The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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From RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer diagnostic approach (2020) · cites this paper as [31]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400 · cited 5×
“Instead, we selected several tools representing different approaches to database search for comparison, including exhaustive semiglobal gapped alignment (18, 19), heuristic gapped alignment using the Burrows-Wheeler transformation (23), and k-mer-based searching (21, 25).”
MethodInfluentialStructure, function and diversity of the healthy human microbiome
The Human Microbiome Project Consortium (2012) · Nature · doi:10.1038/nature11234 · Cited by 11,726 · cited 4×
“5 million sequences per sample, as demonstrated on deep whole-genome sequencing (WMS) samples from the Human Microbiome Project (HMP) data set (4), the HMP mock community (12), a diabetes study (17), simulated human gut microbiomes, and two novel human stool samples on which we performed ultradeep sequencing of 2.”
BackgroundMethodInfluentialNeedleman, S. B., & Wunsch, C. D. (1970) · Journal of Molecular Biology · doi:10.1016/0022-2836(70)90057-4 · Cited by 11,288 · cited 5×
“Instead, we selected several tools representing different approaches to database search for comparison, including exhaustive semiglobal gapped alignment (18, 19), heuristic gapped alignment using the Burrows-Wheeler transformation (23), and k-mer-based searching (21, 25).”
MethodInfluentialPredictive functional profiling of microbial communities using 16S rRNA marker gene sequences
Langille, M. G. I., Zaneveld, J., Caporaso, J. G., McDonald, D., Knights, D., Reyes, J. A., Clemente, J. C., Burkepile, D. E., Vega Thurber, R. L., Knight, R., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2676 · Cited by 9,116 · cited 3×
“than the correlations of functional profiles predicted from 16S sequencing, which typically has 80% to 90% correlation with the directly observed functions (7, 8).”
BackgroundMethodSelf-citationMcDonald, D. T., Price, M. N., Goodrich, J. K., Nawrocki, E. P., DeSantis, T. Z., Probst, A. J., Andersen, G. L., Knight, R. D., & Hugenholtz, P. (2011) · The ISME Journal · doi:10.1038/ismej.2011.139 · Cited by 5,144 · cited 1×
“16S sequences were aligned to Greengenes version 13_8 (29) at 98% identity with exhaustive gapped alignment (18, 19).”
MethodSelf-citationKEGG for integration and interpretation of large-scale molecular data sets
Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., & Tanabe, M. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr988 · Cited by 4,848 · cited 6×
“Functional profiling was obtained using KEGG Orthology group (KO) (16) annotations for RefSeq-derived genes (20) from directly observed exhaustive gapped alignments in ultradeep WMS sequencing.”
BackgroundMethodInfluentialKraken: ultrafast metagenomic sequence classification using exact alignments
Wood, D. E., & Salzberg, S. L. (2014) · Genome Biology · doi:10.1186/gb-2014-15-3-r46 · Cited by 4,521 · cited 5×
“Instead, we selected several tools representing different approaches to database search for comparison, including exhaustive semiglobal gapped alignment (18, 19), heuristic gapped alignment using the Burrows-Wheeler transformation (23), and k-mer-based searching (21, 25).”
MethodInfluentialBinning metagenomic contigs by coverage and composition
Alneberg, J., Bjarnason, B. S., de Bruijn, I., Schirmer, M., Quick, J., Ijaz, U. Z., Lahti, L., Loman, N. J., Andersson, A. F., & Quince, C. (2014) · Nature Methods · doi:10.1038/nmeth.3103 · Cited by 2,815 · cited 1×
“These concerns can be alleviated to some extent using deep shotgun sequencing and metagenomic assembly (10), coabundance clustering (11), or proximity-based assembly (28), and de novo identification of strains from complex metagenomes remains an active area of research.”
BackgroundA communal catalogue reveals Earth's multiscale microbial diversity
Thompson, L. R., Sanders, J. G., McDonald, D., Amir, A., Ladau, J., Locey, K. J., Prill, R. J., Tripathi, A., Gibbons, S. M., Ackermann, G., et al. (2017) · Nature · doi:10.1038/nature24621 · Cited by 2,684
Self-citationYarza, P., Yilmaz, P., Pruesse, E., Glöckner, F. O., Ludwig, W., SCHLEIFER, K. H., Whitman, W. B., Euzéby, J. P., Amann, R., & Rossello-Mora, R. A. (2014) · Nature Reviews Microbiology · doi:10.1038/nrmicro3330 · Cited by 2,651 · cited 2×
“variable-region amplicon sequences often do not resolve taxa below the genus or family level, although some species can be differentiated with 16S sequencing (6).”
Background
Flagged first, then by citations
Cited by
Sorted by citations
Items 201–300 of 474
Koponen, K., Kambur, O., Joseph, B., Ruuskanen, M. O., Jousilahti, P., Salido, R. A., Brennan, C., Jain, M., Méric, G., Inouye, M., et al. (2023) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.123.319458 · Cited by 17
“57 This in turn increases confidence in species-specific results.”
Self-citationMin, K., Glowacki, A. J., Bosma, M. L., McGuire, J. A., Tian, S., McAdoo, K., DelSasso, A., Fourre, T., Gambogi, R. J., Milleman, J. L., et al. (2024) · BMC Oral Health · doi:10.1186/s12903-024-04365-9 · Cited by 17
“SMS provides more accurate species-level taxonomic and functional profiles of the microbiome than 16S sequencing [11].”
The Function of Probiotics and Prebiotics on Canine Intestinal Health and Their Evaluation Criteria
Xia, J., Cui, Y., Guo, Y., Liu, Y., Deng, B., & Han, S. (2024) · Microorganisms · doi:10.3390/microorganisms12061248 · Cited by 24
“However, it has been reported that shallow shotgun sequencing is a possible cost-effective alternative to 16S rRNA sequencing for large-scale biomarker discovery with improved taxonomic resolution and functional accuracy [48].”
BackgroundNawaz, A., Purahong, W., Herrmann, M., Küsel, K., Buscot, F., & Wubet, T. (2019) · Microorganisms · doi:10.3390/microorganisms7090341 · Cited by 17
“However, owning the fact that metagenome or metatranscriptome based shotgun sequencing is difficult to apply for large sets of samples [54], and that whole genome data for their exhaustive exploitation are still under construction [55,56], performing studies that couple DNA- and RNA-based amplicon analyses would be an…”
BackgroundChen, T., Wang, N., Hao, Y., & Fu, L. (2024) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2024.1488017 · Cited by 17
Thiruppathy, D., Moyne, O., Marotz, C. A., Williams, M. R., Navarro, P., Zaramela, L. S., & Zengler, K. (2025) · Microbiome · doi:10.1186/s40168-025-02063-4 · Cited by 16
Johnstone, N., & Kadosh, K. C. (2025) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2025.06.020 · Cited by 18
More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics
Masoero, L., Camerlenghi, F., Favaro, S., & Broderick, T. (2021) · Biometrika · doi:10.1093/biomet/asab012 · Cited by 17
Tilves, C., Tanaka, T., Differding, M. K., Spira, A. P., Chia, C. W., Ferrucci, L., & Mueller, N. T. (2023) · Obesity · doi:10.1002/oby.23717 · Cited by 16
“Few studies have used shotgun metagenomic sequencing, which can allow for greater resolution at the species taxonomic level, as compared with the more widely used 16s rRNA marker gene sequencing, which provides resolution usually to the genus level [10].”
MethodKato, N. N., Arini, G. S., Silva, R., Bichuette, M. E., Bitencourt, J. A. P., & Lopes, N. P. (2023) · Journal of the Brazilian Chemical Society · doi:10.21577/0103-5053.20230148 · Cited by 17
Jo, J., Lee, H.-G., Kim, K. Y., & Park, C. (2019) · ALGAE · doi:10.4490/algae.2019.34.2.26 · Cited by 17
“…automated and standardized data processing procedures (e.g., MEGAN4, Mothur, QIIME, and UPARSE pipeline) (Schloss et al. 2009, Caporaso et al. 2010, Huson et al. 2011, Edgar 2013) with well-established and curated databases (e.g., NCBI taxonomy, Federhen 2012; IBOL, http://www.ibolproject.org/;…”
MethodGauglitz, J. M., Morton, J. T., Tripathi, A., Hansen, S., Gaffney, M., Carpenter, C. S., Weldon, K. C., Shah, R., Parampil, A., Fidgett, A., et al. (2020) · mSystems · doi:10.1128/msystems.00635-19 · Cited by 15
“io/wol/data/genomes/) 526 using Bowtie 2 within the alignment pipeline SHOGUN (12) using standard parameters.”
BackgroundMethodInfluentialSelf-citationTarracchini, C., Lugli, G. A., Mancabelli, L., Milani, C., Turroni, F., & Ventura, M. (2020) · Applied and Environmental Microbiology · doi:10.1128/aem.02188-20 · Cited by 16
“The resulting 175 vaginal metagenomic data sets were analyzed through a shallow shotgun metagenomics approach (63), allowing us to achieve high taxonomic resolution at the species level.”
MethodAlili, R., Belda, E., Le, P. T., Wirth, T., Zucker, J., Prifti, E., & Clément, K. (2021) · Genes · doi:10.3390/genes12101496 · Cited by 19
“sequencing” method in the line of proposed low-sequencing depth approaches to characterize microbial ecosystems more accurately than 16S barcoding approaches and with lower costs than deep shotgun sequencing[62].”
MethodBuilding up a clinical microbiota profiling: a quality framework proposal
Scherz, V., Greub, G., & Bertelli, C. (2021) · Critical Reviews in Microbiology · doi:10.1080/1040841x.2021.1975642 · Cited by 19
“a given sequencing yield may be sufficient to generate a broad taxonomic profile but not to assemble genomes from metagenomics data (Hillmann et al. 2018)).”
BackgroundDelivery mode impacts newborn gut colonization efficiencyPreprint
Mitchell, C. M., Hogstrom, L. J., Bryant, A., Bergerat, A., Cher, A., Pochan, S., Herman, P., Carrigan, M., Sharp, K., Huttenhower, C., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.29.919993 · Cited by 16
Comparison of 16S and whole genome dog microbiomes using machine learning
Lewis, S. A., Nash, A. K., Li, Q., & Ahn, T.-H. (2021) · BioData Mining · doi:10.1186/s13040-021-00270-x · Cited by 15
“cluding shallow shotgun sequencing [17].”
Nobel, Y. R., Rozenberg, F. D., Park, H., Freedberg, D. E., Blaser, M. J., Green, P. H. R., Uhlemann, A., & Lebwohl, B. (2021) · Clinical and Translational Gastroenterology · doi:10.14309/ctg.0000000000000441 · Cited by 18
“5 million reads, has recently been validated for beginning to investigate species-level taxonomic and functional microbiome data (16).”
BackgroundNienhold, R., Mensah, N., Frank, A., Graber, A., Koike, J., Schwab, N., Hernach, C., Zsikla, V., Willi, N., Cathomas, G., et al. (2022) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2022.968135 · Cited by 16
Baek, H.-W., Kim, S.-A., Min, W.-K., Kang, S. D., Shim, S., Han, N. S., & Seo, J. (2020) · Food Analytical Methods · doi:10.1007/s12161-020-01920-2 · Cited by 15
“Besides, the current metagenomic method based on 16S rRNA gene amplicon sequencing often does not reveal microbial diversity past the genus level and exhibits only moderately accurate profile (Hillmann et al. 2018).”
MethodTanprasertsuk, J., Shmalberg, J. W., Maughan, H., Tate, D. E., Perry, L. M., Jha, A. R., & Honaker, R. W. (2021) · PeerJ · doi:10.7717/peerj.11648 · Cited by 19
“It has been shown that this particular method of low-depth metagenomic sequencing can reach species-level taxonomic assignment and allows for the assessment of functional profiles (Hillmann et al., 2018).”
MethodLugli, G. A., & Ventura, M. (2022) · Microbiome Research Reports · doi:10.20517/mrr.2021.07 · Cited by 15
Mancabelli, L., Milani, C., Fontana, F., Lugli, G. A., Tarracchini, C., Viappiani, A., Ciociola, T., Ticinesi, A., Nouvenne, A., Meschi, T., et al. (2022) · Environmental Microbiology · doi:10.1111/1462-2920.16201 · Cited by 15
“In order to optimize the taxonomical analysis, in accordance with the modern shallow shotgun metagenomics approach (Hillmann et al., 2018; Milani et al., 2021), we decided to analyse a subset of 100,000 reads for each sample, obtaining a total of 22,021,244 classified reads with an average per sample of 68,177 10,074…”
MethodWhy Are Long-Read Sequencing Methods Revolutionizing Microbiome Analysis?
González, A., Fullaondo, A., & Odriozola, A. (2025) · Microorganisms · doi:10.3390/microorganisms13081861 · Cited by 17
“Shallow shotgun metagenomics has a cost per sample comparable to amplicon sequencing and allows for obtaining taxonomic profiles but not performing functional profiling or genome reassembly due to the lack of coverage [229].”
Mhuireach, G. Á., Dietz, L. G., & Gillett, T. (2022) · Frontiers in Sustainable Food Systems · doi:10.3389/fsufs.2022.926824 · Cited by 14
“Shallow shotgun metagenomics methodology was used to characterize microbial community structure and antibiotic resistance capacity, as it has been demonstrated to generate more accurate taxonomic and functional information about bacterial communities than amplicon analysis, while also providing information about viral…”
MethodLuo, K., Wang, Z., Peters, B. A., Hanna, D. B., Wang, T., Sollecito, C. C., Grassi, E., Wiek, F., Peter, L. S., Usyk, M., et al. (2023) · AIDS · doi:10.1097/qad.0000000000003596 · Cited by 15
Self-citationVelsko, I. M., Gallois, S., Stahl, R., Henry, A. G., & Warinner, C. (2023) · Molecular Ecology · doi:10.1111/mec.16988 · Cited by 13
“The average sequencing depth across samples was 14.5 ± 3 million paired-end reads, which is sufficient to obtain an accurate species profile (Hillmann et al., 2018 ).”
InfluentialKhan, M. W., de Jesus, V. C., Mittermuller, B.-A., Sareen, S., Lee, V., Schroth, R. J., Hu, P., & Chelikani, P. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114635 · Cited by 15
Zhang, Y., Luo, K., Peters, B. A., Mossavar‐Rahmani, Y., Moon, J., Wang, Y., Daviglus, M. L., Horn, L. V., McClain, A. C., Cordero, C., et al. (2025) · Cell Metabolism · doi:10.1016/j.cmet.2024.12.004 · Cited by 15
Dhungel, E., Mreyoud, Y., Gwak, H., Rajeh, A. A., Rho, M., & Ahn, T.-H. (2021) · BMC Bioinformatics · doi:10.1186/s12859-020-03933-4 · Cited by 14
Fontana, F., Alessandri, G., Tarracchini, C., Bianchi, M. G., Rizzo, S. M., Mancabelli, L., Lugli, G. A., Argentini, C., Vergna, L. M., Anzalone, R., et al. (2022) · Environmental Microbiology · doi:10.1111/1462-2920.16205 · Cited by 13
Meadows, V., Marakovits, C. N., Ekser, B., Kundu, D., Zhou, T., Kyritsi, K., Pham, L., Chen, L., Kennedy, L. L., Ceci, L., et al. (2022) · American Journal of Physiology-Gastrointestinal and Liver Physiology · doi:10.1152/ajpgi.00112.2022 · Cited by 14
A primer and discussion on DNA-based microbiome data and related bioinformatics analyses
Douglas, G. M., & Langille, M. G. I. (2021) · Peer Community Journal · doi:10.24072/pcjournal.2 · Cited by 17
Yan, J., Liao, C., Taylor, B. P., Fontana, E., Amoretti, L. A., Wright, R. J., Littmann, E. R., Dai, A., Waters, N. R., Peled, J. U., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01302-9 · Cited by 21
Bağcı, C., Negri, T., Buena‐Atienza, E., Groß, C., Ossowski, S., & Ziemert, N. (2025) · GigaScience · doi:10.1093/gigascience/giaf135 · Cited by 19
Serum Vitamin D Level and Gut Microbiota in Women
Alkhaldy, N. S., Al-Musharaf, S., Aljazairy, E. A., Hussain, S. D., Alnaami, A. M., Al‐Daghri, N. M., & Aljuraiban, G. S. (2023) · Healthcare · doi:10.3390/healthcare11030351 · Cited by 13
“Recent evidence revealed that alpha diversity was strongly associated with ethnicity, highlighting the role of ethnicity in the interindividual differences of gut microbiota [79].”
BackgroundAnalysis of vaginal microbiota during postpartum and postmenopausal periods based on metagenomics
Li, X., Zhang, Z., Bai, H., & Liu, Z. (2024) · BMC Microbiology · doi:10.1186/s12866-024-03648-z · Cited by 13
Hellmann, K. T., Tuura, C., Fish, J., Patel, J. M., & Robinson, D. A. (2021) · mSphere · doi:10.1128/msphere.00538-21 · Cited by 11
Accurate profiling of microbial communities for shotgun metagenomic sequencing with Meteor2
Ghozlane, A., Thirion, F., Oñate, F. P., Gauthier, F., Chatelier, E. L., Annamalé, A., Almeida, M., Ehrlich, S. D., & Pons, N. (2025) · Microbiome · doi:10.1186/s40168-025-02249-w · Cited by 20
“This feature is particularly valuable for samples with a high proportion of host DNA (e.g., salivary or skin samples), and where obtaining a high number of microbial reads at a reasonable cost is challenging [60].”
Next-generation sequencing approaches for soil microbiome research
Reznikova, D. A., Barannikova, M. V., Shnakhova, L. M., Mitkin, N. A., & Vatlin, A. A. (2026) · Frontiers in Soil Science · doi:10.3389/fsoil.2025.1706999 · Cited by 14
Dissecting and tuning primer editing by proofreading polymerases
Gohl, D. M., Auch, B., Certano, A. K., François, B. L., Bouevitch, A., Doukhanine, E., Fragel, C. G., Macklaim, J. M., Hollister, E. B., Garbe, J., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab471 · Cited by 15
Self-citationLugli, G. A., Alessandri, G., Milani, C., Viappiani, A., Fontana, F., Tarracchini, C., Mancabelli, L., Argentini, C., Ruíz, L., Margollés, A., et al. (2021) · Environmental Microbiology · doi:10.1111/1462-2920.15559 · Cited by 12
“While taxonomic classification of low abundance sequences may still allow compositional analysis (Hillmann et al., 2018), associated microbial genome reconstruction attempts are likely to result in an inconsistent genome assembly (Malmstrom and EloeFadrosh, 2019).”
BackgroundGuidelines for microbiome studies in renal physiology
Muralitharan, R. R., Snelson, M., Méric, G., Coughlan, M. T., & Marques, F. Z. (2023) · American Journal of Physiology-Renal Physiology · doi:10.1152/ajprenal.00072.2023 · Cited by 11
Temporal and spatial differences in the vaginal microbiome of Chinese healthy women
Du, L., Dong, X., Song, J., Lei, T., Liu, X., Lan, Y., Liu, X., Wang, J., Yue, B., He, M., et al. (2023) · PeerJ · doi:10.7717/peerj.16438 · Cited by 11
“Due to low resolution and limited functional analysis at the species level, studies based on 16S rRNA amplicon sequencing technology have some limitations ( Hillmann et al., 2018 ).”
BackgroundBashir, Z., Hugerth, L. W., Krog, M. C., Prast‐Nielsen, S., Edfeldt, G., Boulund, F., Schacht, S. R., Tetens, I., Engstrand, L. G., Schuppe‐Koistinen, I., et al. (2024) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2024.1324794 · Cited by 11
“BoosterShot is designed to obtain accurate species-level information from a few reads as described in (Hillmann et al., 2018).”
MethodChristman, L. M., Benedetto, A. D., Johnson, E. J., Khoo, C., & Gu, L. (2024) · Nutrients · doi:10.3390/nu16183126 · Cited by 11
“This is because 0.5 million reads is the lowest sequencing depth to correlate with 2.5 billion reads for species composition by 97% [24].”
BackgroundInfluentialDecadt, H., Díaz-Muñoz, C., Vermote, L., Pradal, I., Vuyst, L. D., & Weckx, S. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1543079 · Cited by 14
Guccione, C., Yadlapati, R. H., Shah, S. C., Knight, R. D., & Curtius, K. (2021) · Microorganisms · doi:10.3390/microorganisms9102003 · Cited by 11
Self-citationMetagenomic-based approach to a comprehensive understanding of cave microbial diversity
Wiseschart, A., & Pootanakit, K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821265-3.00023-2 · Cited by 11
Evaluation of the microbiota-sparing properties of the anti-staphylococcal antibiotic afabicin
Nowakowska, J., Cameron, D. R., Martino, A. D., Kühn, J., Fresne, S. L., Leuillet, S., Amouzou, Y., Wittke, F., Carton, T., Vacon, F. L., et al. (2023) · Journal of Antimicrobial Chemotherapy · doi:10.1093/jac/dkad181 · Cited by 12
Wang, Y., Sharma, A., Weber, K. M., Topper, E. T., Appleton, A. A., Gustafson, D., Clish, C. B., Kaplan, R. C., Burk, R. D., Qi, Q., et al. (2023) · Menopause The Journal of The North American Menopause Society · doi:10.1097/gme.0000000000002287 · Cited by 10
“26,27 Shallow shotgun sequencing conducted in the Knight laboratory at the University of California San Diego 28 was applied to generate species-level taxonomic and functional data.”
MethodBounaga, A., Alsanea, A., Danouche, M., Rittmann, B. E., Zhou, C., Boulif, R., Zeroual, Y., Benhida, R., & Lyamlouli, K. (2024) · Bioresource Technology · doi:10.1016/j.biortech.2024.130680 · Cited by 10
The Characteristics of Gut Microbiota and Its Relation with Diet in Postmenopausal Osteoporosis
Chen, T., Meng, F., Wang, N., Hao, Y., & Fu, L. (2024) · Calcified Tissue International · doi:10.1007/s00223-024-01260-x · Cited by 10
Zhang, Z., Huang, J.-E., Phurbu, D., Qu, Z.-S., Liu, F., & Cai, L. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116483 · Cited by 13
Short-Term Tomato Consumption Alters the Pig Gut Microbiome toward a More Favorable Profile
Goggans, M. L., Bilbrey, E. A., Quiroz-Moreno, C. D., Francis, D. M., Jacobi, S. K., Kovač, J., & Cooperstone, J. L. (2022) · Microbiology Spectrum · doi:10.1128/spectrum.02506-22 · Cited by 11
Roman, L. J., Snijders, A. M., Chang, H., Mao, J., Jones, K. J., & Lawson, G. W. (2023) · Journal of the American Association for Laboratory Animal Science · doi:10.30802/aalas-jaalas-22-000068 · Cited by 12
ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning
Mineeva, O., Danciu, D., Schölkopf, B., Ley, R. E., Rätsch, G., & Youngblut, N. D. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011001 · Cited by 10
Kapustina, Ž., Medžiūnė, J., Alzbutas, G., Rokaitis, I., Matjošaitis, K., Mackevičius, G., Žeimytė, S., Karpus, L., & Lubys, A. (2021) · Microbial Genomics · doi:10.1099/mgen.0.000624 · Cited by 11
Edwin, N. R., Duff, A. M., Deveautour, C., Brennan, F., Abram, F., & O’Sullivan, Ó. (2025) · mSystems · doi:10.1128/msystems.01059-24 · Cited by 11
“This could be provided as a link to an online repository for the paper on something like figshare or the Open Science Framework.”
Faecal microbiota transplant for Parkinson’s disease: promises and future directions
Tan, E. K., Wang, J. D. J., Pettersson, S., Wang, Q., Takahashi, R., Poewe, W. H., Jankovic, J. J., & Rascol, O. (2025) · Brain · doi:10.1093/brain/awaf419 · Cited by 10
Youngblut, N. D., de la Cuesta‐Zuluaga, J., & Ley, R. E. (2022) · Environmental Microbiology · doi:10.1111/1462-2920.15910 · Cited by 9
“Recent work has shown that shallow sequencing depths can provide similar or greater coverage of microbial diversity compared to 16S rRNA sequencing (20) .”
BackgroundKopera, K., Gromowski, T., Wydmański, W., Skonieczna‐Żydecka, K., Muszyńska, A., Zielińska, K., Wierzbicka-Woś, A., Kaczmarczyk, M., Kadaj-Lipka, R., Cembrowska-Lech, D., et al. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1342749 · Cited by 9
“Gut microbiome investigations among patients with COVID-19 to date characterized the makeup and diversity of the microbiota through one of two sequencing strategies.”
BackgroundAnalysis of the Soil Microbiome of a Los Angeles Urban Farm
Clair, S. S., Saraylou, M., Melendez, D., Senn, N., Reitz, S., Kananipour, D., & Alvarez, A. (2020) · Applied and Environmental Soil Science · doi:10.1155/2020/5738237 · Cited by 9
Detecting Flavobacterial Fish Pathogens in the Environment via High-Throughput Community Analysis
Testerman, T., Beka, L., McClure, E. A., Reichley, S. R., King, S., Welch, T. J., & Graf, J. (2021) · Applied and Environmental Microbiology · doi:10.1128/aem.02092-21 · Cited by 9
“While shotgun approaches 410 can generally provide better resolution, they are significantly more expensive (37), 411 suffer from background or host DNA interfering with detection (34), and require a more 412 complex bioinformatic approach.”
BackgroundGEM-based computational modeling for exploring metabolic interactions in a microbial community
Mirzaei, S., & Tefagh, M. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012233 · Cited by 8
“Due to the falling cost of 16S rRNA and shallow shotgun sequencing, we now have access to information about the composition and abundance of microbes in a community [5, 6].”
BackgroundSiew, S. W., Khairi, M. H. F., Hamid, N. A., Asras, M. F. F., & Ahmad, H. F. (2024) · Environmental Pollution · doi:10.1016/j.envpol.2024.125330 · Cited by 8
Longhi, G., Argentini, C., Fontana, F., Tarracchini, C., Mancabelli, L., Lugli, G. A., Alessandri, G., Lahner, E., Pivetta, G., Turroni, F., et al. (2023) · Microbiome Research Reports · doi:10.20517/mrr.2023.02 · Cited by 8
Recovery of microbial community profile information hidden in chimeric sequence reads
Ho, M., Moon, D., Pires-Alves, M., Thornton, P. D., McFarlin, B. L., & Wilson, B. A. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.08.050 · Cited by 7
Differential richness inference for 16S rRNA marker gene surveys
Kumar, M. S. B., SLUD, E. V., Hehnly, C., Zhang, L., Broach, J. R., Irizarry, R. A., Schiff, S. J., & Paulson, J. N. (2022) · Genome biology · doi:10.1186/s13059-022-02722-x · Cited by 8
Estimating consensus proteomes and metabolic functions from taxonomic affiliationsPreprint
Belcour, A., Hamon-Giraud, P., Mataigne, A., Ruiz, B., Cunff, Y. L., Got, J., Awhangbo, L., Lebreton, M., Frioux, C., Dittami, S. M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.16.484574 · Cited by 8
“In addition, there are other sequencing methods that provide taxonomic characterisation from environmental samples, such as shallow whole genome sequencing [19, 20] or metatranscriptomics [21].”
Tepekule, B., Barcik, W., Staiger, W. I., Bergadà-Pijuan, J., Scheier, T., Brülisauer, L., Hall, A. R., Günthard, H. F., Hilty, M., Kouyos, R. D., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2412742122 · Cited by 7
Metagenomic analysis of the faecal microbiota and AMR in roe deer in Western Pomerania
Lechleiter, N., Wedemeyer, J., Schütz, A. K., Sehl‐Ewert, J., Schaufler, K., & Homeier‐Bachmann, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-93602-4 · Cited by 7
Maukonen, M., Koponen, K. K., Havulinna, A. S., Kaartinen, N. E., Niiranen, T., Méric, G., Pajari, A., Knight, R. D., Salomaa, V. V., & Männistö, S. (2024) · European Journal of Nutrition · doi:10.1007/s00394-024-03406-x · Cited by 7
“The samples were sequenced based on whole-genome, untargeted shallow shotgun sequencing at the University of California San Diego [20].”
MethodSelf-citationMetagenomic characterisation of canine skin reveals a core healthy skin microbiome
Whittle, M. J., Fernandez, J. C., Amos, G. C. A., & Watson, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-63999-5 · Cited by 7
Neuronal Activation of the Gastrointestinal Tract Shapes the Gut Environment in Mice
Yoo, B. B., Griffiths, J. A., Thuy-Boun, P. S., Cantu, V., Weldon, K. C., Challis, C., Sweredoski, M. J., Chan, K. Y., Thron, T., Sharon, G., et al. (2021) · CaltechAUTHORS (California Institute of Technology) · Cited by 8
Self-citationLiu, P., Hu, S., He, Z., Feng, C., Dong, G., An, S., Liu, R., Xu, F., Chen, Y., & Ying, X. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.828254 · Cited by 7
Le, L. T., Huang, Z., Whiteson, K., & Jiang, S. C. (2022) · Environmental Science Water Research & Technology · doi:10.1039/d1ew00820j · Cited by 7
Ross, D. E., Lipus, D., & Gulliver, D. M. (2022) · Environmental Microbiology · doi:10.1111/1462-2920.16251 · Cited by 7
Dietary Polyphenols in Relation to Gut Microbiota Composition in Saudi Arabian Females
Alsuhaibani, M. N., Aljuraiban, G. S., Aljazairy, E. A., Abudawood, M., Hussain, S., Alnaami, A. M., Sabico, S., Al‐Daghri, N. M., & Al-Musharaf, S. (2022) · Metabolites · doi:10.3390/metabo13010006 · Cited by 7
“We investigated the association between dietary polyphenols and gut microbiota relative to body weight using WGS for taxonomic identification to species level, which is considered to be a gold standard technique when compared to other methods [57].”
MethodZhang, Q., Campos, M. A., Larama, G., Acuña, J. J., Valenzuela, B., Solís, F. J. T., Zamorano, P. L., Araya, R., Sadowsky, M. J., & Jorquera, M. A. (2020) · Archives of Microbiology · doi:10.1007/s00203-020-02020-9 · Cited by 7
Gweon, H. S., Shaw, L. P., Swann, J., Maio, N. D., AbuOun, M., Hubbard, A. T. M., Bowes, M. J., Bailey, M., Peto, T., Hoosdally, S., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/593301 · Cited by 7
Cell‐free DNA‐associated multi‐feature applications in cancer diagnosis and treatment
Zhang, X., Li, J., Lan, X., & Li, J. (2024) · Clinical and Translational Discovery · doi:10.1002/ctd2.280 · Cited by 6
Yu, Y., Feng, S., Zhou, C., Yang, Y., & Zhang, T. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.126136 · Cited by 6
Dang, T., Lysenko, A., Boroevich, K. A., & Tsunoda, T. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf300 · Cited by 8
Liu, K. Y. P., Huang, A., Pepin, C., Shen, Y., Tsang, P., & Poh, C. F.-Y. (2025) · Cancers · doi:10.3390/cancers18010145 · Cited by 6
“Host DNA contamination, particularly in oral lesion or brush samples, remains a significant challenge [112].”
Jurgiel, J., Gromowski, T., Król, J. W., Bomba-Opon, D. A., Kościółek, T., & Wielgoś, M. (2026) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2025.09.001 · Cited by 7
Rodents consuming the same toxic diet harbor a unique functional core microbiome
Stapleton, T. E., Lindsey, L. M., Sundar, H., & Dearing, M. D. (2024) · Animal Microbiome · doi:10.1186/s42523-024-00330-7 · Cited by 7
“Metagenomics is often touted as providing more in-depth and accurate results in comparison to 16S rRNA sequencing and this has been demonstrated in well studied systems, such as the human microbiome where microbial functions have been established [34, 47, 59, 75].”
ResultPedro, N., Brucato, N., Cavadas, B., Lisant, V., Camacho, R. C., Kinipi, C., Leavesley, M., Pereira, L., & Ricaut, F. (2022) · Molecular Ecology · doi:10.1111/mec.16702 · Cited by 7
“Previous research has shown that microbiome results might differ quite substantially depending on the underlying method used (Durazzi et al., 2021; Hillmann et al., 2018), but a high correlation has been found between these different methods regarding the most abundant species in the samples.”
BackgroundSignatures of HIV and Major Depressive Disorder in the Plasma Microbiome
Taylor, B. C., Andalibi, M. S. S., Wandro, S., Weldon, K. C., Sepich‐Poore, G. D., Carpenter, C. S., Fraraccio, S., Franklin, D., Iudicello, J. E., Letendre, S. L., et al. (2023) · Microorganisms · doi:10.3390/microorganisms11041022 · Cited by 6
“In addition, it is argued that in studies on blood and biopsy specimens where there is low bacterial biomass, and the risk of host DNA contamination is relatively high, shallow-shotgun sequencing may not be a viable replacement for 16S sequencing [21].”
BackgroundSelf-citationCampos-Madueno, E. I., Aldeia, C., Perreten, V., Sendi, P., Moser, A. I., & Endimiani, A. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1236208 · Cited by 6
Inwood, S. N., McLaughlin, G. M., Buckley, T. R., Cox, M. P., Handley, K. M., Steeves, T. E., Strabala, T. J., McDougal, R. L., & Dearden, P. K. (2020) · New Zealand Journal of Ecology · doi:10.20417/nzjecol.44.22 · Cited by 6
Genome-Based Targeted Sequencing as a Reproducible Microbial Community Profiling Assay
Benjamino, J., Leopold, B., Phillips, D. E., & Adams, M. D. (2021) · mSphere · doi:10.1128/msphere.01325-20 · Cited by 6
Two-Target Quantitative PCR To Predict Library Composition for Shallow Shotgun Sequencing
Cho, M. Y., Oliva, M., Spreafico, A., Chen, B., Xu, W., Choi, Y., Kaul, R., Siu, L. L., Coburn, B. A., & Schneeberger, P. H. H. (2021) · mSystems · doi:10.1128/msystems.00552-21 · Cited by 7
Nugent, J. L., Singh, A. R., Wirth, K. M., Oppler, S. H., Stone, L. H., Janecek, J. L., Sheka, A. C., Kizy, S., Moore, M. E. G., Staley, C. M., et al. (2021) · iScience · doi:10.1016/j.isci.2021.103421 · Cited by 6
“Libraries for whole genome shotgun sequencing were prepared using 1/4 NexteraXT reactions (Hillmann et al., 2018), and paired end sequencing was performed at a read length of 150 nt on the Illumina MiSeq platform by the University of Minnesota Genomics Center (Minneapolis, MN, USA).”
MethodBioremediation Assessment in Industrial Wastewater Treatment: The Omics Approach
Chaurasia, P., Jasuja, N. D., & Kumar, S. (2022) · Springer eBooks · doi:10.1007/978-981-19-4320-1_20 · Cited by 6
Quantifying Metagenomic Strain Associations from Microbiomes with AnpanPreprint
Ghazi, A. R., Thompson, K. N., Bhosle, A., Mei, Z., Yan, Y., Wang, F., Wang, K., Franzosa, E. A., & Huttenhower, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.06.631550 · Cited by 7
XAI-based Data Visualization in Multimodal Medical DataPreprint
Sharma, S., Singh, M., Mcdaid, L. J., & Bhattacharyya, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.11.664302 · Cited by 7
Zouiouich, S., Wan, Y., Vogtmann, E., Porras, C., Abnet, C. C., Shi, J., & Sinha, R. R. (2025) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-24-0839 · Cited by 5
Chitcharoen, S., Sawaswong, V., Klomkliew, P., Chanchaem, P., & Payungporn, S. (2025) · BioScience Trends · doi:10.5582/bst.2024.01393 · Cited by 5
Mubaraki, F. A. (2025) · PLoS ONE · doi:10.1371/journal.pone.0328194 · Cited by 5
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
