ArticleNature communications2022
Strain level microbial detection and quantification with applications to single cell metagenomics.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Strain-level metagenomic profiling using pangenome graphs with PanTax.Genome research · 2026Article
- Bioinformatic approaches to blood and tissue microbiome analyses: challenges and perspectives.Briefings in bioinformatics · 2025Review
- Article
- kMetaShot: a fast and reliable taxonomy classifier for metagenome-assembled genomes.Briefings in bioinformatics · 2024Article
- Beyond the Gut: The intratumoral microbiome's influence on tumorigenesis and treatment response.Cancer communications (London, England) · 2024Review
- Identification of intracellular bacteria from multiple single-cell RNA-seq platforms using CSI-Microbes.Science advances · 2024Article
- Fast, parallel, and cache-friendly suffix array construction.Algorithms for molecular biology : AMB · 2024Article
- Isolation and Cultivation of Human Gut Microorganisms: A Review.Microorganisms · 2023Review
- Sketching and sampling approaches for fast and accurate long read classification.BMC bioinformatics · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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.
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Registered trials
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