Approximate, simultaneous comparison of microbial genome architectures via syntenic anchoring of quiver representations

Alex N. Salazar, Thomas Abeel*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
59 Downloads (Pure)


Motivation A long-standing limitation in comparative genomic studies is the dependency on a reference genome, which hinders the spectrum of genetic diversity that can be identified across a population of organisms. This is especially true in the microbial world where genome architectures can significantly vary. There is therefore a need for computational methods that can simultaneously analyze the architectures of multiple genomes without introducing bias from a reference. Results In this article, we present Ptolemy: a novel method for studying the diversity of genome architectures - such as structural variation and pan-genomes - across a collection of microbial assemblies without the need of a reference. Ptolemy is a 'top-down' approach to compare whole genome assemblies. Genomes are represented as labeled multi-directed graphs - known as quivers - which are then merged into a single, canonical quiver by identifying 'gene anchors' via synteny analysis. The canonical quiver represents an approximate, structural alignment of all genomes in a given collection encoding structural variation across (sub-) populations within the collection. We highlight various applications of Ptolemy by analyzing structural variation and the pan-genomes of different datasets composing of Mycobacterium, Saccharomyces, Escherichia and Shigella species. Our results show that Ptolemy is flexible and can handle both conserved and highly dynamic genome architectures. Ptolemy is user-friendly - requires only FASTA-formatted assembly along with a corresponding GFF-formatted file - and resource-friendly - can align 24 genomes in ∼10 mins with four CPUs and <2 GB of RAM. Availability and implementation Github: Supplementary information Supplementary data are available at Bioinformatics online.

Original languageEnglish
Pages (from-to)i732-i742
Number of pages11
Issue number17
Publication statusPublished - 2018


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