Neville E. Sanjana

26.1k total citations · 10 hit papers
74 papers, 12.5k citations indexed

About

Neville E. Sanjana is a scholar working on Molecular Biology, Plant Science and Cellular and Molecular Neuroscience. According to data from OpenAlex, Neville E. Sanjana has authored 74 papers receiving a total of 12.5k indexed citations (citations by other indexed papers that have themselves been cited), including 62 papers in Molecular Biology, 8 papers in Plant Science and 6 papers in Cellular and Molecular Neuroscience. Recurrent topics in Neville E. Sanjana's work include CRISPR and Genetic Engineering (45 papers), RNA and protein synthesis mechanisms (19 papers) and Single-cell and spatial transcriptomics (15 papers). Neville E. Sanjana is often cited by papers focused on CRISPR and Genetic Engineering (45 papers), RNA and protein synthesis mechanisms (19 papers) and Single-cell and spatial transcriptomics (15 papers). Neville E. Sanjana collaborates with scholars based in United States, Israel and Japan. Neville E. Sanjana's co-authors include Feng Zhang, Ophir Shalem, Xi Shi, Tarjei S. Mikkelsen, David Scott, Dirk Heckl, Ella Hartenian, John G. Doench, David E. Root and Benjamin L. Ebert and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Neville E. Sanjana

71 papers receiving 12.4k citations

Hit Papers

Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells 2012 2026 2016 2021 2013 2014 2015 2017 2015 1000 2.0k 3.0k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Neville E. Sanjana United States 35 10.3k 1.7k 1.6k 1.3k 1.1k 74 12.5k
Luke A. Gilbert United States 38 16.2k 1.6× 2.8k 1.6× 1.4k 0.9× 1.2k 0.9× 796 0.7× 68 18.0k
Naomi Habib United States 20 15.6k 1.5× 3.2k 1.9× 1.3k 0.8× 986 0.8× 961 0.9× 34 18.0k
Ophir Shalem United States 21 11.6k 1.1× 2.3k 1.4× 745 0.5× 1.1k 0.8× 830 0.8× 42 13.1k
Tae‐Young Roh South Korea 34 11.9k 1.2× 1.7k 1.0× 1.7k 1.0× 843 0.6× 2.4k 2.2× 83 14.8k
Jason Wright United States 12 7.9k 0.8× 1.5k 0.9× 936 0.6× 843 0.6× 914 0.8× 16 9.8k
Silvana Konermann United States 18 15.2k 1.5× 2.7k 1.6× 791 0.5× 795 0.6× 524 0.5× 26 16.3k
Le Cong United States 34 17.5k 1.7× 4.1k 2.4× 706 0.4× 1.4k 1.1× 1.2k 1.1× 60 20.7k
Vineeta Agarwala United States 10 10.0k 1.0× 2.2k 1.3× 811 0.5× 976 0.7× 1.0k 0.9× 18 12.0k
Sayda M. Elbashir United States 16 12.4k 1.2× 2.1k 1.2× 2.7k 1.7× 752 0.6× 1.3k 1.2× 26 14.2k
Lei S. Qi United States 55 19.9k 1.9× 3.9k 2.3× 1.0k 0.6× 1.1k 0.8× 649 0.6× 186 22.2k

Countries citing papers authored by Neville E. Sanjana

Since Specialization
Citations

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

Fields of papers citing papers by Neville E. Sanjana

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Neville E. Sanjana

This figure shows the co-authorship network connecting the top 25 collaborators of Neville E. Sanjana. A scholar is included among the top collaborators of Neville E. Sanjana 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 Neville E. Sanjana. Neville E. Sanjana 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.
Müller, Simon, et al.. (2025). Precise RNA targeting with CRISPR–Cas13d. Nature Biotechnology. 44(1). 64–69. 6 indexed citations
2.
Schertzer, Megan D., Keren Isaev, Laura C. J. Pereira, et al.. (2025). Cas13d-mediated isoform-specific RNA knockdown with a unified computational and experimental toolbox. Nature Communications. 16(1). 6948–6948. 2 indexed citations
4.
Yan, Rachel, Wang Xiao, Xinhe Xue, et al.. (2024). Pooled CRISPR screens with joint single-nucleus chromatin accessibility and transcriptome profiling. Nature Biotechnology. 43(10). 1628–1634. 5 indexed citations
5.
Morris, John, Christina M. Caragine, Zharko Daniloski, et al.. (2023). Discovery of target genes and pathways at GWAS loci by pooled single-cell CRISPR screens. Science. 380(6646). eadh7699–eadh7699. 84 indexed citations
6.
Garipler, Görkem, Congyi Lu, Simon E. Vidal, et al.. (2022). The BTB transcription factors ZBTB11 and ZFP131 maintain pluripotency by repressing pro-differentiation genes. Cell Reports. 38(11). 110524–110524. 11 indexed citations
7.
Wessels, Hans‐Hermann, Alejandro Méndez‐Mancilla, Yuhan Hao, et al.. (2022). Efficient combinatorial targeting of RNA transcripts in single cells with Cas13 RNA Perturb-seq. Nature Methods. 20(1). 86–94. 47 indexed citations
8.
Daniloski, Zharko, Tristan X. Jordan, Xinyi Guo, et al.. (2021). The Spike D614G mutation increases SARS-CoV-2 infection of multiple human cell types. eLife. 10. 149 indexed citations
9.
Liscovitch‐Brauer, Noa, Antonino Montalbano, Alejandro Méndez‐Mancilla, et al.. (2021). Profiling the genetic determinants of chromatin accessibility with scalable single-cell CRISPR screens. Nature Biotechnology. 39(10). 1270–1277. 64 indexed citations
10.
Méndez‐Mancilla, Alejandro, Hans‐Hermann Wessels, Mateusz Legut, et al.. (2021). Chemically modified guide RNAs enhance CRISPR-Cas13 knockdown in human cells. Cell chemical biology. 29(2). 321–327.e4. 39 indexed citations
11.
Wessels, Hans‐Hermann, et al.. (2020). Massively parallel Cas13 screens reveal principles for guide RNA design. Nature Biotechnology. 38(6). 722–727. 272 indexed citations breakdown →
12.
Legut, Mateusz & Neville E. Sanjana. (2019). Immunomagnetic cell sorting. Nature Biomedical Engineering. 3(10). 759–760. 5 indexed citations
13.
Erb, Michael A., Thomas G. Scott, Bin E. Li, et al.. (2017). Transcription control by the ENL YEATS domain in acute leukaemia. RePEc: Research Papers in Economics. 1 indexed citations
14.
Jain, Isha H., L. Zazzeron, Kristen Alexa, et al.. (2016). Hypoxia as a therapy for mitochondrial disease. DSpace@MIT (Massachusetts Institute of Technology). 1 indexed citations
15.
Erb, Michael A., Georg Winter, Shiva Dastjerdi, et al.. (2016). Transcription control by the ENL YEATS domain in acute leukemia. European Journal of Cancer. 69. S85–S86.
16.
Canver, Matthew C., Elenoe C. Smith, Falak Sher, et al.. (2015). BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis. RePEc: Research Papers in Economics. 2 indexed citations
17.
Shalem, Ophir, Neville E. Sanjana, & Feng Zhang. (2015). High-throughput functional genomics using CRISPR–Cas9. DSpace@MIT (Massachusetts Institute of Technology). 16. 26 indexed citations
18.
Busskamp, Volker, Nathan E. Lewis, Patrick Guye, et al.. (2014). Rapid neurogenesis through transcriptional activation in human stem cells. Molecular Systems Biology. 10(11). 760–760. 158 indexed citations
19.
Shalem, Ophir, Neville E. Sanjana, Ella Hartenian, et al.. (2013). Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells. Science. 343(6166). 84–87. 3656 indexed citations breakdown →
20.
Sanjana, Neville E. & Joshua B. Tenenbaum. (2002). Bayesian Models of Inductive Generalization. Neural Information Processing Systems. 15. 59–66. 21 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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