Swapna Uplekar

2.2k total citations · 1 hit paper
22 papers, 1.2k citations indexed

About

Swapna Uplekar is a scholar working on Infectious Diseases, Epidemiology and Molecular Biology. According to data from OpenAlex, Swapna Uplekar has authored 22 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Infectious Diseases, 14 papers in Epidemiology and 9 papers in Molecular Biology. Recurrent topics in Swapna Uplekar's work include Tuberculosis Research and Epidemiology (16 papers), Mycobacterium research and diagnosis (12 papers) and RNA and protein synthesis mechanisms (5 papers). Swapna Uplekar is often cited by papers focused on Tuberculosis Research and Epidemiology (16 papers), Mycobacterium research and diagnosis (12 papers) and RNA and protein synthesis mechanisms (5 papers). Swapna Uplekar collaborates with scholars based in Switzerland, United States and United Kingdom. Swapna Uplekar's co-authors include Stewart T. Cole, Ruben C. Hartkoorn, Claudia Sala, Jacques Rougemont, Florence Pojer, Jeffrey M. Chen, Christophe Guilhot, João Neres, Sophie Magnet and Stefanie Boy‐Röttger and has published in prestigious journals such as Nucleic Acids Research, PLoS ONE and Scientific Reports.

In The Last Decade

Swapna Uplekar

22 papers receiving 1.2k 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.

Cross-Resistance between Clofazimine and Bedaquiline through Upregulation of MmpL5 in Mycobacterium tuberculosis

2014 334 citations Ruben C. Hartkoorn, Swapna Uplekar et al. Antimicrobial Agents and Chemotherapy profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Swapna Uplekar Switzerland	14	905	740	490	180	171	22	1.2k
Jong Seok Lee South Korea	14	882 1.0×	717 1.0×	543 1.1×	200 1.1×	153 0.9×	26	1.2k
Bhavna G. Gordhan South Africa	13	742 0.8×	627 0.8×	391 0.8×	151 0.8×	194 1.1×	33	1.1k
Omar Vandal United States	12	660 0.7×	498 0.7×	417 0.9×	109 0.6×	165 1.0×	13	1.0k
Angelo Scorpio United States	13	1.0k 1.1×	987 1.3×	665 1.4×	259 1.4×	140 0.8×	15	1.6k
Marı́a de la Paz Santangelo Argentina	15	889 1.0×	762 1.0×	516 1.1×	191 1.1×	150 0.9×	34	1.3k
Laura Inés Klepp Argentina	16	883 1.0×	735 1.0×	427 0.9×	192 1.1×	113 0.7×	31	1.1k
Jeffrey M. Chen Canada	18	707 0.8×	638 0.9×	373 0.8×	166 0.9×	175 1.0×	27	1.1k
Amol Amin United States	13	782 0.9×	671 0.9×	376 0.8×	275 1.5×	177 1.0×	13	1.1k
Mandira Varma‐Basil India	19	965 1.1×	844 1.1×	377 0.8×	356 2.0×	162 0.9×	67	1.3k
Tige R. Rustad United States	23	1.6k 1.8×	1.3k 1.7×	869 1.8×	210 1.2×	259 1.5×	33	2.0k

Countries citing papers authored by Swapna Uplekar

Since Specialization

Citations

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

Fields of papers citing papers by Swapna Uplekar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Swapna Uplekar

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

All Works

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20 of 20 papers shown

Georghiou, Sophia B., Nestani Tukvadze, Camilla Rodrigues, et al.. (2025). Targeted next-generation sequencing for drug-resistant tuberculosis diagnosis: implementation considerations for bacterial load, regimen selection and diagnostic algorithm placement. BMJ Global Health. 10(11). e019135–e019135. 1 indexed citations

Carpi, Giovanna, Marva Seifert, Swapna Uplekar, et al.. (2025). Comparative assessment of line probe assays and targeted next-generation sequencing in drug-resistant tuberculosis diagnosis. EBioMedicine. 119. 105875–105875. 1 indexed citations

Colman, Rebecca E., Marva Seifert, Sophia B. Georghiou, et al.. (2024). Evaluating culture-free targeted next-generation sequencing for diagnosing drug-resistant tuberculosis: a multicentre clinical study of two end-to-end commercial workflows. The Lancet Infectious Diseases. 25(3). 325–334. 13 indexed citations

Akande, Oluwatosin Wuraola, Babak Afrough, Jane Cunningham, et al.. (2024). Investing in health preparedness, response and resilience: a genomics costing tool focused on next generation sequencing. Frontiers in Public Health. 12. 1404243–1404243. 3 indexed citations

Uplekar, Swapna, Zeeshan Ali Syed, Surya K Sharma, et al.. (2024). Antimalarial drug resistance profiling of Plasmodium falciparum infections in India using Ion Torrent deep sequencing. 2. 2 indexed citations

Chindelevitch, Leonid, Maarten van Dongen, Anita Suresh, et al.. (2023). Ten simple rules for the sharing of bacterial genotype—Phenotype data on antimicrobial resistance. PLoS Computational Biology. 19(6). e1011129–e1011129. 4 indexed citations

Brankin, Alice, Marva Seifert, Sophia B. Georghiou, et al.. (2022). In silico evaluation of WHO-endorsed molecular methods to detect drug resistant tuberculosis. Scientific Reports. 12(1). 17741–17741. 4 indexed citations

Cabibbe, Andrea Maurizio, Andrea Spitaleri, Simone Battaglia, et al.. (2020). Application of Targeted Next-Generation Sequencing Assay on a Portable Sequencing Platform for Culture-Free Detection of Drug-Resistant Tuberculosis from Clinical Samples. Journal of Clinical Microbiology. 58(10). 89 indexed citations

Benjak, Andrej, Swapna Uplekar, Ming Zhang, et al.. (2016). Genomic and transcriptomic analysis of the streptomycin-dependent Mycobacterium tuberculosis strain 18b. BMC Genomics. 17(1). 190–190. 15 indexed citations

10.

Carlton, Jane M., Sarah K. Volkman, Swapna Uplekar, et al.. (2015). Population Genetics, Evolutionary Genomics, and Genome-Wide Studies of Malaria: A View Across the International Centers of Excellence for Malaria Research. American Journal of Tropical Medicine and Hygiene. 93(3_Suppl). 87–98. 9 indexed citations

11.

Solans, Luis, Jesús Gonzalo‐Asensio, Claudia Sala, et al.. (2014). The PhoP-Dependent ncRNA Mcr7 Modulates the TAT Secretion System in Mycobacterium tuberculosis. PLoS Pathogens. 10(5). e1004183–e1004183. 116 indexed citations

12.

Hartkoorn, Ruben C., Swapna Uplekar, & Stewart T. Cole. (2014). Cross-Resistance between Clofazimine and Bedaquiline through Upregulation of MmpL5 in Mycobacterium tuberculosis. Antimicrobial Agents and Chemotherapy. 58(5). 2979–2981. 334 indexed citations breakdown →

13.

Uplekar, Swapna, Jacques Rougemont, Stewart T. Cole, & Claudia Sala. (2012). High-resolution transcriptome and genome-wide dynamics of RNA polymerase and NusA in Mycobacterium tuberculosis. Nucleic Acids Research. 41(2). 961–977. 39 indexed citations

14.

Blasco, Benjamin, Jeffrey M. Chen, Ruben C. Hartkoorn, et al.. (2012). Virulence Regulator EspR of Mycobacterium tuberculosis Is a Nucleoid-Associated Protein. PLoS Pathogens. 8(3). e1002621–e1002621. 106 indexed citations

15.

Chen, Jeffrey M., Swapna Uplekar, Stephen V. Gordon, & Stewart T. Cole. (2012). A Point Mutation in cycA Partially Contributes to the D-cycloserine Resistance Trait of Mycobacterium bovis BCG Vaccine Strains. PLoS ONE. 7(8). e43467–e43467. 41 indexed citations

16.

Hartkoorn, Ruben C., Claudia Sala, João Neres, et al.. (2012). Towards a new tuberculosis drug: pyridomycin – nature's isoniazid. EMBO Molecular Medicine. 4(10). 1032–1042. 143 indexed citations

17.

Hartkoorn, Ruben C., Claudia Sala, Swapna Uplekar, et al.. (2012). Genome-Wide Definition of the SigF Regulon in Mycobacterium tuberculosis. Journal of Bacteriology. 194(8). 2001–2009. 38 indexed citations

18.

Kirksey, Meghan, Anna D. Tischler, Roxane Siméone, et al.. (2011). Spontaneous Phthiocerol Dimycocerosate-Deficient Variants of Mycobacterium tuberculosis Are Susceptible to Gamma Interferon-Mediated Immunity. Infection and Immunity. 79(7). 2829–2838. 61 indexed citations

19.

Uplekar, Swapna, et al.. (2011). Comparative Genomics ofesxGenes from Clinical Isolates of Mycobacterium tuberculosis Provides Evidence for Gene Conversion and Epitope Variation. Infection and Immunity. 79(10). 4042–4049. 55 indexed citations

20.

Uplekar, Swapna, Andrew Keniry, Thierry Garnier, et al.. (2009). A Comprehensive Survey of Single Nucleotide Polymorphisms (SNPs) across Mycobacterium bovis Strains and M. bovis BCG Vaccine Strains Refines the Genealogy and Defines a Minimal Set of SNPs That Separate Virulent M. bovis Strains and M. bovis BCG Strains. Infection and Immunity. 77(5). 2230–2238. 52 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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