geneorder

The Python Gene Order plotter

This package was born out of my continuous efforts to write streamlined code to plot Hox gene clusters. It seems like this is a task I will be performing many times, and there don’t seem to be too many tools out there, so I wrote my own. This is my matplotlib-based answer, and I hope that it is useful to you, too.

Input

Generally, the package expects you to have, at least, knowledge of the gene IDs that you want to visualize, and knowledge of their coordinates in the genome. This can be manually encoded or come in table form - crucially, it can be read from a GFF3 file.

Usage

Installation

Install latest from the GitHub repository:

$ pip install git+https://github.com/galicae/geneorder.git

or from pypi

$ pip install geneorder

Documentation

Documentation can be found hosted on this GitHub repository’s pages. Additionally you can find package manager specific guidelines on pypi.

How to use

The most basic geneorder use case is when you have a list of genes that are co-linear and would like to plot them in their chromosomal context. The least information that is needed for that is

• the chromosome name
• the gene IDs/names
• the starts/ends of all genes
• the strand and orientation of the genes

import pandas as pd

from geneorder.core import plot_synteny_schematic

gene_dict = {
    "gene_name": [
        "Hox1",
        "Hox2",
        "Hox3",
        "Hox4",
        "Hox5",
        "Hox6",
        "Hox7",
        "Hox8",
        "Hox10",
    ],
    "gene_id": [
        "PB.8615",
        "g9718",
        "PB.8616",
        "g9720",
        "g9721",
        "PB.8617",
        "g9723",
        "g9724",
        "g9725",
    ],
    "start": [
        1927066,
        1998922,
        2058396,
        2195412,
        2351936,
        2373415,
        2565196,
        2916314,
        2986021,
    ],
    "end": [
        1936157,
        2024148,
        2065953,
        2206712,
        2354374,
        2375678,
        2594468,
        2926445,
        2996225,
    ],
}

minimal = pd.DataFrame(gene_dict)
minimal["seqid"] = "pseudochrom_56"
minimal["strand"] = "-"

minimal

	gene_name	gene_id	start	end	seqid	strand
0	Hox1	PB.8615	1927066	1936157	pseudochrom_56	-
1	Hox2	g9718	1998922	2024148	pseudochrom_56	-
2	Hox3	PB.8616	2058396	2065953	pseudochrom_56	-
3	Hox4	g9720	2195412	2206712	pseudochrom_56	-
4	Hox5	g9721	2351936	2354374	pseudochrom_56	-
5	Hox6	PB.8617	2373415	2375678	pseudochrom_56	-
6	Hox7	g9723	2565196	2594468	pseudochrom_56	-
7	Hox8	g9724	2916314	2926445	pseudochrom_56	-
8	Hox10	g9725	2986021	2996225	pseudochrom_56	-

plot_synteny_schematic(minimal)

The plot can be customized, e.g. by including color:

minimal["color"] = [
    "red",
    "orange",
    "gold",
    "lightgreen",
    "forestgreen",
    "royalblue",
    "darkblue",
    "darkmagenta",
    "magenta",
]

plot_synteny_schematic(minimal)

We can also edit the dataframe to indicate missing genes:

from geneorder import util

minimal = util.insert_gap(
    minimal,
    "Hox8",
    "Hox10",
    "gene_name",
    no_gaps=1,
    purge_columns=["gene_id", "color"],
)

plot_synteny_schematic(minimal)

For more details, please refer to the documentation.