One is not enough: On the effects of reference genome for the mapping and subsequent analyses of short-reads

Abstract

Mapping of high-throughput sequencing (HTS) reads to a single arbitrary reference genome is a frequently used approach in microbial genomics. However, the choice of a reference may represent a source of errors that may affect subsequent analyses such as the detection of single nucleotide polymorphisms (SNPs) and phylogenetic inference. In this work, we evaluated the effect of reference choice on short-read sequence data from five clinically and epidemiologically relevant bacteria (Klebsiella pneumoniae, Legionella pneumophila, Neisseria gonorrhoeae, Pseudomonas aeruginosa and Serratia marcescens). Publicly available whole-genome assemblies encompassing the genomic diversity of these species were selected as reference sequences, and read alignment statistics, SNP calling, recombination rates, dN/dS ratios, and phylogenetic trees were evaluated depending on the mapping reference. The choice of different reference genomes proved to have an impact on almost all the parameters considered in the five species. In addition, these biases had potential epidemiological implications such as including/excluding isolates of particular clades and the estimation of genetic distances. These findings suggest that the single reference approach might introduce systematic errors during mapping that affect subsequent analyses, particularly for data sets with isolates from genetically diverse backgrounds. In any case, exploring the effects of different references on the final conclusions is highly recommended.

Evaluation of within-host evolution of methicillin-resistant Staphylococcus aureus (MRSA) by comparing cgMLST and SNP analysis approaches

“…The selection of a reference genome with a well-defined core genome can be a source of bias if it is not closely related to the analysed sequences and can affect the subsequent analyses such as the detection of genetic events 28 . This could explain why the cgMLST analysis performed with 1928 showed a lower estimated genomic variation rate compared to SeqSphere+ since they are based on two different references, i.e.…”

Keeping up with the Bacillus cereus group: taxonomy through the genomics era and beyond

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Bacterial low-abundant taxa are key determinants of a healthy airway metagenome in the early years of human life

“…Our recent software publication raspir in combination with gapseq , a tool introduced by Zimmermann et al (2021) facilitated the taxonomic and functional identification of core and rare species from shotgun metagenomic sequencing data and reference genomes, respectively, with reduced false discovery and omission rates [27] , [28] . Since previous reports have demonstrated that metagenome investigations are affected by the reference database of choice [29] and the normalisation strategy of count data for addressing the compositional behaviour of microbiome sequencing data [30] , [31] , [32] , [33] , we tested our model simulations, random forest bootstrapping aggregations, ecological network analysis and kernel-based machine learning applications on infant metagenome datasets, generated from read alignments towards either a pan-genome or a one-strain-per-species reference database. Moreover, we generated datasets based on three different read count normalisation strategies, namely variance-stabilising transformations (VST), relative log expression (RLE) and bacterial to human cell ratios (BCPHC) and worked with three distinct rarity thresholds (15th, 25th and 35th species abundance percentile) to define the core and rare species biosphere.…”