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

Kaiyuan Zhu, Alejandro A. Schäffer, Welles Robinson, Junyan Xu, Eytan Ruppin, A. Funda Ergun, Yuzhen Ye and S. Cenk Sahinalp ()
Additional contact information
Kaiyuan Zhu: National Cancer Institute, National Institutes of Health
Alejandro A. Schäffer: National Cancer Institute, National Institutes of Health
Welles Robinson: 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

Nature Communications, 2022, vol. 13, issue 1, 1-19

Abstract: 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.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-022-33869-7 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33869-7

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-022-33869-7

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().