The transcription factor downstream target annotation enricher (TFTenricher) package is a bioinformatics tool to enable users to do an enrichment analyses on lists of transcription factors (TFs).
Transcription factors (TFs) are the upstream regulators that orchestrate gene expression, and therefore a centrepiece in bioinformatics studies. A popular strategy that helps understanding the biological context of genes/proteins includes basic annotation enrichment. There are several annotation enrichment methods available, yet these methods are not well suited for analysing groups of TFs, particularly since such methods fail to include downstream processes. Here, we present TFTenricher, a Python toolbox that focuses specifically at identifying gene ontologies, cellular pathways, and diseases that are overrepresented among genes that are downstream of user-defined sets of TFs. Given a set of TFs, TFTenricher infers downstream genes and calculates enrichments in some of the most common databases of gene functionalities, including GO, KEGG, and Reactome. The TFTenricher package enables users to search for biological context in any set of TFs and their downstream genes.
Examples of how to use TFTenricher are found below, and in a Jupyter notebook under ./examples/
The package can be used under the GNU GENERAL PUBLIC LICENSE V3
To run the default version of TFTenricher, a correlation look-up table that is used in the TF-to-target mappings needs to be constructed. This is done when the setup.py file is run. We recommend that TFTenricher is installed using pip:
pip install .
- Python 3.9
Except for the standard, built-in Python modules, the following dependencies are needed:
Here are the basic usages of the TFTenricher. A more comprehensive example can e found as a Jupyter notebook in the ./example/ folder
In python:
# Comment
>>> from TFTenricher import TFTenricher
enr = TFTenricher(list_of_tfs)
# to print the inferred target genes
print(enr.target_genes)
# Do ontology analysis. To set dataset to calculate overlaps with,
# set parameter 'db' to either 'GO', 'KEGG', 'GWAS', or 'REACTOME'.
# Default is 'GO'
enr.downstream_enrich(db='GO')
# Save the results
# The 'enrichments' variable is a pandas dataframe, with all its methods
enr.enrichments.to_csv('savename.csv')
# Plot the results
enr.plot(savename='fig.svg')
# Since TFTenricher increases statistical power, we might want to only plot
# the top n terms. We also choose to sort on odds ratios or p values ('OR' or 'p')
enr.plot(savename='fig_top_5.png', plot_Ntop=5, sorton='OR')
# Write the reference to TFTenricher and third-party software
enr.cite()Or from the command line:
python TFTenrich.py --TFs tfs.txt
# To get a full list of input parameters, run
python TFTenrich.py --help The TFTenricher algorithm works in two distinct steps. First, it maps a user-defined list of TFs to putative downstream genes using lookup-tables of co-expression that comes included with the software. In detail, the expression correlation was extracted using the ARCHS4 database and is based on data from >100k gene expression profiles, making it one of the most extensive co-expression analyses currently available. This mapping can, however, easily be replaced to a method defined by the user.
The second step takes the mapped target genes and performs enrichment analyses on gene sets annotated in, as per the choice of the user, KEGG, GO, REACTOME, the GWAS catalogue, or, alternatively, any set defined by the user. Moreover, enrichments are calculated using a Fisher’s exact test, and multiple testing correction is available using either a Bonferroni or Benjamini-Hochberg correction, or any correction provided by the user.
Rasmus Magnusson: Development of the package.
- @rasma774
Magnusson, R., Lubovac-Pilav, Z. TFTenricher: a python toolbox for annotation enrichment analysis of transcription factor target genes. BMC Bioinformatics 22, 440 (2021). https://doi.org/10.1186/s12859-021-04357-4