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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From RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer diagnostic approach (2020) · cites this paper as [31]
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Alteration of Gut Microbiome and Correlated Lipid Metabolism in Post-Stroke Depression
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“, analysis of a limited number of reads (500,000 or lower), generated by shotgun metagenomic sequencing, has become popular as a cost-effective alternative to 16S rRNA gene-based microbial profiling as it allows species-level accuracy with similar requirements in terms of sequencing data (11).”
BackgroundInfluentialOptimizing sequencing protocols for leaderboard metagenomics by combining long and short reads
Sanders, J. G., Nurk, S., Salido, R. A., Minich, J. J., Xu, Z. Z., Zhu, Q., Martino, C., Fedarko, M. W., Arthur, T. D., Chen, F., et al. (2019) · Genome biology · doi:10.1186/s13059-019-1834-9 · Cited by 77
“And while recent research has demonstrated that even shallow metagenomic sequencing can identify the same kinds of biological signals that are recovered from (typically lower-cost) 16S rRNA marker gene sequencing [42], these shallow metagenomic analyses are highly dependent on the quality of the available genome refer…”
BackgroundSelf-citationGehrig, J. L., Portik, D. M., Driscoll, M. D., Jackson, E., Chakraborty, S., Gratalo, D., Ashby, M., & Valladares, R. B. (2022) · Microbial Genomics · doi:10.1099/mgen.0.000794 · Cited by 80
Mas-Lloret, J., Obón‐Santacana, M., Ibáñez‐Sanz, G., Guinó, E., Pato, M. L., Rodríguez‐Moranta, F., Mata, A., García‐Rodríguez, A., Moreno, V. R., & Pimenoff, V. N. (2020) · Scientific Data · doi:10.1038/s41597-020-0427-5 · Cited by 82
Thompson, R. S., Gaffney, M., Hopkins, S., Kelley, T., González, A., Bowers, S. J., Vitaterna, M. H., Turek, F. W., Foxx, C. L., Lowry, C. A., et al. (2021) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2021.07.006 · Cited by 82
Self-citationJo, J., Oh, J., & Park, C. (2020) · The Journal of Microbiology · doi:10.1007/s12275-020-9525-5 · Cited by 80
Links between gut microbiome composition and fatty liver disease in a large population sample
Ruuskanen, M. O., Åberg, F. O., Männistö, V., Havulinna, A. S., Méric, G., Liu, Y., Loomba, R. S., Vázquez‐Baeza, Y., Tripathi, A., Valsta, L. M., et al. (2021) · Gut Microbes · doi:10.1080/19490976.2021.1888673 · Cited by 78
Self-citationEvaluation of the Effect of Storage Methods on Fecal, Saliva, and Skin Microbiome Composition
Marotz, C. A., Cavagnero, K. J., Song, S. J., McDonald, D. T., Wandro, S., Humphrey, G. C., Bryant, M., Ackermann, G. L., Diaz, E., & Knight, R. D. (2021) · mSystems · doi:10.1128/msystems.01329-20 · Cited by 80
Self-citationJha, A. R., Shmalberg, J. W., Tanprasertsuk, J., Perry, L. M., Massey, D., & Honaker, R. W. (2020) · PLoS ONE · doi:10.1371/journal.pone.0227289 · Cited by 80
“If deep shotgun sequencing is prohibitively expensive, shallow-depth shotgun sequencing may be alternative to 16S sequencing as it can provide species and/or strain level taxonomic resolution as well as functional data at approximately the same cost as 16S sequencing [96].”
Alessandri, G., Argentini, C., Milani, C., Turroni, F., Ossiprandi, M. C., van Sinderen, D., & Ventura, M. (2020) · Microbial Biotechnology · doi:10.1111/1751-7915.13656 · Cited by 76
“…the limitations of the 16S rRNA or ITS microbial profiling, shallow shotgun sequencing, which is a low-depth WMS, has recently been proposed to combine species-level taxonomic assignment with reduced cost while avoiding amplification bias of 16S rRNA-based sequencing (Hillmann et al., 2018).”
Molecular diagnostic assays for COVID-19: an overview
Habibzadeh, P., Mofatteh, M., Silawi, M., Ghavami, S., & Faghihi, M. A. (2021) · Critical Reviews in Clinical Laboratory Sciences · doi:10.1080/10408363.2021.1884640 · Cited by 77
“use in the setting of an outbreak [85].”
BackgroundEvolving approaches to profiling the microbiome in skin disease
Chen, Y., Knight, R. D., & Gallo, R. L. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1151527 · Cited by 86
Self-citationThe gut microbiome is a significant risk factor for future chronic lung disease
Liu, Y., Teo, S. M., Méric, G., Tang, H. H. F., Zhu, Q., Sanders, J. G., Vázquez‐Baeza, Y., Verspoor, K. M., Vartiainen, V. A., Jousilahti, P., et al. (2022) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2022.12.810 · Cited by 76
“30,31,47 Logistic regression naturally models binary outcomes; Cox regressiontakesintoaccountthetimeuntileventsoccur;Spearmancorrelation is a nonparametric measure of the strength and direction of nonlinear correlation.”
BackgroundSelf-citationGut Microbiota and Blood Metabolites Related to Fiber Intake and Type 2 Diabetes
Wang, Z., Peters, B. A., Yu, B., Grove, M. L., Wang, T., Xue, X., Thyagarajan, B., Daviglus, M. L., Boerwinkle, E., Hu, G., et al. (2024) · Circulation Research · doi:10.1161/circresaha.123.323634 · Cited by 76
Self-citationShaffer, J. P., Carpenter, C. S., Martino, C., Salido, R. A., Minich, J. J., Bryant, M., Sanders, K., Schwartz, T., Humphrey, G., Swafford, A. D.-E., et al. (2022) · BioTechniques · doi:10.2144/btn-2022-0032 · Cited by 77
Self-citationCurrent Trends in Diagnostics of Viral Infections of Unknown Etiology
Kiselev, D., Дмитриевна, М. А., Abramov, I. A., Dedkov, V. G., Shipulin, G. A., & Khafizov, K. (2020) · Viruses · doi:10.3390/v12020211 · Cited by 75
“Even though the cost per raw megabase of DNA sequence has plummeted over the years [73], HTS remains a costly and time-consuming method, requiring a complex procedure of sample preparation, expensive equipment and at least some bioinformatic training with laboratory personnel, therefore rendering it unsuitable for lar…”
MethodSynthetic glycans control gut microbiome structure and mitigate colitis in mice
Tolonen, A. C., Beauchemin, N., Bayne, C. F., Li, L., Tan, J., Lee, J., Meehan, B. M., Meisner, J. S., Millet, Y. A., LeBlanc, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28856-x · Cited by 72
Gut microbiome and atrial fibrillation—results from a large population-based study
Palmu, J., Börschel, C. S., Ortega‐Alonso, A., Markó, L., Inouye, M., Jousilahti, P., Salido, R. A., Sanders, K., Brennan, C., Humphrey, G., et al. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104583 · Cited by 72
Self-citationStatistical normalization methods in microbiome data with application to microbiome cancer research
Xia, Y. (2023) · Gut Microbes · doi:10.1080/19490976.2023.2244139 · Cited by 76
“Like in 16S rRNA-seq data, rarefying is also commonly used in shotgun metagenomics.(89,90) For a review article, the reader is referred to.”
MethodApplication of Microbiome in Forensics
Zhang, J., Liu, W., Simayijiang, H., Hu, P., & Yan, J. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.07.007 · Cited by 75
“In contrast, shotgun metagenomic sequencing provides functional gene information and strain-level resolution, rather than just taxonomic composition as determined by amplicon sequencing [25].”
BackgroundRobustness of cancer microbiome signals over a broad range of methodological variation
Sepich‐Poore, G. D., McDonald, D. T., Kopylova, E., Guccione, C., Zhu, Q., Austin, G. I., Carpenter, C. S., Fraraccio, S., Wandro, S., Kościółek, T., et al. (2024) · Oncogene · doi:10.1038/s41388-024-02974-w · Cited by 69
“All human fi ltered short reads were mapped to the masked RS210 database with bowtie2 (v. 2.5.1) [67] using the SHOGUN [66] parameter set, speci fi cally “ -p ${threads} -x ${db} -q - --seed 42 --very-sensitive -k 16 --np 1 --mp “ 1,1 ” --rdg “ 0,1 ” --rfg “ 0,1 ” --score-min “ L,0,-0.05 ” --no-head…”
MethodInfluentialSelf-citationEvolving understanding of rumen methanogen ecophysiology
Khairunisa, B. H., Heryakusuma, C., Ike, K. A., Mukhopadhyay, B., & Susanti, D. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1296008 · Cited by 78
Bastiaanssen, T. F. S., Quinn, T. P., & Loughman, A. (2023) · Nature Mental Health · doi:10.1038/s44220-023-00148-3 · Cited by 71
“First of all, the price of 16S is lower than that of shotgun - though this difference is decreasing and shallow shotgun sequencing has been used as a cost-friendly alternative for 16S (Hillmann et al., 2018).”
MethodXu, W., Chen, T., Pei, Y., Guo, H., Li, Z., Yang, Y., Zhang, F., Yu, J., Li, X., Yang, Y. F., et al. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.678319 · Cited by 63
Wang, Z., Usyk, M., Vázquez‐Baeza, Y., Chen, G., Isasi, C. R., Williams-Nguyen, J. S., Hua, S., McDonald, D. T., Thyagarajan, B., Daviglus, M. L., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02559-w · Cited by 64
“Functional profiles were obtained using SHOGUN and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database release 94.0 [36].”
InfluentialSelf-citationGreay, T. L., Gofton, A. W., Zahedi, A., Paparini, A., Linge, K. L., Joll, C. A., & Ryan, U. M. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.03.278 · Cited by 61
“shotgun sequencing", which can produce more accurate species level taxonomic and functional profiles of the human microbiome than 16S sequencing (Hillmann et al. 2018).”
BackgroundCross, S. T., Kapuscinski, M. L., Jacquelyn, P., Maertens, B. L., Weger‐Lucarelli, J., Ebel, G. D., & Stenglein, M. D. (2018) · Viruses · doi:10.3390/v10070388 · Cited by 61
Mei, Z., Chen, G., Wang, Z., Usyk, M., Yu, B., Baeza, Y. V., Humphrey, G. C., Benítez, R. A. S., Li, J., Williams-Nguyen, J. S., et al. (2021) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqab001 · Cited by 62
“Bowtie2 (33) was used to build the SHOGUN assemblies.”
BackgroundMethodInfluentialSelf-citationOral Microbiome and Subsequent Risk of Head and Neck Squamous Cell Cancer
Kwak, S., Wang, C., Usyk, M., Wu, F. C., Freedman, N. D., Huang, W., McCullough, M. L., Um, C. Y., Shrubsole, M. J., Cai, Q., et al. (2024) · JAMA Oncology · doi:10.1001/jamaoncol.2024.4006 · Cited by 61
Giampetruzzi, A., Baptista, P. C., Morelli, M., Cameirão, C., Lino‐Neto, T., Costa, D., D’Attoma, G., Kubaa, R. A., Altamura, G., Saponari, M., et al. (2020) · Pathogens · doi:10.3390/pathogens9090723 · Cited by 61
“However, information on the whole microbiome can be also obtained through whole metagenome shotgun sequencing (WMSS) [26,27], a strategy that allows gathering microbial data at very high depth.”
BackgroundShallow shotgun sequencing reduces technical variation in microbiome analysis
Reau, A. J. L., Strom, N., Filvaroff, E. H., Mavrommatis, K., Ward, T., & Knights, D. (2023) · Scientific Reports · doi:10.1038/s41598-023-33489-1 · Cited by 60
“Although it is possible to predict functional profiles from 16S sequencing with approximately 80–90% accuracy 11,12,20 , we have previously found that direct observation of functional profiles in shallow shotgun data had higher concordance with functional profiles from deep shotgun metagenomics 15 .”
MethodSelf-citationMancabelli, L., Milani, C., Biase, R. D., Bocchio, F., Fontana, F., Lugli, G. A., Alessandri, G., Tarracchini, C., Viappiani, A., Conto, F. D., et al. (2024) · mSystems · doi:10.1128/msystems.01294-23 · Cited by 64
“Additionally, it promotes reliable profile comparisons by mitigating disparities resulting from variations in the total number of analyzed reads (42, 43).”
BackgroundMethodGut microbiome: An intermediary to neurotoxicity
Dempsey, J. L., Little, M., & Cui, J. Y. (2019) · NeuroToxicology · doi:10.1016/j.neuro.2019.08.005 · Cited by 63
“Deep whole-metagenome shotgun sequencing, as well as a more costeffective approach, namely shallow shotgun metagenomic sequencing, are highly recommended alternatives for high resolution taxonomic and functional microbiome analysis (Hillmann et al., 2018).”
MethodMicrobial enrichment and storage for metagenomics of vaginal, skin, and saliva samples
Ahannach, S., Delanghe, L., Spacova, I., Wittouck, S., Beeck, W. V., Boeck, I. D., & Lebeer, S. (2021) · iScience · doi:10.1016/j.isci.2021.103306 · Cited by 56
“Here, we specifically aimed to optimize the workflow for metagenomic shotgun sequencing, since this technique allows a high-resolutionmicrobiome analysis (Hillmann et al., 2018).”
MethodVaginotypes of the human vaginal microbiome
Mancabelli, L., Tarracchini, C., Milani, C., Lugli, G. A., Fontana, F., Turroni, F., van Sinderen, D., & Ventura, M. (2021) · Environmental Microbiology · doi:10.1111/1462-2920.15441 · Cited by 57
Wang, Z., Peters, B. A., Bryant, M., Hanna, D. B., Schwartz, T., Wang, T., Sollecito, C. C., Usyk, M., Grassi, E., Wiek, F., et al. (2023) · Microbiome · doi:10.1186/s40168-023-01566-2 · Cited by 54
Self-citationBorsom, E. M., Lee, K., & Cope, E. K. (2020) · Brain Sciences · doi:10.3390/brainsci10110814 · Cited by 56
“While SSMA does not provide the depth of taxonomic and functional resolution that deep shotgun metagenomic sequencing does, it is relatively low cost compared to deep shotgun metagenomics, and therefore more feasible to apply in large-scale studies [43].”
BackgroundLane, M., Howland, G., West, M., Hockey, M., Marx, W., Loughman, A., O’Hely, M., Jacka, F., & Rocks, T. (2020) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2020.04.006 · Cited by 50
The root canal microbiome diversity and function. A whole‐metagenome shotgun analysis
Ordinola‐Zapata, R., Costalonga, M., Dietz, M. A., Lima, B. P., & Staley, C. M. (2023) · International Endodontic Journal · doi:10.1111/iej.13911 · Cited by 50
Oliva, M., Schneeberger, P. H. H., Rey, V., Cho, M., Taylor, R., Hansen, A. R., Taylor, K., Hosni, A., Bayley, A., Hope, A. J., et al. (2021) · British Journal of Cancer · doi:10.1038/s41416-020-01253-1 · Cited by 48
“05% but it did not allow us to conduct functional analyses.(36) This pilot study shows that prospective characterisation of both oral and stool microbiome is feasible in this patient population, Table 1.”
BackgroundYang, J., Zhang, Z., Xie, Z., Bai, L., Xiong, P., Chen, F., Zhu, T., Peng, Q., Wu, H., Zhou, Y. C., et al. (2022) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2022.112837 · Cited by 49
Approaches for characterizing and tracking hospital-associated multidrug-resistant bacteria
Blake, K. S., Choi, J., & Dantas, G. A. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-020-03717-2 · Cited by 51
Complex interactions between the microbiome and cancer immune therapy
Schwartz, D. J., Rebeck, O. N., & Dantas, G. A. (2019) · Critical Reviews in Clinical Laboratory Sciences · doi:10.1080/10408363.2019.1660303 · Cited by 52
A Comparison of DNA/RNA Extraction Protocols for High-Throughput Sequencing of Microbial Communities
Shaffer, J. P., Marotz, C. A., Belda‐Ferre, P., Martino, C., Wandro, S., Estaki, M., Salido, R. A., Carpenter, C. S., Zaramela, L. S., Minich, J. J., et al. (2021) · BioTechniques · doi:10.2144/btn-2020-0153 · Cited by 50
“Filtered reads were aligned to the Web of Life database [36] using Shogun [31] with default parameters and using bowtie2 as the aligner, followed by read classification with the Web of Life Toolkit App [36,37].”
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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