The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
GOATOOLS: A Python library for Gene Ontology analyses
Open accessAbstract
The biological interpretation of gene lists with interesting shared properties, such as up- or down-regulation in a particular experiment, is typically accomplished using gene ontology enrichment analysis tools. Given a list of genes, a gene ontology (GO) enrichment analysis may return hundreds of statistically significant GO results in a “flat” list, which can be challenging to summarize. It can also be difficult to keep pace with rapidly expanding biological knowledge, which often results in daily changes to any of the over 47,000 gene ontologies that describe biological knowledge. GOATOOLS, a Python-based library, makes it more efficient to stay current with the latest ontologies and annotations, perform gene ontology enrichment analyses to determine over- and under-represented terms, and organize results for greater clarity and easier interpretation using a novel GOATOOLS GO grouping method. We performed functional analyses on both stochastic simulation data and real data from a published RNA-seq study to compare the enrichment results from GOATOOLS to two other popular tools: DAVID and GOstats. GOATOOLS is freely available through GitHub: https://github.com/tanghaibao/goatools.
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
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn923 · Cited by 14,108
Compilers: Principles, Techniques, and Tools
Aho, A. V., Sethi, R., & Ullman, J. D. (1986) · Cited by 8,082
Statsmodels: Econometric and Statistical Modeling with Python
Seabold, S., & Perktold, J. (2010) · Proceedings of the Python in Science Conference · doi:10.25080/majora-92bf1922-011 · Cited by 6,465
REVIGO summarizes and visualizes long lists of gene ontology terms
Supek, F., Bošnjak, M., Škunca, N., & Šmuc, T. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021800 · Cited by 6,264
An Information-Theoretic Definition of Similarity
Lin, D. (1998) · Cited by 3,790
Using Information Content to Evaluate Semantic Similarity in a Taxonomy
Resnik, P. (1995) · arXiv (Cornell University) · doi:10.48550/arxiv.cmp-lg/9511007 · Cited by 3,193
Boyle, E. I., Weng, S., Gollub, J., Jin, H., Botstein, D., Cherry, J. M., & Sherlock, G. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth456 · Cited by 2,067
Using GOstats to test gene lists for GO term association
Falcon, S., & Gentleman, R. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btl567 · Cited by 2,015
Cited by
Generative modeling of brain maps with spatial autocorrelation
Burt, J. B., Helmer, M., Shinn, M., Anticevic, A., & Murray, J. D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117038 · Cited by 528
Building a knowledge graph to enable precision medicine
Chandak, P., Huang, K., & Zitnik, M. (2023) · Scientific Data · doi:10.1038/s41597-023-01960-3 · Cited by 522
Quantifying the local adaptive landscape of a nascent bacterial community
Ascensao, J. A., Wetmore, K. M., Good, B. H., Arkin, A. P., & Hallatschek, O. (2023) · Nature Communications · doi:10.1038/s41467-022-35677-5 · Cited by 447
An Activity-Guided Map of Electrophile-Cysteine Interactions in Primary Human T Cells
Vinogradova, E. V., Zhang, X., Remillard, D., Lazar, D. C., Suciu, R. M., Wang, Y., Bianco, G., Yamashita, Y., Crowley, V. M., Schafroth, M. A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.07.001 · Cited by 351
Cuomo, A. S. E., Seaton, D. D., McCarthy, D. J., Martinez, I., Bonder, M. J., Garcia-Bernardo, J., Amatya, S., Madrigal, P., Isaacson, A., Buettner, F., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14457-z · Cited by 331
Metadata
- Authors
- Klopfenstein, D. V., Zhang, L., Pedersen, B. S., Ramírez, F., Warwick Vesztrocy, A., Naldi, A., Mungall, C. J., Yunes, J. M., Botvinnik, O., Weigel, M., Dampier, W., Dessimoz, C., Flick, P., & Tang, H.
- Venue
- Scientific Reports
- Published
- 2018-07-18
- Volume
- 8
- Issue
- 1
- Pages
- 10872-10872
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2845591014
- Cited by
- 1,290
- References
- 30
- FWCI
- 33.04
- Type
- journal article
- Language
- en
- Open access
- gold
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: GOATOOLS: A Python library for Gene Ontology analyses. https://app.cochat.ai/papers/10.1038/s41598-018-28948-z
- The paper
- Klopfenstein, D. V., Zhang, L., Pedersen, B. S., Ramírez, F., Warwick Vesztrocy, A., Naldi, A., Mungall, C. J., Yunes, J. M., Botvinnik, O., Weigel, M., Dampier, W., Dessimoz, C., Flick, P., & Tang, H. (2018). GOATOOLS: A Python library for Gene Ontology analyses. Scientific Reports. https://doi.org/10.1038/s41598-018-28948-z
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.
