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.
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
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?
Sequence-based methods 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.
“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]
Sequencing depth is essential for deciphering microbial composition of a sample, but deeper sequencing comes with higher costs and analytical complexity.
“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]
Shotgun metagenomics has not experienced widespread use because of limitations including issues related to sequencing depth and partial incompleteness of available reference databases.
“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]
Shotgun metagenomics is replacing 16S rRNA gene sequencing and provides higher taxonomic resolution and insight into functions encoded by the microbial community.
“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]
The visibility of species with particularly high genetic diversity within individual communities remains limited; in most cases, only the most dominant strain of each taxon per community is currently analyzable.
“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
Unlike 16S ribosomal RNA sequencing, shotgun metagenomics sequences permit a parallel assessment of all microbial kingdoms (bacterial, fungal, viral) in a given sample.
“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.
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 25,691 · see citing works
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 · see citing works
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 · see citing works
Self-citationA 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 · see citing works
Li, D., Liu, C.-M., Luo, R., Sadakane, K., & Lam, T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv033 · Cited by 9,781 · see citing works
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 · see citing works
Richness of human gut microbiome correlates with metabolic markers
Le Chatelier, E., Nielsen, T., Qin, J., Prifti, E., Hildebrand, F., Falony, G., Almeida, M., Arumugam, M., Batto, J.-M., Kennedy, S., et al. (2013) · Nature · doi:10.1038/nature12506 · Cited by 4,662 · see citing works
Kraken: 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 · see citing works
The COG database: an updated version includes eukaryotes
Tatusov, R. L., Fedorova, N. D., Jackson, J. D., Jacobs, A. R., Kiryutin, B., Koonin, E. V., Krylov, D. M., Mazumder, R., Mekhedov, S., Nikolskaya, A. N., et al. (2003) · BMC Bioinformatics · doi:10.1186/1471-2105-4-41 · Cited by 4,410 · see citing works
Environmental genome shotgun sequencing of the sargasso sea
Venter, J. C., Remington, K., Heidelberg, J. F., Halpern, A. L., Rusch, D., Eisen, J. A., Wu, D., Paulsen, I., Nelson, K. E., Nelson, W., et al. (2004) · Science · doi:10.1126/science.1093857 · Cited by 4,376 · see citing works
ABySS: A parallel assembler for short read sequence dataPreprint
Simpson, J. T., Wong, K., Jackman, S. D., Schein, J. E., Jones, S. J. M., & Birol, İ. (2009) · Genome Research · doi:10.1101/gr.089532.108 · Cited by 3,583 · see citing works
Self-citationReagent and laboratory contamination can critically impact sequence-based microbiome analyses
Salter, S. J., Cox, M. J., Turek, E. M., Calus, S. T., Cookson, W. O., Moffatt, M. F., Turner, P., Parkhill, J., Loman, N. J., & Walker, A. W. (2014) · BMC Biology · doi:10.1186/s12915-014-0087-z · Cited by 3,463 · see citing works
Self-citationThe Treatment-Naive Microbiome in New-Onset Crohn’s Disease
Gevers, D., Kugathasan, S., Denson, L. A., Vázquez-Baeza, Y., Van Treuren, W., Ren, B., Schwager, E., Knights, D., Song, S. J., Yassour, M., et al. (2014) · Cell Host & Microbe · doi:10.1016/j.chom.2014.02.005 · Cited by 3,220 · see citing works
Gut metagenome in European women with normal, impaired and diabetic glucose control
Karlsson, F. H., Tremaroli, V., Nookaew, I., Bergström, G., Behre, C. J., Fagerberg, B., Nielsen, J., & Bäckhed, F. (2013) · Nature · doi:10.1038/nature12198 · Cited by 3,115 · see citing works
Data, information, knowledge and principle: back to metabolism in KEGG
Kanehisa, M. I., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., & Tanabe, M. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1076 · Cited by 3,103 · see citing works
Peng, Y., Leung, H. C. M., Yiu, S., & Chin, F. Y. L. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts174 · Cited by 2,997 · see citing works
Structure and function of the global ocean microbiome
Sunagawa, S., Coelho, L. P., Chaffron, S., Kultima, J. R., Labadie, K., Salazar, G., Djahanschiri, B., Zeller, G., Mende, D. R., Alberti, A., et al. (2015) · Science · doi:10.1126/science.1261359 · Cited by 2,945 · see citing works
Complete nitrification by Nitrospira bacteria
Daims, H., Lebedeva, E. V., Pjevac, P., Han, P., Herbold, C. W., Albertsen, M., Jehmlich, N., Palatinszky, M., Vierheilig, J., Булаев, А. Г., et al. (2015) · Nature · doi:10.1038/nature16461 · Cited by 2,732 · see citing works
Binning 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 2,707 · see citing works
Self-citationInferring Correlation Networks from Genomic Survey Data
Friedman, J., & Alm, E. J. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002687 · Cited by 2,688 · see citing works
Differential abundance analysis for microbial marker-gene surveys
Paulson, J. N., Stine, O. C., Bravo, H. C., & Pop, M. (2013) · Nature Methods · doi:10.1038/nmeth.2658 · Cited by 2,437 · see citing works
Community structure and metabolism through reconstruction of microbial genomes from the environment
Tyson, G. W., Chapman, J., Hugenholtz, P., Allen, E. E., Ram, R. J., Richardson, P. M., Solovyev, V. V., Rubin, E. M., Rokhsar, D. S., & Banfield, J. F. (2004) · Nature · doi:10.1038/nature02340 · Cited by 2,417 · see citing works
Population-level analysis of gut microbiome variation
Falony, G., Joossens, M., Vieira-Silva, S., Wang, J., Darzi, Y., Faust, K., Kurilshikov, A., Bonder, M. J., Valles-Colomer, M., Vandeputte, D., et al. (2016) · Science · doi:10.1126/science.aad3503 · Cited by 2,400 · see citing works
Insights into the phylogeny and coding potential of microbial dark matter
Rinke, C., Schwientek, P., Sczyrba, A., Ivanova, N. N., Anderson, I. J., Cheng, J.-F., Darling, A., Malfatti, S., Swan, B. K., Gies, E. A., et al. (2013) · Nature · doi:10.1038/nature12352 · Cited by 2,394 · see citing works
MetaPhlAn2 for enhanced metagenomic taxonomic profiling
Truong, D. T., Franzosa, E. A., Tickle, T. L., Scholz, M., Weingart, G., Pasolli, E., Tett, A., Huttenhower, C., & Segata, N. (2015) · Nature Methods · doi:10.1038/nmeth.3589 · Cited by 2,185 · see citing works
Self-citationAlterations of the human gut microbiome in liver cirrhosis
Qin, N., Yang, F., Li, A., Prifti, E., Chen, Y., Shao, L., Guo, J., Le Chatelier, E., Yao, J., Wu, L., et al. (2014) · Nature · doi:10.1038/nature13568 · Cited by 2,177 · see citing works
An integrated catalog of reference genes in the human gut microbiome
MetaHIT Consortium, Li, J., Jia, H., Cai, X., Zhong, H., Feng, Q., Sunagawa, S., Arumugam, M., Kultima, J. R., Prifti, E., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2942 · Cited by 2,164 · see citing works
Interactive metagenomic visualization in a Web browser
Ondov, B., Bergman, N. H., & Phillippy, A. M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-385 · Cited by 2,122 · see citing works
A new view of the tree of life
Hug, L. A., Baker, B. J., Anantharaman, K., Brown, C. T., Probst, A. J., Castelle, C. J., Butterfield, C. N., Hernsdorf, A. W., Amano, Y., Ise, K., et al. (2016) · Nature Microbiology · doi:10.1038/nmicrobiol.2016.48 · Cited by 2,039 · see citing works
Complete nitrification by a single microorganism
van Kessel, M. A. H. J., Speth, D. R., Albertsen, M., Nielsen, P. H., den Camp, H. J. M. O., Kartal, B., Jetten, M. S. M., & Lücker, S. (2015) · Nature · doi:10.1038/nature16459 · Cited by 1,911 · see citing works
Anvi’o: an advanced analysis and visualization platform for ‘omics data
Eren, A. M., Esen, Ö. C., Quince, C., Vineis, J. H., Morrison, H. G., Sogin, M. L., & Delmont, T. O. (2015) · PeerJ · doi:10.7717/peerj.1319 · Cited by 1,823 · see citing works
Self-citationAb initio gene identification in metagenomic sequences
Zhu, W., Lomsadze, A., & Borodovsky, M. Y. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq275 · Cited by 1,793 · see citing works
Metagenomic microbial community profiling using unique clade-specific marker genes
Segata, N., Waldron, L., Ballarini, A., Narasimhan, V., Jousson, O., & Huttenhower, C. (2012) · Nature Methods · doi:10.1038/nmeth.2066 · Cited by 1,776 · see citing works
Self-citationActivities at the Universal Protein Resource (UniProt)
The UniProt Consortium (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1140 · Cited by 1,736 · see citing works
Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples
White, J. R., Nagarajan, N., & Pop, M. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000352 · Cited by 1,581 · see citing works
Integrative analysis of environmental sequences using MEGAN4Preprint
Huson, D. H., Mitra, S., Ruscheweyh, H., Weber, N., & Schuster, S. C. (2011) · Genome Research · doi:10.1101/gr.120618.111 · Cited by 1,534 · see citing works
Microbial co-occurrence relationships in the human microbiome
Faust, K., Sathirapongsasuti, J. F., Izard, J., Segata, N., Gevers, D., Raes, J., & Huttenhower, C. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002606 · Cited by 1,527 · see citing works
Self-citationPotential of fecal microbiota for early-stage detection of colorectal cancer
Zeller, G., Tap, J., Voigt, A. Y., Sunagawa, S., Kultima, J. R., Costea, P. I., Amiot, A., Böhm, J., Brunetti, F., Habermann, N., et al. (2014) · Molecular Systems Biology · doi:10.15252/msb.20145645 · Cited by 1,375 · see citing works
An Eulerian path approach to DNA fragment assembly
Pevzner, P. A., Tang, H., & Waterman, M. S. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.171285098 · Cited by 1,352 · see citing works
Disease-Specific Alterations in the Enteric Virome in Inflammatory Bowel Disease
Norman, J. M., Handley, S. A., Baldridge, M. T., Droit, L., Liu, C. Y., Keller, B. C., Kambal, A., Monaco, C. L., Zhao, G., Fleshner, P., et al. (2015) · Cell · doi:10.1016/j.cell.2015.01.002 · Cited by 1,285 · see citing works
Albertsen, M., Hugenholtz, P., Skarshewski, A., Nielsen, K. L., Tyson, G. W., & Nielsen, P. H. (2013) · Nature Biotechnology · doi:10.1038/nbt.2579 · Cited by 1,253 · see citing works
ARDB--Antibiotic Resistance Genes Database
Liu, B., & Pop, M. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn656 · Cited by 1,252 · see citing works
A complete bacterial genome assembled de novo using only nanopore sequencing data
Loman, N., Quick, J., & Simpson, J. (2015) · Nature Methods · doi:10.1038/nmeth.3444 · Cited by 1,248 · see citing works
Self-citationUnusual biology across a group comprising more than 15% of domain Bacteria
Brown, C. T., Hug, L. A., Thomas, B. C., Sharon, I., Castelle, C. J., Singh, A., Wilkins, M. J., Wrighton, K. C., Williams, K. H., & Banfield, J. F. (2015) · Nature · doi:10.1038/nature14486 · Cited by 1,216 · see citing works
MetaHIT Consortium, Nielsen, H. B., Almeida, M., Juncker, A. S., Rasmussen, S., Li, J., Sunagawa, S., Plichta, D. R., Gautier, L., Pedersen, A. G., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2939 · Cited by 1,184 · see citing works
Stewart, E. J. (2012) · Journal of Bacteriology · doi:10.1128/jb.00345-12 · Cited by 1,154 · see citing works
Biogeography and individuality shape function in the human skin metagenome
Program, N. C. S., Oh, J., Byrd, A. L., Deming, C., Conlan, S., Kong, H. H., & Segre, J. A. (2014) · Nature · doi:10.1038/nature13786 · Cited by 1,148 · see citing works
Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome
Abubucker, S., Segata, N., Goll, J. B., Schubert, A. M., Izard, J., Cantarel, B. L., Rodriguez-Mueller, B., Zucker, J., Thiagarajan, M., Henrissat, B., et al. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002358 · Cited by 1,050 · see citing works
Self-citationMicrobial community profiling for human microbiome projects: Tools, techniques, and challengesPreprint
Hamady, M., & Knight, R. D. (2009) · Genome Research · doi:10.1101/gr.085464.108 · Cited by 998 · see citing works
Olfactory exposure to males, including men, causes stress and related analgesia in rodents
Sorge, R. E., Martin, L. J., Isbester, K. A., Sotocinal, S. G., Rosen, S., Tuttle, A. H., Wieskopf, J. S., Acland, E. L., Dokova, A., Kadoura, B., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2935 · Cited by 905 · see citing works
Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software
Sczyrba, A., Hofmann, P., Belmann, P., Koslicki, D., Janssen, S., Dröge, J., Gregor, I., Majda, S., Fiedler, J., Dahms, E., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4458 · Cited by 873 · see citing works
Self-citationMicrobial strain-level population structure and genetic diversity from metagenomes
Truong, D. T., Tett, A., Pasolli, E., Huttenhower, C., & Segata, N. (2017) · Genome Research · doi:10.1101/gr.216242.116 · Cited by 839 · see citing works
Self-citationThe effect of host genetics on the gut microbiome
Bonder, M. J., Kurilshikov, A., Tigchelaar, E. F., Mujagic, Z., Imhann, F., Vila, A. V., Deelen, P., Vatanen, T., Schirmer, M., Smeekens, S. P., et al. (2016) · Nature Genetics · doi:10.1038/ng.3663 · Cited by 819 · see citing works
Compact graphical representation of phylogenetic data and metadata with GraPhlAn
Asnicar, F., Weingart, G., Tickle, T. L., Huttenhower, C., & Segata, N. (2015) · PeerJ · doi:10.7717/peerj.1029 · Cited by 806 · see citing works
Self-citationYilmaz, P., Kottmann, R., Field, D., Knight, R. D., Cole, J. R., Amaral‐Zettler, L., Gilbert, J. A., Karsch‐Mizrachi, I., Johnston, A., Cochrane, G., et al. (2011) · Nature Biotechnology · doi:10.1038/nbt.1823 · Cited by 802 · see citing works
Goodrich, J. K., Di Rienzi, S. C., Poole, A. C., Koren, O., Walters, W. A., Caporaso, J. G., Knight, R., & Ley, R. E. (2014) · Cell · doi:10.1016/j.cell.2014.06.037 · Cited by 714 · see citing works
Donia, M. S., Cimermancic, P., Schulze, C. J., Wieland Brown, L. C., Martin, J., Mitreva, M., Clardy, J., Linington, R. G., & Fischbach, M. A. (2014) · Cell · doi:10.1016/j.cell.2014.08.032 · Cited by 705 · see citing works
A Catalog of Reference Genomes from the Human Microbiome
The Human Microbiome Jumpstart Reference Strains Consortium, Nelson, K. E., Weinstock, G. M., Highlander, S. K., Worley, K. C., Creasy, H. H., Wortman, J. R., Rusch, D. B., Mitreva, M., Sodergren, E., et al. (2010) · Science · doi:10.1126/science.1183605 · Cited by 702 · see citing works
De novo assembly and genotyping of variants using colored de Bruijn graphs
Iqbal, Z., Cáccamo, M., Turner, I., Flicek, P., & McVean, G. (2012) · Nature Genetics · doi:10.1038/ng.1028 · Cited by 695 · see citing works
Inexpensive Multiplexed Library Preparation for Megabase-Sized Genomes
Baym, M., Kryazhimskiy, S., Lieberman, T. D., Chung, H., Desai, M. M., & Kishony, R. (2015) · PLOS ONE · doi:10.1371/journal.pone.0128036 · Cited by 685 · see citing works
CDD: a database of conserved domain alignments with links to domain three-dimensional structure
Marchler‐Bauer, A. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.281 · Cited by 678 · see citing works
Namiki, T., Hachiya, T., Tanaka, H. L., & Sakakibara, Y. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks678 · Cited by 677 · see citing works
Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights
Pasolli, E., Truong, D. T., Malik, F., Waldron, L., & Segata, N. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004977 · Cited by 659 · see citing works
Self-citationAssemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species
Bradnam, K. R., Fass, J., Alexandrov, A., Baranay, P., Bechner, M., Birol, İ., Boisvert, S., Chapman, J., Chapuis, G., Chikhi, R., et al. (2013) · GigaScience · doi:10.1186/2047-217x-2-10 · Cited by 658 · see citing works
Self-citationGibson, M. K., Forsberg, K. J., & Dantas, G. A. (2014) · The ISME Journal · doi:10.1038/ismej.2014.106 · Cited by 656 · see citing works
Gene and translation initiation site prediction in metagenomic sequences
Hyatt, D., LoCascio, P. F., Hauser, L., & Uberbacher, E. C. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts429 · Cited by 655 · see citing works
Interconnected microbiomes and resistomes in low-income human habitats
Pehrsson, E. C., Tsukayama, P., Patel, S., Mejía-Bautista, M., Sosa-Soto, G., Navarrete, K. M., Calderon, M., Cabrera, L., Hoyos-Arango, W., Bertoli, M. T., et al. (2016) · Nature · doi:10.1038/nature17672 · Cited by 607 · see citing works
Ray Meta: scalable de novo metagenome assembly and profiling
Boisvert, S., Raymond, F., Godzaridis, É., Laviolette, F., & Corbeil, J. (2012) · Genome biology · doi:10.1186/gb-2012-13-12-r122 · Cited by 593 · see citing works
Accurate phylogenetic classification of variable-length DNA fragments
Mchardy, A., Martín, H., Tsirigos, A., Hugenholtz, P., & Rigoutsos, I. (2007) · Nature Methods · doi:10.1038/nmeth976 · Cited by 552 · see citing works
Durable coexistence of donor and recipient strains after fecal microbiota transplantation
Li, S. S., Zhu, A., Beneš, V., Costea, P. I., Hercog, R., Hildebrand, F., Huerta‐Cepas, J., Nieuwdorp, M., Salojärvi, J., Voigt, A. Y., et al. (2016) · Science · doi:10.1126/science.aad8852 · Cited by 544 · see citing works
Community-wide analysis of microbial genome sequence signatures
Dick, G. J., Andersson, A. F., Baker, B. J., Simmons, S. L., Thomas, B. C., Yelton, A. P., & Banfield, J. F. (2009) · Genome biology · doi:10.1186/gb-2009-10-8-r85 · Cited by 537 · see citing works
Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models
Brady, A., & Salzberg, S. L. (2009) · Nature Methods · doi:10.1038/nmeth.1358 · Cited by 526 · see citing works
Metagenomic species profiling using universal phylogenetic marker genes
Sunagawa, S., Mende, D. R., Zeller, G., Izquierdo-Carrasco, F., Berger, S. A., Kultima, J. R., Coelho, L. P., Arumugam, M., Tap, J., Nielsen, H. B., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2693 · Cited by 517 · see citing works
A catalog of the mouse gut metagenome
Xiao, L., Feng, Q., Liang, S., Sonne, S. B., Xia, Z., Qiu, X., Li, X., Long, H., Zhang, J., Zhang, D., et al. (2015) · Nature Biotechnology · doi:10.1038/nbt.3353 · Cited by 514 · see citing works
Accessible, curated metagenomic data through ExperimentHub
Pasolli, E., Schiffer, L., Manghi, P., Renson, A., Obenchain, V., Truong, D. T., Beghini, F., Malik, F., Ramos, M., Dowd, J. B., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4468 · Cited by 514 · see citing works
Self-citationEvaluation of Methods for the Extraction and Purification of DNA from the Human Microbiome
Yuan, S., Cohen, D. B., Ravel, J., Abdo, Z., & Forney, L. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033865 · Cited by 511 · see citing works
Sharon, I., Morowitz, M. J., Thomas, B. C., Costello, E. K., Relman, D. A., & Banfield, J. F. (2012) · Genome Research · doi:10.1101/gr.142315.112 · Cited by 481 · see citing works
Nayfach, S., Rodriguez-Mueller, B., Garud, N., & Pollard, K. S. (2016) · Genome Research · doi:10.1101/gr.201863.115 · Cited by 470 · see citing works
Compositional biases of bacterial genomes and evolutionary implications
Karlin, S., Mrázek, J., & Campbell, A. (1997) · Journal of Bacteriology · doi:10.1128/jb.179.12.3899-3913.1997 · Cited by 442 · see citing works
Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples
Korem, T., Zeevi, D., Suez, J., Weinberger, A., Avnit-Sagi, T., Lotan‐Pompan, M., Matot, E., Jona, G., Harmelin, A., Cohen, N., et al. (2015) · Science · doi:10.1126/science.aac4812 · Cited by 437 · see citing works
Meta-analyses of studies of the human microbiota
Lozupone, C. A., Stombaugh, J., Gonzalez, A., Ackermann, G., Wendel, D., Vázquez-Baeza, Y., Jansson, J. K., Gordon, J. I., & Knight, R. (2013) · Genome Research · doi:10.1101/gr.151803.112 · Cited by 424 · see citing works
D’Amore, R., Ijaz, U. Z., Schirmer, M., Kenny, J. G., Gregory, R., Darby, A. C., Shakya, M., Podar, M., Quince, C., & Hall, N. (2016) · BMC Genomics · doi:10.1186/s12864-015-2194-9 · Cited by 405 · see citing works
Self-citationNiche and host-associated functional signatures of the root surface microbiome
Ofek‐Lalzar, M., Sela, N., Goldman-Voronov, M., Green, S. J., Hadar, Y., & Minz, D. (2014) · Nature Communications · doi:10.1038/ncomms5950 · Cited by 403 · see citing works
Heterogeneity of the gut microbiome in mice: guidelines for optimizing experimental design
Laukens, D., Brinkman, B. M. N., Raes, J., Vos, M. D., & Vandenabeele, P. (2015) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuv036 · Cited by 378 · see citing works
Strain-level microbial epidemiology and population genomics from shotgun metagenomics
Scholz, M., Ward, D. V., Pasolli, E., Tolio, T., Zolfo, M., Asnicar, F., Truong, D. T., Tett, A., Morrow, A. L., & Segata, N. (2016) · Nature Methods · doi:10.1038/nmeth.3802 · Cited by 374 · see citing works
Self-citationLoman, N. J., Constantinidou, C., Christner, M., Rohde, H., Chan, J. Z.-M., Quick, J., Weir, J., Quince, C., Smith, G. P., Betley, J. R., et al. (2013) · JAMA · doi:10.1001/jama.2013.3231 · Cited by 339 · see citing works
Self-citationHigh-Specificity Targeted Functional Profiling in Microbial Communities with ShortBRED
Kaminski, J., Gibson, M. K., Franzosa, E. A., Segata, N., Dantas, G. A., & Huttenhower, C. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004557 · Cited by 334 · see citing works
Self-citationComputational meta'omics for microbial community studies
Segata, N., Boernigen, D., Tickle, T. L., Morgan, X. C., Garrett, W. S., & Huttenhower, C. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.22 · Cited by 327 · see citing works
Self-citationMeta-IDBA: a de Novo assembler for metagenomic data
Peng, Y., Leung, H. C. M., Yiu, S., & Chin, F. Y. L. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr216 · Cited by 326 · see citing works
Analysis, Optimization and Verification of Illumina-Generated 16S rRNA Gene Amplicon Surveys
Nelson, M. C., Morrison, H. G., Benjamino, J., Grim, S. L., & Graf, J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094249 · Cited by 325 · see citing works
Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data
Rosa, P. S. L., Brooks, J. P., Deych, E., Boone, E. L., Edwards, D., Wang, Q., Sodergren, E. J., Weinstock, G. M., & Shannon, W. D. (2012) · PLoS ONE · doi:10.1371/journal.pone.0052078 · Cited by 325 · see citing works
Kennedy, N. A., Walker, A. W., Berry, S. H., Duncan, S. H., Farquarson, F. M., Louis, P., Thomson, J., surname):, O. M. N. N. W. T. M. A. L. (. B., Satsangi, J. J., Flint, H. J., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0088982 · Cited by 311 · see citing works
Self-citationGroopM: an automated tool for the recovery of population genomes from related metagenomes
Imelfort, M., Parks, D. H., Woodcroft, B. J., Dennis, P. G., Hugenholtz, P., & Tyson, G. W. (2014) · PeerJ · doi:10.7717/peerj.603 · Cited by 310 · see citing works
A Quantitative Comparison of Single-Cell Whole Genome Amplification Methods
de Bourcy, C. F. A., Vlaminck, I. D., Kanbar, J. N., Wang, J., Gawad, C., & Quake, S. R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105585 · Cited by 305 · see citing works
Unlocking the potential of metagenomics through replicated experimental design
Knight, R. D., Jansson, J. K., Field, D., Fierer, N., Desai, N., Fuhrman, J. A., Hugenholtz, P., van der Lelie, D., Meyer, F., Stevens, R. L., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2235 · Cited by 278 · see citing works
Wesolowska-Andersen, A., Bahl, M. I., Carvalho, V., Kristiansen, K., Sicheritz-Pontén, T., Gupta, R., & Licht, T. R. (2014) · Microbiome · doi:10.1186/2049-2618-2-19 · Cited by 277 · see citing works
Tanner, M. A., Goebel, B. M., Dojka, M. A., & Pace, N. R. (1998) · Applied and Environmental Microbiology · doi:10.1128/aem.64.8.3110-3113.1998 · Cited by 270 · see citing works
ConStrains identifies microbial strains in metagenomic datasets
Luo, C., Knight, R. D., Siljander, H., Knip, M., Xavier, R. J., & Gevers, D. (2015) · Nature Biotechnology · doi:10.1038/nbt.3319 · Cited by 267 · see citing works
McCafferty, J., Mühlbauer, M., Gharaibeh, R. Z., Arthur, J. C., Perez-Chanona, E., Sha, W., Jobin, C., & Fodor, A. A. (2013) · The ISME Journal · doi:10.1038/ismej.2013.106 · Cited by 259 · see citing works
Scaling metagenome sequence assembly with probabilistic de Bruijn graphs
Jason, P., Hintze, A., Canino-Koning, R., Howe, A., Tiedje, J. M., & Brown, C. T. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1121464109 · Cited by 250 · see citing works
Phylogeny, culturing, and metagenomics of the human gut microbiota
Walker, A. W., Duncan, S. H., Louis, P., & Flint, H. J. (2014) · Trends in Microbiology · doi:10.1016/j.tim.2014.03.001 · Cited by 239 · see citing works
Self-citationThe Binning of Metagenomic Contigs for Microbial Physiology of Mixed Cultures
Strous, M., Kraft, B., Bisdorf, R., & Tegetmeyer, H. E. (2012) · Frontiers in Microbiology · doi:10.3389/fmicb.2012.00410 · Cited by 226 · see citing works
Quick, J., Quinlan, A. R., & Loman, N. J. (2014) · GigaScience · doi:10.1186/2047-217x-3-22 · Cited by 224 · see citing works
Self-citationSinha, R., Stanley, G., Gulati, G. S., Ezran, C., Travaglini, K. J., Wei, E., Chan, C. K. F., Nabhan, A. N., Su, T., Morganti, R. M., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/125724 · Cited by 209 · see citing works
Jones, M. B., Highlander, S. K., Anderson, E. L., Li, W., Dayrit, M., Klitgord, N., Fabani, M. M., Seguritan, V., Green, J., Pride, D. T., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1519288112 · Cited by 204 · see citing works
Direct sequencing of the human microbiome readily reveals community differences
Kuczynski, J., Costello, E. K., Nemergut, D. R., Zaneveld, J., Lauber, C. L., Knights, D., Koren, O., Fierer, N., Kelley, S. T., Ley, R. E., et al. (2010) · Genome biology · doi:10.1186/gb-2010-11-5-210 · Cited by 154 · see citing works
Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning
Cleary, B., Brito, I., Huang, K. H., Gevers, D., Shea, T., Young, S., & Alm, E. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3329 · Cited by 151 · see citing works
Exploring genome characteristics and sequence quality without a reference
Simpson, J. T. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu023 · Cited by 141 · see citing works
Self-citationAccurate, multi-kb reads resolve complex populations and detect rare microorganismsPreprint
Sharon, I., Kertesz, M., Hug, L. A., Pushkarev, D., Blauwkamp, T. A., Castelle, C. J., Amirebrahimi, M., Thomas, B. C., Burstein, D. E., Tringe, S. G., et al. (2015) · Genome Research · doi:10.1101/gr.183012.114 · Cited by 133 · see citing works
Clustering metagenomic sequences with interpolated Markov models
Kelley, D. R., & Salzberg, S. L. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-544 · Cited by 116 · see citing works
The Theory and Practice of Genome Sequence Assembly
Simpson, J. T., & Pop, M. (2015) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-090314-050032 · Cited by 108 · see citing works
Self-citationMetagenome Fragment Classification Using N‐Mer Frequency Profiles
ROSEN, G. L., Garbarine, E., Caseiro, D., Robi Polikar, A., & Sokhansanj, B. A. (2008) · Advances in Bioinformatics · doi:10.1155/2008/205969 · Cited by 103 · see citing works
Kent, B. N., Salichos, L., Gibbons, J. G., Rokas, A., Newton, I. L. G., Clark, M. E., & Bordenstein, S. R. (2011) · Genome Biology and Evolution · doi:10.1093/gbe/evr007 · Cited by 100 · see citing works
Synthetic long-read sequencing reveals intraspecies diversity in the human microbiome
Kuleshov, V., Jiang, C., Zhou, W., Jahanbani, F., Batzoglou, S., & Snyder, M. P. (2015) · Nature Biotechnology · doi:10.1038/nbt.3416 · Cited by 98 · see citing works
Fixation-free fluorescence in situ hybridization for targeted enrichment of microbial populations
Yilmaz, S., Haroon, M. F., Rabkin, B. A., Tyson, G. W., & Hugenholtz, P. (2010) · The ISME Journal · doi:10.1038/ismej.2010.73 · Cited by 92 · see citing works
Uncovering oral Neisseria tropism and persistence using metagenomic sequencing
Donati, C., Zolfo, M., Albanese, D., Truong, D., Asnicar, F., Iebba, V., Cavalieri, D., Jousson, O., de Filippo, C., Huttenhower, C., et al. (2016) · Nature Microbiology · doi:10.1038/nmicrobiol.2016.70 · Cited by 92 · see citing works
Self-citationReconstructing rare soil microbial genomes using in situ enrichments and metagenomics
Delmont, T. O., Eren, A. M., Maccario, L., Prestat, E., Esen, Ö. C., Pelletier, É., Paslier, D. L., Simonet, P., & Vogel, T. M. (2015) · Frontiers in Microbiology · doi:10.3389/fmicb.2015.00358 · Cited by 89 · see citing works
The effect of DNA extraction methodology on gut microbiota research applications
Gerasimidis, K., Bertz, M., Quince, C., Brunner, K., Bruce, A., Combet, E., Calus, S., Loman, N. J., & Ijaz, U. Z. (2016) · BMC Research Notes · doi:10.1186/s13104-016-2171-7 · Cited by 83 · see citing works
Self-citationAverage genome size: a potential source of bias in comparative metagenomics
Beszteri, B., Temperton, B., Frickenhaus, S., & Giovannoni, S. J. (2010) · The ISME Journal · doi:10.1038/ismej.2010.29 · Cited by 72 · see citing works
Cuthbertson, L., Rogers, G. B., Walker, A. W., Oliver, A., Hafiz, T., Hoffman, L. R., Carroll, M. P., Parkhill, J., Bruce, K. D., & Gast, C. (2014) · Journal of Clinical Microbiology · doi:10.1128/jcm.00764-14 · Cited by 72 · see citing works
Self-citationAccessible, curated metagenomic data through ExperimentHubPreprint
Pasolli, E., Schiffer, L., Manghi, P., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/103085 · Cited by 65 · see citing works
Integrity of the Human Faecal Microbiota following Long-Term Sample Storage
Kia, E., Mackenzie, B. W., Middleton, D., Lau, A., Waite, D. W., Lewis, G. D., Chan, Y., Silvestre, M. P., Cooper, G. J. S., Poppitt, S. D., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0163666 · Cited by 56 · see citing works
Sczyrba, A., Hofmann, P., Belmann, P., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/099127 · Cited by 53 · see citing works
New Perspectives on Microbial Community Distortion after Whole-Genome Amplification
Probst, A. J., Weinmaier, T., DeSantis, T. Z., Domingo, J. W. S., & Ashbolt, N. J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124158 · Cited by 52 · see citing works
Improved Multiple Displacement Amplification (iMDA) and Ultraclean Reagents
Motley, S. T., Picuri, J. M., Crowder, C. D., Minich, J. J., Hofstadler, S. A., & Eshoo, M. W. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-443 · Cited by 50 · see citing works
Two-stage microbial community experimental design
Tickle, T. L., Segata, N., Waldron, L., Weingart, U., & Huttenhower, C. (2013) · The ISME Journal · doi:10.1038/ismej.2013.139 · Cited by 46 · see citing works
Self-citationAge and Microenvironment Outweigh Genetic Influence on the Zucker Rat Microbiome
Lees, H., Swann, J. R., Poucher, S. M., Nicholson, J. K., Holmes, E. C., Wilson, I. D., & Marchesi, J. R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0100916 · Cited by 42 · see citing works
Seth-Smith, H. M. B., Harris, S. R., Scott, P., Parmar, S., Marsh, P., Unemo, M., Clarke, I. N., Parkhill, J., & Thomson, N. R. (2013) · Nature Protocols · doi:10.1038/nprot.2013.147 · Cited by 41 · see citing works
Microbiology: the road to strain-level identification
Marx, V. (2016) · Nature Methods · doi:10.1038/nmeth.3837 · Cited by 35 · see citing works
Huson, D. H., Tappu, R., Bazinet, A. L., Xie, C., Cummings, M. P., Nieselt, K., & Williams, R. B. H. (2017) · Microbiome · doi:10.1186/s40168-017-0233-2 · Cited by 34 · see citing works
Lim, Y. W., Haynes, M. R., Furlan, M., Robertson, C. E., Harris, J. K., & Rohwer, F. L. (2014) · Journal of Visualized Experiments · doi:10.3791/52117 · Cited by 30 · see citing works
Quick, J. C., Quinlan, A. R., & Loman, N. J. (2015) · GigaScience · doi:10.1186/s13742-015-0043-z · Cited by 30 · see citing works
Self-citationMcKain, N., Genç, B., Snelling, T. J., & Wallace, R. J. (2013) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2013.10.009 · Cited by 30 · see citing works
A Bayesian Approach to Inferring the Phylogenetic Structure of Communities from Metagenomic Data
O’Brien, J., Didelot, X., Iqbal, Z., Amenga-Etego, L. N.-K., Ahiska, B., & Falush, D. (2014) · Genetics · doi:10.1534/genetics.114.161299 · Cited by 26 · see citing works
Improving the sensitivity of negative controls in ancient DNA extractions
Xu, Z., Zhang, F., Xu, B., Tan, J., Li, S., & Li, J. (2009) · Electrophoresis · doi:10.1002/elps.200800473 · Cited by 12 · see citing works
De novo extraction of microbial strains from metagenomes reveals intra-species niche partitioningPreprint
Quince, C., Connelly, S., Raguideau, S., Alneberg, J., Shin, S., Collins, G., & Eren, A. M. (2016) · bioRxiv · doi:10.1101/073825 · Cited by 11 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- [53]53. Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. Journal of Computational Biology 19, 455-477 (2012).
Cited by
Sorted by citations
MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis
Uritskiy, G. V., DiRuggiero, J., & Taylor, J. (2018) · Microbiome · doi:10.1186/s40168-018-0541-1 · Cited by 2,642 · see citing works
Beghini, F., McIver, L. J., Blanco-Míguez, A., Dubois, L., Asnicar, F., Maharjan, S., Mailyan, A., Manghi, P., Scholz, M., Thomas, A. M., et al. (2021) · eLife · doi:10.7554/elife.65088 · Cited by 2,303 · see citing works
Self-citationBest practices for analysing microbiomes
Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., Gonzalez, A., Kosciolek, T., McCall, L.-I., McDonald, D., et al. (2018) · Nature Reviews Microbiology · doi:10.1038/s41579-018-0029-9 · Cited by 2,030 · see citing works
Pasolli, E., Asnicar, F., Manara, S., Zolfo, M., Karcher, N., Armanini, F., Beghini, F., Manghi, P., Tett, A., Ghensi, P., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.001 · Cited by 1,591 · see citing works
Self-citationChiu, C. Y., & Miller, S. A. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0113-7 · Cited by 1,620 · see citing works
Blanco-Míguez, A., Beghini, F., Cumbo, F., McIver, L. J., Thompson, K. N., Zolfo, M., Manghi, P., Dubois, L., Huang, K. D., Thomas, A. M., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01688-w · Cited by 1,442 · see citing works
Self-citationFerretti, P., Pasolli, E., Tett, A., Asnicar, F., Gorfer, V., Fedi, S., Armanini, F., Truong, D. T., Manara, S., Zolfo, M., et al. (2018) · Cell Host & Microbe · doi:10.1016/j.chom.2018.06.005 · Cited by 1,292 · see citing works
Self-citationA new genomic blueprint of the human gut microbiota
Almeida, A., Mitchell, A. L., Boland, M., Forster, S. C., Gloor, G. B., Tarkowska, A., Lawley, T. D., & Finn, R. D. (2019) · Nature · doi:10.1038/s41586-019-0965-1 · Cited by 1,314 · see citing works
VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses
Guo, J.-R., Bolduc, B., Zayed, A., Varsani, A., Domínguez-Huerta, G., Delmont, T., Pratama, A. A., Gazitúa, M. C., Vik, D., Sullivan, M., et al. (2021) · Microbiome · doi:10.1186/s40168-020-00990-y · Cited by 1,250 · see citing works
Thomas, A. M., Manghi, P., Asnicar, F., Pasolli, E., Armanini, F., Zolfo, M., Beghini, F., Manara, S., Karcher, N., Pozzi, C., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0405-7 · Cited by 1,030 · see citing works
Self-citationAsnicar, F., Berry, S. E., Valdes, A. M., Nguyen, L. H., Piccinno, G., Drew, D. A., Leeming, E., Gibson, R., Le Roy, C., Khatib, H. A., et al. (2021) · Nature Medicine · doi:10.1038/s41591-020-01183-8 · Cited by 995 · see citing works
Self-citationA practical guide to amplicon and metagenomic analysis of microbiome data
Liu, Y., Qin, Y., Chen, T., Lu, M., Qian, X., Guo, X., & Bai, Y. (2020) · Protein & Cell · doi:10.1007/s13238-020-00724-8 · Cited by 875 · see citing works
Chen, I.-M. A., Chu, K., Palaniappan, K., Pillay, M., Ratner, A., Huang, J., Huntemann, M., Varghese, N. J., White, J. R., Seshadri, R., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky901 · Cited by 811 · see citing works
The oral microbiome: Role of key organisms and complex networks in oral health and disease
Sedghi, L., DiMassa, V., Harrington, A., Lynch, S. V., & Kapila, Y. L. (2021) · Periodontology 2000 · doi:10.1111/prd.12393 · Cited by 755 · see citing works
Prevotella Diversity, Niches and Interactions with the Human Host
Tett, A., Pasolli, E., Masetti, G., Ercolini, D., & Segata, N. (2021) · Nature Reviews Microbiology · doi:10.1038/s41579-021-00559-y · Cited by 748 · see citing works
Self-citationImproved metagenome binning and assembly using deep variational autoencoders
Nissen, J. N., Johansen, J., Allesøe, R. L., Sønderby, C. K., Armenteros, J. J. A., Grønbech, C. H., Jensen, L. J., Nielsen, H. B., Petersen, T. N., Winther, O., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-00777-4 · Cited by 634 · see citing works
Current challenges and best-practice protocols for microbiome analysis
Bharti, R., & Grimm, D. G. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbz155 · Cited by 592 · see citing works
A human gut bacterial genome and culture collection for improved metagenomic analyses
Forster, S. C., Kumar, N., Anonye, B. O., Almeida, A., Viciani, E., Stares, M., Dunn, M., Mkandawire, T. T., Zhu, A., Shao, Y., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-018-0009-7 · Cited by 554 · see citing works
Genetic and biological hallmarks of colorectal cancerPreprint
Li, J., Ma, X., Chakravarti, D., Shalapour, S., & DePinho, R. A. (2021) · Genes & Development · doi:10.1101/gad.348226.120 · Cited by 588 · see citing works
Regulation of lipid peroxidation and ferroptosis in diverse species
Conrad, M., Kagan, V. E., Bayir, H., Pagnussat, G. C., Head, B., Traber, M. G., & Stockwell, B. R. (2018) · Genes & Development · doi:10.1101/gad.314674.118 · Cited by 544 · see citing works
Systematic Review of Gut Microbiota and Major Depression
Cheung, S. G., Goldenthal, A. R., Uhlemann, A.-C., Mann, J. J., Miller, J. M., & Sublette, M. E. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00034 · Cited by 597 · see citing works
Sequencing-based methods and resources to study antimicrobial resistance
Boolchandani, M., D’Souza, A. W., & Dantas, G. A. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0108-4 · Cited by 530 · see citing works
KrakenUniq: confident and fast metagenomics classification using unique k-mer counts
Breitwieser, F. P., Baker, D. N., & Salzberg, S. L. (2018) · Genome biology · doi:10.1186/s13059-018-1568-0 · Cited by 480 · see citing works
Lee, K. A., Thomas, A. M., Bolte, L. A., Björk, J. R., de Ruijter, L. K., Armanini, F., Asnicar, F., Blanco-Miguez, A., Board, R., Calbet-Llopart, N., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01695-5 · Cited by 464 · see citing works
Self-citationDiversity within Species: Interpreting Strains in Microbiomes
Van Rossum, T., Ferretti, P., Maistrenko, O. M., & Bork, P. (2020) · Nature Reviews Microbiology · doi:10.1038/s41579-020-0368-1 · Cited by 441 · see citing works
Plant developmental stage drives the differentiation in ecological role of the maize microbiome
Xiong, C., Singh, B. K., He, J., Han, Y.-L., Li, P., Wan, L.-H., Meng, G.-Z., Liu, S., Wang, J., Wu, C., et al. (2021) · Microbiome · doi:10.1186/s40168-021-01118-6 · Cited by 445 · see citing works
Consistent and correctable bias in metagenomic sequencing experiments
McLaren, M. R., Willis, A. D., & Callahan, B. J. (2019) · eLife · doi:10.7554/elife.46923 · Cited by 424 · see citing works
Antibiotic perturbations to the gut microbiome
Fishbein, S. R. S., Mahmud, B., & Dantas, G. (2023) · Nature Reviews Microbiology · doi:10.1038/s41579-023-00933-y · Cited by 440 · see citing works
Vasta, V., Daghio, M., Cappucci, A., Buccioni, A., Serra, A., Viti, C., & Mele, M. L. (2019) · Journal of Dairy Science · doi:10.3168/jds.2018-14985 · Cited by 432 · see citing works
Horizontal transfer of antibiotic resistance genes in the human gut microbiome
McInnes, R. S., McCallum, G. E., Lamberte, L. E., & van Schaik, W. (2020) · Current Opinion in Microbiology · doi:10.1016/j.mib.2020.02.002 · Cited by 401 · see citing works
Genome-Based Prediction of Bacterial Antibiotic Resistance
Su, M., Satola, S. W., & Read, T. D. (2018) · Journal of Clinical Microbiology · doi:10.1128/jcm.01405-18 · Cited by 373 · see citing works
Best practices in metabarcoding of fungi: From experimental design to results
Tedersoo, L., Bahram, M., Zinger, L., Nilsson, R. H., Kennedy, P. G., Yang, T., Anslan, S., & Mikryukov, V. S. (2022) · Molecular Ecology · doi:10.1111/mec.16460 · Cited by 351 · see citing works
Advances in Metagenomics and Its Application in Environmental Microorganisms
Zhang, L., Chen, F., Zeng, Z., Xu, M., Sun, F., Yang, L., Bi, X., Lin, Y., Gao, Y., Hao, H., et al. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.766364 · Cited by 334 · see citing works
Key determinants of success in fecal microbiota transplantation: From microbiome to clinic
Porcari, S., Benech, N., Valles-Colomer, M., Segata, N., Gasbarrini, A., Cammarota, G., Sokol, H., & Ianiro, G. (2023) · Cell Host & Microbe · doi:10.1016/j.chom.2023.03.020 · Cited by 331 · see citing works
Self-citationNeugent, M. L., Hulyalkar, N. V., Nguyen, V. H., Zimmern, P. E., & Nisco, N. J. D. (2020) · mBio · doi:10.1128/mbio.00218-20 · Cited by 331 · see citing works
Gut microbiome, big data and machine learning to promote precision medicine for cancer
Cammarota, G., Ianiro, G., Ahern, A., Carbone, C., Temko, A., Claesson, M. J., Gasbarrini, A., & Tortora, G. (2020) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-020-0327-3 · Cited by 324 · see citing works
Pérez-Cobas, A. E., Gomez-Valero, L., & Buchrieser, C. (2020) · Microbial Genomics · doi:10.1099/mgen.0.000409 · Cited by 314 · see citing works
Zimmermann, J., Kaleta, C., & Waschina, S. (2021) · Genome biology · doi:10.1186/s13059-021-02295-1 · Cited by 318 · see citing works
Pérez-Burillo, S., Molino, S., Navajas-Porras, B., Valverde-Moya, Á. J., Hinojosa-Nogueira, D., López-Maldonado, A., Pastoriza, S., & Rufián‐Henares, J. Á. (2021) · Nature Protocols · doi:10.1038/s41596-021-00537-x · Cited by 314 · see citing works
Sencio, V., Machado, M. G., & Trottein, F. (2021) · Mucosal Immunology · doi:10.1038/s41385-020-00361-8 · Cited by 315 · see citing works
Ultra-deep, long-read nanopore sequencing of mock microbial community standards
Nicholls, S. M., Quick, J., Tang, S., & Loman, N. J. (2019) · GigaScience · doi:10.1093/gigascience/giz043 · Cited by 305 · see citing works
Self-citationThe food-gut axis: lactic acid bacteria and their link to food, the gut microbiome and human health
De Filippis, F., Pasolli, E., & Ercolini, D. (2020) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuaa015 · Cited by 303 · see citing works
Metagenomics-enabled microbial surveillance
Ko, K. K. K., Chng, K. R., & Nagarajan, N. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01089-w · Cited by 280 · see citing works
Sheng, L., & Wang, L. (2020) · Comprehensive Reviews in Food Science and Food Safety · doi:10.1111/1541-4337.12671 · Cited by 304 · see citing works
Melkonian, C., Gottstein, W., Blasche, S., Kim, Y., Abel-Kistrup, M., Swiegers, H., Saerens, S., Edwards, N. K., Patil, K. R., Teusink, B., et al. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.01347 · Cited by 277 · see citing works
Cordier, T., Alonso‐Sáez, L., Apothéloz‐Perret‐Gentil, L., Aylagas, E., Bohan, D. A., Bouchez, A., Chariton, A. A., Creer, S., Frühe, L., Keck, F., et al. (2020) · Molecular Ecology · doi:10.1111/mec.15472 · Cited by 282 · see citing works
Choi, S.-C., Brown, J., Gong, M., Ge, Y., Zadeh, M., Li, W., Croker, B. P., Michailidis, G., Garrett, T. J., Mohamadzadeh, M., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aax2220 · Cited by 266 · see citing works
Legrand, T. P. R. A., Wynne, J. W., Weyrich, L. S., & Oxley, A. P. A. (2019) · Reviews in Aquaculture · doi:10.1111/raq.12375 · Cited by 269 · see citing works
Pereira‐Marques, J., van der Hout, A., Ferreira, R. M., Weber, M., Pinto-Ribeiro, I., van Doorn, L., Knetsch, C. W., & Figueiredo, C. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.01277 · Cited by 256 · see citing works
Population genomics of bacterial host adaptation
Sheppard, S. K., Guttman, D. S., & Fitzgerald, J. R. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0032-z · Cited by 251 · see citing works
Wong, J., Manoil, D., Näsman, P., Belibasakis, G. N., & Neelakantan, P. (2021) · Frontiers in Oral Health · doi:10.3389/froh.2021.672887 · Cited by 249 · see citing works
Expanded catalog of microbial genes and metagenome-assembled genomes from the pig gut microbiome
Chen, C., Zhou, Y., Fu, H., Xiong, X., Fang, S., Jiang, H., Wu, J., Yang, H., Gao, J., & Huang, L. (2021) · Nature Communications · doi:10.1038/s41467-021-21295-0 · Cited by 247 · see citing works
A guide to the application of Hill numbers to DNA‐based diversity analyses
Alberdi, A., & Gilbert, M. T. P. (2019) · Molecular Ecology Resources · doi:10.1111/1755-0998.13014 · Cited by 246 · see citing works
Promises and pitfalls of using high‐throughput sequencing for diet analysis
Alberdi, A., Aizpurua, O., Bohmann, K., Gopalakrishnan, S., Lynggaard, C., Nielsen, M., & Gilbert, M. T. P. (2018) · Molecular Ecology Resources · doi:10.1111/1755-0998.12960 · Cited by 242 · see citing works
Shaiber, A., Willis, A. D., Delmont, T. O., Roux, S., Chen, L.-X., Schmid, A. C., Yousef, M., Watson, A. R., Lolans, K., Esen, Ö. C., et al. (2020) · Genome biology · doi:10.1186/s13059-020-02195-w · Cited by 227 · see citing works
Standardized multi-omics of Earth’s microbiomes reveals microbial and metabolite diversity
Shaffer, J. P., Nothias-Scaglia, L.-F., Thompson, L., Sanders, J. G., Salido, R. A., Couvillion, S., Brejnrod, A., Lejzerowicz, F., Haiminen, N., Huang, S. W., et al. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01266-x · Cited by 218 · see citing works
Sunbeam: an extensible pipeline for analyzing metagenomic sequencing experiments
Clarke, E., Taylor, L. J., Zhao, C., Connell, A. J., Lee, J., Fett, B., Bushman, F. D., & Bittinger, K. L. (2019) · Microbiome · doi:10.1186/s40168-019-0658-x · Cited by 212 · see citing works
Microbiota in Tumors: From Understanding to Application
Xie, Y., Xie, F., Zhou, X., Zhang, L., Yang, B., Huang, J., Wang, F., Yan, H., Zhang, L., Zhang, L., et al. (2022) · Advanced Science · doi:10.1002/advs.202200470 · Cited by 211 · see citing works
Current and emerging trends in techniques for plant pathogen detection
Venbrux, M., Crauwels, S., & Rediers, H. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1120968 · Cited by 211 · see citing works
Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities
Chiara, M., D’Erchia, A. M., Gissi, C., Manzari, C., Parisi, A., Resta, N., Zambelli, F., Picardi, E., Pavesi, G., Horner, D. S., et al. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa297 · Cited by 202 · see citing works
Chemical and microbial diversity covary in fresh water to influence ecosystem functioning
Tanentzap, A. J., Fitch, A. A., Orland, C., Emilson, E. J. S., Yakimovich, K. M., Osterholz, H., & Dittmar, T. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1904896116 · Cited by 208 · see citing works
Breast and Gut Microbiota Action Mechanisms in Breast Cancer Pathogenesis and Treatment
Laborda-Illanes, A., Sánchez‐Alcoholado, L., Domínguez-Recio, M. E., Jiménez-Rodríguez, B., Lavado-Valenzuela, R., Comino‐Méndez, I., Alba, E., & Queipo‐Ortuño, M. I. (2020) · Cancers · doi:10.3390/cancers12092465 · Cited by 208 · see citing works
Pan, S., Zhao, X., & Coelho, L. P. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad209 · Cited by 215 · see citing works
Tools for Analysis of the Microbiome
Galloway-Peña, J., & Hanson, B. (2020) · Digestive Diseases and Sciences · doi:10.1007/s10620-020-06091-y · Cited by 203 · see citing works
High-quality metagenome assembly from long accurate reads with metaMDBG
Benoit, G., Raguideau, S., James, R. S., Phillippy, A. M., Chikhi, R., & Quince, C. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01983-6 · Cited by 199 · see citing works
Self-citationNanopore sequencing and its application to the study of microbial communities
Ciuffreda, L., Rodríguez-Pérez, H., & Flores, C. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.02.020 · Cited by 169 · see citing works
CAMISIM: simulating metagenomes and microbial communities
Fritz, A., Hofmann, P., Majda, S., Dahms, E., Dröge, J., Fiedler, J., Lesker, T. R., Belmann, P., DeMaere, M. Z., Darling, A. E., et al. (2019) · Microbiome · doi:10.1186/s40168-019-0633-6 · Cited by 194 · see citing works
SCycDB: A curated functional gene database for metagenomic profiling of sulphur cycling pathways
Yu, X., Zhou, J., Song, W., Xu, M., He, Q., Peng, Y., Tian, Y., Wang, C., Shu, L., Wang, S., et al. (2020) · Molecular Ecology Resources · doi:10.1111/1755-0998.13306 · Cited by 193 · see citing works
DeepMicrobes: taxonomic classification for metagenomics with deep learning
Liang, Q., Bible, P. W., Liu, Y., Zou, B., & Wei, L. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa009 · Cited by 189 · see citing works
Microbial Biodiversity and Bioremediation Assessment Through Omics Approaches
Chandran, H., Meena, M., & Sharma, K. (2020) · Frontiers in Environmental Chemistry · doi:10.3389/fenvc.2020.570326 · Cited by 188 · see citing works
Alexa, E. A., Likotrafiti, E., Álvarez-Molina, A., Maradona, M. P., Santos, J. A., & Álvarez‐Ordóñez, A. (2018) · Genes · doi:10.3390/genes9050268 · Cited by 187 · see citing works
Life history strategies of soil bacterial communities across global terrestrial biomes
Piton, G., Allison, S., Bahram, M., Hildebrand, F., Martiny, J. B. H., Treseder, K. K., & Martiny, A. C. (2023) · Nature Microbiology · doi:10.1038/s41564-023-01465-0 · Cited by 187 · see citing works
Metagenomics: An Effective Approach for Exploring Microbial Diversity and Functions
Nam, N. N., Do, H. D. K., Trinh, K. T. L., & Lee, N. Y. (2023) · Foods · doi:10.3390/foods12112140 · Cited by 203 · see citing works
Gut microbiome modulates efficacy of immune checkpoint inhibitors
Yi, M., Yu, S., Qin, S., Liu, Q., Xu, H., Zhao, W., Chu, Q., & Wu, K. (2018) · Journal of Hematology & Oncology · doi:10.1186/s13045-018-0592-6 · Cited by 179 · see citing works
Li, L.-G., Huang, Q., Yin, X., & Zhang, T. (2020) · Water Research · doi:10.1016/j.watres.2020.116127 · Cited by 178 · see citing works
Laboratory Diagnosis of Lyme Borreliosis
Branda, J. A., & Steere, A. C. (2021) · Clinical Microbiology Reviews · doi:10.1128/cmr.00018-19 · Cited by 180 · see citing works
Human microbiome myths and misconceptions
Walker, A. W., & Hoyles, L. (2023) · Nature Microbiology · doi:10.1038/s41564-023-01426-7 · Cited by 163 · see citing works
Self-citationMultiple levels of the unknown in microbiome research
Thomas, A. M., & Segata, N. (2019) · BMC Biology · doi:10.1186/s12915-019-0667-z · Cited by 176 · see citing works
Self-citationLinking metagenomics to aquatic microbial ecology and biogeochemical cycles
Grossart, H., Massana, R., McMahon, K. D., & Walsh, D. A. (2019) · Limnology and Oceanography · doi:10.1002/lno.11382 · Cited by 174 · see citing works
Panel), E. P. O. B. H. (. B., Koutsoumanis, K., Allende, A., Álvarez‐Ordóñez, A., Bolton, D., Bover‐Cid, S., Chemaly, M., Davies, R., Cesare, A. D., Hilbert, F., et al. (2019) · EFSA Journal · doi:10.2903/j.efsa.2019.5898 · Cited by 172 · see citing works
Difficulty in inferring microbial community structure based on co-occurrence network approaches
Hirano, H., & Takemoto, K. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2915-1 · Cited by 170 · see citing works
Strain-level metagenomic assignment and compositional estimation for long reads with MetaMaps
Dilthey, A., Jain, C., Koren, S., & Phillippy, A. M. (2019) · Nature Communications · doi:10.1038/s41467-019-10934-2 · Cited by 170 · see citing works
Gut microbiota–bile acid–skeletal muscle axis
Mancin, L., Wu, G. D., & Paoli, A. (2022) · Trends in Microbiology · doi:10.1016/j.tim.2022.10.003 · Cited by 176 · see citing works
Maguire, F., Jia, B., Gray, K. L., Lau, W. Y. V., Beiko, R. G., & Brinkman, F. S. L. (2020) · Microbial Genomics · doi:10.1099/mgen.0.000436 · Cited by 152 · see citing works
An Introduction to Next Generation Sequencing Bioinformatic Analysis in Gut Microbiome Studies
Gao, B., Chi, L., Zhu, Y., Shi, X., Tu, P., Li, B. J., Yin, J., Gao, N., Shen, W., & Schnabl, B. (2021) · Biomolecules · doi:10.3390/biom11040530 · Cited by 165 · see citing works
Liu, S., Wang, H., Chen, L., Wang, J., Zheng, M., Liu, S., Chen, Q., & Ni, J. (2020) · The ISME Journal · doi:10.1038/s41396-020-0701-8 · Cited by 159 · see citing works
Veillonellae: Beyond Bridging Species in Oral Biofilm Ecology
Zhou, P., Manoil, D., Belibasakis, G. N., & Kotsakis, G. A. (2021) · Frontiers in Oral Health · doi:10.3389/froh.2021.774115 · Cited by 159 · see citing works
Nazir, A., Nazir, A., Zuhair, V., Aman, S., Sadiq, S. U. R., Hasan, A. H., Tariq, M., Rehman, L. U., Mustapha, M. J., & Bulimbe, D. B. (2025) · Health Science Reports · doi:10.1002/hsr2.71077 · Cited by 178 · see citing works
Aquaculture industry prospective from gut microbiome of fish and shellfish: An overview
Diwan, A. D., Harke, S. N., Gopalkrishna, & Panche, A. N. (2021) · Journal of Animal Physiology and Animal Nutrition · doi:10.1111/jpn.13619 · Cited by 157 · see citing works
Holo-Omics: Integrated Host-Microbiota Multi-omics for Basic and Applied Biological Research
Nyholm, L., Koziol, A., Marcos, S., Botnen, A. B., Aizpurua, O., Gopalakrishnan, S., Limborg, M. T., Gilbert, M. T. P., & Alberdi, A. (2020) · iScience · doi:10.1016/j.isci.2020.101414 · Cited by 156 · see citing works
Rumen Microbiome Composition Is Altered in Sheep Divergent in Feed Efficiency
McLoughlin, S., Spillane, C., Claffey, N. A., Smith, P. E., O’Rourke, T., Diskin, M. G., & Waters, S. M. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.01981 · Cited by 155 · see citing works
Ghensi, P., Manghi, P., Zolfo, M., Armanini, F., Pasolli, E., Bolzan, M., Bertelle, A., Dell'Acqua, F., Dellasega, E., Waldner, R., et al. (2020) · npj Biofilms and Microbiomes · doi:10.1038/s41522-020-00155-7 · Cited by 148 · see citing works
Self-citationMacroecology to Unite All Life, Large and Small
Shade, A. L., Dunn, R. R., Blowes, S. A., Keil, P., Bohannan, B. J. M., Herrmann, M., Küsel, K., Lennon, J. T., Sanders, N. J., Štorch, D., et al. (2018) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2018.08.005 · Cited by 152 · see citing works
King, W. L., Jenkins, C., Seymour, J. R., & Labbate, M. (2018) · Marine Environmental Research · doi:10.1016/j.marenvres.2018.11.007 · Cited by 151 · see citing works
A multi-omics approach to solving problems in plant disease ecology
Crandall, S. G., Gold, K. M., Jiménez-Gasco, M. D. M., Filgueiras, C. C., & Willett, D. S. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237975 · Cited by 150 · see citing works
Analysis of the gut microbiome in dogs and cats
Suchodolski, J. S. (2021) · Veterinary Clinical Pathology · doi:10.1111/vcp.13031 · Cited by 153 · see citing works
Strain-level epidemiology of microbial communities and the human microbiome
Yan, Y., Nguyen, L. H., Franzosa, E. A., & Huttenhower, C. (2020) · Genome Medicine · doi:10.1186/s13073-020-00765-y · Cited by 147 · see citing works
Mannaa, M., Han, G., Seo, Y., & Park, I. (2021) · Foods · doi:10.3390/foods10112861 · Cited by 148 · see citing works
Unifying the known and unknown microbial coding sequence space
Vanni, C., Schechter, M. S., Acinas, S. G., Barberán, A., Buttigieg, P. L., Casamayor, E. O., Delmont, T. O., Duarte, C. M., Eren, A. M., Finn, R. D., et al. (2022) · eLife · doi:10.7554/elife.67667 · Cited by 134 · see citing works
The microbiome: An emerging key player in aging and longevity
Kim, M., & Benayoun, B. A. (2020) · Translational Medicine of Aging · doi:10.1016/j.tma.2020.07.004 · Cited by 142 · see citing works
- 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 · 2015: 2
- 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