Jonathan S. Packer

8.9k total citations · 5 hit papers
12 papers, 4.0k citations indexed

About

Jonathan S. Packer is a scholar working on Molecular Biology, Aging and Ecology. According to data from OpenAlex, Jonathan S. Packer has authored 12 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 2 papers in Aging and 1 paper in Ecology. Recurrent topics in Jonathan S. Packer's work include Single-cell and spatial transcriptomics (8 papers), RNA Research and Splicing (3 papers) and Advanced biosensing and bioanalysis techniques (3 papers). Jonathan S. Packer is often cited by papers focused on Single-cell and spatial transcriptomics (8 papers), RNA Research and Splicing (3 papers) and Advanced biosensing and bioanalysis techniques (3 papers). Jonathan S. Packer collaborates with scholars based in United States, Germany and South Korea. Jonathan S. Packer's co-authors include Cole Trapnell, Xiaojie Qiu, Andrew J. Hill, Yi-An Ma, Dejun Lin, Jay Shendure, Frank J. Steemers, Riza M. Daza, Andrew C. Adey and Darren A. Cusanovich and has published in prestigious journals such as Science, Molecular Cell and Bioinformatics.

In The Last Decade

Jonathan S. Packer

12 papers receiving 4.0k citations

Hit Papers

Single-cell mRNA quantification and differential analysis... 2017 2026 2020 2023 2017 2017 2018 2018 2019 250 500 750 1000
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.

  1. Single-cell mRNA quantification and differential analysis with Census
    2017 1.1k citations Xiaojie Qiu, Andrew J. Hill et al. Nature Methods profile →
  2. Comprehensive single-cell transcriptional profiling of a multicellular organism
    2017 896 citations Junyue Cao, Jonathan S. Packer et al. Science profile →
  3. Joint profiling of chromatin accessibility and gene expression in thousands of single cells
    2018 585 citations Junyue Cao, Darren A. Cusanovich et al. Science profile →
  4. Cicero Predicts cis-Regulatory DNA Interactions from Single-Cell Chromatin Accessibility Data
    2018 413 citations Hannah A. Pliner, Jonathan S. Packer et al. Molecular Cell profile →
  5. A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution
    2019 301 citations Jonathan S. Packer, Qin Zhu et al. Science profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jonathan S. Packer United States 11 3.1k 713 692 393 342 12 4.0k
Junyue Cao United States 19 4.1k 1.3× 811 1.1× 956 1.4× 544 1.4× 260 0.8× 40 5.3k
Darren A. Cusanovich United States 18 4.1k 1.3× 818 1.1× 528 0.8× 422 1.1× 227 0.7× 28 4.7k
Tamir Chandra United Kingdom 25 3.4k 1.1× 606 0.8× 1.2k 1.7× 198 0.5× 164 0.5× 47 4.6k
Beijing Wu United States 10 4.8k 1.5× 1.0k 1.5× 1.1k 1.6× 303 0.8× 135 0.4× 10 5.8k
Monika S. Kowalczyk United States 15 2.1k 0.7× 561 0.8× 743 1.1× 167 0.4× 79 0.2× 17 3.1k
M. Ryan Corces United States 28 5.1k 1.6× 2.4k 3.4× 909 1.3× 154 0.4× 175 0.5× 42 6.9k
Jeffrey A. Farrell United States 11 3.5k 1.1× 743 1.0× 1.1k 1.6× 361 0.9× 70 0.2× 18 4.9k
Carmen Bravo González‐Blas Belgium 14 3.4k 1.1× 783 1.1× 1.2k 1.8× 224 0.6× 68 0.2× 16 4.7k
Caleb A. Lareau United States 34 5.3k 1.7× 1.1k 1.5× 1.2k 1.7× 418 1.1× 81 0.2× 71 6.5k
Lena Christiansen United States 10 4.1k 1.3× 930 1.3× 894 1.3× 487 1.2× 45 0.1× 13 5.0k

Countries citing papers authored by Jonathan S. Packer

Since Specialization
Citations

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

Fields of papers citing papers by Jonathan S. Packer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan S. Packer. 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 Jonathan S. Packer. The network helps show where Jonathan S. Packer may publish in the future.

Co-authorship network of co-authors of Jonathan S. Packer

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

All Works

12 of 12 papers shown
1.
Massouridès, Emmanuelle, Virginie Mournetas, Dana L. Jackson, et al.. (2024). Dystrophin deficiency impairs cell junction formation during embryonic myogenesis from pluripotent stem cells. iScience. 27(7). 110242–110242. 1 indexed citations
2.
Srivatsan, Sanjay, Mary C. Regier, Eliza Barkan, et al.. (2021). Embryo-scale, single-cell spatial transcriptomics. Science. 373(6550). 111–117. 170 indexed citations
3.
Srivatsan, Sanjay, José L. McFaline‐Figueroa, Vijay Ramani, et al.. (2019). Massively multiplex chemical transcriptomics at single-cell resolution. Science. 367(6473). 45–51. 198 indexed citations
4.
Packer, Jonathan S., Qin Zhu, Chau Huynh, et al.. (2019). A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution. Science. 365(6459). 301 indexed citations breakdown →
5.
Saunders, Lauren M., Andrew J. Aman, Victor M. Lewis, et al.. (2019). Thyroid hormone regulates distinct paths to maturation in pigment cell lineages. eLife. 8. 107 indexed citations
6.
Cao, Junyue, Darren A. Cusanovich, Vijay Ramani, et al.. (2018). Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science. 361(6409). 1380–1385. 585 indexed citations breakdown →
7.
Packer, Jonathan S. & Cole Trapnell. (2018). Single-Cell Multi-omics: An Engine for New Quantitative Models of Gene Regulation. Trends in Genetics. 34(9). 653–665. 60 indexed citations
8.
Pliner, Hannah A., Jonathan S. Packer, José L. McFaline‐Figueroa, et al.. (2018). Cicero Predicts cis-Regulatory DNA Interactions from Single-Cell Chromatin Accessibility Data. Molecular Cell. 71(5). 858–871.e8. 413 indexed citations breakdown →
9.
Hill, Andrew J., José L. McFaline‐Figueroa, Lea M. Starita, et al.. (2018). On the design of CRISPR-based single-cell molecular screens. Nature Methods. 15(4). 271–274. 134 indexed citations
10.
Cao, Junyue, Jonathan S. Packer, Vijay Ramani, et al.. (2017). Comprehensive single-cell transcriptional profiling of a multicellular organism. Science. 357(6352). 661–667. 896 indexed citations breakdown →
11.
Qiu, Xiaojie, Andrew J. Hill, Jonathan S. Packer, et al.. (2017). Single-cell mRNA quantification and differential analysis with Census. Nature Methods. 14(3). 309–315. 1069 indexed citations breakdown →
12.
Packer, Jonathan S., Evan K. Maxwell, Colm O’Dushlaine, et al.. (2015). CLAMMS: a scalable algorithm for calling common and rare copy number variants from exome sequencing data. Bioinformatics. 32(1). 133–135. 50 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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