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Evaluating the Information Content of Shallow Shotgun Metagenomics
Open accessAbstract
A common refrain in recent microbiome-related academic meetings is that the field needs to move away from broad taxonomic surveys using 16S sequencing and toward more powerful longitudinal studies using shotgun sequencing. However, performing deep shotgun sequencing in large longitudinal studies remains prohibitively expensive for all but the most well-funded research labs and consortia, which leads many researchers to choose 16S sequencing for large studies, followed by deep shotgun sequencing on a subset of targeted samples. Here, we show that shallow- or moderate-depth shotgun sequencing may be used by researchers to obtain species-level taxonomic and functional data at approximately the same cost as amplicon sequencing. While shallow shotgun sequencing is not intended to replace deep shotgun sequencing for strain-level characterization, we recommend that microbiome scientists consider using shallow shotgun sequencing instead of 16S sequencing for large-scale human microbiome studies.
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“we repeated all bioinformatic, normalization, and ML steps using bacterial assignments from SHOGUN31 and its separate database48, which showed highly concordant performances”
From RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer diagnostic approach (2020) · cites this paper as [31]
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Ruuskanen, M. O., Vats, D., Potbhare, R., RaviKumar, A., Munukka, E., Ashma, R., & Lahti, L. (2022) · Environmental Microbiology · doi:10.1111/1462-2920.15945 · Cited by 24
“Recently, shallow shotgun sequencing has shown to be a cost-effective alternative to 16S rRNA amplicon sequencing (Hillmann et al., 2018).”
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Transforming Big Data into AI‐ready data for nutrition and obesity research
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“Follow-on [48, 51].”
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Cross‐Cohort Gut Microbiome Signatures of Irritable Bowel Syndrome Presentation and Treatment
Li, J., Ghosh, T. S., Arendt, E. K., Shanahan, F., & O’Toole, P. W. (2024) · Advanced Science · doi:10.1002/advs.202308313 · Cited by 25
Santiago-Rodríguez, T. M., Garoutte, A., Adams, E., Nasser, W., Ross, M. C., Reau, A. L., Henseler, Z. M., Ward, T., Knights, D., Petrosino, J. F., et al. (2020) · Genes · doi:10.3390/genes11111380 · Cited by 24
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“Research on non-model organisms will require assembly and binning-based approaches to be implemented, which entails deeper sequencing [121,122].”
The role of microbiota in allogeneic hematopoietic stem cell transplantation
Chang, C.-C., Hayase, E., & Jenq, R. R. (2021) · Expert Opinion on Biological Therapy · doi:10.1080/14712598.2021.1872541 · Cited by 20
“Shallow shotgun sequencing has been recommended as an alternative to 16S to provide better taxonomical resolution, while deep wholegenome sequencing (WGS) remains the gold standard [111] and additionally can provide functional data.”
MethodTap water as a natural vehicle for microorganisms shaping the human gut microbiome
Lugli, G. A., Longhi, G., Mancabelli, L., Alessandri, G., Tarracchini, C., Fontana, F., Turroni, F., Milani, C., van Sinderen, D., & Ventura, M. (2022) · Environmental Microbiology · doi:10.1111/1462-2920.15988 · Cited by 23
“Sequencing output constituted about one million paired-end reads, with an average of 64 thousand reads per sample (Table S1), thus allowing accurate assessment of the microbial species inhabiting each water sample (Hillmann et al., 2018).”
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Cauwenberghs, E., Boeck, I. D., Spacova, I., Tente, I. V., Bastiaenssen, J., Lammertyn, E. J., Verhulst, S. L., Hoorenbeeck, K. V., & Lebeer, S. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101371 · Cited by 22
The Microbiome and Its Potential for Pharmacology
Chavira, A., Belda‐Ferre, P., Kościółek, T., Ali, F., Dorrestein, P. C., & Knight, R. D. (2019) · Handbook of experimental pharmacology · doi:10.1007/164_2019_317 · Cited by 21
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Biodegradation of synthetic organic pollutants: principles, progress, problems, and perspectives
Huang, Y. L., Deng, Y., Yu, K., Li, B. J., & Zhang, T. (2025) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuaf043 · Cited by 23
“However, this improved comprehensiveness is achieved at the cost of reduced detection sensitivity, necessitating deeper sequencing to mitigate the high detection limits (Hillmann et al. 2018 ).”
High-Throughput Assessment of Changes in the Caenorhabditis elegans Gut Microbiome
Zhang, F., Weckhorst, J. L., Assié, A., Khodakova, A. S., Cabrera, M. L., Vidal, D., & Samuel, B. S. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0592-9_12 · Cited by 21
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Calprotectin (S100A8/A9) Is an Innate Immune Effector in Experimental Periodontitis
Johnstone, K. F., Wei, Y., Bittner‐Eddy, P. D., Vreeman, G. W., Stone, I. A., Clayton, J. B., Reilly, C., Walbon, T. B., Wright, E. N., Hoops, S., et al. (2021) · Infection and Immunity · doi:10.1128/iai.00122-21 · Cited by 22
“Taxonomic Unit (OTU) picking against the NCBI RefSeq82 16S targeted loci reference 198 database was performed using the taxonomy-aware exhaustive optimal alignment software 199 BURST (64).”
MethodBloemen, B., Gand, M., Vanneste, K., Kathleen Marchal, D., Roosens, N. H. C., & Keersmaecker, S. C. J. D. (2023) · Scientific Reports · doi:10.1038/s41598-023-46771-z · Cited by 21
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“Fecal microbiomes were characterized using a shotgun DNA sequencing-based approach similar to one previously described [27].”
MethodRajan, S., Lindqvist, C. M., Brummer, R. J., Schoultz, I., & Repsilber, D. (2019) · PLoS ONE · doi:10.1371/journal.pone.0222171 · Cited by 21
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Healthy microbiome—moving towards functional interpretation
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Analyzing human gut microbiome data from global populations: challenges and resources
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“As previously described, the samples were all uniquely dual-indexed (98).”
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“Our results corroborate previous findings, that taxonomic profiles based on shallow shotgun sequencing (0.5–4 million reads) can reflect those obtained at a higher sequencing depths 2,3,6,8 .”
InfluentialInfluence of Adiposity on the Gut Microbiota Composition of Arab Women: A Case-Control Study
Aljazairy, E. A., Al-Musharaf, S., Abudawood, M., Almaarik, B. M., Hussain, S. D., Alnaami, A. M., Sabico, S., Al‐Daghri, N. M., Clerici, M., & Aljuraiban, G. S. (2022) · Biology · doi:10.3390/biology11111586 · Cited by 19
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“These fi ltering thresholds also correspond to the abundance at which a shotgun metagenomic data-set of similar depth might be expected to accurately estimate feature abundance 79 .”
OGUs enable effective, phylogeny-aware analysis of even shallow metagenome community structuresPreprint
Zhu, Q., Huang, S. W., Gonzalez, A., McGrath, I., McDonald, D. T., Haiminen, N., Armstrong, G., Vázquez‐Baeza, Y., Yu, J., Kuczynski, J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.04.438427 · Cited by 19
“It can also work well with cost-efficient “shallow” 107 shotgun metagenomics (12), where limited sequencing depth (even below the previously recommended 108 lower threshold of 500,000 sequences per sample) is adequate for assessing community structure.”
BackgroundMethodSelf-citationA Review on Microbial Species for Forensic Body Fluid Identification in Healthy and Diseased Humans
Dass, M., Singh, Y., & Ghai, M. (2023) · Current Microbiology · doi:10.1007/s00284-023-03413-x · Cited by 18
Identifying Robust Microbiota Signatures and Interpretable Rules to Distinguish Cancer Subtypes
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“…to more facultative anaerobic bacteria such as Proteobacteria, Firmicutes, and Bacteroidetes to obligate anaerobic organisms such as Clostridium, and finally sporeforming microorganisms such as Clostridium and Bacillus (Figure 1; Hyde et al., 2015; Javan et al., 2016b, 2019; García et al., 2020).”
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Sex Hormones, the Stool Microbiome, and Subclinical Atherosclerosis in Women With and Without HIV
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Self-citation
Metadata
- Authors
- Hillmann, B., Al-Ghalith, G. A., Shields-Cutler, R. R., Zhu, Q., Gohl, D. M., Beckman, K. B., Knight, R., & Knights, D.
- Affiliations
- University of Minnesota (US)
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota, USA
- Biotechnology Institute, University of Minnesota, Minneapolis, Minnesota, USA
- University of California San Diego (US)
- Corresponding author
- Knights, Dan
- ORCID
- Venue
- mSystems
- Publisher
- American Society for Microbiology
- Published
- 2018-10-30
- Volume
- 3
- Issue
- 6
- ISSN
- 2379-5077 (ISSN-L)
- PMC
- PMC6234283
- OpenAlex
- W2951076599
- Cited by
- 474
- References
- 26
- Citations by year
- 2026: 52 · 2025: 66 · 2024: 57 · 2023: 61 · 2022: 60 · 2021: 82 · 2020: 68 · 2019: 24 · 2018: 2
- FWCI
- 15.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- mSystems (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- HHS | National Institutes of Health (R01AI121383, P01DK078669)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Keywords
- 16S rRNA gene sequencing, functional metagenomics, amplicon sequencing
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