Traver Hart

11.3k total citations · 2 hit papers
61 papers, 4.8k citations indexed

About

Traver Hart is a scholar working on Molecular Biology, Oncology and Genetics. According to data from OpenAlex, Traver Hart has authored 61 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Molecular Biology, 9 papers in Oncology and 9 papers in Genetics. Recurrent topics in Traver Hart's work include CRISPR and Genetic Engineering (30 papers), Bioinformatics and Genomic Networks (10 papers) and Single-cell and spatial transcriptomics (8 papers). Traver Hart is often cited by papers focused on CRISPR and Genetic Engineering (30 papers), Bioinformatics and Genomic Networks (10 papers) and Single-cell and spatial transcriptomics (8 papers). Traver Hart collaborates with scholars based in United States, Canada and China. Traver Hart's co-authors include Jason Moffat, Edward M. Marcotte, Kevin R. Brown, Arun Ramani, Stéphane Angers, Insuk Lee, Zachary Steinhart, Eiru Kim, Medina Colic and Graham MacLeod and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Traver Hart

61 papers receiving 4.7k citations

Hit Papers

High-Resolution CRISPR Screens Reveal Fitness Genes and G... 2015 2026 2018 2022 2015 2023 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Traver Hart United States 28 3.8k 819 671 562 456 61 4.8k
Kevin R. Brown Canada 25 3.7k 1.0× 565 0.7× 660 1.0× 195 0.3× 377 0.8× 59 4.5k
Céline Lefèbvre France 29 2.6k 0.7× 643 0.8× 675 1.0× 548 1.0× 414 0.9× 54 3.8k
Sabrina L. Spencer United States 25 2.6k 0.7× 442 0.5× 855 1.3× 208 0.4× 214 0.5× 45 3.7k
Nadine K. Kolas United States 15 5.1k 1.3× 596 0.7× 813 1.2× 170 0.3× 648 1.4× 18 5.8k
Christine Sers Germany 41 2.6k 0.7× 987 1.2× 1.4k 2.1× 550 1.0× 266 0.6× 114 4.5k
Thomas F. Westbrook United States 28 4.4k 1.1× 1.5k 1.9× 1.4k 2.1× 906 1.6× 541 1.2× 41 5.9k
Berend Snijder Switzerland 23 2.4k 0.6× 729 0.9× 508 0.8× 727 1.3× 276 0.6× 56 3.9k
Martin L. Miller United States 21 3.2k 0.8× 1.2k 1.5× 914 1.4× 372 0.7× 371 0.8× 32 4.5k
Syed Haider United Kingdom 36 3.3k 0.9× 1.6k 1.9× 1.0k 1.5× 597 1.1× 602 1.3× 111 5.0k
Mathieu Lupien Canada 40 5.3k 1.4× 1.4k 1.7× 1.1k 1.6× 836 1.5× 1.6k 3.4× 101 6.7k

Countries citing papers authored by Traver Hart

Since Specialization
Citations

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

Fields of papers citing papers by Traver Hart

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Traver Hart

This figure shows the co-authorship network connecting the top 25 collaborators of Traver Hart. A scholar is included among the top collaborators of Traver Hart 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 Traver Hart. Traver Hart 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.
Chen, Junjie, et al.. (2025). Z-scores outperform similar methods for analyzing CRISPR paralog synthetic lethality screens. Genome biology. 26(1). 188–188. 2 indexed citations
2.
Lin, Chenchu, Xingdi Ma, Lori Wilson, et al.. (2024). Efficient gene knockout and genetic interaction screening using the in4mer CRISPR/Cas12a multiplex knockout platform. Nature Communications. 15(1). 3577–3577. 26 indexed citations
3.
Dede, Merve & Traver Hart. (2023). Recovering false negatives in CRISPR fitness screens with JLOE. Nucleic Acids Research. 51(4). 1637–1651. 3 indexed citations
4.
Liu, Xiaoguang, Litong Nie, Yilei Zhang, et al.. (2023). Actin cytoskeleton vulnerability to disulfide stress mediates disulfidptosis. Nature Cell Biology. 25(3). 404–414. 648 indexed citations breakdown →
5.
Patel, Lalit R., Sabrina A. Stratton, Megan M. McLaughlin, et al.. (2023). Genome-wide CRISPR-Cas9 screen analyzed by SLIDER identifies network of repressor complexes that regulate TRIM24. iScience. 26(7). 107126–107126. 1 indexed citations
6.
Colic, Medina, et al.. (2022). PICKLES v3: the updated database of pooled in vitro CRISPR knockout library essentiality screens. Nucleic Acids Research. 51(D1). D1117–D1121. 6 indexed citations
7.
Feng, Xu, Mengfan Tang, Merve Dede, et al.. (2022). Genome-wide CRISPR screens using isogenic cells reveal vulnerabilities conferred by loss of tumor suppressors. Science Advances. 8(19). eabm6638–eabm6638. 25 indexed citations
8.
Hart, Traver, et al.. (2022). Optimal construction of a functional interaction network from pooled library CRISPR fitness screens. BMC Bioinformatics. 23(1). 510–510. 4 indexed citations
9.
Yoshihama, Yohei, Kyle A. LaBella, Eiru Kim, et al.. (2021). AR-negative prostate cancer is vulnerable to loss of JMJD1C demethylase. Proceedings of the National Academy of Sciences. 118(36). 10 indexed citations
10.
Kim, Eiru & Traver Hart. (2021). Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier. Genome Medicine. 13(1). 2–2. 61 indexed citations
11.
Su, Dan, Xu Feng, Medina Colic, et al.. (2020). CRISPR/CAS9-based DNA damage response screens reveal gene-drug interactions. DNA repair. 87. 102803–102803. 26 indexed citations
12.
Nixon, Allison M.L., M. A. McLaughlin, Jennifer Haynes, et al.. (2019). A rapid in vitro methodology for simultaneous target discovery and antibody generation against functional cell subpopulations. Scientific Reports. 9(1). 842–842. 10 indexed citations
13.
Komori, H. Kiyomi, Sarah LaMere, Traver Hart, et al.. (2017). Microdroplet PCR for Highly Multiplexed Targeted Bisulfite Sequencing. Methods in molecular biology. 1708. 333–348. 1 indexed citations
14.
Hart, Traver & Jason Moffat. (2016). BAGEL: a computational framework for identifying essential genes from pooled library screens. BMC Bioinformatics. 17(1). 164–164. 155 indexed citations
15.
Vu, Victoria, Adrian J. Verster, Michael R Schertzberg, et al.. (2015). Natural Variation in Gene Expression Modulates the Severity of Mutant Phenotypes. Cell. 162(2). 391–402. 95 indexed citations
16.
Hart, Traver, Kevin R. Brown, Fabrice Sircoulomb, Robert Rottapel, & Jason Moffat. (2014). Measuring error rates in genomic perturbation screens: gold standards for human functional genomics. Molecular Systems Biology. 10(7). 733–733. 215 indexed citations
17.
Hart, Traver, H. Kiyomi Komori, Sarah LaMere, Katie Podshivalova, & Daniel R. Salomon. (2013). Finding the active genes in deep RNA-seq gene expression studies. BMC Genomics. 14(1). 778–778. 166 indexed citations
18.
Hart, Traver, et al.. (2009). Human Cell Chips: Adapting DNA Microarray Spotting Technology to Cell-Based Imaging Assays. PLoS ONE. 4(10). e7088–e7088. 21 indexed citations
19.
Hart, Traver, Arun Ramani, & Edward M. Marcotte. (2006). How complete are current yeast and human protein-interaction networks?. Genome Biology. 7(11). 120–120. 288 indexed citations
20.
Narayanaswamy, Rammohan, et al.. (2006). Systematic profiling of cellular phenotypes with spotted cell microarrays reveals mating-pheromone response genes. Genome biology. 7(1). R6–R6. 36 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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