The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Using ggtree to Visualize Data on Tree‐Like Structures
Abstract
Ggtree is an R/Bioconductor package for visualizing tree‐like structures and associated data. After 5 years of continual development, ggtree has been evolved as a package suite that contains treeio for tree data input and output, tidytree for tree data manipulation, and ggtree for tree data visualization. Ggtree was originally designed to work with phylogenetic trees, and has been expanded to support other tree‐like structures, which extends the application of ggtree to present tree data in other disciplines. This article contains five basic protocols describing how to visualize trees using the grammar of graphics syntax, how to visualize hierarchical clustering results with associated data, how to estimate bootstrap values and visualize the values on the tree, how to estimate continuous and discrete ancestral traits and visualize ancestral states on the tree, and how to visualize a multiple sequence alignment with a phylogenetic tree. The ggtree package is freely available at https://www.bioconductor.org/packages/ggtree. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Using grammar of graphics for visualizing trees Basic Protocol 2: Visualizing hierarchical clustering using ggtree Basic Protocol 3: Visualizing bootstrap values as symbolic points Basic Protocol 4: Visualizing ancestral status Basic Protocol 5: Visualizing a multiple sequence alignment with a phylogenetic tree
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Guindon, S., Dufayard, J.-F., Lefort, V., Anisimova, M., Hordijk, W., & Gascuel, O. (2010) · Systematic Biology · doi:10.1093/sysbio/syq010 · Cited by 18,376
Cited by
Emergence of SARS-CoV-2 Omicron lineages BA.4 and BA.5 in South Africa
Tegally, H., Moir, M., Everatt, J., Giovanetti, M., Scheepers, C., Wilkinson, E., Subramoney, K., Makatini, Z., Moyo, S., Amoako, D. G., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01911-2 · Cited by 707
A DNA methylation atlas of normal human cell types
Loyfer, N., Magenheim, J., Peretz, A., Cann, G., Bredno, J., Klochendler, A., Fox-Fisher, I., Shabi-Porat, S., Hecht, M., Pelet, T., et al. (2023) · Nature · doi:10.1038/s41586-022-05580-6 · Cited by 656
Addetia, A., Crawford, K. H. D., Dingens, A., Zhu, H., Roychoudhury, P., Huang, M.-L., Jerome, K. R., Bloom, J. D., & Greninger, A. L. (2020) · Journal of Clinical Microbiology · doi:10.1128/jcm.02107-20 · Cited by 525
Genomic characterization and Epidemiology of an emerging SARS-CoV-2 variant in Delhi, India
Dhar, M. S., Marwal, R., Radhakrishnan, V., Ponnusamy, K., Jolly, B., Bhoyar, R. C., Sardana, V., Naushin, S., Rophina, M., Mellan, T. A., et al. (2021) · doi:10.1101/2021.06.02.21258076 · Cited by 296
Engineered phage with antibacterial CRISPR–Cas selectively reduce E. coli burden in mice
Gencay, Y. E., Jasinskytė, D., Robert, C., Semsey, S., Martínez, V., Petersen, A. Ø., Brunner, K., de Santiago Torio, A., Salazar, A., Turcu, I. C., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01759-y · Cited by 282
Metadata
- Authors
- Yu, G.
- Venue
- Current Protocols in Bioinformatics
- Publisher
- Wiley
- Published
- 2020-03
- Volume
- 69
- Issue
- 1
- Pages
- e96-e96
- ISSN
- 1934-3396 (ISSN-L)
- 1934-340X
- OpenAlex
- W3009737049
- Cited by
- 1,377
- References
- 1
- FWCI
- 52.97
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Using ggtree to Visualize Data on Tree‐Like Structures. https://app.cochat.ai/papers/10.1002/cpbi.96
- The paper
- Yu, G. (2020). Using ggtree to Visualize Data on Tree‐Like Structures. Current Protocols in Bioinformatics. https://doi.org/10.1002/cpbi.96
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.