Improving metagenomic binning results with overlapped bins using assembly graphs

Vijini G. Mallawaarachchi, Anuradha S. Wickramarachchi, Yu Lin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Background: Metagenomic sequencing allows us to study the structure, diversity and ecology in microbial communities without the necessity of obtaining pure cultures. In many metagenomics studies, the reads obtained from metagenomics sequencing are first assembled into longer contigs and these contigs are then binned into clusters of contigs where contigs in a cluster are expected to come from the same species. As different species may share common sequences in their genomes, one assembled contig may belong to multiple species. However, existing tools for binning contigs only support non-overlapped binning, i.e., each contig is assigned to at most one bin (species). Results: In this paper, we introduce GraphBin2 which refines the binning results obtained from existing tools and, more importantly, is able to assign contigs to multiple bins. GraphBin2 uses the connectivity and coverage information from assembly graphs to adjust existing binning results on contigs and to infer contigs shared by multiple species. Experimental results on both simulated and real datasets demonstrate that GraphBin2 not only improves binning results of existing tools but also supports to assign contigs to multiple bins. Conclusion: GraphBin2 incorporates the coverage information into the assembly graph to refine the binning results obtained from existing binning tools. GraphBin2 also enables the detection of contigs that may belong to multiple species. We show that GraphBin2 outperforms its predecessor GraphBin on both simulated and real datasets. GraphBin2 is freely available at https://github.com/Vini2/GraphBin2.

Original languageEnglish
Article number3
Number of pages18
JournalAlgorithms for Molecular Biology
Volume16
DOIs
Publication statusPublished - 2021
Externally publishedYes

Keywords

  • Assembly graphs
  • Contigs
  • Metagenomics binning
  • Overlapped binning

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