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Strain level microbial detection and quantification with applications to single cell metagenomics

Author

Listed:
  • Kaiyuan Zhu

    (National Cancer Institute, National Institutes of Health
    UC San Diego
    Indiana University)

  • Alejandro A. Schäffer

    (National Cancer Institute, National Institutes of Health)

  • Welles Robinson

    (National Cancer Institute, National Institutes of Health
    National Cancer Institute, National Institutes of Health)

  • Junyan Xu

    (National Cancer Institute, National Institutes of Health)

  • Eytan Ruppin

    (National Cancer Institute, National Institutes of Health)

  • A. Funda Ergun

    (Indiana University)

  • Yuzhen Ye

    (Indiana University)

  • S. Cenk Sahinalp

    (National Cancer Institute, National Institutes of Health
    Indiana University)

Abstract

Computational identification and quantification of distinct microbes from high throughput sequencing data is crucial for our understanding of human health. Existing methods either use accurate but computationally expensive alignment-based approaches or less accurate but computationally fast alignment-free approaches, which often fail to correctly assign reads to genomes. Here we introduce CAMMiQ, a combinatorial optimization framework to identify and quantify distinct genomes (specified by a database) in a metagenomic dataset. As a key methodological innovation, CAMMiQ uses substrings of variable length and those that appear in two genomes in the database, as opposed to the commonly used fixed-length, unique substrings. These substrings allow to accurately decouple mixtures of highly similar genomes resulting in higher accuracy than the leading alternatives, without requiring additional computational resources, as demonstrated on commonly used benchmarking datasets. Importantly, we show that CAMMiQ can distinguish closely related bacterial strains in simulated metagenomic and real single-cell metatranscriptomic data.

Suggested Citation

  • Kaiyuan Zhu & Alejandro A. Schäffer & Welles Robinson & Junyan Xu & Eytan Ruppin & A. Funda Ergun & Yuzhen Ye & S. Cenk Sahinalp, 2022. "Strain level microbial detection and quantification with applications to single cell metagenomics," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33869-7
    DOI: 10.1038/s41467-022-33869-7
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    References listed on IDEAS

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    3. Peter Menzel & Kim Lee Ng & Anders Krogh, 2016. "Fast and sensitive taxonomic classification for metagenomics with Kaiju," Nature Communications, Nature, vol. 7(1), pages 1-9, September.
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