Purnima Ravisankar

1.3k total citations · 2 hit papers
6 papers, 768 citations indexed

About

Purnima Ravisankar is a scholar working on Molecular Biology, Social Psychology and Virology. According to data from OpenAlex, Purnima Ravisankar has authored 6 papers receiving a total of 768 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 1 paper in Social Psychology and 1 paper in Virology. Recurrent topics in Purnima Ravisankar's work include CRISPR and Genetic Engineering (5 papers), RNA and protein synthesis mechanisms (2 papers) and Advanced biosensing and bioanalysis techniques (2 papers). Purnima Ravisankar is often cited by papers focused on CRISPR and Genetic Engineering (5 papers), RNA and protein synthesis mechanisms (2 papers) and Advanced biosensing and bioanalysis techniques (2 papers). Purnima Ravisankar collaborates with scholars based in United States, Austria and Canada. Purnima Ravisankar's co-authors include Jun Yan, Britt Adamson, Jonathan S. Weissman, Jeffrey A. Hussmann, Peter J. Chen, David R. Liu, Cidi Chen, Friederike Knipping, Mustafa Şahin and Gregory A. Newby and has published in prestigious journals such as Nature, Cell and Nature Biotechnology.

In The Last Decade

Purnima Ravisankar

6 papers receiving 761 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.

Enhanced prime editing systems by manipulating cellular determinants of editing outcomes

2021 488 citations Peter J. Chen, Jeffrey A. Hussmann et al. Cell profile →
Improving prime editing with an endogenous small RNA-binding protein

2024 107 citations Jun Yan, Paul Oyler et al. Nature profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Purnima Ravisankar United States	6	722	202	89	47	47	6	768
Alvin Hsu United States	6	691 1.0×	192 1.0×	91 1.0×	42 0.9×	44 0.9×	8	744
Gemma L. Curie United States	2	751 1.0×	168 0.8×	73 0.8×	65 1.4×	38 0.8×	3	787
Amanda L. Waterbury United States	7	669 0.9×	197 1.0×	68 0.8×	68 1.4×	34 0.7×	11	705
Gayoung Baek South Korea	8	945 1.3×	284 1.4×	83 0.9×	66 1.4×	36 0.8×	8	989
Cidi Chen United States	4	491 0.7×	155 0.8×	66 0.7×	36 0.8×	29 0.6×	7	516
Karl Petri United States	10	485 0.7×	165 0.8×	56 0.6×	33 0.7×	54 1.1×	14	528
Katelynn R. Kazane United States	7	552 0.8×	135 0.7×	51 0.6×	64 1.4×	51 1.1×	9	596
Graham J. Ray United States	4	834 1.2×	180 0.9×	64 0.7×	71 1.5×	51 1.1×	4	875
Jianhang Yin China	10	509 0.7×	125 0.6×	77 0.9×	69 1.5×	44 0.9×	12	545
Su Bin Moon South Korea	7	623 0.9×	132 0.7×	79 0.9×	87 1.9×	25 0.5×	8	650

Countries citing papers authored by Purnima Ravisankar

Since Specialization

Citations

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

Fields of papers citing papers by Purnima Ravisankar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Purnima Ravisankar

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

All Works

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6 of 6 papers shown

1.

Yan, Jun, Paul Oyler, Purnima Ravisankar, et al.. (2024). Improving prime editing with an endogenous small RNA-binding protein. Nature. 628(8008). 639–647. 107 indexed citations breakdown →

2.

Sanidad, Katherine Z., Aparna Ananthanarayanan, Tingting Li, et al.. (2024). Gut bacteria–derived serotonin promotes immune tolerance in early life. Science Immunology. 9(93). eadj4775–eadj4775. 43 indexed citations

3.

Simpson, Danny, Purnima Ravisankar, Sabrina C. Solley, et al.. (2024). A benchmarked, high-efficiency prime editing platform for multiplexed dropout screening. Nature Methods. 22(1). 92–101. 11 indexed citations

4.

Kim, Rebecca, Bradley Joyce, Peter J. Chen, et al.. (2024). Efficient prime editing in two-cell mouse embryos using PEmbryo. Nature Biotechnology. 42(12). 1822–1830. 10 indexed citations

5.

Chen, Peter J., Jeffrey A. Hussmann, Jun Yan, et al.. (2021). Enhanced prime editing systems by manipulating cellular determinants of editing outcomes. Cell. 184(22). 5635–5652.e29. 488 indexed citations breakdown →

6.

Hussmann, Jeffrey A., Ling Jia, Purnima Ravisankar, et al.. (2021). Mapping the genetic landscape of DNA double-strand break repair. Cell. 184(22). 5653–5669.e25. 109 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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