A review of computational tools for generating metagenome-assembled genomes from metagenomic sequencing data.

Metagenomic sequencing provides a culture-independent avenue to investigate the complex microbial communities by constructing metagenome-assembled genomes (MAGs). A MAG represents a microbial genome by a group of sequences from genome assembly with similar characteristics. It enables us to identify novel species and understand their potential functions in a dynamic ecosystem. Many computational tools have been developed to construct and annotate MAGs from metagenomic sequencing, however, there is a prominent gap to comprehensively introduce their background and practical performance. In this paper, we have thoroughly investigated the computational tools designed for both upstream and downstream analyses, including metagenome assembly, metagenome binning, gene prediction, functional annotation, taxonomic classification, and profiling. We have categorized the commonly used tools into unique groups based on their functional background and introduced the underlying core algorithms and associated information to demonstrate a comparative outlook. Furthermore, we have emphasized the computational requisition and offered guidance to the users to select the most efficient tools. Finally, we have indicated current limitations, potential solutions, and future perspectives for further improving the tools of MAG construction and annotation. We believe that our work provides a consolidated resource for the current stage of MAG studies and shed light on the future development of more effective MAG analysis tools on metagenomic sequencing.

The binning of metagenomic contigs for microbial physiology of mixed cultures.

So far, microbial physiology has dedicated itself mainly to pure cultures. In nature, cross feeding and competition are important aspects of microbial physiology and these can only be addressed by studying complete communities such as enrichment cultures. Metagenomic sequencing is a powerful tool to characterize such mixed cultures. In the analysis of metagenomic data, well established algorithms exist for the assembly of short reads into contigs and for the annotation of predicted genes. However, the binning of the assembled contigs or unassembled reads is still a major bottleneck and required to understand how the overall metabolism is partitioned over different community members. Binning consists of the clustering of contigs or reads that apparently originate from the same source population. In the present study eight metagenomic samples from the same habitat, a laboratory enrichment culture, were sequenced. Each sample contained 13-23 Mb of assembled contigs and up to eight abundant populations. Binning was attempted with existing methods but they were found to produce poor results, were slow, dependent on non-standard platforms or produced errors. A new binning procedure was developed based on multivariate statistics of tetranucleotide frequencies combined with the use of interpolated Markov models. Its performance was evaluated by comparison of the results between samples with BLAST and in comparison to existing algorithms for four publicly available metagenomes and one previously published artificial metagenome. The accuracy of the new approach was comparable or higher than existing methods. Further, it was up to a 100 times faster. It was implemented in Java Swing as a complete open source graphical binning application available for download and further development (http://sourceforge.net/projects/metawatt).