Aniket Mane, Haley Sanderson, Aaron P White, Rahat Zaheer, Robert Beiko, Cédric Chauve
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引用次数: 0
Abstract
Background: Plasmids play a major role in the transfer of antimicrobial resistance (AMR) genes among bacteria via horizontal gene transfer. The identification of plasmids in short-read assemblies is a challenging problem and a very active research area. Plasmid binning aims at detecting, in a draft genome assembly, groups (bins) of contigs likely to originate from the same plasmid. Several methods for plasmid binning have been developed recently, such as PlasBin-flow, HyAsP, gplas, MOB-suite, and plasmidSPAdes. This motivates the problem of evaluating the performances of plasmid binning methods, either against a given ground truth or between them.
Results: We describe PlasEval, a novel method aimed at comparing the results of plasmid binning tools. PlasEval computes a dissimilarity measure between two sets of plasmid bins, that can originate either from two plasmid binning tools, or from a plasmid binning tool and a ground truth set of plasmid bins. The PlasEval dissimilarity accounts for the contig content of plasmid bins, the length of contigs and is repeat-aware. Moreover, the dissimilarity score computed by PlasEval is broken down into several parts, that allows to understand qualitative differences between the compared sets of plasmid bins. We illustrate the use of PlasEval by benchmarking four recently developed plasmid binning tools-PlasBin-flow, HyAsP, gplas, and MOB-recon-on a data set of 53 E. coli bacterial genomes.
Conclusion: Analysis of the results of plasmid binning methods using PlasEval shows that their behaviour varies significantly. PlasEval can be used to decide which specific plasmid binning method should be used for a specific dataset. The disagreement between different methods also suggests that the problem of plasmid binning on short-read contigs requires further research. We believe that PlasEval can prove to be an effective tool in this regard. PlasEval is publicly available at https://github.com/acme92/PlasEval.
期刊介绍:
BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology.
BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.