Pangrowth

pangrowth is an efficient tool designed for genomic researchers to predict the openness of a pangenome and estimate the core genome size. This tool is capable of analyzing fasta sequences using k-mers, as well as other genomic elements such as genes, CDS, ORFs, provided as either a frequency histogram or a pan-matrix (with columns representing genomes and rows representing items; see panmatrix_ecoli_n50.txt for an example).

Key features

• k-mer counting: utilizes a modified version of yak to count k-mers
• growth/core calculation: computes the exact expected genomic growth/core size quadratically in the number of genomes

Publications

Parmigiani, L., Wittler, R., Stoye, J.,: Revisiting pangenome openness with k-mers. PCI Comp & Biol. (2024).

Table of Contents

• Pangrowth
  • Table of Contents
  • Install
    • Build Pangrowth
  • Usage
    • Histogram from fasta files
    • Pangenome growth (from histogram or pan-matrix)
    • Pangenome core (from histogram or pan-matrix)

Install

git clone https://gitlab.ub.uni-bielefeld.de/gi/pangrowth.git
cd pangrowth
make 

To plot the results we need the following python libraries: numpy, pandas, matplotlib, scipy and searbon. You can install them with:

pip install -r dependencies.txt

Usage

Histogram from fasta files

./pangrowth hist -k 17 -t 12 data/fa/*.fna.gz > hist.txt

• pangrowth also accepts a file containing a list of fasta files (each one on a single line) passed with the paremeter -i fasta_list.txt

To visualize the histogram:

python scripts/plot_hist.py hist.txt hist.pdf

If you have multiple histograms that you want to compare with different number of genomes you can use:

python scripts/plot_hist.py --norm_x --norm_y=both hist.txt data/hist_ecoli_n50.txt data/hist_ecoli_n200.txt hist_multiple.pdf

• The flag --norm_x normalize the x-axis to be between (0,1].
• The flag --norm_y allows two types of normalization:
  • multiplicity which adjusts each histogram value h[i] multiplying it by its index i (i.e., h[i] * i, this means that values appearing once remain the same, values appearing twice are doubled, and so on)
  • percentage which divides the values of h[i] by the total sum of h (its total sum equals 1) The --norm_y=both applies both in series.

Pangenome growth from histogram (or pan-matrix)

./pangrowth growth -h data/hist_ecoli_n50.txt > growth.txt
#./pangrowth growth -p data/panmatrix_ecoli_n50.txt > growth.txt

To fit the openness and visualize the growth:

python scripts/plot_growth.py growth.txt growth.pdf

We can again pass multiple growth files to scripts/plot_growth.py to compare with other species.

python scripts/plot_growth.py growth.txt data/growth_ecoli_n200.txt growth_multiple.pdf

Pangenome core from histogram (or pan-matrix)

./pangrowth core -h data/hist_ecoli_n50.txt > core.txt
#./pangrowth core -p data/panmatrix_ecoli_n50.txt > core.txt
./pangrowth core -h data/hist_ecoli_n50.txt -q 0.9 > core_q90.txt

• The -q takes a quorum to considered the item in the core (default 1.0).

To fit the core genome and report the percentage of core item over the expected genome size:

python scripts/plot_core.py core_q90.txt data/core_q90_ecoli_n200.txt core.pdf

The expected genome size is calculated as the total sum of the histogram divided by the number of genomes.