GOATOOLS: A Python library for Gene Ontology analyses

Abstract

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.

Major host transitions are modulated through transcriptome‐wide reprogramming events in Schistocephalus solidus, a threespine stickleback parasite

“…We used the Python package ‘ goatools’ (Klopfenstein et al . ) to perform Fisher's exact tests on GO annotation terms found in clusters of significantly differentially expressed gene. Annotation of GO terms for each gene was based on the published transcriptome of S. solidus (Hébert et al .…”

“…We identified functional categories over-represented in each coexpression module to characterize the biological functions associated with each life stage. We used the Python package 'GOATOOLS' (Klopfenstein et al 2015) to perform Fisher's exact tests on GO annotation terms found in clusters of significantly differentially expressed gene. Annotation of GO terms for each gene was based on the published transcriptome of S. solidus (H ebert et al 2016b).…”

Polygenic selection drives the evolution of convergent transcriptomic landscapes across continents within a Nearctic sister species complex

“…DEGs were determined by controlling for false discovery rate (FDR) as implemented in deseq2 (Benjamini-Hochberg correction; Benjamini & Hochberg, 1995), with a threshold of a FDR < 0.05. Then, GO enrichment analysis was performed with GOATOOLS (Klopfenstein et al, 2018), based on Fisher's exact test. For all tested lists of genes, GO enrichment was associated with FDR < 0.05 (Benjamini & Hochberg, 1995) and a minimum of three genes represented per category.…”

The impact of the genetic background on gene deletion phenotypes in Saccharomyces cerevisiae

“…Gene ontology (GO) annotations were downloaded from the SGD database (Cherry et al, 2012), while the GO slim yeast dataset was downloaded from the gene ontology website (Ashburner et al, 2000;The Gene Ontology Consortium, 2017). GO term enrichments were assessed using goatools (Klopfenstein et al, 2018), v0.8.2, using a FDR-corrected P-value threshold of 0.01.…”