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Review Articles

Bioinformatic approaches for studying the microbiome of fermented food

, , , ORCID Icon & ORCID Icon
Pages 693-725 | Received 11 May 2022, Accepted 28 Sep 2022, Published online: 26 Oct 2022

References

  • Abe M, Takaoka N, Idemoto Y, Takagi C, Imai T, Nakasaki K. 2008. Characteristic fungi observed in the fermentation process for Puer tea. Int J Food Microbiol. 124(2):199–203.
  • Aggarwala V, Liang G, Bushman FD. 2017. Viral communities of the human gut: metagenomic analysis of composition and dynamics. Mob DNA. 8(1):12.
  • Albrecht B, Bağcı C, Huson DH. 2020. MAIRA- real-time taxonomic and functional analysis of long reads on a laptop. BMC Bioinf. 21(Suppl 13):390.
  • Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M, Edalatmand A, Huynh W, Nguyen A-LV, Cheng AA, Liu S, et al. 2019. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res. 48(D1):D517–D525.
  • Alexa EA, Walsh CJ, Coughlan LM, Awad A, Simon CA, Ruiz L, Crispie F, Cotter PD, Alvarez-Ordóñez A. 2020. Dairy products and dairy-processing environments as a reservoir of antibiotic resistance and quorum-quenching determinants as revealed through functional metagenomics. mSystems. 5(1):e00723-19.
  • Almeida OGG, De Martinis ECP. 2019. Bioinformatics tools to assess metagenomic data for applied microbiology. Appl Microbiol Biotechnol. 103(1):69–82.
  • Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, Lahti L, Loman NJ, Andersson AF, Quince C. 2014. Binning metagenomic contigs by coverage and composition. Nat Methods. 11(11):1144–1146.
  • Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. 1990. Basic local alignment search tool. J Mol Biol. 215(3):403–410.
  • Angers-Loustau A, Petrillo M, Bengtsson-Palme J, Berendonk T, Blais B, Chan KG, Coque TM, Hammer P, Heß S, Kagkli DM, et al. 2018. The challenges of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance determinants using next generation sequencing technologies. F1000Res. 7:ISCB Comm J-459.
  • Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, et al. 2004. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 32(Database issue):D115–D119.
  • Arango-Argoty G, Garner E, Pruden A, Heath LS, Vikesland P, Zhang L. 2018. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome. 6(1):23.
  • Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, Wishart DS. 2016. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 44(W1):W16–21.
  • Asnicar F, Thomas AM, Beghini F, Mengoni C, Manara S, Manghi P, Zhu Q, Bolzan M, Cumbo F, May U, et al. 2020. Precise phylogenetic analysis of microbial isolates and genomes from metagenomes using PhyloPhlAn 3.0. Nat Commun. 11(1):2500.
  • Asnicar F, Weingart G, Tickle TL, Huttenhower C, Segata N. 2015. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ. 3:e1029.
  • Ayling M, Clark MD, Leggett RM. 2020. New approaches for metagenome assembly with short reads. Brief Bioinform. 21(2):584–594.
  • Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, et al. 2012. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 19(5):455–477.
  • Bauer N, Evans P, Leopold B, Levine J, White P. 2014. White paper: current and future development and use of molecular subtyping by USDA-FSIS. Washington (DC): Food Safety and Inspection Service, U.S. Department of Agriculture. Available from: https://www.fsis.usda.gov/wps/wcm/connect/6c7f71fd-2c0c-4ff0-b2bc-4977c7947516/Molecular-Subtyping-White-Paper.pdf?MOD=AJPERES.
  • Beghini F, McIver LJ, Blanco-Míguez A, Dubois L, Asnicar F, Maharjan S, Mailyan A, Manghi P, Scholz M, Thomas AM, et al. 2021. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife. 10:e65088.
  • Behera S, Bal P, Das S, Panda S, Mohanty N. 2018. Advances in microbial fermentation and fermented food for health. In: Panda S, Shetty P, editors. Innovations in technologies for fermented food and beverage industries. Food microbiology and food safety. Cham: Springer; p. 53–69.
  • Beier S, Tappu R, Huson DH. 2017. Functional analysis in metagenomics using MEGAN 6. In: Charles TC, Liles MR, Sessitsch A, editors. Functional metagenomics: tools and applications. Cham: Springer International Publishing; p. 65–74.
  • Belcour A, Frioux C, Aite M, Bretaudeau A, Hildebrand F, Siegel A. 2020. Metage2Metabo, microbiota-scale metabolic complementarity for the identification of key species. eLife. 9:e61968.
  • Bellon JR, Yang F, Day MP, Inglis DL, Chambers PJ. 2015. Designing and creating Saccharomyces interspecific hybrids for improved, industry relevant, phenotypes. Appl Microbiol Biotechnol. 99(20):8597–8609.
  • Bengtsson-Palme J, Ryberg M, Hartmann M, Branco S, Wang Z, Godhe A, De Wit P, Sánchez-García M, Ebersberger I, de Sousa F, et al. 2013. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods Ecol Evol. 4(10):914–919.
  • Berglund F, Österlund T, Boulund F, Marathe NP, Larsson DGJ, Kristiansson E. 2019. Identification and reconstruction of novel antibiotic resistance genes from metagenomes. Microbiome. 7(1):52.
  • Bertuzzi AS, Walsh AM, Sheehan JJ, Cotter PD, Crispie F, McSweeney PLH, Kilcawley KN, Rea MC. 2018. Omics-based insights into flavor development and microbial succession within surface-ripened cheese. mSystems. 3(1):e00211-17.
  • Blair JMA, Webber MA, Baylay AJ, Ogbolu DO, Piddock LJV. 2015. Molecular mechanisms of antibiotic resistance. Nat Rev Microbiol. 13(1):42–51.
  • Bourrie BCT, Willing BP, Cotter PD. 2016. The microbiota and health promoting characteristics of the fermented beverage kefir. Front Microbiol. 7:647.
  • Bove P, Russo P, Capozzi V, Gallone A, Spano G, Fiocco D. 2013. Lactobacillus plantarum passage through an oro-gastro-intestinal tract simulator: carrier matrix effect and transcriptional analysis of genes associated to stress and probiosis. Microbiol Res. 168(6):351–359.
  • Boyd JA, Woodcroft BJ, Tyson GW. 2018. GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. Nucleic Acids Res. 46(10):e59–e59.
  • Brown ED, Wright GD. 2016. Antibacterial drug discovery in the resistance era. Nature. 529(7586):336–343.
  • Calle ML. 2019. Statistical analysis of metagenomics data. Genom Inform. 17(1):e6.
  • Cao Y, Fanning S, Proos S, Jordan K, Srikumar S. 2017. A review on the applications of next generation sequencing technologies as applied to food-related microbiome studies. Front Microbiol. 8:1829.
  • Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. 2019. GTDB-Tk: a toolkit to classify genomes with the genome taxonomy database. Bioinformatics. 36(6):1925–1927.
  • Chaves-López C, Serio A, Grande-Tovar CD, Cuervo-Mulet R, Delgado-Ospina J, Paparella A. 2014. Traditional fermented foods and beverages from a microbiological and nutritional perspective: the Colombian heritage. Compr Rev Food Sci Food Saf. 13(5):1031–1048.
  • Chen LX, Anantharaman K, Shaiber A, Eren AM, Banfield JF. 2020. Accurate and complete genomes from metagenomes. Genome Res. 30(3):315–333.
  • Chen W, Narbad A. 2018. Lactic acid bacteria in foodborne hazards reduction. Cham: Springer.
  • Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. 2016. GenBank. Nucleic Acids Res. 44(D1):D67–D72.
  • Colombo S, Arioli S, Gargari G, Neri E, Della Scala G, Mora D. 2018. Characterization of airborne viromes in cheese production plants. J Appl Microbiol. 125(5):1444–1454.
  • Couto N, Schuele L, Raangs EC, Machado MP, Mendes CI, Jesus TF, Chlebowicz M, Rosema S, Ramirez M, Carriço JA, et al. 2018. Critical steps in clinical shotgun metagenomics for the concomitant detection and typing of microbial pathogens. Sci Rep. 8(1):13767. PubMed. (Accessed 2018/09//).
  • Cuevas DA, Edirisinghe J, Henry CS, Overbeek R, O'Connell TG, Edwards RA. 2016. From DNA to FBA: how to build your own genome-scale metabolic model. Front Microbiol. 7:907.
  • De Filippis F, Parente E, Ercolini D. 2017. Metagenomics insights into food fermentations. Microb Biotechnol. 10(1):91–102.
  • de Jong A, van Hijum SA, Bijlsma JJ, Kok J, Kuipers OP. 2006. BAGEL: a web-based bacteriocin genome mining tool. Nucleic Acids Res. 34(Web Server issue):W273–9.
  • Delcher AL, Harmon D, Kasif S, White O, Salzberg SL. 1999. Improved microbial gene identification with GLIMMER. Nucleic Acids Res. 27(23):4636–4641.
  • Dong X, Strous M. 2019. An integrated pipeline for annotation and visualization of metagenomic contigs. Front Genet. 10:999–999.
  • Drost H-G, Paszkowski J. 2017. Biomartr: genomic data retrieval with R. Bioinformatics. 33(8):1216–1217.
  • Dugat-Bony E, Lossouarn J, De Paepe M, Sarthou AS, Fedala Y, Petit MA, Chaillou S. 2020. Viral metagenomic analysis of the cheese surface: a comparative study of rapid procedures for extracting viral particles. Food Microbiol. 85:103278.
  • Edgar RC. 2004. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32(5):1792–1797.
  • Eddy SR. 2011. Accelerated Profile HMM Searches. PLOS Computational Biology. 7(10):e1002195.
  • Edgar RC. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 26(19):2460–2461.
  • Ercolini D, Ferrocino I, Nasi A, Ndagijimana M, Vernocchi P, La Storia A, Laghi L, Mauriello G, Guerzoni ME, Villani F. 2011. Monitoring of microbial metabolites and bacterial diversity in beef stored under different packaging conditions. Appl Environ Microbiol. 77(20):7372–7381.
  • Fedarko MW, Martino C, Morton JT, González A, Rahman G, Marotz CA, Minich JJ, Allen EE, Knight R. 2020. Visualizing ’omic feature rankings and log-ratios using Qurro. NAR Genom Bioinform. 2(2):lqaa023.
  • Fernández L, Escobedo S, Gutiérrez D, Portilla S, Martínez B, García P, Rodríguez A. 2017. Bacteriophages in the dairy environment: from enemies to allies. Antibiotics. 6(4):27.
  • Ferrocino I, Bellio A, Giordano M, Macori G, Romano A, Rantsiou K, Decastelli L, Cocolin L. 2018. Shotgun metagenomics and volatilome profile of the microbiota of fermented sausages. Appl Environ Microbiol. 84(3):e02120-17.
  • Flores M, Corral S, Cano-García L, Salvador A, Belloch C. 2015. Yeast strains as potential aroma enhancers in dry fermented sausages. Int J Food Microbiol. 212:16–24.
  • Fritz A, Hofmann P, Majda S, Dahms E, Dröge J, Fiedler J, Lesker TR, Belmann P, DeMaere MZ, Darling AE, et al. 2019. CAMISIM: simulating metagenomes and microbial communities. Microbiome. 7(1):17.
  • Garneau JE, Moineau S. 2011. Bacteriophages of lactic acid bacteria and their impact on milk fermentations. Microb Cell Fact. 10(Suppl 1):S20.
  • Gille D, Schmid A, Walther B, Vergères G. 2018. Fermented food and non-communicable chronic diseases: a review. Nutrients. 10(4):448.
  • Gillings MR, Stokes HW. 2012. Are humans increasing bacterial evolvability? Trends Ecol Evol. 27(6):346–352.
  • Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. 2017. Microbiome datasets are compositional: and this is not optional. Front Microbiol. 8:2224.
  • Graham ED, Heidelberg JF, Tully BJ. 2017. BinSanity: unsupervised clustering of environmental microbial assemblies using coverage and affinity propagation. PeerJ. 5:e3035.
  • Gruening B, Sallou O, Moreno P, da Veiga Leprevost F, Ménager H, Søndergaard D, Röst H, Sachsenberg T, O'Connor B, Madeira F, et al. 2018. Recommendations for the packaging and containerizing of bioinformatics software. F1000Res. 7:ISCB Comm J-742.
  • Guitor AK, Raphenya AR, Klunk J, Kuch M, Alcock B, Surette MG, McArthur AG, Poinar HN, Wright GD. 2019. Capturing the resistome: a targeted capture method to reveal antibiotic resistance determinants in metagenomes. Antimicrob Agents Chemother. 64(1):e01324-19.
  • Guo J, Bolduc B, Zayed AA, Varsani A, Dominguez-Huerta G, Delmont TO, Pratama AA, Gazitúa MC, Vik D, Sullivan MB, et al. 2021. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome. 9(1):37.
  • Guo J, Quensen JF, Sun Y, Wang Q, Brown CT, Cole JR, Tiedje JM. 2019. Review, evaluation, and directions for gene-targeted assembly for ecological analyses of metagenomes. Front Genet. 10:957.
  • Gupta SK, Padmanabhan BR, Diene SM, Lopez-Rojas R, Kempf M, Landraud L, Rolain JM. 2014. ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob Agents Chemother. 58(1):212–220.
  • Hazards E, Panel B, Koutsoumanis K, Allende A, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, Davies R, De Cesare A, et al. 2019. Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne microorganisms. EFSA J. 17(12):e05898.
  • Hendrix RW. 2002. Bacteriophages: evolution of the majority. Theor Popul Biol. 61(4):471–480.
  • Hillmann B, Al-Ghalith GA, Shields-Cutler RR, Zhu Q, Gohl DM, Beckman KB, Knight R, Knights D, Rawls JF. 2018. Evaluating the information content of shallow shotgun metagenomics. mSystems. 3(6):e00069-18.
  • Hillmann B, Al-Ghalith GA, Shields-Cutler RR, Zhu Q, Knight R, Knights D. 2020. SHOGUN: a modular, accurate and scalable framework for microbiome quantification. Bioinformatics. 36(13):4088–4090.
  • Huang Y, Niu B, Gao Y, Fu L, Li W. 2010. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics. 26(5):680–682.
  • Hübler R, Key FM, Warinner C, Bos KI, Krause J, Herbig A. 2019. HOPS: automated detection and authentication of pathogen DNA in archaeological remains. Genome Biol. 20(1):280.
  • Hutchins BI, Baker KL, Davis MT, Diwersy MA, Haque E, Harriman RM, Hoppe TA, Leicht SA, Meyer P, Santangelo GM. 2019. The NIH open citation collection: a public access, broad coverage resource. PLoS Biol. 17(10):e3000385.
  • Hutchins BI, Yuan X, Anderson JM, Santangelo GM. 2016. Relative citation ratio (RCR): a new metric that uses citation rates to measure influence at the article level. PLoS Biol. 14(9):e1002541.
  • Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. 2010. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 11:119–119.
  • Illeghems K, Weckx S, De Vuyst L. 2015. Applying meta-pathway analyses through metagenomics to identify the functional properties of the major bacterial communities of a single spontaneous cocoa bean fermentation process sample. Food Microbiol. 50:54–63.
  • Imelfort M, Parks D, Woodcroft BJ, Dennis P, Hugenholtz P, Tyson GW. 2014. GroopM: an automated tool for the recovery of population genomes from related metagenomes. PeerJ. 2:e603.
  • Jung JY, Lee SH, Kim JM, Park MS, Bae J-W, Hahn Y, Madsen EL, Jeon CO. 2011. Metagenomic analysis of kimchi, a traditional Korean fermented food. Appl Environ Microbiol. 77(7):2264–2274.
  • Kabak B, Dobson AD. 2011. An introduction to the traditional fermented foods and beverages of Turkey. Crit Rev Food Sci Nutr. 51(3):248–260.
  • Kable ME, Srisengfa Y, Laird M, Zaragoza J, McLeod J, Heidenreich J, Marco ML. 2016. The core and seasonal microbiota of raw bovine milk in tanker trucks and the impact of transfer to a milk processing facility. MBio. 7(4):e00836–16.
  • Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M. 2014. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42(Database issue):D199–D205.
  • Kanehisa M, Sato Y, Morishima K. 2016. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 428(4):726–731.
  • Kang DD, Froula J, Egan R, Wang Z. 2015. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 3:e1165.
  • Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, Wang Z. 2019. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 7:e7359.
  • Karp PD, Latendresse M, Paley SM, Krummenacker M, Ong QD, Billington R, Kothari A, Weaver D, Lee T, Subhraveti P, et al. 2016. Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology. Brief Bioinform. 17(5):877-90. PMID: 26454094
  • Kopylova E, Noé L, Touzet H. 2012. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 28(24):3211-3217.
  • Karlsen E, Schulz C, Almaas E. 2018. Automated generation of genome-scale metabolic draft reconstructions based on KEGG. BMC Bioinf. 19(1):467.
  • Lakin SM, Dean C, Noyes NR, Dettenwanger A, Ross AS, Doster E, Rovira P, Abdo Z, Jones KL, Ruiz J, et al. 2017. MEGARes: an antimicrobial resistance database for high throughput sequencing. Nucleic Acids Res. 45(D1):D574–D580.
  • Laranjo M, Potes ME, Elias M. 2019. Role of starter cultures on the safety of fermented meat products. Front Microbiol. 10:853.
  • Laserson J, Jojic V, Koller D. 2011. Genovo: de novo assembly for metagenomes. J Comput Biol. 18(3):429–443.
  • LaSarre B, Federle MJ. 2013. Exploiting quorum sensing to confuse bacterial pathogens. Microbiology and Molecular Biology Reviews. 77(1):73–111.
  • Lee J-H, Yi H, Chun J. 2011. rRNASelector: a computer program for selecting ribosomal RNA encoding sequences from metagenomic and metatranscriptomic shotgun libraries. J Microbiol. 49(4):689–691.
  • Leech J, Cabrera-Rubio R, Walsh AM, Macori G, Walsh CJ, Barton W, Finnegan L, Crispie F, O’Sullivan O, Claesson MJ, et al. 2020. Fermented-food metagenomics reveals substrate-associated differences in taxonomy and health-associated and antibiotic resistance determinants. mSystems. 5(6):e00522-20.
  • Leimbach A, Hacker J, Dobrindt U. 2013. E. coli as an all-rounder: the thin line between commensalism and pathogenicity. Curr Top Microbiol Immunol. 358:3–32.
  • Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, et al. 2010. BioModels database: an enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol. 4(1):92.
  • Li D, Luo R, Liu CM, Leung CM, Ting HF, Sadakane K, Yamashita H, Lam TW. 2016. MEGAHIT v1.0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 102:3–11.
  • Li H. 2013. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv. [Preprint].
  • Li Z, Feng C, Luo X, Yao H, Zhang D, Zhang T. 2018. Revealing the influence of microbiota on the quality of Pu-erh tea during fermentation process by shotgun metagenomic and metabolomic analysis. Food Microbiol. 76:405–415.
  • Liang H, Chen H, Ji C, Lin X, Zhang W, Li L. 2018. Dynamic and functional characteristics of predominant species in industrial paocai as revealed by combined DGGE and metagenomic sequencing. Front Microbiol. 9:2416–2416.
  • Lieven C, Beber ME, Olivier BG, Bergmann FT, Ataman M, Babaei P, Bartell JA, Blank LM, Chauhan S, Correia K, et al. 2020. MEMOTE for standardized genome-scale metabolic model testing. Nat Biotechnol. 38(3):272–276.
  • Liu B, Pop M. 2009. ARDB—antibiotic resistance genes database. Nucleic Acids Res. 37(Database issue):D443–D447.
  • Liu J, Wang H, Yang H, Zhang Y, Wang J, Zhao F, Qi J. 2013. Composition-based classification of short metagenomic sequences elucidates the landscapes of taxonomic and functional enrichment of microorganisms. Nucleic Acids Res. 41(1):e3.
  • Liu Y-X, Qin Y, Chen T, Lu M, Qian X, Guo X, Bai Y. 2021. A practical guide to amplicon and metagenomic analysis of microbiome data. Protein Cell. 12(5):315–330.
  • Luo R, Liu B, Xie Y, Li Z, Huang W, Yuan J, He G, Chen Y, Pan Q, Liu Y, et al. 2012. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience,. 1(1):18.
  • Macesic N, Polubriaginof F, Tatonetti NP. 2017. Machine learning: novel bioinformatics approaches for combating antimicrobial resistance. Curr Opin Infect Dis. 30(6):511–517.
  • Machado D, Andrejev S, Tramontano M, Patil KR. 2018. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 46(15):7542–7553.
  • Marco ML, Heeney D, Binda S, Cifelli CJ, Cotter PD, Foligné B, Gänzle M, Kort R, Pasin G, Pihlanto A, et al. 2017. Health benefits of fermented foods: microbiota and beyond. Curr Opin Biotechnol. 44:94–102.
  • Marco ML, Sanders ME, Gänzle M, Arrieta MC, Cotter PD, De Vuyst L, Hill C, Holzapfel W, Lebeer S, Merenstein D, et al. 2021. The International Scientific Association for Probiotics and Prebiotics (ISAPP) consensus statement on fermented foods. Nat Rev Gastroenterol Hepatol. 18(3):196–208.
  • Martinez-Villaluenga C, Peñas E, Frias J. 2017. Chapter 2 - bioactive peptides in fermented foods: production and evidence for health effects. In: Frias J, Martinez-Villaluenga C, Peñas E, editors. Fermented foods in health and disease prevention. Boston: Academic Press; p. 23–47.
  • Matsen FA, Kodner RB, Armbrust EV. 2010. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinf. 11(1):538.
  • McDonald D, Hyde E, Debelius JW, Morton JT, Gonzalez A, Ackermann G, Aksenov AA, Behsaz B, Brennan C, Chen Y, et al. 2018. American gut: an open platform for citizen science microbiome research. mSystems. 3(3):e00031–18.
  • McHardy A, Sczyrba A, Rattei T. 2014. The critical assessment of metagenome interpretation (CAMI) competition. Nat Methods. 19:429–440.
  • McIver LJ, Abu-Ali G, Franzosa EA, Schwager R, Morgan XC, Waldron L, Segata N, Huttenhower C. 2017. bioBakery: a meta’omic analysis environment. Bioinformatics. 34(7):1235–1237.
  • Meersman E, Steensels J, Paulus T, Struyf N, Saels V, Mathawan M, Koffi J, Vrancken G, Verstrepen KJ. 2015. Breeding strategy to generate robust yeast starter cultures for cocoa pulp fermentations. Appl Environ Microbiol. 81(18):6166–6176.
  • Melkonian C, Gottstein W, Blasche S, Kim Y, Abel-Kistrup M, Swiegers H, Saerens S, Edwards N, Patil KR, Teusink B, et al. 2019. Finding functional differences between species in a microbial community: case studies in wine fermentation and kefir culture. Front Microbiol. 10:1347.
  • Mendoza SN, Olivier BG, Molenaar D, Teusink B. 2019. A systematic assessment of current genome-scale metabolic reconstruction tools. Genome Biol. 20(1):158.
  • Menzel P, Gudbergsdottir SR, Rike AG, Lin L, Zhang Q, Contursi P, Moracci M, Kristjansson JK, Bolduc B, Gavrilov S, et al. 2015. Comparative metagenomics of eight geographically remote terrestrial hot springs. Microb Ecol. 70(2):411–424.
  • Menzel P, Ng KL, Krogh A. 2016. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 7(1):11257.
  • Meyer F, Bremges A, Belmann P, Janssen S, McHardy AC, Koslicki D. 2019. Assessing taxonomic metagenome profilers with OPAL. Genome Biol. 20(1):51.
  • Meyer F, Hofmann P, Belmann P, Garrido-Oter R, Fritz A, Sczyrba A, McHardy AC. 2018. AMBER: assessment of metagenome BinnERs. GigaScience. 7(6):giy069.
  • Meyer F, Lesker T-R, Koslicki D, Fritz A, Gurevich A, Darling AE, Sczyrba A, Bremges A, McHardy AC. 2021. Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit. Nat Protoc. 16:1785–1801.
  • Mikheenko A, Saveliev V, Gurevich A. 2016. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics. 32(7):1088–1090.
  • Miller JR, Delcher AL, Koren S, Venter E, Walenz BP, Brownley A, Johnson J, Li K, Mobarry C, Sutton G. 2008. Aggressive assembly of pyrosequencing reads with mates. Bioinformatics. 24(24):2818–2824.
  • Morton JT, Marotz C, Washburne A, Silverman J, Zaramela LS, Edlund A, Zengler K, Knight R. 2019. Establishing microbial composition measurement standards with reference frames. Nat Commun. 10(1):2719.
  • Mukherjee S, Stamatis D, Bertsch J, Ovchinnikova G, Katta HY, Mojica A, Chen IMA, Kyrpides NC, Reddy T. 2019. Genomes OnLine database (GOLD) v.7: updates and new features. Nucleic Acids Res. 47(D1):D649–D659.
  • Murovec B, Deutsch L, Stres B. 2020. Computational framework for high-quality production and large-scale evolutionary analysis of metagenome assembled genomes. Mol Biol Evol. 37(2):593–598.
  • Nampoothiri KM, Beena DJ, Vasanthakumari DS, Ismail B. 2017. Chapter 3 – health benefits of exopolysaccharides in fermented foods. In: Frias J, Martinez-Villaluenga C, Peñas E, editors. Fermented foods in health and disease prevention. Boston: Academic Press; p. 49–62.
  • Norsigian CJ, Pusarla N, McConn JL, Yurkovich JT, Dräger A, Palsson BO, King Z. 2019. BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree. Nucleic Acids Res. 48(D1):D402–D406.
  • Nout MJR. 2014. Food technologies: fermentation. In: Motarjemi Y, editor. Encyclopedia of food safety. Waltham: Academic Press; p. 168–177.
  • Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. 2017. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27(5):824–834.
  • O'Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, Rajput B, Robbertse B, Smith-White B, Ako-Adjei D, et al. 2016. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44(D1):D733–45.
  • Olm MR, Crits-Christoph A, Bouma-Gregson K, Firek BA, Morowitz MJ, Banfield JF. 2021. inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains. Nat Biotechnol. 39(6):727–736.
  • Oniciuc E-A, Likotrafiti E, Alvarez-Molina A, Prieto M, López M, Alvarez-Ordóñez A. 2019. Food processing as a risk factor for antimicrobial resistance spread along the food chain. Curr Opin Food Sci. 30:21–26.
  • Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. 2015. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25(7):1043–1055.
  • Pasolli E, De Filippis F, Mauriello IE, Cumbo F, Walsh AM, Leech J, Cotter PD, Segata N, Ercolini D. 2020. Large-scale genome-wide analysis links lactic acid bacteria from food with the gut microbiome. Nat Commun. 11(1):2610.
  • Peng Y, Leung HC, Yiu SM, Chin FY. 2012. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics. 28(11):1420–1428.
  • Petersen TN, Lukjancenko O, Thomsen MCFM, Sperotto M, Lund O, Møller Aarestrup F, Sicheritz-Pontén T. 2017. MGmapper: reference based mapping and taxonomy annotation of metagenomics sequence reads. PLoS One. 12(5):e0176469.
  • Pevzner PA, Tang H, Waterman MS. 2001. An Eulerian path approach to DNA fragment assembly. Proc Natl Acad Sci U S A. 98(17):9748–9753.
  • Piro VC, Matschkowski M, Renard BY. 2017. MetaMeta: integrating metagenome analysis tools to improve taxonomic profiling. Microbiome. 5(1):101.
  • Pride DT, Meinersmann RJ, Wassenaar TM, Blaser MJ. 2003. Evolutionary implications of microbial genome tetranucleotide frequency biases. Genome Res. 13(2):145–158.
  • Prosser JI. 2015. Dispersing misconceptions and identifying opportunities for the use of 'omics’ in soil microbial ecology. Nat Rev Microbiol. 13(7):439–446.
  • Punta M, Coggill PC, Eberhardt RY, Mistry J, Tate J, Boursnell C, Pang N, Forslund K, Ceric G, Clements J, et al. 2012. The Pfam protein families database. Nucleic Acids Res. 40(Database issue):D290–D301.
  • Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. 2017. Shotgun metagenomics, from sampling to analysis. Nat Biotechnol. 35(9):833–844.
  • Ren J, Ahlgren NA, Lu YY, Fuhrman JA, Sun F. 2017. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome. 5(1):69.
  • Rho M, Tang H, Ye Y. 2010. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res. 38(20):e191.
  • Roux S, Emerson JB, Eloe-Fadrosh EA, Sullivan MB. 2017. Benchmarking viromics: an in silico evaluation of metagenome-enabled estimates of viral community composition and diversity. PeerJ. 5:e3817.
  • Roux S, Enault F, Hurwitz BL, Sullivan MB. 2015. VirSorter: mining viral signal from microbial genomic data. PeerJ. 3:e985.
  • Roux S. 2019. A viral ecogenomics framework to uncover the secrets of nature’s “microbe whisperers”. mSystems. 4(3):e00111-19.
  • Salazar JK, Carstens CK, Ramachandran P, Shazer AG, Narula SS, Reed E, Ottesen A, Schill KM. 2018. Metagenomics of pasteurized and unpasteurized gouda cheese using targeted 16S rDNA sequencing. BMC Microbiol. 18(1):189.
  • Şanlier N, Gökcen BB, Sezgin AC. 2019. Health benefits of fermented foods. Crit Rev Food Sci Nutr. 59(3):506–527.
  • Satlin MJ, Cohen N, Ma KC, Gedrimaite Z, Soave R, Askin G, Chen L, Kreiswirth BN, Walsh TJ, Seo SK. 2016. Bacteremia due to carbapenem-resistant Enterobacteriaceae in neutropenic patients with hematologic malignancies. J Infect. 73(4):336–345.
  • Sato K, Sakakibara Y. 2015. MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning. DNA Res. 22(1):69–77.
  • Scholz M, Ward DV, Pasolli E, Tolio T, Zolfo M, Asnicar F, Truong DT, Tett A, Morrow AL, Segata N. 2016. Strain-level microbial epidemiology and population genomics from shotgun metagenomics. Nat Methods. 13(5):435–438.
  • Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S, Dröge J, Gregor I, Majda S, Fiedler J, Dahms E, et al. 2017. Critical assessment of metagenome interpretation—a benchmark of metagenomics software. Nat Methods. 14(11):1063–1071.
  • Sedlar K, Kupkova K, Provaznik I. 2017. Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics. Comput Struct Biotechnol J. 15:48–55.
  • Seemann T. 2014. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 30(14):2068–2069.
  • Segata N, Waldron L, Ballarini A, Narasimhan V, Jousson O, Huttenhower C. 2012. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat Methods. 9(8):811–814.
  • Segata N. 2018. On the road to strain-resolved comparative metagenomics. mSystems. 3(2):e00190-17.
  • Selhub EM, Logan AC, Bested AC. 2014. Fermented foods, microbiota, and mental health: ancient practice meets nutritional psychiatry. J Physiol Anthropol. 33(1):2.
  • Shahbandeh M. 2019. Global functional food market revenue 2019–2025.
  • Shakya M, Lo C-C, Chain PSG. 2019. Advances and challenges in metatranscriptomic analysis. Front Genet. 10:904.
  • Silva GG, Green KT, Dutilh BE, Edwards RA. 2016. SUPER-FOCUS: a tool for agile functional analysis of shotgun metagenomic data. Bioinformatics. 32(3):354–361.
  • Silva GGZ, Cuevas DA, Dutilh BE, Edwards RA. 2014. FOCUS: an alignment-free model to identify organisms in metagenomes using non-negative least squares. PeerJ. 2:e425.
  • Slattery C, Cotter PD, O'Toole PW. 2019. Analysis of health benefits conferred by Lactobacillus species from kefir. Nutrients. 11(6):1252.
  • Solden L, Lloyd K, Wrighton K. 2016. The bright side of microbial dark matter: lessons learned from the uncultivated majority. Curr Opin Microbiol. 31:217–226.
  • Stanke M, Morgenstern B. 2005. AUGUSTUS: a web server for gene prediction in eukaryotes that allows user-defined constraints. Nucleic Acids Res. 33(Web Server issue):W465–W467.
  • Stasiewicz MJ, den Bakker HC, Wiedmann M. 2015. Genomics tools in microbial food safety. Curr Opin Food Sci. 4:105–110.
  • Sulaiman J, Gan HM, Yin WF, Chan KG. 2014. Microbial succession and the functional potential during the fermentation of Chinese soy sauce brine. Front Microbiol. 5:556.
  • Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, Djahanschiri B, Zeller G, Mende DR, Alberti A, et al. 2015. Structure and function of the global ocean microbiome. Science. 348(6237):1261359.
  • Tamames J, Puente-Sánchez F. 2018. SqueezeMeta, a highly portable, fully automatic metagenomic analysis pipeline. Front Microbiol. 9:3349.
  • Tatusova T, Ciufo S, Fedorov B, O'Neill K, Tolstoy I. 2014. RefSeq microbial genomes database: new representation and annotation strategy. Nucleic Acids Res. 42(Database issue):D553–D559.
  • Taylor BC, Lejzerowicz F, Poirel M, Shaffer JP, Jiang L, Aksenov A, Litwin N, Humphrey G, Martino C, Miller-Montgomery S, et al. 2020. Consumption of fermented foods is associated with systematic differences in the gut microbiome and metabolome. mSystems. 5(2):e00901-19.
  • Thiele I, Palsson B. 2010. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc. 5(1):93–121.
  • Tian J, Zhu Z, Wu B, Wang L, Liu X. 2013. Bacterial and fungal communities in Pu’er tea samples of different ages. J Food Sci. 78(8):M1249–M1256.
  • Truong DT, Franzosa EA, Tickle TL, Scholz M, Weingart G, Pasolli E, Tett A, Huttenhower C, Segata N. 2015. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods. 12(10):902–903.
  • Truong DT, Tett A, Pasolli E, Huttenhower C, Segata N. 2017. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Research. 27(4):626–638.
  • Uritskiy GV, DiRuggiero J, Taylor J. 2018. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 6(1):158.
  • Van Rossum T, Ferretti P, Maistrenko OM, Bork P. 2020. Diversity within species: interpreting strains in microbiomes. Nat Rev Microbiol. 18(9):491–506.
  • Ventola CL. 2015. The antibiotic resistance crisis: part 1: causes and threats. P T. 40(4):277–283.
  • Verce M, De Vuyst L, Weckx S. 2019. Shotgun metagenomics of a water kefir fermentation ecosystem reveals a novel Oenococcus species. Front Microbiol. 10(479):479.
  • Wallace JC, Port JA, Smith MN, Faustman EM. 2017. FARME DB: a functional antibiotic resistance element database. Database. 2017:baw165.
  • Walsh AM, Crispie F, Daari K, O'Sullivan O, Martin JC, Arthur CT, Claesson MJ, Scott KP, Cotter PD. 2017. Strain-level metagenomic analysis of the fermented dairy beverage nunu highlights potential food safety risks. Appl Environ Microbiol. 83(16):1–13.
  • Walsh AM, Crispie F, Kilcawley K, O'Sullivan O, O'Sullivan MG, Claesson MJ, Cotter PD. 2016. Microbial succession and flavor production in the fermented dairy beverage kefir. mSystems. 1(5):e00052-16.
  • Walsh AM, Crispie F, O'Sullivan O, Finnegan L, Claesson MJ, Cotter PD. 2018. Species classifier choice is a key consideration when analysing low-complexity food microbiome data. Microbiome. 6(1):50–50.
  • Wang Z, Wang Y, Fuhrman JA, Sun F, Zhu S. 2019. Assessment of metagenomic assemblers based on hybrid reads of real and simulated metagenomic sequences. Briefings Bioinf. 21(3):777–790.
  • Weinbauer MG, Rassoulzadegan F. 2004. Are viruses driving microbial diversification and diversity? Environ Microbiol. 6(1):1–11.
  • Wickham H. 2016. ggplot2: elegant graphics for data analysis. Cham: Springer.
  • Wolfe BE, Button JE, Santarelli M, Dutton RJ. 2014. Cheese rind communities provide tractable systems for in situ and in vitro studies of microbial diversity. Cell. 158(2):422–433.
  • Wood DE, Lu J, Langmead B. 2019. Improved metagenomic analysis with Kraken 2. Genome Biol. 20(1):257.
  • Wood DE, Salzberg SL. 2014. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15(3):R46.
  • World Health Organization. 2017. The burden of foodborne diseases in the WHO European Region. https://www.euro.who.int/__data/assets/pdf_file/0005/402989/50607-WHO-Food-Safety-publicationV4_Web.pdf
  • Wu L-H, Lu Z-M, Zhang X-J, Wang Z-M, Yu Y-J, Shi J-S, Xu Z-H. 2017. Metagenomics reveals flavour metabolic network of cereal vinegar microbiota. Food Microbiol. 62:23–31.
  • Wu YW, Simmons BA, Singer SW. 2016. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 32(4):605–607.
  • Ye SH, Siddle KJ, Park DJ, Sabeti PC. 2019. Benchmarking metagenomics tools for taxonomic classification. Cell. 178(4):779–794.
  • Yu G. 2020. Using ggtree to visualize data on tree-like structures. Curr Protoc Bioinformatics. 69(1):e96.
  • Zahn JA, Halter MC. 2018. Surveillance and elimination of bacteriophage contamination in an industrial fermentation process. In: Savva R, editor. Bacteriophages. London: IntechOpen.
  • Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, Lund O, Aarestrup FM, Larsen MV. 2012. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother. 67(11):2640–2644.
  • Zelezniak A, Andrejev S, Ponomarova O, Mende DR, Bork P, Patil KR. 2015. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc Natl Acad Sci U S A. 112(20):6449–6454.
  • Zepeda Mendoza ML, Sicheritz-Pontén T, Gilbert MT. 2015. Environmental genes and genomes: understanding the differences and challenges in the approaches and software for their analyses. Brief Bioinform. 16(5):745–758.
  • Zepeda-Mendoza ML, Edwards NK, Madsen MG, Abel-Kistrup M, Puetz L, Sicheritz-Ponten T, Swiegers JH. 2018. Influence of Oenococcus oeni and Brettanomyces bruxellensis on wine microbial taxonomic and functional potential profiles. American Journal of Enology and Viticulture. 69(4):321–333.
  • Zhang C, Hua Q. 2015. Applications of genome-scale metabolic models in biotechnology and systems medicine. Front Physiol. 6:413.
  • Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, Busk PK, Xu Y, Yin Y. 2018. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 46(W1):W95–W101.
  • Zolfo M, Tett A, Jousson O, Donati C, Segata N. 2017. MetaMLST: multi-locus strain-level bacterial typing from metagenomic samples. Nucleic Acids Res. 45(2):e7.
  • Zomorrodi AR, Maranas CD. 2012. OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLoS Comput Biol. 8(2):e1002363.