Nucleotide-resolution bacterial pan-genomics with reference graphs

Abstract

AbstractBackgroundBacterial genomes follow a U-shaped frequency distribution whereby most genomic loci are either rare (accessory) or common (core); the union of these is the pan-genome. The alignable fraction of two genomes from a single species can be low (e.g. 50-70%), such that no single reference genome can access all single nucleotide polymorphisms (SNPs). The pragmatic solution is to choose a close reference, and analyse SNPs only in the core genome. Given much bacterial adaptability hinges on the accessory genome, this is an unsatisfactory limitation.ResultsWe present a novel pan-genome graph structure and algorithms implemented in the software pandora, which approximates a sequenced genome as a recombinant of reference genomes, detects novel variation and then pan-genotypes multiple samples. The method takes fastq as input and outputs a multi-sample VCF with respect to an inferred data-dependent reference genome, and is available at https://github.com/rmcolq/pandora.Constructing a reference graph from 578 E. coli genomes, we analyse a diverse set of 20 E. coli isolates. We show pandora recovers at least 13k more rare SNPs than single-reference based tools, achieves equal or better error rates with Nanopore as with Illumina data, 6-24x lower Nanopore error rates than other tools, and provides a stable framework for analysing diverse samples without reference bias. We also show that our inferred recombinant VCF reference genome is significantly better than simply picking the closest RefSeq reference.ConclusionsThis is a step towards comprehensive cohort analysis of bacterial pan-genomic variation, with potential impacts on genotype/phenotype and epidemiological studies.