Comparison Of Multi-locus Sequence Typing Software For Next Generation Sequencing Data

Abstract

Multi-locus sequence typing (MLST) is a widely used method for categorising bacteria. Increasingly MLST is being performed using next generation sequencing data by reference labs and for clinical diagnostics. Many software applications have been developed to calculate sequence types from NGS data; however, there has been no comprehensive review to date on these methods. We have compared six of these applications against real and simulated data and present results on: 1. the accuracy of each method against traditional typing methods, 2. the performance on real outbreak datasets, 3. in the impact of contamination and varying depth of coverage, and 4. the computational resource requirements.\n\nDATA SUMMARYO_LISimulated reads for datasets testing coverage and mixed samples have been deposited in Figshare; DOI: https://doi.org/10.6084/m9.figshare.4602301.vl\nC_LIO_LIOutbreak databases are available from Github; url - https://github.com/WGS-standards-and-analysis/datasets\nC_LIO_LIDocker containers used to run each of the applications are available from Github; url - https://tinyurl.com/z7ks2ft\nC_LIO_LIAccession numbers for the data used in this paper are available in the Supplementary material.\nC_LI\n\nWe confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. {ballotx}\n\nIMPACT STATEMENTSequence typing is rapidly transitioning from traditional sequencing methods to using whole genome sequencing. A number of in silico prediction methods have been developed on an ad hoc basis and aim to replicate Multi-locus sequence typing (MLST). This is the first study to comprehensively evaluate multiple MLST software applications on real validated datasets and on common simulated difficult cases. It will give researchers a clearer understanding of the accuracy, limitations and computational performance of the methods they use, and will assist future researchers to choose the most appropriate method for their experimental goals.