Comparing Apples and Oranges?: Next Generation Sequencing and Its Impact on Microbiome Analysis
Open accessAbstract
Rapid advancements in sequencing technologies along with falling costs present widespread opportunities for microbiome studies across a vast and diverse array of environments. These impressive technological developments have been accompanied by a considerable growth in the number of methodological variables, including sampling, storage, DNA extraction, primer pairs, sequencing technology, chemistry version, read length, insert size, and analysis pipelines, amongst others. This increase in variability threatens to compromise both the reproducibility and the comparability of studies conducted. Here we perform the first reported study comparing both amplicon and shotgun sequencing for the three leading next-generation sequencing technologies. These were applied to six human stool samples using Illumina HiSeq, MiSeq and Ion PGM shotgun sequencing, as well as amplicon sequencing across two variable 16S rRNA gene regions. Notably, we found that the factor responsible for the greatest variance in microbiota composition was the chosen methodology rather than the natural inter-individual variance, which is commonly one of the most significant drivers in microbiome studies. Amplicon sequencing suffered from this to a large extent, and this issue was particularly apparent when the 16S rRNA V1-V2 region amplicons were sequenced with MiSeq. Somewhat surprisingly, the choice of taxonomic binning software for shotgun sequences proved to be of crucial importance with even greater discriminatory power than sequencing technology and choice of amplicon. Optimal N50 assembly values for the HiSeq was obtained for 10 million reads per sample, whereas the applied MiSeq and PGM sequencing depths proved less sufficient for shotgun sequencing of stool samples. The latter technologies, on the other hand, provide a better basis for functional gene categorisation, possibly due to their longer read lengths. Hence, in addition to highlighting methodological biases, this study demonstrates the risks associated with comparing data generated using different strategies. We also recommend that laboratories with particular interests in certain microbes should optimise their protocols to accurately detect these taxa using different techniques.
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Differences in protocols make pooling data from two different studies more complicated.
“This makes pooling the data from two different studies more complicated (Clooney et al., 2016)”
From The Microbiota-Gut-Brain Axis (2019) · cites this paper as Clooney et al., 2016
16S sequencing and whole genome shotgun sequencing provide complementary readouts that can inform each other.
“While the techniques are arguably similar, they are not mutually exclusive but provide complementary readouts that can inform each other (Clooney et al., 2016), see Fig. 2.”
From The Microbiota-Gut-Brain Axis (2019) · cites this paper as Clooney et al., 2016
Almost all cases in which sequencing platforms differed specifically investigated Ion Torrent 16S amplicon sequencing.
“Almost all cases in which platforms differed31–33 specifically investigated Ion Torrent 16S amplicon sequencing”
From Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium (2017) · cites this paper as [32]
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BackgroundSafarchi, A., Al-Qadami, G. H., Tran, C. D., & Conlon, M. A. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1559521 · Cited by 133 · see citing works
Boix-Amorós, A., Puente‐Sánchez, F., du Toit, E., Linderborg, K. M., Zhang, Y., Yang, B., Salminen, S., Isolauri, E., Tamames, J., Mira, Á., et al. (2019) · Applied and Environmental Microbiology · doi:10.1128/aem.02994-18 · Cited by 122 · see citing works
Monteiro, C., Marques, P., Cavadas, B., Damião, I., Almeida, V., Barros, N., Barros, A., Carvalho, F., Gomes, S. C. V., & Seixas, S. (2018) · American Journal of Reproductive Immunology · doi:10.1111/aji.12838 · Cited by 120 · see citing works
Gruninger, R. J., Ribeiro, G. O., Cameron, A., & McAllister, T. A. (2019) · animal · doi:10.1017/s1751731119000752 · Cited by 119 · see citing works
Tourlousse, D. M., Narita, K., Miura, T., Sakamoto, M., Ohashi, A., Shiina, K., Matsuda, M., Miura, D., Shimamura, M., Ohyama, Y., et al. (2021) · Microbiome · doi:10.1186/s40168-021-01048-3 · Cited by 118 · see citing works
“Workflows for metagenomics are however complex and prone to bias and errors at all steps, from sample collection and storage [6, 7] to DNA extraction [8, 9], sequencing and bioinformatics analysis [10, 11].”
BackgroundThe Fungal Frontier: A Comparative Analysis of Methods Used in the Study of the Human Gut Mycobiome
Huseyin, C. E., Cabrera‐Rubio, R., O’Sullivan, Ó., Cotter, P. D., & Scanlan, P. D. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.01432 · Cited by 108 · see citing works
“This is of crucial importance as microbiome studies have consistently shown that the use of different methodologies can account for considerable variation in the resulting data output (Yu and Morrison, 2004; Bahl et al., 2012; Clooney et al., 2016; Fouhy et al., 2016).”
Self-citationShi, Z. J., Yin, H., Nostrand, J. D. V., Voordeckers, J. W., Tu, Q., Deng, Y., Yuan, M., Zhou, A., Zhang, P., Xiao, N., et al. (2019) · mSystems · doi:10.1128/msystems.00296-19 · Cited by 107 · see citing works
The emerging role of the gut microbiota and its application in inflammatory bowel disease
Xiu, W., Peng, J., Cai, P., Xia, Y., Yi, C., Shang, A., Akanyibah, F. A., & Mao, F. (2024) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2024.117302 · Cited by 105 · see citing works
Species classifier choice is a key consideration when analysing low-complexity food microbiome data
Walsh, A. M., Crispie, F., O’Sullivan, Ó., Finnegan, L., Claesson, M. J., & Cotter, P. D. (2018) · Microbiome · doi:10.1186/s40168-018-0437-0 · Cited by 99 · see citing works
“However, when these platforms were compared with the HiSeq for shotgun metagenomic applications, it was apparent that compositional results were similar across platforms but varied depending on the species classification tools used [34].”
BackgroundResultSelf-citationAssessment and Comparison of Molecular Subtyping and Characterization Methods for Salmonella
Tang, S., Orsi, R. H., Luo, H., Ge, C., Zhang, G., Baker, R. C., Stevenson, A., & Wiedmann, M. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.01591 · Cited by 94 · see citing works
Omics-Based Insights into Flavor Development and Microbial Succession within Surface-Ripened Cheese
Bertuzzi, A. S., Walsh, A. M., Sheehan, J. J., Cotter, P. D., Crispie, F., McSweeney, P. L. H., Kilcawley, K. N., & Rea, M. C. (2018) · mSystems · doi:10.1128/msystems.00211-17 · Cited by 90 · see citing works
“Whole-metagenome shotgun libraries were prepared in accordance with the Nextera XT DNA library preparation guide from Illumina (53).”
MethodInfluentialSelf-citationWiseschart, A., Mhuantong, W., Tangphatsornruang, S., Chantasingh, D., & Pootanakit, K. (2019) · BMC Microbiology · doi:10.1186/s12866-019-1521-8 · Cited by 90 · see citing works
Metagenomics Approaches for Improving Food Safety: A Review
Billington, C., Kingsbury, J. M., & Rivas, L. (2021) · Journal of Food Protection · doi:10.4315/jfp-21-301 · Cited by 87 · see citing works
García-López, R., Cornejo‐Granados, F., López-Zavala, A. A., Sánchez-López, F., Cota-Huízar, A., Sotelo‐Mundo, R. R., Guerrero, A., Mendoza-Vargas, A., Gómez‐Gil, B., & Ochoa‐Leyva, A. (2020) · Microorganisms · doi:10.3390/microorganisms8010134 · Cited by 86 · see citing works
“On the other hand, it has been reported that different sequencing platforms impact the microbiota analysis [14,68].”
BackgroundEnvironmental DNA: A New Low-Cost Monitoring Tool for Pathogens in Salmonid Aquaculture
Peters, L., Spatharis, S., Dario, M. A., Dwyer, T., Roca, I. J. T., Kintner, A. H., Kanstad-Hanssen, Ø., Llewellyn, M. S., & Præbel, K. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.03009 · Cited by 85 · see citing works
“Two commonly used NGS methods are Ion Torrent and Illumina MiSeq – the efficiency of which in detecting organisms from community samples has been compared in multiple studies mostly focused on bacterial diversity (e.g., Salipante et al., 2014; Clooney et al., 2016; Lahens et al., 2017).”
MethodIsolation and Identification of the Follicular Microbiome: Implications for Acne Research
Hall, J. B., Cong, Z., Kawasawa, Y. I., Kidd, B. A., Dudley, J. T., Thiboutot, D. M., & Nelson, A. M. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.02.038 · Cited by 83 · see citing works
Walsh, A. M., Crispie, F., Daari, K., O’Sullivan, Ó., Martin, J. C., Arthur, C. T., Claesson, M. J., Scott, K. P., & Cotter, P. D. (2017) · Applied and Environmental Microbiology · doi:10.1128/aem.01144-17 · Cited by 86 · see citing works
“Whole-metagenome shotgun libraries were prepared in accordance with the Nextera XT DNA Library Preparation Guide from Illumina (26).”
InfluentialSelf-citationKarimi, E., Keller‐Costa, T., Slaby, B. M., Cox, C. J., da Rocha, U. N., Hentschel, U., & Costa, R. (2019) · Scientific Reports · doi:10.1038/s41598-019-38737-x · Cited by 79 · see citing works
Akaçin, İ., Ersoy, Ş., Doluca, O., & Güngörmüşler, M. (2022) · Microbiological Research · doi:10.1016/j.micres.2022.127154 · Cited by 86 · see citing works
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 78 · see citing works
Wu, J., Singleton, S. S., Bhuiyan, U., Krammer, L., & Mazumder, R. (2024) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1337373 · Cited by 83 · see citing works
Detection and Enumeration of Spore-Forming Bacteria in Powdered Dairy Products
McHugh, A. J., Feehily, C., Hill, C., & Cotter, P. D. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.00109 · Cited by 75 · see citing works
“To provide thorough and reproducible analysis of dairy powders in this fashion, it will be particularly important to arrive at a consensus regarding the standardized sample preparation, use of specific sequencing platforms and analysis methodologies to facilitate comparison across multiple investigations (Clooney et a…”
InfluentialSelf-citationApproaches to Improve Endometrial Receptivity in Case of Repeated Implantation Failures
Makrigiannakis, A., Makrygiannakis, F., & Vrekoussis, T. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.613277 · Cited by 75 · see citing works
Videnska, P., Šmerková, K., Zwinsova, B., Popovici, V. C., Micenková, L., Sedlář, K., & Budínská, E. (2019) · Scientific Reports · doi:10.1038/s41598-019-49520-3 · Cited by 74 · see citing works
Global Ramifications of Dust and Sandstorm Microbiota
Behzad, H., Mineta, K., & Gojobori, T. (2018) · Genome Biology and Evolution · doi:10.1093/gbe/evy134 · Cited by 73 · see citing works
Influence of 16S rRNA target region on the outcome of microbiome studies in soil and saliva samples
Soriano‐Lerma, A., Pérez‐Carrasco, V., Sánchez‐Marañón, M., Ortiz-González, M., Sánchez-Martín, V., Gijón, J., Navarro‐Marí, J. M., Garcia-Salcedo, J. A., & Soriano, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-70141-8 · Cited by 70 · see citing works
Quantification of variation and the impact of biomass in targeted 16S rRNA gene sequencing studies
Bender, J. M., Li, F., Adisetiyo, H., Lee, D., Zabih, S., Hung, L. T., Wilkinson, T. A., Pannaraj, P. S., She, R. C., Bard, J. D., et al. (2018) · Microbiome · doi:10.1186/s40168-018-0543-z · Cited by 71 · see citing works
Vera‐Gargallo, B., & Ventosa, A. (2018) · Genes · doi:10.3390/genes9030152 · Cited by 65 · see citing works
“Since sequencing technology and analysis workflow has been shown to greatly affect comparability of metagenomic studies results [66], publicly available 454 metagenomic databases from salterns were analyzed in parallel to the newly sequenced soil databases.”
BackgroundPálková, L., Tomova, A., Repiská, G., Babinská, K., Bokor, B., Mikula, I., Minárik, G., Ostatníková, D., & Šoltýs, K. (2021) · Scientific Reports · doi:10.1038/s41598-021-86378-w · Cited by 64 · see citing works
Bastiaanssen, T. F. S., Quinn, T. P., & Loughman, A. (2023) · Nature Mental Health · doi:10.1038/s44220-023-00148-3 · Cited by 70 · see citing works
“Although downstream bioinformatics analyses of both types of microbiome sequencing techniques converge, the actual techniques are distinct 57 .”
BackgroundMethods for Studying Gut Microbiota: A Primer for Physicians
Sarangi, A. N., Goel, A., & Aggarwal, R. (2018) · Journal of Clinical and Experimental Hepatology · doi:10.1016/j.jceh.2018.04.016 · Cited by 63 · see citing works
“Of these, the V4–V5 region is particularly suited for the study of microbiota, since it provides the most comparable results across platforms(10) and provides a high taxonomic resolution.(11,12) However, the sequencing of these hypervariable regions requires a technique with a longer read length than themethods that u…”
BackgroundRubbmark, O. R., Sint, D., Horngacher, N., & Traugott, M. (2018) · Ecology and Evolution · doi:10.1002/ece3.4520 · Cited by 63 · see citing works
“On one hand, this may not be of concern when comparing structural differences between samples (beta diversity), but could become very problematic for comparing differences in alpha diversity (Clooney et al., 2016).”
BackgroundAn, R., Wilms, E., Smolinska, A., Hermes, G. D. A., Masclee, A., de Vos, P., Schols, H. A., van Schooten, F., Smidt, H., Jonkers, D. M. A. E., et al. (2019) · Nutrients · doi:10.3390/nu11092193 · Cited by 63 · see citing works
“The varying effects on microbiota composition and/or fecal SCFA levels are likely due to different methodologies [29], and differences in dosage [19], chemical structure [30], and/or source (e.”
BackgroundA snapshot of gut microbiota of an adult urban population from Western region of India
Tandon, D., Haque, M. M., R., S., Shaikh, S., P., S., Dubey, A., & Mande, S. S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195643 · Cited by 54 · see citing works
Next-Generation Sequencing (NGS): Platforms and Applications
Mandlik, J., Patil, A., & Singh, S. (2024) · Journal of Pharmacy And Bioallied Sciences · doi:10.4103/jpbs.jpbs_838_23 · Cited by 55 · see citing works
“[24] NGS and oral microbiome Lederberg and McCray referred to all the microbial species along with their genome harboring the human oral cavity as the human oral microbiome.”
The porcine respiratory microbiome: recent insights and future challenges
Pirolo, M., Espinosa‐Gongora, C., Bogaert, D., & Guardabassi, L. (2021) · Animal Microbiome · doi:10.1186/s42523-020-00070-4 · Cited by 54 · see citing works
“Among these, differences in the i) 16S rRNA gene variable region targeted, ii) primers employed, iii) length of the reads produced by the sequencing platforms, iv) data processing software and v) reference databases are among the major methodological problem with the largest impact on the outcomes of the studies [58,…”
BackgroundDistribution of apple and blackcurrant microbiota in Lithuania and the Czech Republic
Vepštaitė‐Monstavičė, I., Lukša, J., Stanevičienė, R., Strazdaitė‐Žielienė, Ž., Yurchenko, V., Serva, S., & Servienė, E. (2017) · Microbiological Research · doi:10.1016/j.micres.2017.09.004 · Cited by 54 · see citing works
“Only a limited number of studies on microorganisms residing on plums, apples, pears, cherries, and strawberries have been reported (Janisiewicz et al., 2014; Abdelfattah et al., 2016a, 2016b; Clooney et al., 2016; Volschenk et al., 2016).”
BackgroundGancao-Gansui combination impacts gut microbiota diversity and related metabolic functions
Yu, J., Guo, J., Tao, W., Liu, P., Shang, E., Zhu, Z., Fan, X., Shen, J., Hua, Y., Zhu, K. Y., et al. (2017) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2017.11.031 · Cited by 53 · see citing works
“Due to the application of 16S rDNA sequencing (Clooney et al., 2016), an increasing number of literatures have reported the relevance between gut microbiota and diseases, such as obesity, diabetes, various tumors and even endocrinopathy (Markle et al., 2013; Schulz et al., 2014; Turnbaugh et al.,…”
BackgroundEsophageal microbiome in active eosinophilic esophagitis and changes induced by different therapies
Mendieta, E. J. L., FitzGerald, J. A., Arias‐González, L., Olalla, J. M., Bernardo, D., Claesson, M. J., & Lucendo, A. J. (2021) · Scientific Reports · doi:10.1038/s41598-021-86464-z · Cited by 52 · see citing works
Self-citationPoulsen, C. S., Ekstrøm, C. T., Aarestrup, F. M., & Pamp, S. J. (2022) · Microbiology Spectrum · doi:10.1128/spectrum.00090-22 · Cited by 50 · see citing works
“effect on the characterization of microbiomes (30).”
BackgroundIndividual Signatures Define Canine Skin Microbiota Composition and Variability
Cuscó, A., Sánchez, A., Altet, L., Ferrer, L., & Francino, O. (2017) · Frontiers in Veterinary Science · doi:10.3389/fvets.2017.00006 · Cited by 50 · see citing works
“This problem was larger in Illumina MiSeq rather than in Ion Torrent PGM when analyzing 16S rRNA V1–V2 region amplicons (48).”
MethodClark, K., Taggart, D. M., Baldwin, B. R., Ritalahti, K. M., Murdoch, R. W., Hatt, J. K., & Löffler, F. E. (2018) · Environmental Science & Technology · doi:10.1021/acs.est.8b04373 · Cited by 49 · see citing works
Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine
Contreras, A. V., Cocom-Chan, B., Hernández‐Montes, G., Portillo-Bobadilla, T., & Reséndis-Antonio, O. (2016) · Frontiers in Physiology · doi:10.3389/fphys.2016.00606 · Cited by 46 · see citing works
“Furthermore, MiSeq and PGM technologies provide a better functional categorization for predicting core genes from assembled contigs, possibly due to their longer read lengths (Clooney et al., 2016).”
MethodSchell, S. L., Schneider, A. M., & Nelson, A. M. (2021) · Experimental Dermatology · doi:10.1111/exd.14398 · Cited by 46 · see citing works
Evidence for the contribution of the gut microbiome to obesity and its reversal
de Wit, D. F., Hanssen, N. M. J., Wortelboer, K., Herrema, H., Rampanelli, E., & Nieuwdorp, M. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.adg2773 · Cited by 47 · see citing works
Effects of Actinomycete Secondary Metabolites on Sediment Microbial Communities
Patin, N., Schorn, M., Aguinaldo, K., Lincecum, T. L., Moore, B. S., & Jensen, P. R. (2016) · Applied and Environmental Microbiology · doi:10.1128/aem.02676-16 · Cited by 42 · see citing works
“To minimize next-generation sequencing biases and platform-specific errors (62), we applied rigorous quality controls (QCs) at every step of sample preparation, sequencing, and data analysis.”
MethodThe respiratory microbiome in idiopathic pulmonary fibrosis
Hewitt, R. J., & Molyneaux, P. L. (2017) · Annals of Translational Medicine · doi:10.21037/atm.2017.01.56 · Cited by 41 · see citing works
Call, L., Stoll, B., Oosterloo, B. C., Ajami, N. J., Sheikh, F., Wittke, A., Waworuntu, R. V., Berg, B. M., Petrosino, J. F., Olutoye, O. O., et al. (2018) · Microbiome · doi:10.1186/s40168-018-0498-0 · Cited by 41 · see citing works
“non-culture-based, different sequencing platforms) [33, 34], and the challenges of interpreting results across the various animal models and human patients, each harboring its own co-evolved microbiota [35–38].”
BackgroundMeta‐analysis of caries microbiome studies can improve upon disease prediction outcomes
Butcher, M., Short, B., Veena, C. L. R., Bradshaw, D., Pratten, J., McLean, W., Shaban, S., Ramage, G., & Delaney, C. (2022) · Acta Pathologica, Microbiologica et Immunologica Scandinavica (APMIS) · doi:10.1111/apm.13272 · Cited by 26 · see citing works
“As stated previously, it has been well defined in the literature that the selected 16S rRNA region amplicon can have a profound impact on the amplification of select taxa within the sample (60,61).”
BackgroundCabral, D. J., Wurster, J. I., Korry, B. J., Penumutchu, S., & Belenky, P. A. (2020) · mSystems · doi:10.1128/msystems.00317-20 · Cited by 42 · see citing works
Vaher, K., Bogaert, D., Richardson, H., & Boardman, J. P. (2022) · Developmental Review · doi:10.1016/j.dr.2022.101038 · Cited by 39 · see citing works
“This could explain variations of results between the different studies because the choice of 16S rRNA region can significantly alter the estimation of taxonomic composition and diversity (Clooney et al., 2016; Rintala et al., 2017; Yang et al., 2016).”
ResultXiong, Z., Sogin, J. H., & Worobo, R. W. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.1099522 · Cited by 38 · see citing works
Caverly, L. J., Carmody, L. A., Haig, S., Kotlarz, N., Kalikin, L. M., Raskin, L. M., & LiPuma, J. J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0153876 · Cited by 37 · see citing works
Marine, R. L., Magaña, L. C., Castro, C. J., Zhao, K., Montmayeur, A. M., Schmidt, A., Diez‐Valcarce, M., Ng, T. F. F., Vinjé, J., Burns, C. C., et al. (2019) · Journal of Virological Methods · doi:10.1016/j.jviromet.2020.113865 · Cited by 38 · see citing works
Parente, E., Filippis, F. D., Ercolini, D., Ricciardi, A., & Zotta, T. (2019) · International Journal of Food Microbiology · doi:10.1016/j.ijfoodmicro.2019.108249 · Cited by 37 · see citing works
Microbiome as an Immunological Modifier
Kumar, M., Singh, P., Murugesan, S., Vétizou, M., McCulloch, J. A., Badger, J. H., Trinchieri, G., & Khodor, S. A. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9773-2_27 · Cited by 36 · see citing works
Breitenwieser, F., Doll, E. V., Clavel, T., Scherer, S., & Wenning, M. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.01557 · Cited by 37 · see citing works
Sevillano, M., Dai, Z., Calus, S., Santos, Q. M. B.-D. L., Eren, A. M., van der Wielen, P. W. J. J., Ijaz, U. Z., & Pinto, A. J. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.141451 · Cited by 41 · see citing works
Urolithins: Diet-Derived Bioavailable Metabolites to Tackle Diabetes
Raimundo, A., Ferreira, S., Tomás-Barberán, F. A., dos Santos, C. N., & Menezes, R. (2021) · Nutrients · doi:10.3390/nu13124285 · Cited by 36 · see citing works
“However, the causality in humans is not yet fully seen and reports seem to be slightly contradicting [69,70].”
ResultImpact of micro‐environmental changes on respiratory tract infections with intracellular bacteria
Shima, K., Coopmeiners, J., Graspeuntner, S., Dalhoff, K., & Rupp, J. (2016) · FEBS Letters · doi:10.1002/1873-3468.12353 · Cited by 36 · see citing works
Emerging microbiota during cold storage and temperature abuse of ready-to-eat salad
Söderqvist, K., Osman, O. A., Wolff, C., Bertilsson, S., Vågsholm, I., & Boqvist, S. (2017) · Infection Ecology & Epidemiology · doi:10.1080/20008686.2017.1328963 · Cited by 36 · see citing works
“Another example is that different DNA extraction methods or primers may be used in different studies, which may have a major effect on the outcome of the analysis and should be considered when comparing studies.[39] Standardized methodologies for analyzing the microbiota of leafy vegetables would be helpful to reduce…”
ResultVeli, M., & Özcan, A. (2018) · ACS Nano · doi:10.1021/acsnano.7b08375 · Cited by 35 · see citing works
Guerrero-Sánchez, V. M., Maldonado-Alconada, A. M., Amil‐Ruíz, F., Verardi, A., Jorrín-Novo, J. V., & Rey, M.-D. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210356 · Cited by 35 · see citing works
An introduction to microbiome analysis for human biology applications
Amato, K. R. (2016) · American Journal of Human Biology · doi:10.1002/ajhb.22931 · Cited by 35 · see citing works
“Nevertheless, IonTorrent is a useful tool outside of core facilities due to its low cost and ease of maintenance (Glenn, 2011; Clooney et al., 2016; Pylro et al., 2014) For the same reasons, it also represents a potential solution for generating data in the field.”
BackgroundMethod
- Authors
- Clooney, A. G., Fouhy, F., Sleator, R. D., O’ Driscoll, A., Stanton, C., Cotter, P. D., & Claesson, M. J.
- Affiliations
- University College Cork (IE)
- Teagasc - The Irish Agriculture and Food Development Authority (IE)
- Corresponding author
- Cotter, Paul D.
- Claesson, Marcus J.
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-02-05
- Volume
- 11
- Issue
- 2
- Pages
- e0148028
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4746063
- OpenAlex
- W2260059978
- Cited by
- 270
- References
- 38
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 18 · 2023: 25 · 2022: 18 · 2021: 42 · 2020: 37 · 2019: 34 · 2018: 38 · 2017: 21 · 2016: 13
- FWCI
- 18.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- creativecommons.org/publicdomain/zero/1.0/ (vor)
- Locations
- PLoS ONE (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- T-Stór (Teagasc) (acceptedVersion)
- Arrow@dit (Dublin Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Arrow@dit (Dublin Institute of Technology) (submittedVersion, public-domain)
- Cork Open Research Archive (University College Cork, Ireland) (submittedVersion, public-domain)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- European Commission (603038)
- Science Foundation Ireland (SFI/12/RC/, 11/PI/1137, 12/RC/2273, 12/RC/, SFI/12/RC/2273.)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Keywords
- microbiome analysis, next-generation sequencing, amplicon sequencing, shotgun sequencing, 16S rRNA gene, stool microbiome, sequencing platform comparison, methodological bias, reproducibility, sequencing depth, read length, human gut microbiome
- MeSH terms
- Aged, Feces, Humans, RNA, Ribosomal, 16S, Sequence Analysis, RNA, DNA Primers, Metagenomics, High-Throughput Nucleotide Sequencing, Microbiota
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