1.2× 143 0.8× 44 0.3× 88 1.2× 48 2.2k
Cécile Segonzac South Korea 22 3.2k 1.7× 936 1.3× 167 1.0× 96 0.6× 84 1.2× 47 3.4k
Hernán G. Rosli Argentina 16 1.3k 0.7× 539 0.7× 84 0.5× 101 0.7× 43 0.6× 24 1.5k
Roger Thilmony United States 24 2.8k 1.5× 1.1k 1.4× 224 1.3× 216 1.5× 161 2.3× 46 3.2k
Frederikke Gro Malinovsky Denmark 15 1.9k 1.0× 774 1.0× 148 0.9× 44 0.3× 57 0.8× 15 2.1k
Kamil Witek United Kingdom 22 2.0k 1.1× 488 0.7× 162 0.9× 105 0.7× 51 0.7× 34 2.1k
Qingzhen Zhao China 17 1.9k 1.0× 1.4k 1.9× 166 1.0× 45 0.3× 78 1.1× 26 2.4k
Zhendong Tian China 26 1.8k 1.0× 690 0.9× 202 1.2× 44 0.3× 54 0.8× 74 2.0k
Tianqiao Song China 14 1.3k 0.7× 430 0.6× 288 1.7× 55 0.4× 50 0.7× 44 1.4k

Countries citing papers authored by Suma Chakravarthy

Since Specialization
Citations

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

Fields of papers citing papers by Suma Chakravarthy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Suma Chakravarthy

This figure shows the co-authorship network connecting the top 25 collaborators of Suma Chakravarthy. A scholar is included among the top collaborators of Suma Chakravarthy 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 Suma Chakravarthy. Suma Chakravarthy 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.
Waters, Stephen P., et al.. (2021). Recommendations for Science-Based Safety Assessment of Genetically Modified (GM) Plants for Food and Feed Uses. 9(1). 16–21. 12 indexed citations
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Brauer, Elizabeth K., George V. Popescu, Dharmendra Kumar Singh, et al.. (2018). Integrative network-centric approach reveals signaling pathways associated with plant resistance and susceptibility to Pseudomonas syringae. PLoS Biology. 16(12). e2005956–e2005956. 9 indexed citations
6.
Senthil‐Kumar, Muthappa, Mingyi Wang, Venkategowda Ramegowda, et al.. (2018). Virus‐induced gene silencing database for phenomics and functional genomics in Nicotiana benthamiana. Plant Direct. 2(4). e00055–e00055. 14 indexed citations
7.
Hatsugai, Noriyuki, Daisuke Igarashi, Keisuke Mase, et al.. (2017). A plant effector‐triggered immunity signaling sector is inhibited by pattern‐triggered immunity. The EMBO Journal. 36(18). 2758–2769. 69 indexed citations
8.
Chakravarthy, Suma, Bethany Huot, & Brian H. Kvitko. (2017). Effector Translocation: Cya Reporter Assay. Methods in molecular biology. 1615. 473–487. 12 indexed citations
9.
Butcher, Bronwyn G., et al.. (2016). Disruption of the carA gene in Pseudomonas syringae results in reduced fitness and alters motility. BMC Microbiology. 16(1). 194–194. 12 indexed citations
10.
Wei, Hai‐Lei, Suma Chakravarthy, Johannes Mathieu, et al.. (2015). Pseudomonas syringae pv. tomato DC3000 Type III Secretion Effector Polymutants Reveal an Interplay between HopAD1 and AvrPtoB. Cell Host & Microbe. 17(6). 752–762. 95 indexed citations
11.
Chakravarthy, Suma, Hai‐Lei Wei, Hoang C.B. Nguyen, et al.. (2014). Global Analysis of the HrpL Regulon in the Plant Pathogen Pseudomonas syringae pv. tomato DC3000 Reveals New Regulon Members with Diverse Functions. PLoS ONE. 9(8). e106115–e106115. 43 indexed citations
12.
Bao, Zhongmeng, Paul Stodghill, Christopher R. Myers, et al.. (2014). Genomic Plasticity Enables Phenotypic Variation of Pseudomonas syringae pv. tomato DC3000. PLoS ONE. 9(2). e86628–e86628. 11 indexed citations
13.
Torre, Fernando De la, et al.. (2013). The Tomato Calcium Sensor Cbl10 and Its Interacting Protein Kinase Cipk6 Define a Signaling Pathway in Plant Immunity. The Plant Cell. 25(7). 2748–2764. 120 indexed citations
14.
Lee, Sangjik, C. M. B. Damasceno, Suma Chakravarthy, et al.. (2010). A secreted effector protein (SNE1) from Phytophthora infestans is a broadly acting suppressor of programmed cell death. The Plant Journal. 62(3). 357–366. 101 indexed citations
15.
Chakravarthy, Suma, André C. Velásquez, Sophia Ekengren, Alan Collmer, & Gregory B. Martin. (2010). Identification of Nicotiana benthamiana Genes Involved in Pathogen-Associated Molecular Pattern–Triggered Immunity. Molecular Plant-Microbe Interactions. 23(6). 715–726. 64 indexed citations
16.
Chakravarthy, Suma, André C. Velásquez, & Gregory B. Martin. (2009). Assay for Pathogen-Associated Molecular Pattern (PAMP)-Triggered Immunity (PTI) in Plants. Journal of Visualized Experiments. 4 indexed citations
17.
Bhullar, Simran, Suma Chakravarthy, Deepak Pental, & Pradeep Kumar Burma. (2009). Analysis of promoter activity in transgenic plants by normalizing expression with a reference gene: anomalies due to the influence of the test promoter on the reference promoter. Journal of Biosciences. 34(6). 953–962. 12 indexed citations
18.
Chakravarthy, Suma, André C. Velásquez, & Gregory B. Martin. (2009). Assay for Pathogen-Associated Molecular Pattern (PAMP)-Triggered Immunity (PTI) in Plants. Journal of Visualized Experiments. 20 indexed citations
19.
Bhullar, Simran, et al.. (2007). Functional analysis of cauliflower mosaic virus 35S promoter: re‐evaluation of the role of subdomains B5, B4 and B2 in promoter activity. Plant Biotechnology Journal. 5(6). 696–708. 33 indexed citations
20.

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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