Which application is right for my microbiome studies?

Shallow shotgun metagenomics? 16S Amplicon? or Deep shotgun metagenomics?

Valerie TAN, Product Manager, Novogene AMEA
11 September 2022 | Metagenome

Shallow shotgun metagenomic sequencing is a relatively recent application that bridges the gap between amplicon sequencing and deep shotgun metagenomic sequencing, offering a cost friendly alternative to 16S rRNA amplicon sequencing, in particular for large-scale human microbiome studies.

What are the advantages of shallow shotgun metagenomic sequencing (SSMS) and the other metagenomic strategies in uncovering the microbiota taxonomy? We'll tell you more..
Taxonomy annotation results obtained by shallow shotgun metagenomic sequencing are similar to deep shotgun metagenomic sequencing

With SSMS, you can achieve comparable taxonomy annotation results to deep shotgun metagenomic sequencing at a cost similar to 16S rRNA amplicon sequencing. It provides more reliable taxonomy profiles than amplicon sequencing at the species level (3, 5, 7).

Shallow shotgun metagenomic sequencing provides more comprehensive taxonomy annotation than 16S amplicon sequencing

Characterization of the microbial community is typically accomplished with 16S rRNA amplicon sequencing due to its affordability. However, short-read sequencing of one variable region of the 16S rRNA gene is not sufficient to reflect the complete composition of a complex sample. Primer bias may also influence the data received as some region primers are superior at identifying specific species of bacteria over others (2,5,6).

SSMS provides taxonomy annotation data that is representative of the complete microbiome community in the sample, and overcomes the limitations of amplification bias associated with amplicon sequencing. Furthermore, SSMS requires less input materials than amplicon sequencing for multiple regions, which simplifies its DNA process and is more cost-effective (3,5,7).

Shallow shotgun metagenomic sequencing is a good alternative to amplicon sequencing for large-scale human microbiome studies

Amplicon sequencing is commonly employed in large-scale longitudinal studies for both preliminary microbial composition characterization and subsequent metagenomic analysis. However, research has shown that amplicon sequencing results are not as comparable and accurate as previously thought (1,2,3,5,7). Instead, shallow shotgun metagenomic sequencing results provide taxonomy annotation results that are superior to amplicon sequencing in regards to recovery, taxonomy resolution, and annotation of species. Compared with deep shotgun metagenomic analysis results, SSMS surpasses amplicon sequencing for the initial investigation of the microbiome characterization with its better recall rate, accuracy and consistency (3,5,7).

Which application is right for my microbiome studies among the many metagenomic strategies available?

Shotgun metagenomic sequencing Shallow shotgun metagenomic sequencing Amplicon sequencing
16S Full-length amplicon sequencing
Data Requirement 6-12 Gb+ 1-2 Gb 100K raw tags 10-40K clean reads
Taxonomic Resolution Species-level Species-level Genus level
Species/ Sub-species level
Taxonomic Coverage All taxa All taxa Amplification region specific
Bacteria and Archea (16S)
Gene Reconstruction Yes No No No
Bias Lower Lower Medium-High Medium-High
Taxonomy Annotation Micro_NR/ NR database Kraken 2 Qimme 1/ Qiime 2 Qiime 2
Sequencing Platform Illumina Novaseq
PE150
Illumina Novaseq
PE150
Illumina Novaseq
PE250
PacBio Sequel II/IIe
Want to learn more about our Shallow Shotgun Metagenomic Sequencing?

References:
  1. Fricker, A. M., Podlesny, D., & Fricke, W. F. (2019). What is new and relevant for sequencing-based microbiome research? A mini-review. Journal of advanced research, 19, 105–112. https://doi.org/10.1016/j.jare.2019.03.006
  2. Han, D., Gao, P., Li, R., Tan, P., Xie, J., Zhang, R., & Li, J. (2020). Multicenter assessment of microbial community profiling using 16S rRNA gene sequencing and shotgun metagenomic sequencing. Journal of advanced research, 26, 111–121. https://doi.org/10.1016/j.jare.2020.07.010
  3. Hillmann, B., Al-Ghalith, G. A., Shields-Cutler, R. R., Zhu, Q., Gohl, D. M., Beckman, K. B., Knight, R., & Knights, D. (2018). Evaluating the Information Content of Shallow Shotgun Metagenomics. mSystems, 3(6), e00069-18. https://doi.org/10.1128/mSystems.00069-18
  4. Liu, Y. X., Qin, Y., Chen, T., Lu, M., Qian, X., Guo, X., & Bai, Y. (2021). A practical guide to amplicon and metagenomic analysis of microbiome data. Protein & cell, 12(5), 315–330. https://doi.org/10.1007/s13238-020-00724-8
  5. Lugli GA, Ventura M. A breath of fresh air in microbiome science: shallow shotgun metagenomics for a reliable disentangling of microbial ecosystems. Microbiome Res Rep 2022;1:8. http://dx.doi.org/10.20517/mrr.2021.07
  6. Sharpton T. J. (2014). An introduction to the analysis of shotgun metagenomic data. Frontiers in plant science, 5, 209. https://doi.org/10.3389/fpls.2014.00209
  7. Xu, W., Chen, T., Pei, Y., Guo, H., Li, Z., Yang, Y., Zhang, F., Yu, J., Li, X., Yang, Y., Zhao, B., & Wu, C. (2021). Characterization of Shallow Whole-Metagenome Shotgun Sequencing as a High-Accuracy and Low-Cost Method by Complicated Mock Microbiomes. Frontiers in microbiology, 12, 678319. https://doi.org/10.3389/fmicb.2021.678319
 
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