Dollive, S. A tool kit for quantifying eukaryotic rRNA gene sequences from human microbiome samples. Genome Biol 13, 60 (2012).
Google Scholar
Pausan, M. R. Exploring the archaeome: Detection of archaeal signatures in the human body. Front. Microbiol 10, 2796 (2019).
Google Scholar
Shkoporov, A. N. & Hill, C. Bacteriophages of the human gut: The “known unknown” of the microbiome. Cell Host Microbe 25, 195–209 (2019).
Google Scholar
Clark, D. P., Pazdernik, N. J. & McGehee, M. R. Plasmids. in Molecular Biology, 712–748 (Elsevier, 2019). https://doi.org/10.1016/B978-0-12-813288-3.00023-9.
Meinhardt, F., Schaffrath, R. & Larsen, M. Microbial linear plasmids. Appl. Microbiol. Biotechnol 47, 329–336 (1997).
Google Scholar
Lacroix, B. & Citovsky, V. Transfer of DNA from bacteria to eukaryotes. MBio 7, 00863–16 (2016).
Google Scholar
Łobocka, M. B. Genome of bacteriophage P1. J. Bacteriol. 186, 7032–7068 (2004).
Google Scholar
Roux, S., Enault, F., Hurwitz, B. L. & Sullivan, M. B. VirSorter: Mining viral signal from microbial genomic data. PeerJ 3, e985 (2015).
Google Scholar
Spaziante, M., Oliva, A., Ceccarelli, G. & Venditti, M. What are the treatment options for resistant Klebsiella pneumoniae carbapenemase (KPC)-producing bacteria?. Expert Opin. Pharmacother. 21, 1781–1787 (2020).
Google Scholar
Kopotsa, K., Osei Sekyere, J. & Mbelle, N. M. Plasmid evolution in carbapenemase-producing Enterobacteriaceae: A review. Ann. N. Y. Acad. Sci. 1457, 61–91 (2019).
Google Scholar
Ogilvie, L. A., Firouzmand, S. & Jones, B. V. Evolutionary, ecological and biotechnological perspectives on plasmids resident in the human gut mobile metagenome. Bioengineered 3, 13–31 (2012).
Google Scholar
Jørgensen, T. S., Xu, Z., Hansen, M. A., Sørensen, S. J. & Hansen, L. H. Hundreds of circular novel plasmids and DNA elements identified in a rat cecum metamobilome. PLoS ONE 9, 87924 (2014).
Google Scholar
Kav, A. B. Insights into the bovine rumen plasmidome. Proc. Natl. Acad. Sci. 109, 5452–5457 (2012).
Google Scholar
Brown Kav, A. Unravelling plasmidome distribution and interaction with its hosting microbiome. Environ. Microbiol. 22, 32–44 (2020).
Google Scholar
Norman, J. M. et al. Disease-specific alterations in the enteric virome in inflammatory bowel disease. Cell 160, 447–460 (2015).
Google Scholar
Krishnamurthy, S. R. & Wang, D. Origins and challenges of viral dark matter. Virus Res. 239, 136–142 (2017).
Google Scholar
Clooney, A. G. et al. Whole-virome analysis sheds light on viral dark matter in inflammatory bowel disease. Cell Host. Microbe 26, 764-778.e5 (2019).
Google Scholar
Sutton, T. D. S., Clooney, A. G. & Hill, C. Giant oversights in the human gut virome. Gut 69, 1357–1358 (2020).
Google Scholar
Zuo, T. Gut mucosal virome alterations in ulcerative colitis. Gut 68, 1169–1179 (2019).
Google Scholar
Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 176, 649-662.e20 (2019).
Google Scholar
Tamminen, M., Virta, M., Fani, R. & Fondi, M. Large-scale analysis of plasmid relationships through gene-sharing networks. Mol. Biol. Evol. 29, 1225–1240 (2012).
Google Scholar
Angelakis, E. et al. Treponema species enrich the gut microbiota of traditional rural populations but are absent from urban individuals. New Microbes New Infect 27, 14–21 (2019).
Google Scholar
Mackie, R. I. et al. Ecology of uncultivated oscillospira species in the rumen of cattle, sheep, and reindeer as assessed by microscopy and molecular approaches. Appl. Environ. Microbiol. 69, 6808–6815 (2003).
Google Scholar
Konikoff, T. & Gophna, U. Oscillospira: A central, enigmatic component of the human gut microbiota. Trends Microbiol. 24, 523–524 (2016).
Google Scholar
Chen, Y. et al. High Oscillospira abundance indicates constipation and low BMI in the Guangdong Gut Microbiome Project. Sci. Rep. 10, (2020).
Bushman, F. D. Multi-omic analysis of the interaction between clostridioides difficile infection and pediatric inflammatory bowel disease. Cell Host Microbe 28, 422–433 (2020).
Google Scholar
Willing, B. P. et al. A pyrosequencing study in twins shows that gastrointestinal microbial profiles vary with inflammatory bowel disease phenotypes. Gastroenterology 139, 1844–1854 (2010).
Google Scholar
Wills, E. S. et al. Fecal microbial composition of ulcerative colitis and Crohn’s disease patients in remission and subsequent exacerbation. PLoS ONE 9, e90981 (2014).
Google Scholar
Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014).
Google Scholar
Halfvarson, J. Dynamics of the human gut microbiome in inflammatory bowel disease. Nat. Microbiol. 2, 17004 (2017).
Google Scholar
Pascal, V. A microbial signature for Crohn’s disease. Gut 66, 813–822 (2017).
Google Scholar
Nitzan, O., Elias, M., Chazan, B., Raz, R. & Saliba, W. Clostridium difficile and inflammatory bowel disease: Role in pathogenesis and implications in treatment. World J. Gastroenterol. 19, 7577–7585 (2013).
Google Scholar
Clayton, E. M. et al. The vexed relationship between Clostridium difficile and inflammatory bowel disease: an assessment of carriage in an outpatient setting among patients in remission. Am. J. Gastroenterol. 104, 1162–1169 (2009).
Google Scholar
Tariq, R. et al. Efficacy of fecal microbiota transplantation for recurrent C.
Marcella, C. Systematic review: The global incidence of faecal microbiota transplantation-related adverse events from 2000 to 2020. Aliment. Pharmacol. Ther. https://doi.org/10.1111/apt.16148 (2020).
Google Scholar
Shkoporov, A. N. et al. The human gut virome is highly diverse, stable, and individual specific. Cell Host Microbe 26, 527-541.e5 (2019).
Google Scholar
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Google Scholar
Fraser-Liggett, C. Metagenomic analysis of the structure and function of the human gut microbiota in Crohn’s disease. Nat. Preced. [Internet] (2010).
Barton, W. et al. The microbiome of professional athletes differs from that of more sedentary subjects in composition and particularly at the functional metabolic level. Gut (2017).
Mira-Pascual, L. Microbial mucosal colonic shifts associated with the development of colorectal cancer reveal the presence of different bacterial and archaeal biomarkers. J. Gastroenterol. 50, 167–179 (2015).
Google Scholar
Rampelli, S. Shotgun metagenomics of gut microbiota in humans with up to extreme longevity and the increasing role of xenobiotic degradation. mSystems 5, (2020).
Monaghan, T. M. Metagenomics reveals impact of geography and acute diarrheal disease on the Central Indian human gut microbiome. Gut Microbes 12, 1752605 (2020).
Google Scholar
Chu, D. M. Maturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery. Nat. Med. 23, 314–326 (2017).
Google Scholar
MD, D. G., K, F., C, C. & EL, C. Whole genome metagenomic analysis of the gut microbiome of differently fed infants identifies differences in microbial composition and functional genes, including an absent CRISPR/Cas9 gene in the formula-fed cohort. Hum. Microbiome J. 12, (2019).
Qian, Y. et al. Gut metagenomics-derived genes as potential biomarkers of Parkinson’s disease. Brain J. Neurol. 143, 2474–2489 (2020).
Google Scholar
Kao, D. Effect of oral capsule- vs colonoscopy-delivered fecal microbiota transplantation on recurrent clostridium difficile infection: A randomized clinical trial. JAMA 318, 1985–1993 (2017).
Google Scholar
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: A new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).
Google Scholar
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).
Google Scholar
Finn, R. D., Clements, J. & Eddy, S. R. HMMER web server: Interactive sequence similarity searching. Nucleic Acids Res. 39, W29–W37 (2011).
Google Scholar
Guerin, E. et al. Biology and taxonomy of crAss-like bacteriophages, the most abundant virus in the human gut. (2018). https://doi.org/10.1101/295642.
Grazziotin, A. L., Koonin, E. V. & Kristensen, D. M. Prokaryotic Virus Orthologous Groups (pVOGs): A resource for comparative genomics and protein family annotation. Nucleic Acids Res. 45, D491–D498 (2017).
Google Scholar
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Google Scholar
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Google Scholar
Quinlan, A. R. & Hall, I. M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Google Scholar
Edgar, R. C. PILER-CR: Fast and accurate identification of CRISPR repeats. BMC Bioinform. 8, 18 (2007).
Google Scholar
R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/. (2019). Accessed Aug 2021–Mar 2022.
Wickham, H. Reshaping Data with the reshape Package. J. Stat. Softw. 21, 1–20 (2007).
Google Scholar
Jari Oksanen et al. vegan: Community Ecology Package. (2019).
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
Google Scholar
Kassambara, A. ggpubr: ‘ggplot2’ based publication ready plots. (2019).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).
Google Scholar
Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. Circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).
Google Scholar
Flor M. chorddiag: Interactive Chord Diagrams [Internet]. (2020).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Google Scholar
Hulsen, T., Vlieg, J. & Alkema, W. BioVenn—A web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genom. 9, (2008).
Stothard, P. & Wishart, D. S. Circular genome visualization and exploration using CGView. Bioinform. Oxf. Engl. 21, 537–539 (2005).
Google Scholar
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinform. Oxf. Engl. 30, 2068–2069 (2014).
Google Scholar
Huerta-Cepas, J. et al. eggNOG 4.5: A hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 44, D286–D293 (2016).
Google Scholar
McArthur, A. G. et al. The comprehensive antibiotic resistance database. Antimicrob. Agents Chemother. 57, 3348–3357 (2013).
Google Scholar
Li, W. & Godzik, A. Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
Google Scholar
Source: Ecology - nature.com
