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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)


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

Original languageEnglish
Title of host publicationResearch in Computational Molecular Biology - 26th Annual International Conference, RECOMB 2022, Proceedings
EditorsItsik Pe’er
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Electronic)978-3-031-04749-7
ISBN (Print)978-3-031-04748-0
Publication statusPublished - 2022
Externally publishedYes
Event26th International Conference on Research in Computational Molecular Biology, RECOMB 2022 - San Diego, United States
Duration: 22 May 202225 May 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13278 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference26th International Conference on Research in Computational Molecular Biology, RECOMB 2022
Country/TerritoryUnited States
CitySan Diego


  • Assembly graphs
  • Binning
  • Contigs
  • Metagenomics


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