An integrated strain-level analytic pipeline utilizing longitudinal metagenomic data

Zhou, Boyan; Wang, Chan; Putzel, Gregory G.; Hu, Jiyuan; Liu, Menghan; Wu, Fen; Pironti, Alejandro; Li, Huilin

doi:10.1101/2022.02.15.480548

Cited by 1 publication

(1 citation statement)

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“…Methods for identifying significant changes throughout a metagenome series is an active area of research (43). Currently, a common approach is to first simplify each metagenome into a profile that can be logically aligned and compared, such as taxonomic classification relative abundance, gene function presence, and counts of short sub-sequences (k-mers) (10).…”

Section: Discussionmentioning

confidence: 99%

Reference-free Structural Variant Detection in Microbiomes via Long-read Coassembly Graphs

Curry,

Yu,

Vance

et al. 2024

Preprint

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Bacterial genome dynamics are vital for understanding the mechanisms underlying microbial adaptation, growth, and their broader impact on host phenotype. Structural variants (SVs), genomic alterations of 10 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to absence of clear reference genomes and presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing a single metagenome coassembly graph constructed from all samples in a series. The log fold change in graph coverage between subsequent samples is then calculated to call SVs that are thriving or declining throughout the series. We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, which is particularly noticeable as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between subsequent time and temperature samples, suggesting host advantage. Our innovative approach leverages raw read patterns rather than references or MAGs to include all sequencing reads in analysis, and thus provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial genome dynamics.

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Section: Discussionmentioning

confidence: 99%