METAMVGL: a multi-view graph-based metagenomic contig binning algorithm by integrating assembly and paired-end graphs

Background Due 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...

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Vydané v:	BMC bioinformatics Ročník 22; číslo Suppl 10; s. 1 - 14
Hlavní autori:	Zhang, Zhenmiao, Zhang, Lu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 22.07.2021 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	Algorithms Analysis Assembly Assembly graph Bioinformatics Biomedical and Life Sciences Clustering Clusters Computational Biology/Bioinformatics Computer Appl. in Life Sciences Contig binning Dead end Evolution & development Genomes Genomics Graph theory Graphs Infants Information management Innovations Life Sciences Metagenomics Methodology Methods Microarrays Microbial activity Microorganisms Multi-view label propagation Next-generation sequencing Nucleotides Paired-end graph Propagation China Multi-view label propagation Dead end Contig binning Assembly graph Paired-end graph
ISSN:	1471-2105, 1471-2105
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Shrnutí:	Background Due 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. Results We 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. Conclusions Our 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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-021-04284-4