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LRBinner: Binning Long Reads in Metagenomics Datasets

Authors Anuradha Wickramarachchi , Yu Lin

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Anuradha Wickramarachchi
  • School of Computing, Australian National University, Canberra, Australia
Yu Lin
  • School of Computing, 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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Anuradha Wickramarachchi and Yu Lin. LRBinner: Binning Long Reads in Metagenomics Datasets. In 21st International Workshop on Algorithms in Bioinformatics (WABI 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 201, pp. 11:1-11:18, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)


Advancements in metagenomics sequencing allow the study of microbial communities directly from their environments. Metagenomics binning is a key step in the species characterisation of microbial communities. Next-generation sequencing reads are usually assembled into contigs for metagenomics binning mainly due to the limited information within short reads. Third-generation sequencing provides much longer reads that have lengths similar to the contigs assembled from short reads. However, existing contig-binning tools cannot be directly applied on long reads due to the absence of coverage information and the presence of high error rates. The few existing long-read binning tools either use only composition or use composition and coverage information separately. This may ignore bins that correspond to low-abundance species or erroneously split bins that correspond to species with non-uniform coverages. Here we present a reference-free binning approach, LRBinner, that combines composition and coverage information of complete long-read datasets. LRBinner also uses a distance-histogram-based clustering algorithm to extract clusters with varying sizes. The experimental results on both simulated and real datasets show that LRBinner achieves the best binning accuracy against the baselines. Moreover, we show that binning reads using LRBinner prior to assembly reduces computational resources for assembly while attaining satisfactory assembly qualities.

Subject Classification

ACM Subject Classification
  • Applied computing → Bioinformatics
  • Applied computing → Computational genomics
  • Metagenomics binning
  • long reads
  • machine learning
  • clustering


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