GOATOOLS: A Python library for Gene Ontology analyses.
- Klopfenstein DV School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA.
- Zhang L Center for Genomics and Biotechnology, Fujian Agriculture and Forestry University, Fuzhou, China.
- Pedersen BS Department of Human Genetics, University of Utah, Salt Lake City, UT, USA.
- Ramírez F Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany.
- Warwick Vesztrocy A Department of Genetics, Evolution and Environment, University College London, London, UK.
- Naldi A Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
- Mungall CJ Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
- Yunes JM UC Berkeley - UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA, USA.
- Botvinnik O Bioinformatics and Systems Biology Program, University of California, San Diego, CA, USA.
- Weigel M Independent Researcher, Philadelphia, PA, USA.
- Dampier W School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA.
- Dessimoz C Department of Genetics, Evolution and Environment, University College London, London, UK.
- Flick P School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
- Tang H Center for Genomics and Biotechnology, Fujian Agriculture and Forestry University, Fuzhou, China. tanghaibao@gmail.com.
- 2018-07-20
Published in:
- Scientific reports. - 2018
Algorithms
Alzheimer Disease
Animals
Biomarkers
Computational Biology
Disease Models, Animal
Gene Expression Profiling
Gene Expression Regulation, Developmental
Gene Ontology
Mice
Software
English
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 .
- Language
-
- English
- Open access status
- gold
- Identifiers
-
- DOI 10.1038/s41598-018-28948-z
- PMID 30022098
- Persistent URL
- https://folia.unifr.ch/global/documents/123265
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