PaReBrick: PArallel REarrangements and BReakpoints identification toolkit

Motivation

High plasticity of bacterial genomes is provided by numerous mechanisms including horizontal gene transfer and recombination via numerous flanking repeats. Genome rearrangements such as inversions, deletions, insertions, and duplications may independently occur in different strains, providing parallel adaptation or phenotypic diversity. Specifically, such rearrangements might be responsible for virulence, antibiotic resistance, and antigenic variation. However, identification of such events requires laborious manual inspection and verification of phyletic pattern consistency.

Methods and Results

We present tool PaReBrick — implementation of an algorithmic solution for the identification of parallel rearrangements in bacterial population. We define the term "parallel rearrangements" as events that occur independently in phylogenetically distant bacterial strains and present a formalization of the problem of parallel rearrangements calling.

The tool takes a collection of strains represented as a sequence of oriented synteny blocks and a phylogenetic tree as input data. It identifies rearrangements, tests them for consistency with a tree, and sorts the events by their parallelism score. The tool provides diagrams of the neighbors for each block of interest, allowing the detection of horizontally transferred blocks or their extra copies and the inversions in which copied blocks are involved.

We demonstrated PaReBrick’s efficiency and accuracy and showed its potential to detect genome rearrangements responsible for pathogenicity and adaptation in bacterial genomes.

Installation

PaReBrick can be installed with pip:

pip install PaReBrick

Now you can run tool from any directory as PaReBrick (or parebrick).

Script parameters

Main script of project including all modules together can be run from anywhere as console tool.

Required input

Important for input: Identifiers in tree and on in blocks must be equal;

--tree/t

Tree in newick format, must be parsable by ete3 library. You can read more about formats supported by ete3

--blocks_folder/-b

Path to folder with blocks resulted as output of original Sibelia or maf2synteny tool.

BLOCKS-OBTAIN.md — instruction about how to obtain synteny blocks using SibeliaZ.

Optional input

--labels/-l

Path to csv file with tree labels for showing on phylogenetic tree. Must contain two columns: strain and label.

--output/-o

Path to output folder. Default is ./parebrick_output

Output

Output consist of tree folders.

1. preprocessed_data — here you can find both all and common synteny blocks in both infercars, GRIMM and csv formats. As well as same genomes_lengths.csv file with lengths of provided genomes.
2. balanced_rearrangements_output — this folder contains stats.csv file with all non-convex characters statistics of balanced rearrangements. And folders characters, tree_colorings with character representation in rendered .pdf tree and .csv formats.
3. unbalanced_rearrangements_output — this folder contains stats.csv file with all non-convex characters statistics of unbalanced rearrangements. And folders characters, tree_colorings with character in rendered .pdf tree and .csv formats in subfolders according to their clustering representation.

Example run and data

Example data is available in example-data folder.

How to run example:

1. Clone all repository with data:

git clone https://github.com/ctlab/parallel-rearrangements

2. Change directory to data folder:

cd parallel-rearrangements/example-data/streptococcus_pyogenes

3. (a) Running tool on example input:

PaReBrick -t input/tree.nwk -b input/maf2synteny-output -l input/labels.csv

3. (b) Or with minimal required arguments (no labels):

PaReBrick -t input/tree.nwk -b input/maf2synteny-output

Citation

If you use PaReBrick in your research, please cite:

Alexey Zabelkin, Yulia Yakovleva, Olga Bochkareva, Nikita Alexeev, PaReBrick: PArallel REarrangements and BReaks identification toolkit, Bioinformatics, 2021; btab691, https://doi.org/10.1093/bioinformatics/btab691