karyoplot 0.1.0

karyoplot: annotations along the genome

karyoplot

What is karyoplot ?

karyoplot is a python package developed to draw simple karyoplot/ideogram with a simple configuration file like Circos [1], without any lines of code like karyoploteR [2]. karyoplot deals with conventional bioinformatics formats (.bed, .fasta, .gff, .gtf, .vcf, ...) to avoid file manipulations before drawing.

How does it work ?

karyoplot takes as an argument a configuration file with different sections which will have a sequence Fasta file as reference for ideograms. Every plot to draw on reference sequences is described in a track section with link to associated data and drawing options. karyoplot is mainly based on matplotlib for graph rendering and pysam for bioinformatics file format parsing.

karyoplot is under developpement, currently supported format are:

• fasta
• bed

available soon:

• gff
• vcf
• bam
• wig/bigWig

Install

create your virtual env in a dedicated directory

python3 -m venv $HOME/myvenvs/karyoplot

set up the new env

source $HOME/myvenvs/karyoplot/bin/activate

install karyoplot

pip install karyoplot 

enjoy

karyoplot -h

to leave the env

deactivate

Usage

Simple usage

karyoplot config.ini -o genome_karyo.png -v 2

The config.ini file is a configuration file in Microsoft Windows INI style, readable by the configparser Python module. Some sections are expected with defined options to customize your plot. See the associated documentation here for full options and required sections/options.

Support

For any request please contact nicolas.lapalu[at]inrae.fr or adeline.simon[at]inrae.fr

Authors and acknowledgment

• Rosanne Phebe
• Nicolas Lapalu
• Adeline Simon

License

licensed under GNU General Public License v3.0

References

• [1] Krzywinski, M. et al. Circos: an Information Aesthetic for Comparative Genomics. Genome Res (2009) 19:1639-1645
• [2] Bernat Gel & Eduard Serra. (2017). karyoploteR: an R/Bioconductor package to plot customizable genomes displaying arbitrary data. Bioinformatics, 31–33. doi:10.1093/bioinformatics/btx346