PaReBrick: PArallel REarrangements and BReaks 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. Results Here 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. We implement an algorithmic solution for the identification of parallel rearrangements in bacterial populations as a tool PaReBrick. 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. Availability PaReBrick is written in Python and is available on GitHub https://github.com/ctlab/parallelrearrangements Supplementary information Supplementary data are available at Bioinformatics online.

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. Specifically, such rearrangements might be responsible for multi-virulence, antibiotic resistance, and antigenic variation. However, identification of such events requires laborious manual inspection and verification of phyletic pattern consistency. Results: Here 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. We implement an algorithmic solution for the identification of parallel rearrangements in bacterial population, as a tool PaReBrick. The tool takes synteny blocks and a phylogenetic tree as input and outputs rearrangement events. The tool tests each rearrangement for consistency with a tree, and sorts the events by their parallelism score and 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 proved PaReBrick's efficiency and accuracy and showed its potential to detect genome rearrangements responsible for pathogenicity and adaptation in bacterial genomes. Availability: PaReBrick is written in Python and is available on GitHub: https://github.com/ctlab/parallel-rearrangements .

IslandCafe: Compositional Anomaly and Feature Enrichment Assessment for Delineation of Genomic Islands

One of the evolutionary forces driving bacterial genome evolution is the acquisition of clusters of genes through horizontal gene transfer (HGT). These genomic islands may confer adaptive advantages to the recipient bacteria, such as, the ability to thwart antibiotics, become virulent or hypervirulent, or acquire novel metabolic traits. Methods for detecting genomic islands either search for markers or features typical of islands or examine anomaly in oligonucleotide composition against the genome background. The former tends to underestimate, missing islands that have the markers either lost or degraded, while the latter tends to overestimate, due to their inability to discriminate compositional atypicality arising because of HGT from those that are a consequence of other biological factors. We propose here a framework that exploits the strengths of both these approaches while bypassing the pitfalls of either. Genomic islands lacking markers are identified by their association with genomic islands with markers. This was made possible by performing marker enrichment and phyletic pattern analyses within an integrated framework of recursive segmentation and clustering. The proposed method, IslandCafe, compared favorably with frequently used methods for genomic island detection on synthetic test datasets and on a test-set of known islands from 15 well-characterized bacterial species. Furthermore, IslandCafe identified novel islands with imprints of likely horizontal acquisition.