Abstract
BackgroundDue to the complexity of microbial communities, de novo assembly on next generation sequencing data is commonly unable to produce complete microbial genomes. Metagenome assembly binning becomes an essential step that could group the fragmented contigs into clusters to represent microbial genomes based on contigs’ nucleotide compositions and read depths. These features work well on the long contigs, but are not stable for the short ones. Contigs can be linked by sequence overlap (assembly graph) or by the paired-end reads aligned to them (PE graph), where the linked contigs have high chance to be derived from the same clusters.ResultsWe developed METAMVGL, a multi-view graph-based metagenomic contig binning algorithm by integrating both assembly and PE graphs. It could strikingly rescue the short contigs and correct the binning errors from dead ends. METAMVGL learns the two graphs’ weights automatically and predicts the contig labels in a uniform multi-view label propagation framework. In experiments, we observed METAMVGL made use of significantly more high-confidence edges from the combined graph and linked dead ends to the main graph. It also outperformed many state-of-the-art contig binning algorithms, including MaxBin2, MetaBAT2, MyCC, CONCOCT, SolidBin and GraphBin on the metagenomic sequencing data from simulation, two mock communities and Sharon infant fecal samples.ConclusionsOur findings demonstrate METAMVGL outstandingly improves the short contig binning and outperforms the other existing contig binning tools on the metagenomic sequencing data from simulation, mock communities and infant fecal samples.
Highlights
Due to the complexity of microbial communities, de novo assembly on generation sequencing data is commonly unable to produce complete microbial genomes
Our experiments indicate METAMVGL substantially improves the binning performance of the state-ofthe-art algorithms, including MaxBin2, MetaBAT2, MyCC, CONCOCT, SolidBin and GraphBin in the simulated, mock and Sharon datasets
In step 1, METAMVGL constructs the assembly graph and PE graph with contig labels generated by the existing binning tools
Summary
Due to the complexity of microbial communities, de novo assembly on generation sequencing data is commonly unable to produce complete microbial genomes. Metagenome assembly binning becomes an essential step that could group the fragmented contigs into clusters to represent microbial genomes based on contigs’ nucleotide compositions and read depths. These features work well on the long contigs, but are not stable for the short ones. Contig binning algorithms provide a supplement to genome assembly that group the contigs into clusters to represent the complete microbial genomes This strategy has been widely adopted to explore the novel microbes from the human gut metagenomic sequencing data [3,4,5,6,7,8,9,10]
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