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2022 | OriginalPaper | Buchkapitel

MetaCoAG: Binning Metagenomic Contigs via Composition, Coverage and Assembly Graphs

verfasst von : Vijini Mallawaarachchi, Yu Lin

Erschienen in: Research in Computational Molecular Biology

Verlag: Springer International Publishing

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Abstract

Metagenomics has allowed us to obtain various genetic material from different species and gain valuable insights into microbial communities. Binning plays an important role in the early stages of metagenomic analysis pipelines. A typical pipeline in metagenomics binning is to assemble short reads into longer contigs and then bin into groups representing different species in the metagenomic sample. While existing binning tools bin metagenomic contigs, they do not make use of the assembly graphs that produce such assemblies. Here we propose MetaCoAG, a tool that utilizes assembly graphs with the composition and coverage information to bin metagenomic contigs. MetaCoAG uses single-copy marker genes to estimate the number of initial bins, assigns contigs into bins iteratively and adjusts the number of bins dynamically throughout the binning process. Experimental results on simulated and real datasets demonstrate that MetaCoAG significantly outperforms state-of-the-art binning tools, producing similar or more high-quality bins than the second-best tool. To the best of our knowledge, MetaCoAG is the first stand-alone contig-binning tool to make direct use of the assembly graph information.
Availability: MetaCoAG is freely available at https://​github.​com/​Vini2/​MetaCoAG.

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Fußnoten
1
Please note that the recently published tool Vamb [27] was not used to evaluate the simHC+ dataset as the number of contigs was less than the number recommended by the authors (https://​github.​com/​RasmussenLab/​vamb#recommended-workflow).
 
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Metadaten
Titel
MetaCoAG: Binning Metagenomic Contigs via Composition, Coverage and Assembly Graphs
verfasst von
Vijini Mallawaarachchi
Yu Lin
Copyright-Jahr
2022
DOI
https://doi.org/10.1007/978-3-031-04749-7_5