The Binning of Metagenomic Contigs for Microbial Physiology of Mixed Cultures

Abstract

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 ().

Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life

“…MAGs are obtained by grouping or 'binning' together assembled contigs with similar sequence composition, depth of coverage across one or more related samples and taxonomic affiliations 16,17 . Several tools have been developed that exploit these sources of information to produce genomes from metagenomic data [18][19][20][21] and there are ongoing efforts to evaluate the effectiveness of different approaches 22 . Although closed genomes have been obtained using metagenomic binning methods 10,23 , MAGs are typically incomplete and may contain contigs from multiple strains or species due to challenges in distinguishing between related community members both in the assembly and binning processes 19,24 .…”

A Previously Uncharacterized, Nonphotosynthetic Member of the Chromatiaceae Is the Primary CO 2 -Fixing Constituent in a Self-Regenerating Biocathode

“…Clusters were defined for groups of AMPHORA2 genes that resolved to the same taxonomic level and for which similar coverage was found for the contigs from which the ORFs were generated. Contigs longer than 200 bp were processed using the Metawatt binner (v1.7), which uses multivariate statistics of tetranucleotide frequencies combined with use of the interpolated Markov model (IMM) (GLIMMER 3.02) to cluster contigs (32). Initial clusters based only on tetranucleotide frequencies were built of at least 0.1 Mbp using the medium confidence level.…”

Microbial dark matter ecogenomics reveals complex synergistic networks in a methanogenic bioreactor

“…Four methods were compared for phylogenetically annotating metagenomic scaffolds and, ultimately, constructing accurate TA-degrading community member draft genomes: PhylopythiaS, ClaMS, Metawatt version 1.7 and BLAST (Camacho et al, 2009;Pati et al, 2011;Patil et al, 2012;Strous et al, 2012). Metagenomic bins generated from these k-mer-and homology-based methods (detailed in Supplementary Note) were compared to attain accurate classification of contigs containing novel genes that BLAST cannot address.…”