Comparative Analysis of Metagenomic (Amplicon and Shotgun) DNA Sequencing to Characterize Microbial Communities in Household On-Site Wastewater Treatment Systems

Vries, Jacob de; Saleem, Faizan; Li, Enze; Chan, Alexander Wing Yip; Naphtali, James; Naphtali, Paul; Paschos, Athanasios; Schellhorn, Herb E.

doi:10.3390/w15020271

Cited by 3 publications

(2 citation statements)

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“…The library sizes (i.e., depths) in the two tables may differ by orders of magnitude, ranging from 1.4 to 1500 fold. Despite their differences, the 16S and SMS taxonomic profiles for the same cohort of samples have frequently been found to yield consistent patterns of microbial signatures [7,[16][17][18][19][20][21][22]. This further underscores the need for an integrative analysis of the two taxonomic profiles to improve power of testing these signatures.…”

Section: Introductionmentioning

confidence: 99%

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency

Yue,

Read,

Fedirko

et al. 2023

Preprint

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Background: The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Interestingly, many microbiome studies have performed both sequencing experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, highlighting the potential for an integrative analysis to improve power of testing these signatures. However, differential experimental biases, partially overlapping samples, and differential library sizes pose tremendous challenges when combining the two datasets. Currently, researchers either discard one dataset entirely or use different datasets for different objectives. Methods: In this article, we introduce the first method of this kind, named Com-2seq, that combines the two sequencing datasets for testing differential abundance at the genus and community levels while overcoming these difficulties. The new method is based on our LOCOM model (Hu et al., 2022), which employs logistic regression for testing taxon differential abundance while remaining robust to experimental bias. To benchmark the performance of Com-2seq, we introduce two ad hoc approaches: applying LOCOM to pooled taxa count data and combining LOCOM p-values from analyzing each dataset separately. Results: Our simulation studies indicate that Com-2seq substantially improves statistical efficiency over analysis of either dataset alone and works better than the two ad hoc approaches. An application of Com-2seq to two real microbiome studies uncovered scientifically plausible findings that would have been missed by analyzing individual datasets. Conclusions: Com-2seq performs integrative analysis of 16S and metagenomic sequencing data, which improves statistical efficiency and has the potential to accelerate the search of microbial communities and taxa that are involved in human health and diseases.

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Section: Introductionmentioning

confidence: 99%

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency

Yue,

Read,

Fedirko

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

mentioning

confidence: 99%

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency

Yue

Read

Fedirko

et al. 2023

Preprint

View full text Add to dashboard Cite

The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Interestingly, many microbiome studies have performed both sequencing experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, highlighting the potential for an integrative analysis to improve power of testing these signatures. However, differential experimental biases, partially overlapping samples, and differential library sizes pose tremendous challenges when combining the two datasets. Currently, researchers either discard one dataset entirely or use different datasets for different objectives. In this article, we introduce the first method of this kind, named Com-2seq, that combines the two sequencing datasets for the objective of testing differential abundance at the genus and community levels while overcoming these difficulties. We demonstrate that Com-2seq substantially improves statistical efficiency over analysis of either dataset alone and works better than two ad hoc approaches.

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Comparison between 16S rRNA and shotgun sequencing in colorectal cancer, advanced colorectal lesions, and healthy human gut microbiota

Bars-Cortina,

Ramon,

Rius-Sansalvador

et al. 2024

BMC Genomics

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Background Gut 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. Results As 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. Conclusions Shotgun 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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Comparative Analysis of Metagenomic (Amplicon and Shotgun) DNA Sequencing to Characterize Microbial Communities in Household On-Site Wastewater Treatment Systems

Cited by 3 publications

References 54 publications

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency

Comparison between 16S rRNA and shotgun sequencing in colorectal cancer, advanced colorectal lesions, and healthy human gut microbiota

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