Abstract

BackgroundComparative genomics has seen the development of many software performing the clustering, polymorphism and gene content analysis of genomes at different phylogenetic levels (isolates, species). These tools rely on de novo assembly and/or multiple alignments that can be computationally intensive for large datasets. With a large number of similar genomes in particular, e.g., in surveillance and outbreak detection, assembling each genome can become a redundant and expensive step in the identification of genes potentially involved in a given clinical feature.ResultsWe have developed deltaRpkm, an R package that performs a rapid differential gene presence evaluation between two large groups of closely related genomes. Starting from a standard gene count table, deltaRpkm computes the RPKM per gene per sample, then the inter-group δRPKM values, the corresponding median δRPKM (m) for each gene and the global standard deviation value of m (sm). Genes with m > = 2 ∗ sm (standard deviation s of all the m values) are considered as “differentially present” in the reference genome group. Our simple yet effective method of differential RPKM has been successfully applied in a recent study published by our group (N = 225 genomes of Listeria monocytogenes) (Aguilar-Bultet et al. Front Cell Infect Microbiol 8:20, 2018).ConclusionsTo our knowledge, deltaRpkm is the first tool to propose a straightforward inter-group differential gene presence analysis with large datasets of related genomes, including non-coding genes, and to output directly a list of genes potentially involved in a phenotype.

Highlights

  • Comparative genomics has seen the development of many software performing the clustering, polymorphism and gene content analysis of genomes at different phylogenetic levels

  • In comparative genomics the gene presence/absence analysis is commonly performed by multiple alignment calculations on whole genomes or on their subsets as pan-core-genome analysis

  • Increasing number of studies in human or veterinary clinical genomics, especially those focusing on outbreak detection and tracking, involve a large number of similar genomes to be compared

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Summary

Introduction

Comparative genomics has seen the development of many software performing the clustering, polymorphism and gene content analysis of genomes at different phylogenetic levels (isolates, species). These tools rely on de novo assembly and/or multiple alignments that can be computationally intensive for large datasets. Increasing number of studies in human or veterinary clinical genomics, especially those focusing on outbreak detection and tracking, involve a large number of similar genomes to be compared For such particular cases, we propose a simple yet effective approach using a canonical gene read count table, short-cutting the intensive genome assembly and annotation tasks. Our user-friendly and open-source R package, deltaRpkm, identifies putative genes involved in a given phenotype by inferring their presence/absence from their differential coverage between a reference genome group and a comparison group

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