SMEAGOL (Sequence Motif Enrichment And Genome annOtation Library)

SMEAGOL is a library to identify and visualize enrichment (or depletion) of sequence motifs in DNA/RNA sequences.

Setup

It is recommended to install SMEAGOL in a conda environment or virtualenv. SMEAGOL is compatible with Python 3.7 and higher.

If you have conda installed on your machine you can create a conda environment like this:

conda create --name SMEAGOL python=3.7

Next, you need to activate the created environment before you can install SMEAGOL into it, which can be done as follows:

conda activate SMEAGOL

1. Clone this git repository

git clone https://github.com/gruber-sciencelab/SMEAGOL && cd SMEAGOL

2. Install SMEAGOL

pip install .

Or, if you want to edit the code:

pip install -e .

3. Run tests locally (optional)

cd tests
pytest

4. Install jupyter lab and add SMEAGOL kernel to Ipython (if running SMEAGOL in Jupyter notebooks)

pip install jupyterlab 
python -m ipykernel install --user --name=SMEAGOL --display-name='Python 3.7 (SMEAGOL)'

Usage

In your python script / notebook, you can import modules or functions from SMEAGOL. For example:

import smeagol.visualize

from smeagol.visualize import plot_background

Modules

The following modules are included:

• smeagol.aggregate: Aggregate results across different genomes.
• smeagol.encode: Encoding nucleic acid sequences into numeric form for PWM scanning.
• smeagol.enrich: Calculate enrichment or depletion of PWM matches in a sequence.
• smeagol.io: Read and write data (motifs and sequences).
• smeagol.matrices: Analyze motifs in the form of PFMs, PPMs and PWMs.
• smeagol.models: Encoding of PWMs into a model to scan sequences.
• smeagol.scan: Scan nucleic acid sequences with PWMs and score putative binding sites.
• smeagol.utils: Miscellaneous functions used by other modules.
• smeagol.variant: Predict the effects of sequence variants on the PWM match score.
• smeagol.visualize: Generating visualizations.

Documentation

https://gruber-sciencelab.github.io/SMEAGOL/

Tutorials

See the vignette for an example workflow using SMEAGOL.

Citation

Please cite our publication: Lal, A., Galvao Ferrarini, M., & Gruber, A. J. (2022). Investigating the Human Host—ssRNA Virus Interaction Landscape Using the SMEAGOL Toolbox. Viruses, 14(7), 1436.