The DOI matches its Crossref record; 141 of the 141 references it cites were found in a registry or an index.
Shotgun metagenomics, from sampling to analysis
Open accessAbstract
Diverse microbial communities of bacteria, archaea, viruses and single-celled eukaryotes have crucial roles in the environment and in human health. However, microbes are frequently difficult to culture in the laboratory, which can confound cataloging of members and understanding of how communities function. High-throughput sequencing technologies and a suite of computational pipelines have been combined into shotgun metagenomics methods that have transformed microbiology. Still, computational approaches to overcome the challenges that affect both assembly-based and mapping-based metagenomic profiling, particularly of high-complexity samples or environments containing organisms with limited similarity to sequenced genomes, are needed. Understanding the functions and characterizing specific strains of these communities offers biotechnological promise in therapeutic discovery and innovative ways to synthesize products using microbial factories and can pinpoint the contributions of microorganisms to planetary, animal and human health.
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?
The cited source says what the claim attributes to it.
In the citing paper
“While sequence-based methods have been transformative for microbiome research, they are not perfect. Biases can be introduced at every step of sequence-based studies, from sample collection and storage, through laboratory-based steps such as DNA extraction, to choice of bioinformatic pipelines and reference databases used to analyse the data 33 .”
From Human microbiome myths and misconceptions (2023) · cites this paper as [33]
In the citing paper
“Specifically, the depth of sequencing is essential for deciphering the microbial composition of a sample, but deeper sequencing comes with higher costs and analytical complexity. 132”
From Key determinants of success in fecal microbiota transplantation: From microbiome to clinic (2023) · cites this paper as [132]
In the citing paper
“Despite its use for more than 15 years, shotgun metagenomics has still not experienced widespread use because of some limitations, including issues related to sequencing depth and partial incompleteness of available reference databases.”
From Key determinants of success in fecal microbiota transplantation: From microbiome to clinic (2023) · cites this paper as [132]
In the citing paper
“Although earlier studies were mostly based on 16S rRNA gene sequencing, the technique is being replaced by shotgun metagenomics, which bypasses amplification steps and provides higher taxonomic resolution and insight into the functions encoded by the microbial community. 132”
From Key determinants of success in fecal microbiota transplantation: From microbiome to clinic (2023) · cites this paper as [132]
In the citing paper
“The visibility of species with particularly high genetic diversity within individual communities also remains limited; in most cases, only the most dominant strain of each taxon per community is currently analyzable, again for both experimental (e.g. sequencing depth) and analytical reasons (Quince et al., 2017).”
From Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3 (2021) · cites this paper as Quince et al., 2017
In the citing paper
“Unlike 16S ribosomal RNA sequencing, shotgun metagenomics sequences permit a parallel assessment of all microbial kingdoms (bacterial, fungal, viral) in a given sample.77, 78”
From The oral microbiome: Role of key organisms and complex networks in oral health and disease (2021) · cites this paper as [78]
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
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
Trimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548
vegan: Community Ecology Package
Oksanen, J., Simpson, G. L., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O’Hara, R. B., Sólymos, P., Stevens, M. H. H., Szöcs, E., et al. (2001) · doi:10.32614/cran.package.vegan · Cited by 27,099
SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing
Bankevich, A., Nurk, S., Antipov, D., Gurevich, A. A., Dvorkin, M. V., Kulikov, A. S., Lesin, V. M., Nikolenko, S., Pham, S., Prjibelski, A. D., et al. (2012) · Journal of Computational Biology · doi:10.1089/cmb.2012.0021 · Cited by 26,312
Metagenomic biomarker discovery and explanation
Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W. S., & Huttenhower, C. (2011) · Genome Biology · doi:10.1186/gb-2011-12-6-r60 · Cited by 16,192
Self-citationStructure, function and diversity of the healthy human microbiome
The Human Microbiome Project Consortium (2012) · Nature · doi:10.1038/nature11234 · Cited by 11,726
A human gut microbial gene catalogue established by metagenomic sequencing
MetaHIT Consortium, Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., et al. (2010) · Nature · doi:10.1038/nature08821 · Cited by 11,624
Li, D., Liu, C.-M., Luo, R., Sadakane, K., & Lam, T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv033 · Cited by 10,133
A metagenome-wide association study of gut microbiota in type 2 diabetes
Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., Liang, S., Zhang, W., Guan, Y., Shen, D., et al. (2012) · Nature · doi:10.1038/nature11450 · Cited by 6,762
Research works printed but not linked: 1
- Knight, R. et al. Unlocking the potential of metagenomics through replicated experimental design. Nat. Biotechnol. 30, 513–520 (2012). · DOI
Cited by
Sorted by citations
Items 401–500 of 1,001
Ptaszek, M., Canfora, L., Pugliese, M., Pinzari, F., Gilardi, G., Trzciński, P., & Malusà, E. (2023) · Microorganisms · doi:10.3390/microorganisms11010224 · Cited by 46
Molecular Diagnostics for Invasive Fungal Diseases: Current and Future Approaches
Pham, D., Sivalingam, V., Tang, H. M., Montgomery, J. M., Chen, S. C. ‐., & Halliday, C. L. (2024) · Journal of Fungi · doi:10.3390/jof10070447 · Cited by 47
An Introduction to Whole-Metagenome Shotgun Sequencing Studies
Joseph, T., & Pe’er, I. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1103-6_6 · Cited by 43
Computational methods and challenges in analyzing intratumoral microbiome data
Wang, Q., Liu, Z., Ma, A., Li, Z., Liu, B., & Ma, Q. (2023) · Trends in Microbiology · doi:10.1016/j.tim.2023.01.011 · Cited by 37
Molecular Methodologies for Improved Polymicrobial Sepsis Diagnosis
Doualeh, M., Payne, M. S., Litton, E., Raby, E., & Currie, A. J. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23094484 · Cited by 44
Bellerba, F., Serrano, D., Johansson, H. A., Pozzi, C., Segata, N., Nabinejad, A., Piperni, E., Gnagnarella, P., Macis, D., Aristarco, V., et al. (2022) · Neoplasia · doi:10.1016/j.neo.2022.100842 · Cited by 40
Self-citationAnthony, W., Allison, S., Broderick, C. M., Rodríguez, L. C., Clum, A., Cross, H., Eloe‐Fadrosh, E. A., Evans, S., Fairbanks, D., Gallery, R. E., et al. (2024) · Environmental Microbiome · doi:10.1186/s40793-024-00599-w · Cited by 44
Metagenomic estimation of dietary intake from human stool
Diener, C., Holscher, H. D., Filek, K., Corbin, K. D., Moissl‐Eichinger, C., & Gibbons, S. M. (2025) · Nature Metabolism · doi:10.1038/s42255-025-01220-1 · Cited by 42
Relevance of phenotypic information for the taxonomy of not-yet-cultured microorganisms.
Overmann, J., Huang, S., Nübel, U., Hahnke, R., & Tindall, B. (2019) · Systematic and Applied Microbiology · doi:10.1016/j.syapm.2018.08.009 · Cited by 35
Uterine microbiome—low biomass and high expectations†
Garcia-Grau, I., Simón, C., & Moreno, I. (2018) · Biology of Reproduction · doi:10.1093/biolre/ioy257 · Cited by 42
Wang, Z., Wang, Y., Fuhrman, J. A., Sun, F., & Zhu, S. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz025 · Cited by 41
Alterations in the Ocular Surface Microbiome in Traumatic Corneal Ulcer Patients
Kang, Y., Zhang, H., Hu, M., Ma, Y., Chen, P., Zhao, Z., Li, J., Ye, Y., Zheng, M., & Lou, Y. (2020) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.61.6.35 · Cited by 39
Romanis, C. S., Pearson, L. A., & Neilan, B. A. (2020) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2020.106123 · Cited by 43
Utembe, W., & Kamng’ona, A. W. (2021) · Chemosphere · doi:10.1016/j.chemosphere.2021.129817 · Cited by 38
Ankersen, D. V., Weimers, P., Bennedsen, M., Haaber, A. B., Fjordside, E. L., Beber, M. E., Lieven, C., Saboori, S., Vad, N., Rannem, T., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/30291 · Cited by 45
Hernández, R., Mares, M. C. D., Jiménez, H., Reyes, A., & Caro‐Quintero, A. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.813002 · Cited by 41
Casting Light on the Adaptation Mechanisms and Evolutionary History of the Widespread Sumerlaeota
Fang, Y., Yuan, Y., Liu, J., Wu, G., Yang, J., Hua, Z., Han, J., Zhang, X., Li, W., & Jiang, H. (2021) · mBio · doi:10.1128/mbio.00350-21 · Cited by 38
Boesch, M., Horvath, L., Baty, F., Pircher, A., Wolf, D. G. F., Spahn, S., Straussman, R., Tilg, H., & Brutsche, M. H. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-005401 · Cited by 41
Barcenilla, C., Cobo-Díaz, J. F., Filippis, F. D., Valentino, V., Cabrera‐Rubio, R., O’Neil, D., de Sanchez, L. M., Armanini, F., Carlino, N., Blanco‐Míguez, A., et al. (2024) · Nature Protocols · doi:10.1038/s41596-023-00949-x · Cited by 43
Self-citationBorgiani, G., Possidente, C., Fabbri, C., Oliva, V., Bloemendaal, M., Vásquez, A. A., Dinan, T. G., Vieta, E., Menchetti, M., Ronchi, D. D., et al. (2024) · International Clinical Psychopharmacology · doi:10.1097/yic.0000000000000533 · Cited by 46
Marcelino, V. R., Irinyi, L., Eden, J., Meyer, W., Holmes, E. C., & Sorrell, T. C. (2019) · IMA Fungus · doi:10.1186/s43008-019-0012-8 · Cited by 36
Escherichia coli triggers α-synuclein pathology in the LRRK2 transgenic mouse model of PD
Liang, D., Liu, H., Jin, R., Feng, R., Wang, J., Qin, C., Zhang, R., Chen, Y., Zhang, J., Teng, J., et al. (2023) · Gut Microbes · doi:10.1080/19490976.2023.2276296 · Cited by 38
Pharmacomicrobiomics: The Role of the Gut Microbiome in Immunomodulation and Cancer Therapy
Bolte, L. A., Björk, J. R., Gaćeša, R., & Weersma, R. K. (2025) · Gastroenterology · doi:10.1053/j.gastro.2025.04.025 · Cited by 39
Dissecting microbial communities with single-cell transcriptome analysis
Pountain, A. W., & Yanai, I. (2025) · Science · doi:10.1126/science.adp6252 · Cited by 37
Bedoya-Urrego, K., Coltell, Ó., Cabarcas, F., & Álzate, J. F. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.08.119 · Cited by 39
Fitri, L. E., Candradikusuma, D., Setia, Y. D., Wibawa, P. A., Iskandar, A., Winaris, N., & Pawestri, A. R. (2022) · Tropical Medicine and Infectious Disease · doi:10.3390/tropicalmed7100253 · Cited by 36
Bioinformatic approaches for studying the microbiome of fermented food
Walsh, L. H., Coakley, M., Walsh, A. M., O’Toole, P. W., & Cotter, P. D. (2022) · Critical Reviews in Microbiology · doi:10.1080/1040841x.2022.2132850 · Cited by 38
Prebiotics, probiotics, synbiotics and postbiotics to adolescents in metabolic syndrome
Luzzi, A., Briata, I. M., Napoli, I. D., Giugliano, S., Sabatino, A. D., Rescigno, M., & Cena, H. (2024) · Clinical Nutrition · doi:10.1016/j.clnu.2024.04.032 · Cited by 42
Comparative evaluation of soil DNA extraction kits for long read metagenomic sequencing
Child, H. T., Wierzbicki, L., Joslin, G. R., & Tennant, R. (2024) · Access Microbiology · doi:10.1099/acmi.0.000868.v3 · Cited by 40
ASaiM: a Galaxy-based framework to analyze microbiota data
Batut, B., Gravouil, K., Defois, C., Hiltemann, S., Brugère, J., Peyretaillade, É., & Peyret, P. (2018) · GigaScience · doi:10.1093/gigascience/giy057 · Cited by 36
Boscaini, S., Cabrera‐Rubio, R., Nychyk, O., Speakman, J. R., Cryan, J. F., Cotter, P. D., & Nilaweera, K. N. (2020) · Physiological Reports · doi:10.14814/phy2.14523 · Cited by 37
Molina, M. A., Andralojc, K. M., Huynen, M. A., Leenders, W. P. J., & Melchers, W. J. G. (2022) · npj Biofilms and Microbiomes · doi:10.1038/s41522-022-00336-6 · Cited by 36
Gut Microbial Metabolism and Nonalcoholic Fatty Liver Disease
Sharpton, S. R., Yong, G. J. M., Terrault, N. A., & Lynch, S. V. (2018) · Hepatology Communications · doi:10.1002/hep4.1284 · Cited by 37
Microbial community structure and function in paddy soil as affected by water-saving irrigation mode
Wu, J., Ma, C., & Li, F. (2022) · European Journal of Soil Biology · doi:10.1016/j.ejsobi.2022.103450 · Cited by 34
Lathe, R., Schultek, N. M., Balin, B. J., Ehrlich, G. D., Alberi, L. A., Perry, G. L., Breitschwerdt, E. B., Corry, D. B., Doty, R. L., Rissman, R. A., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13076 · Cited by 39
Role of microbiome in autoimmune liver diseases
Schneider, K. M., Kummen, M., Trivedi, P. J., & Hov, J. R. (2023) · Hepatology · doi:10.1097/hep.0000000000000506 · Cited by 38
Lee, Y., Cappellato, M., & Camillo, B. D. (2022) · GigaScience · doi:10.1093/gigascience/giad083 · Cited by 36
Liu, Y., Ghaffari, M. H., Ma, T., & Tu, Y. (2024) · aBIOTECH · doi:10.1007/s42994-024-00178-0 · Cited by 35
The gut microbiota in thrombosis
Khuu, M. P., Paeslack, N., Dremova, O., Benakis, C., Kiouptsi, K., & Reinhardt, C. (2024) · Nature Reviews Cardiology · doi:10.1038/s41569-024-01070-6 · Cited by 36
Kim, H., Nelson, P., Nzabarushimana, É., Shen, J., Jensen, J., Bhosle, A., Li, C., Lee, N., Everett, C., Berdy, B. M., et al. (2025) · Nature Metabolism · doi:10.1038/s42255-025-01318-6 · Cited by 39
Microbiota: Overview and Implication in Immunotherapy-Based Cancer Treatments
Brandi, G., & Frega, G. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20112699 · Cited by 36
Recent advances and health implications of dietary fasting regimens on the gut microbiome
Mohr, A. E., Gumpricht, E., Sears, D. D., & Sweazea, K. L. (2021) · American Journal of Physiology-Gastrointestinal and Liver Physiology · doi:10.1152/ajpgi.00475.2020 · Cited by 35
The Application of High-Throughput Technologies for the Study of Microbiome and Cancer
Wei, L., Cheong, I. H., Yang, G., Li, X. G., Kozlakidis, Z., Ding, L., Liu, N. N., & Wang, H. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.699793 · Cited by 38
Dynamic of microbial community in simultaneous nitrification and denitrification process: A review
Vu, H. V., Ngo, M. T. T., Itayama, T., Minh, N. B., Vo, T.-D.-H., Vo, T., Le, V., You, S.-J., Nguyen, P.-T., & Bui, X. (2023) · Bioresource Technology Reports · doi:10.1016/j.biteb.2023.101415 · Cited by 34
Maman, L. G., Palizban, F., Atanaki, F. F., Elmi, N., Ariaeenejad, S., Ghaffari, M. R., Salekdeh, G. H., & Kavousi, K. (2020) · Scientific Reports · doi:10.1038/s41598-020-61942-y · Cited by 38
SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery
Hu‐Lieskovan, S., Bhaumik, S., Dhodapkar, K. M., Grivel, J., Gupta, S. V., Hanks, B. A., Janetzki, S., Kleen, T. O., Koguchi, Y., Lund, A. W., et al. (2020) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-000705 · Cited by 35
Functional genome-centric view of the CO-driven anaerobic microbiome
Duan, H., He, P., Shao, L., & Lü, F. (2021) · The ISME Journal · doi:10.1038/s41396-021-00983-1 · Cited by 34
A Comparative Analysis on the Structure and Function of the Panax notoginseng Rhizosphere Microbiome
Kui, L., Chen, B., Chen, J., Sharifi, R., Dong, Y., Zhang, Z., & Miao, J. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.673512 · Cited by 34
Yang, J., Yuan, Y., Monson, M. S., Wang, P., Mu, F., Zhang, Q., Wei, N., Zhang, K., Wang, Y., Leng, L., et al. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.815538 · Cited by 35
Brandt, C., Bongcam‐Rudloff, E., & Müller, B. (2020) · Applied Sciences · doi:10.3390/app10217518 · Cited by 34
Investigating the Ocular Surface Microbiome: What Can It Tell Us?
Peter, V. G., Morandi, S. C., Herzog, E. L., Zinkernagel, M. S., & Zysset‐Burri, D. C. (2023) · Clinical ophthalmology · doi:10.2147/opth.s359304 · Cited by 36
Carbo, E., Mourik, K., Boers, S. A., Munnink, B. O., Nieuwenhuijse, D., Jonges, M., Welkers, M., Matamoros, S., Slooten, J. V. H. T., Kraakman, M., et al. (2023) · European Journal of Clinical Microbiology and Infectious Diseases · doi:10.1007/s10096-023-04590-0 · Cited by 35
Autoimmune diseases exhibit shared alterations in the gut microbiota
Wang, T., Sternes, P. R., Guo, X.-K., Zhao, H., Xu, C., & Xu, H. (2023) · Rheumatology · doi:10.1093/rheumatology/kead364 · Cited by 40
Interplay Between Antimicrobial Resistance and Global Environmental Change
Zambrano, M. M. (2023) · Annual Review of Genetics · doi:10.1146/annurev-genet-022123-113904 · Cited by 34
Extremozymes: Challenges and opportunities on the road to novel enzymes production
Salas-Bruggink, D. I. J., Martín, J. S.-S., Leiva, G., & Blamey, J. M. (2024) · Process Biochemistry · doi:10.1016/j.procbio.2024.04.035 · Cited by 33
Clinical applications of metagenomics next-generation sequencing in infectious diseases
Liu, Y., & Ma, Y. (2024) · Journal of Zhejiang University SCIENCE B · doi:10.1631/jzus.b2300029 · Cited by 37
Lappan, R. J., Chown, S. L., French, M., Perlaza-Jiménez, L., Maćešić, N., Davis, M., Brown, R. R., Cheng, A., Clasen, T., Conlan, L., et al. (2024) · Environment International · doi:10.1016/j.envint.2024.109046 · Cited by 38
Multi‐Omics Signatures of Periodontitis and Periodontal Therapy on the Oral and Gut Microbiome
Baima, G., Dabdoub, S. M., Thumbigere‐Math, V., Ribaldone, D. G., Caviglia, G. P., Tenori, L., Fantato, L., Vignoli, A., Romandini, M., Ferrocino, I., et al. (2025) · Journal of Periodontal Research · doi:10.1111/jre.70055 · Cited by 34
Applying Genome-Resolved Metagenomics to Deconvolute the Halophilic Microbiome
Uritskiy, G., & DiRuggiero, J. (2019) · Genes · doi:10.3390/genes10030220 · Cited by 33
Li, C., Chng, K. R., Kwah, J. S., Av‐Shalom, T. V., Tucker‐Kellogg, L., & Nagarajan, N. (2019) · Microbiome · doi:10.1186/s40168-019-0729-z · Cited by 32
Current Status and Potential Applications of Underexplored Prokaryotes
Goh, K., Shahar, S., Chan, K.-G., Chong, C., Amran, S., Sani, M. H., Zakaria, I. I., & Kahar, U. M. (2019) · Microorganisms · doi:10.3390/microorganisms7100468 · Cited by 29
High-resolution sweep metagenomics using fast probabilistic inference
Mäklin, T., Kallonen, T., David, S., Boinett, C. J., Pascoe, B., Méric, G., Aanensen, D. M., Feil, E. J., Baker, S. G., Parkhill, J., et al. (2021) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15639.2 · Cited by 31
Constantinou, A., Kanti, V., Polak‐Witka, K., Blume‐Peytavi, U., Spyrou, G. M., & Vogt, A. (2021) · Biomedicines · doi:10.3390/biomedicines9030236 · Cited by 37
Pre-Cambrian roots of novel Antarctic cryptoendolithic bacterial lineages
Albanese, D., Coleine, C., Rota‐Stabelli, O., Onofri, S., Tringe, S. G., Stajich, J., Selbmann, L., & Donati, C. (2021) · Microbiome · doi:10.1186/s40168-021-01021-0 · Cited by 33
Modelling spatial patterns in host‐associated microbial communities
Ruuskanen, M. O., Sommeria‐Klein, G., Havulinna, A. S., Niiranen, T., & Lahti, L. (2021) · Environmental Microbiology · doi:10.1111/1462-2920.15462 · Cited by 33
dos Santos, D. G., Coelho, C. C. D. S., Ferreira, A. B. R., & Freitas‐Silva, O. (2021) · Microorganisms · doi:10.3390/microorganisms9040858 · Cited by 33
Papaiakovou, M., Littlewood, D. T. J., Doyle, S. R., Gasser, R. B., & Cantacessi, C. (2022) · Parasites & Vectors · doi:10.1186/s13071-022-05225-7 · Cited by 33
Benchmarking DNA isolation methods for marine metagenomics
Demkina, A., Slonova, D., Mamontov, V., Konovalova, O., Yurikova, D., Rogozhin, V., Belova, V., Korostin, D. O., Sutormin, D. A., Severinov, K., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-48804-z · Cited by 39
Baudry, L., Foutel-Rodier, T., Thierry, A., Koszul, R., & Marbouty, M. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00753 · Cited by 32
Detecting Protein-Protein Interaction Based on Protein Fragment Complementation Assay
Wang, T., Yang, N., Liang, C., Xu, H., An, Y., Xiao, S., Zheng, M., Liu, L., Wang, G., & Nie, L. (2020) · Current Protein and Peptide Science · doi:10.2174/1389203721666200213102829 · Cited by 32
Identification of Natural CRISPR Systems and Targets in the Human Microbiome
Münch, P. C., Franzosa, E. A., Stecher, B., McHardy, A. C., & Huttenhower, C. (2020) · Cell Host & Microbe · doi:10.1016/j.chom.2020.10.010 · Cited by 31
High-resolution sweep metagenomics using fast probabilistic inference
Mäklin, T., Kallonen, T., David, S., Boinett, C. J., Pascoe, B., Méric, G., Aanensen, D. M., Feil, E. J., Baker, S. G., Parkhill, J., et al. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15639.1 · Cited by 31
Casimiro‐Soriguer, C. S., Loucera, C., Peña-Chilet, M., & Dopazo, J. (2022) · Scientific Reports · doi:10.1038/s41598-021-04182-y · Cited by 38
The Cystic Fibrosis Upper and Lower Airway Metagenome
Pienkowska, K., Pust, M., Gessner, M., Gaedcke, S., Thavarasa, A., Rosenboom, I., Losada, P. M., Minso, R., Arnold, C., Hedtfeld, S., et al. (2023) · Microbiology Spectrum · doi:10.1128/spectrum.03633-22 · Cited by 31
A Renal Clinician's Guide to the Gut Microbiota
Snelson, M., Biruete, A., McFarlane, C. M., & Campbell, K. L. (2020) · Journal of Renal Nutrition · doi:10.1053/j.jrn.2019.11.002 · Cited by 32
Seven facts and five initiatives for gut microbiome research
Li, D., Gao, C., Zhang, F., Yang, R., Lan, C., Ma, Y., & Wang, J. (2020) · Protein & Cell · doi:10.1007/s13238-020-00697-8 · Cited by 36
Community‐scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing
Cerk, K., Ugalde‐Salas, P., Nedjad, C. G., Lecomte, M., Muller, C., Sherman, D. J., Hildebrand, F., Labarthe, S., & Frioux, C. (2024) · Microbial Biotechnology · doi:10.1111/1751-7915.14396 · Cited by 39
Exploring Cereal Metagenomics: Unravelling Microbial Communities for Improved Food Security
Masenya, K., Manganyi, M. C., & Dikobe, T. B. (2024) · Microorganisms · doi:10.3390/microorganisms12030510 · Cited by 36
Deng, J., Kang, D., Zhang, Y., Chen, B., Xia, C., Chen, Y. Z., & Peng, Y. (2024) · Water Research · doi:10.1016/j.watres.2024.122420 · Cited by 31
Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions
Sakagianni, A., Koufopoulou, C., Koufopoulos, P., Kalantzi, S., Theodorakis, N., Nikolaou, M., Paxinou, E., Kalles, D., Verykios, V. S., Myrianthefs, P. M., et al. (2024) · Antibiotics · doi:10.3390/antibiotics13111052 · Cited by 37
Absolute quantification of prokaryotes in the microbiome by 16S rRNA qPCR or ddPCR
Doyle, B., Reynolds, G. Z. M., Dvorak, M., Maghini, D. G., Natarajan, A., & Bhatt, A. S. (2025) · Nature Protocols · doi:10.1038/s41596-025-01165-5 · Cited by 32
Caminero, A., Tropini, C., Valles‐Colomer, M., Shung, D. L., Gibbons, S. M., Surette, M. G., Sokol, H., Tomeo, N. J., Alenghat, T., Awoniyi, M., et al. (2025) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-025-01100-9 · Cited by 38
Microbiota, a Third Player in the Host–Plasmodium Affair
Mukherjee, D., Chora, Â. F., & Mota, M. M. (2019) · Trends in Parasitology · doi:10.1016/j.pt.2019.11.001 · Cited by 31
Engineering the Microbiome to Prevent Adverse Events: Challenges and Opportunities
Khan, S. M., Hauptman, R., & Kelly, L. (2020) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-031620-031509 · Cited by 30
Hu, W., Pan, J., Wang, B., Guo, J., Li, M., & Xu, M. (2020) · Environmental Microbiology · doi:10.1111/1462-2920.15349 · Cited by 33
Jones, C. B., White, J. R., Ernst, S. E., Sfanos, K. S., & Peiffer, L. B. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.799615 · Cited by 31
Clinical Aspergillus Signatures in COPD and Bronchiectasis
Tiew, P. Y., Thng, K. X., & Chotirmall, S. H. (2022) · Journal of Fungi · doi:10.3390/jof8050480 · Cited by 33
Stojanović, Z., Gonçalves-Carvalho, F., Marín, A., Abad, J., Domínguez, J., Latorre, I., Lacoma, A., & Prat, C. (2022) · ERJ Open Research · doi:10.1183/23120541.00113-2022 · Cited by 32
Skin Microbiota and the Cosmetic Industry
Carvalho, M. J., Oliveira, A. L. S., Pedrosa, S. S., Pintado, M., Pinto-Ribeiro, I., & Madureira, A. R. (2022) · Microbial Ecology · doi:10.1007/s00248-022-02070-0 · Cited by 34
Ecosystem-specific microbiota and microbiome databases in the era of big data
Lobanov, V. P., Gobet, A., & Joyce, A. (2022) · Environmental Microbiome · doi:10.1186/s40793-022-00433-1 · Cited by 33
Hickl, O., Queirós, P., Wilmes, P., May, P., & Heintz‐Buschart, A. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac431 · Cited by 30
Song, X., Li, Y., Stirling, E., Zhao, K., Wang, B., Zhu, Y., Luo, Y., Xu, J. M., & Ma, B. (2022) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqac080 · Cited by 31
The link between increased Desulfovibrio and disease severity in Parkinson’s disease
Nie, S., Jing, Z., Wang, J., Deng, Y., Zhang, Y., Ye, Z., & Ge, Y. (2023) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-023-12489-1 · Cited by 32
Site-specialization of human oral Gemella species
Torres‐Morales, J., Welch, J. L. M., Dewhirst, F. E., & Borisy, G. G. (2023) · Journal of Oral Microbiology · doi:10.1080/20002297.2023.2225261 · Cited by 31
Probing the functional significance of wild animal microbiomes using omics data
Worsley, S. F., Videvall, E., Harrison, X. A., Björk, J. R., Mazel, F., & Wanelik, K. M. (2024) · Functional Ecology · doi:10.1111/1365-2435.14650 · Cited by 34
Long and Accurate: How HiFi Sequencing is Transforming Genomics
Wang, B., Jia, P., Gao, S., Zhao, H., Zheng, G., Xu, L., & Ye, K. (2025) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf003 · Cited by 35
Thorn, C. E., Bergesch, C., Joyce, A., Sambrano, G. E., Mcdonnell, K., Brennan, F., Heyer, R., Benndorf, D., & Abram, F. (2018) · Molecular Ecology Resources · doi:10.1111/1755-0998.12979 · Cited by 30
High-throughput DNA sequencing technologies for water and wastewater analysis
Chan, A. W. Y., Naphtali, J., & Schellhorn, H. E. (2019) · Science Progress · doi:10.1177/0036850419881855 · Cited by 32
Applying artificial intelligence in the microbiome for gastrointestinal diseases: A review
Zeng, T., Yu, X., & Chen, Z. (2021) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.15503 · Cited by 30
Mthethwa, N. P., Amoah, I. D., Reddy, P., Bux, F., & Kumari, S. (2021) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2021.106269 · Cited by 30
Metadata
- Authors
- Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J., & Segata, N.
- Affiliations
- University of Warwick (GB)
- University of Aberdeen (GB)
- Ontario Institute for Cancer Research (CA)
- University of Birmingham (GB)
- University of Trento (IT)
- Corresponding author
- Segata, Nicola
- Venue
- Nature Biotechnology
- Published
- 2017-09
- Volume
- 35
- Issue
- 9
- Pages
- 833-844
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W2754579667
- Cited by
- 2,202
- References
- 135
- Citations by year
- 2026: 414 · 2025: 319 · 2024: 292 · 2023: 240 · 2022: 265 · 2021: 231 · 2020: 215 · 2019: 141 · 2018: 72 · 2017: 8
- FWCI
- 11.17
- Type
- journal article
- Language
- en
- Update notices
- erratum (2017-12-01)
- Open access
- green
- hdl.handle.net
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Aberdeen University Research Archive (Aberdeen University) (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, other-oa)
- Iris (University of Trento) (submittedVersion, other-oa)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- Warwick Research Archive Portal (University of Warwick) (submittedVersion)
- Funders
- Rural and Environment Science and Analytical Services Division
- Government of Ontario
- European Commission (618833)
- University of Aberdeen
- Ministero dell’Istruzione, dell’Università e della Ricerca (FIR RBFR13EWWI, RBFR13EWWI)
- Fondazione Cassa Di Risparmio Di Trento E Rovereto (2013.0239, Rif.Int.2013.0239)
- Medical Research Council (MR/M501621/1, MR/L015080/1, L015080, MR/M50161X/1)
- Biotechnology and Biological Sciences Research Council (BB/L027801/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Physical Sciences › Environmental Science › Ecology › Environmental DNA in Biodiversity Studies
- Keywords
- shotgun metagenomics, microbial communities, metagenome assembly, unculturable microorganisms, strain characterization, microbial function, therapeutic discovery, microbial biotechnology, environmental microbiology
- MeSH terms
- Animals, Humans, Biomedical Research, Metagenomics, High-Throughput Nucleotide Sequencing, Microbiota
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
