GraphBin2: Refined and Overlapped Binning of Metagenomic Contigs Using Assembly Graphs

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

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Vijini G. Mallawaarachchi
  • Research School of Computer Science, College of Engineering and Computer Science, Australian National University, Canberra, Australia
Anuradha S. Wickramarachchi
  • Research School of Computer Science, College of Engineering and Computer Science, Australian National University, Canberra, Australia
Yu Lin
  • Research School of Computer Science, College of Engineering and Computer Science, Australian National University, Canberra, Australia


We would like to thank the anonymous reviewers for their valuable comments. Furthermore, this research was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI Australia), an NCRIS enabled capability supported by the Australian Government.

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Vijini G. Mallawaarachchi, Anuradha S. Wickramarachchi, and Yu Lin. GraphBin2: Refined and Overlapped Binning of Metagenomic Contigs Using Assembly Graphs. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 8:1-8:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)


Metagenomic sequencing allows us to study 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 contig binning only support non-overlapped binning, i.e., each contig is assigned to at most one bin (species). 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.

Subject Classification

ACM Subject Classification
  • Applied computing → Bioinformatics
  • Applied computing → Computational genomics
  • Metagenomics binning
  • contigs
  • assembly graphs
  • overlapped binning


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