Motivation One of the most widespread methods used in taxonomy studies to distinguish between strains or taxa is the calculation of average nucleotide identity. It requires a computationally expensive alignment step and is therefore not suitable for large-scale comparisons. Short oligonucleotide-based methods do offer a faster alternative but at the expense of accuracy. Here, we aim to address this shortcoming by providing a software that implements a novel method based on short-oligonucleotide frequencies to compute inter-genomic distances. Results Our tetranucleotide and hexanucleotide implementations, which were optimized based on a taxonomically well-defined set of over 200 newly sequenced bacterial genomes, are as accurate as the short oligonucleotide-based method TETRA and average nucleotide identity, for identifying bacterial species and strains, respectively. Moreover, the lightweight nature of this method makes it applicable for large-scale analyses. Availability and implementation The method introduced here was implemented, together with other existing methods, in a dependency-free software written in C, GenDisCal, available as source code from https://github.com/LM-UGent/GenDisCal. The software supports multithreading and has been tested on Windows and Linux (CentOS). In addition, a Java-based graphical user interface that acts as a wrapper for the software is also available. Supplementary information Supplementary data are available at Bioinformatics online.

The rise of metagenomics offers a leap forward for understanding the genetic diversity of microorganisms in many different complex environments by providing a platform that can identify potentially unlimited numbers of known and novel microorganisms. As such, it is impossible to imagine new major initiatives without metagenomics. Nevertheless, it represents a relatively new discipline with various levels of complexity and demands on bioinformatics. The underlying principles and methods used in metagenomics are often seen as common knowledge and often not detailed or fragmented. Therefore, we reviewed these to guide microbiologists in taking the first steps into metagenomics. We specifically focus on a workflow aimed at reconstructing individual genomes, that is, metagenome‐assembled genomes, integrating DNA sequencing, assembly, binning, identification and annotation.

Background Although the total number of microbial taxa on Earth is under debate, it is clear that only a small fraction of these has been cultivated and validly named. Evidently, the inability to culture most bacteria outside of very specific conditions severely limits their characterization and further studies. In the last decade, a major part of the solution to this problem has been the use of metagenome sequencing, whereby the DNA of an entire microbial community is sequenced, followed by the in silico reconstruction of genomes of its novel component species. The large discrepancy between the number of sequenced type strain genomes (around 12,000) and total microbial diversity (106–1012 species) directs these efforts to de novo assembly and binning. Unfortunately, these steps are error-prone and as such, the results have to be intensely scrutinized to avoid publishing incomplete and low-quality genomes. Results We developed MAGISTA (metagenome-assembled genome intra-bin statistics assessment), a novel approach to assess metagenome-assembled genome quality that tackles some of the often-neglected drawbacks of current reference gene-based methods. MAGISTA is based on alignment-free distance distributions between contig fragments within metagenomic bins, rather than a set of reference genes. For proper training, a highly complex genomic DNA mock community was needed and constructed by pooling genomic DNA of 227 bacterial strains, specifically selected to obtain a wide variety representing the major phylogenetic lineages of cultivable bacteria. Conclusions MAGISTA achieved a 20% reduction in root-mean-square error in comparison to the marker gene approach when tested on publicly available mock metagenomes. Furthermore, our highly complex genomic DNA mock community is a very valuable tool for benchmarking (new) metagenome analysis methods.