ArticleBMC genomics2024
Comparison between 16S rRNA and shotgun sequencing in colorectal cancer, advanced colorectal lesions, and healthy human gut microbiota.
Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed.
- Gut microbiota signatures and machine learning-based candidate feature prioritization in advanced colorectal cancer.Archives of microbiology · 2026Article
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- Advancing Personalized Medicine in Gestational Diabetes Mellitus Management Through Gut Microbiota Insights.International journal of molecular sciences · 2026Review
- Microbiome-Informed Precision Electroconvulsive Therapy: Oral-Gut-Immune Signatures and Seizure Biology as Candidate Predictors of Response-A Narrative Review.Biomedicines · 2026Review
- The gastrointestinal microbiome and constituent short-chain fatty acids: a narrative review of an underexplored axis in acute lymphoblastic leukemia.Gut pathogens · 2026Review
- Functional Metagenomics Insights Into the Allium ampeloprasum Rhizosphere Microbiome Under Different Fertilization Regimes.MicrobiologyOpen · 2026Article
- The trends and hotspots of gut microbiota for breast cancer from 2005 to 2024: A bibliometric and visual analysis.Medicine · 2026Review
- Comparing the performance of functional versus taxonomic metagenomics for detecting ammonia disturbances in the biogas system.FEMS microbiology ecology · 2026Article
- Profiling the Athletes' Gut Microbiome: A Critical Methodological Perspective on 16S Metabarcoding and Shotgun Metagenomics.Biology · 2026Review
- Oral Microbiome Diversity Matters on Nucleos(t)ide Analogue Cessation in Chronic Hepatitis B.The Journal of infectious diseases · 2026Article
- From soil to sequences: mechanisms and tools unravelling plant-rhizomicrobiome interactions.World journal of microbiology & biotechnology · 2026Review
- Amplicon-based prediction of secondary metabolic potential of microbiomes facilitates natural product discovery.Synthetic and systems biotechnology · 2026Article
- Comparison of oral collection methods for 16S rRNA gene and shotgun metagenomic sequencing.Microbiology spectrum · 2026Article
- Understanding the bacteriome, phageome and phage-associated bacteriome in healthy Vietnamese children under two years of age.Archives of microbiology · 2026Article
- Oral Microbiota and Clinical Outcomes in Allogenic Hematopoietic Stem Cell Transplantation: A Systematic Review.Microorganisms · 2026Review
- Personalized Nutrition Through the Gut Microbiome in Metabolic Syndrome and Related Comorbidities.Nutrients · 2026Review
- Dysbiotic shift in the oral microbiota of patients with Alzheimer's disease compared to their healthy life partners-a combinatorial approach and a paired study design.Alzheimer's research & therapy · 2026Article
- Standardizing vaginal microbial profiling: evaluating swab materials, storage conditions, and host DNA depletion strategies.BMC microbiology · 2026Article
- Metagenomic next-generation sequencing: new horizons in microbiology.Frontiers in cellular and infection microbiology · 2026Review
- The role of gut microbiota in autoimmune thyroid diseases: nutritional determinants and diet-based modulation.Frontiers in endocrinology · 2026Review
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
backgroundGut dysbiosis has been associated with colorectal cancer (CRC), the third most prevalent cancer in the world. This study compares microbiota taxonomic and abundance results obtained by 16S rRNA gene sequencing (16S) and whole shotgun metagenomic sequencing to investigate their reliability for bacteria profiling. The experimental design included 156 human stool samples from healthy controls, advanced (high-risk) colorectal lesion patients (HRL), and CRC cases, with each sample sequenced using both 16S and shotgun methods. We thoroughly compared both sequencing technologies at the species, genus, and family annotation levels, the abundance differences in these taxa, sparsity, alpha and beta diversities, ability to train prediction models, and the similarity of the microbial signature derived from these models.
resultsAs expected, the results showed that 16S detects only part of the gut microbiota community revealed by shotgun, although some genera were only profiled by 16S. The 16S abundance data was sparser and exhibited lower alpha diversity. In lower taxonomic ranks, shotgun and 16S highly differed, partially due to a disagreement in reference databases. When considering only shared taxa, the abundance was positively correlated between the two strategies. We also found a moderate correlation between the shotgun and 16S alpha-diversity measures, as well as their PCoAs. Regarding the machine learning models, only some of the shotgun models showed some degree of predictive power in an independent test set, but we could not demonstrate a clear superiority of one technology over the other. Microbial signatures from both sequencing techniques revealed taxa previously associated with CRC development, e.g., Parvimonas micra.
conclusionsShotgun and 16S sequencing provide two different lenses to examine microbial communities. While we have demonstrated that they can unravel common patterns (including microbial signatures), shotgun often gives a more detailed snapshot than 16S, both in depth and breadth. Instead, 16S will tend to show only part of the picture, giving greater weight to dominant bacteria in a sample. Therefore, we recommend choosing one or another sequencing technique before launching a study. Specifically, shotgun sequencing is preferred for stool microbiome samples and in-depth analyses, while 16S is more suitable for tissue samples and studies with targeted aims.
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