ArticleMicrobiome2018
Species classifier choice is a key consideration when analysing low-complexity food microbiome data.
Article in Microbiome, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Temperature-driven metabolic adaptation in thermophilic microbial communities of Western Sichuan hot springs.BMC microbiology · 2026Article
- Species-Level Comparative Metagenomic Analysis of the Bacterial Abundance of the Gut Microbiome in Psoriasis, Hidradenitis Suppurativa, and Pemphigus Foliaceous Patients Using Shotgun Next-Generation Sequencing.International journal of molecular sciences · 2026Article
- Standardizing microbiome research: interlaboratory validation of SOPs for sample preparation and DNA extraction from food and environmental ecosystems.Environmental microbiome · 2025Article
- Consistent microbial insights across sequencing methods in soil studies: the role of reference taxonomies.mSystems · 2025Article
- Metagenomic and Physicochemical Analyses Reveal Microbial Community and Functional Differences Between Three Different Grades of Hongxin Low-Temperature Daqu.Foods (Basel, Switzerland) · 2025Article
- Microbial communities and their association with soil health indicators under row cash crop and cover crop diversification: a case study.Frontiers in microbiology · 2025Article
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- Inflammation, Gut Microbiota, and Metabolomic Shifts in Colorectal Cancer: Insights from Human and Mouse Models.International journal of molecular sciences · 2024Article
- Metagenomic and Metabolomic Profiling Reveals the Differences of Flavor Quality betweenFoods (Basel, Switzerland) · 2024Article
- Comparative metatranscriptome analysis of Brazilian milk and water kefir beverages.International microbiology : the official journal of the Spanish Society for Microbiology · 2024Article
- An in-depth evaluation of metagenomic classifiers for soil microbiomes.Environmental microbiome · 2024Article
- Enhancing Clinical Utility: Utilization of International Standards and Guidelines for Metagenomic Sequencing in Infectious Disease Diagnosis.International journal of molecular sciences · 2024Review
- Host DNA depletion methods and genome-centric metagenomics of bovine hindmilk microbiome.mSphere · 2024Article
- Carbohydrate-active enzyme profiles ofFood chemistry: X · 2023Article
- Differences in Faecal Microbiome Taxonomy, Diversity and Functional Potential in a Bovine Cohort Experimentally Challenged withAnimals : an open access journal from MDPI · 2023Article
- A next-generation sequencing approach for the detection of mixed species in canned tuna.Food chemistry: X · 2023Article
- From defaults to databases: parameter and database choice dramatically impact the performance of metagenomic taxonomic classification tools.Microbial genomics · 2023Article
- The gut microbiome-Does stool represent right?Heliyon · 2023Article
- Crowdsourced benchmarking of taxonomic metagenome profilers: lessons learned from the sbv IMPROVER Microbiomics challenge.BMC genomics · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundThe use of shotgun metagenomics to analyse low-complexity microbial communities in foods has the potential to be of considerable fundamental and applied value. However, there is currently no consensus with respect to choice of species classification tool, platform, or sequencing depth. Here, we benchmarked the performances of three high-throughput short-read sequencing platforms, the Illumina MiSeq, NextSeq 500, and Ion Proton, for shotgun metagenomics of food microbiota. Briefly, we sequenced six kefir DNA samples and a mock community DNA sample, the latter constructed by evenly mixing genomic DNA from 13 food-related bacterial species. A variety of bioinformatic tools were used to analyse the data generated, and the effects of sequencing depth on these analyses were tested by randomly subsampling reads.
resultsCompositional analysis results were consistent between the platforms at divergent sequencing depths. However, we observed pronounced differences in the predictions from species classification tools. Indeed, PERMANOVA indicated that there was no significant differences between the compositional results generated by the different sequencers (p = 0.693, R
conclusionsOur results demonstrate a remarkable similarity in the results generated by the three sequencing platforms at different sequencing depths, and, in fact, the choice of bioinformatics methodology had a more evident impact on results than the choice of sequencer did.
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