FastANI, Mash and Dashing equally differentiate between Klebsiella species

Bacteria of the genus Klebsiella are among the most important multi-drug resistant human pathogens, though they have been isolated from a variety of environments. The importance and ubiquity of these organisms call for quick and accurate methods for their classification. Average Nucleotide Identity (ANI) is becoming a standard for species delimitation based on whole genome sequence comparison. However, much faster genome comparison tools have been appearing in the literature. In this study we tested the quality of different approaches for genome-based species delineation against ANI. To this end, we compared 1,189 Klebsiella genomes using measures calculated with Mash, Dashing, and DNA compositional signatures, all of which run in a fraction of the time required to obtain ANI. Receiver Operating Characteristic (ROC) curve analyses showed equal quality in species discrimination for ANI, Mash and Dashing, with Area Under the Curve (AUC) values above 0.99, followed by DNA signatures (AUC: 0.96). Accordingly, groups obtained at optimized cutoffs largely agree with species designation, with ANI, Mash and Dashing producing 15 species-level groups. DNA signatures broke the dataset into more than 30 groups. Testing Mash to map species after adding draft genomes to the dataset also showed excellent results (AUC above 0.99), producing a total of 26 Klebsiella species-level groups. The ecological niches of Klebsiella strains were found to neither be related to species delimitation, nor to protein functional content, suggesting that a single Klebsiella species can have a wide repertoire of ecological functions.

Computational Framework for High-Quality Production and Large-Scale Evolutionary Analysis of Metagenome Assembled Genomes

Microbial species play important roles in different environments and the production of high-quality genomes from metagenome data sets represents a major obstacle to understanding their ecological and evolutionary dynamics. Metagenome-Assembled Genomes Orchestra (MAGO) is a computational framework that integrates and simplifies metagenome assembly, binning, bin improvement, bin quality (completeness and contamination), bin annotation, and evolutionary placement of bins via detailed maximum-likelihood phylogeny based on multiple marker genes using different amino acid substitution models, next to average nucleotide identity analysis of genomes for delineation of species boundaries and operational taxonomic units. MAGO offers streamlined execution of the entire metagenomics pipeline, error checking, computational resource distribution and compatibility of data formats, governed by user-tailored pipeline processing. MAGO is an open-source-software package released in three different ways, as a singularity image and a Docker container for HPC purposes as well as for running MAGO on a commodity hardware, and a virtual machine for gaining a full access to MAGO underlying structure and source code. MAGO is open to suggestions for extensions and is amenable for use in both research and teaching of genomics and molecular evolution of genomes assembled from small single-cell projects or large-scale and complex environmental metagenomes.