Metagenomic binning through low-density hashing

Yunan Luo, Yun William Yu, Jianyang Zeng, Bonnie Berger, Jian Peng

Research output: Contribution to journalArticlepeer-review


Motivation Vastly greater quantities of microbial genome data are being generated where environmental samples mix together the DNA from many different species. Here, we present Opal for metagenomic binning, the task of identifying the origin species of DNA sequencing reads. We introduce low-density' locality sensitive hashing to bioinformatics, with the addition of Gallager codes for even coverage, enabling quick and accurate metagenomic binning. Results On public benchmarks, Opal halves the error on precision/recall (F1-score) as compared with both alignment-based and alignment-free methods for species classification. We demonstrate even more marked improvement at higher taxonomic levels, allowing for the discovery of novel lineages. Furthermore, the innovation of low-density, even-coverage hashing should itself prove an essential methodological advance as it enables the application of machine learning to other bioinformatic challenges. Availability and implementation Full source code and datasets are available at and Supplementary informationSupplementary dataare available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)219-226
Number of pages8
Issue number2
StatePublished - Jan 15 2019

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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