egger

a python package to plot results from eggnog-mapper

Description

egger can plot COG functional annotations from eggnog-mapper annotation files.

The repository for eggnog-mapper can be found, here.

It has two modes:

• compare: compares the correlation of COG categories across several genomes.
• window: plots sliding window plots of COG category distribution across a genome.

See below for usage examples.

Installation

You can install egger with pip.

Either use the PyPI installation: pip install egger.

Or, clone this repository and install manually.

egger only requires Python dependencies, which should be installed automatically.

Usage and Example Output

As described above, egger has two modes:

compare

compare output

egger compare will calculate the correlation of COG category annotations across genomes and build a dendrogram-heatmap. It will write this as .svg file along with the raw data in .tsv formats. It can perform Spearman's rank (-s) or Pearson's (-p) correlation. It can also produce barcharts (-b). Use: egger compare -h for more information.

Here is an example using this repository's example_data/outputs/compare_outputs as the working directory:

$ egger compare -b barchart -s spearmans -p pearsons ../../inputs/annotations/*

compare output

These heatmaps can help identify relationships between genomes or other sequences.

window

window usage

egger window will take eggnog-mapper annotations and plot them using a sliding window. Please note, as eggnog-mapper annotations do not contain location data, the original genome used for the analysis is also required. You will also need to define outputs with -swo or -swp. You can also modify the window and step size using. Use: egger window -h for more information.

Here is an example using this repository's example_data/outputs/window_outputs as the working directory:

egger window -a ../../inputs/annotations/Lactococcus_lactis.annotations -g ../../inputs/genomes/Lactococcus_lactis.gb -swo output_ -swp plot_

window output

This will produce two files:

1. -swo writes a .tsv file with the raw data used for plotting the sliding window.
2. -swp writes a .html file for an interactive plot of the sliding window data.

This analysis is particularly useful for finding regions of the genome that are dedicated to a specific function. In the example, Lactococcus lactis has three notable islands for the COG categories J, M and P (among others).

Citation

Coming soon...

Patch Notes

• v1.0.0 - Initial version