Choice of Reference Sequence and Assembler for Alignment of Listeria monocytogenes Short-Read Sequence Data Greatly Influences Rates of Error in SNP Analyses

Abstract

The wide availability of whole-genome sequencing (WGS) and an abundance of open-source software have made detection of single-nucleotide polymorphisms (SNPs) in bacterial genomes an increasingly accessible and effective tool for comparative analyses. Thus, ensuring that real nucleotide differences between genomes (i.e., true SNPs) are detected at high rates and that the influences of errors (such as false positive SNPs, ambiguously called sites, and gaps) are mitigated is of utmost importance. The choices researchers make regarding the generation and analysis of WGS data can greatly influence the accuracy of short-read sequence alignments and, therefore, the efficacy of such experiments. We studied the effects of some of these choices, including: i) depth of sequencing coverage, ii) choice of reference-guided short-read sequence assembler, iii) choice of reference genome, and iv) whether to perform read-quality filtering and trimming, on our ability to detect true SNPs and on the frequencies of errors. We performed benchmarking experiments, during which we assembled simulated and real Listeria monocytogenes strain 08-5578 short-read sequence datasets of varying quality with four commonly used assemblers (BWA, MOSAIK, Novoalign, and SMALT), using reference genomes of varying genetic distances, and with or without read pre-processing (i.e., quality filtering and trimming). We found that assemblies of at least 50-fold coverage provided the most accurate results. In addition, MOSAIK yielded the fewest errors when reads were aligned to a nearly identical reference genome, while using SMALT to align reads against a reference sequence that is ∼0.82% distant from 08-5578 at the nucleotide level resulted in the detection of the greatest numbers of true SNPs and the fewest errors. Finally, we show that whether read pre-processing improves SNP detection depends upon the choice of reference sequence and assembler. In total, this study demonstrates that researchers should test a variety of conditions to achieve optimal results.

Phylogenomic grouping of Listeria monocytogenes

“…Hence, SNP-based analysis has been very useful in describing the transmission of a pathogen within a single or closely related outbreak (Harris et al 2013;Walker et al 2013;Schmid et al 2014) but not when applied to distantly related isolates. Also, the quality of the raw data generated, the method of assembly, and the reference strains influence the output of SNPs (Pightling et al 2014). On the basis of the 4 above mentioned principles, several bioinformatics tools have been devised and employed to determine the relation between the strains.…”

Navigating Microbiological Food Safety in the Era of Whole-Genome Sequencing

“…One of the least computationally intensive ways to detect SNPs is referenceguided assembly. After the reads have been mapped to a reference genome, a SNP caller can be used to identify the SNPs between the genomes; however, this introduces the issue of reference bias, and the choice of reference genome is extremely important (31). It is also possible to do a SNP analysis of de novo-assembled genomes.…”

Computational Errors and Biases in Short Read Next Generation Sequencing

“…For comparisons and benchmark tests for aligners see [53][54][55][56][57] and the excellent review [50]. The list of aligners is updated online [58].…”