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Bulletin of Chinese Academy of Sciences (Chinese Version)

Keywords

microbiome; microbiophylome; classification; ecology; synthetic biology

Document Type

Article

Abstract

It was the scientific concept and related technology of metagenomics that initiated the microbiome research. These microbiome research projects conducted globally have led to the acquisition huge amount of data and data sets of microbial genomes related to human health, animals, plants and environments. Consequently, various kinds of microbiome databases and analytical platforms are booming. However, besides the designed specific project-oriented status for some of the databases, most of the current microbiome data platforms merely focus on the development of reference data catalog and metagenome data sets, and mainly support the studies of "molecular ecology" aspect of microbiomes and/or the metagenome of a specific biotype. Thus, commonly expected applications in data integration-dependent megaanalysis, genomic information-based microbial taxonomy or comprehensive functional bioparts mining are largely hindered by lacking of proper data resources or sophisticated bioinformaticians capable of handling the complicated tasks.In this review, we introduce the concept of Microbiophylome, which is the sum of all microbes and member organisms of all kinds of microbiota with their genetic and multiple lifeomics information as well as their related biological structural/functional information. Comparing to the conventional Microbiome, which is the sum of all member microbes of various microbiota in a special ecological biotype with their genetic, mainly metagenome information and related biological function, Microbiophylome emphasizes the total information of every individual taxon of the whole microbial world. In other words, with respect to microbiology as an academic discipline, Microbiophylome is concerned more about the α-phase (taxonomy) and β-phase (phylogeny) of microbial biology while Microbiome is concerned more about the γ-phase (ecology), employing the knowledge of α-and β-phases. With the integration of the concepts of Microbiome and Microbiophylome, we suggest to establish a comprehensive microbiome data warehouse as a hub to integrate the data of microbial taxonomy, evolution and ecology as well as their related omics research. Via further integration of the data of basic research in life science and systems and synthetic biology, this data warehouse will support the development of comprehensive and QA/QC controlled reference databases, high quality standards-guided assembly and annotation and state of the art tools for data integration, searching, shared analysis and deep mining to facilitate future academic research and biotechnology R & D activities in microbiology and related fields. In addition, providing high-quality data standard and data SOPs for safe data integration and sharing, this data warehouse will be attractive for further systematic collection of meta-data of large-scale international projects. We have started this effort aiming at the eventual establishment of a microbiome big data center with complete and integrative data storage, safe and efficiency-guaranteed data management as well as comprehensive and user-friendly data service functions.

First page

280

Last Page

289

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

McGuire A L, Colgrove J, Whitney S N, et al. Ethical, legal, and social considerations in conducting the Human Microbiome Project. Genome Res, 2008, 18(12):1861-1864.

Integrative HMPRNC. The Integrative Human Microbiome Project:dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe, 2014, 16(3):276-289.

Qin J, Li R, Raes J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature, 2010, 464(7285):59-65.

Cardona S, Eck A, Cassellas M, et al. Storage conditions of intestinal microbiota matter in metagenomic analysis. BMC Microbiol, 2012, 12:158.

Mitchell A, Bucchini F, Cochrane G, et al. EBI metagenomics in 2016-an expanding and evolving resource for the analysis and archiving of metagenomic data. Nucleic Acids Res, 2016, 44(D1):D595-603.

Sunagawa S, Coelho LP, Chaffron S, et al. Ocean plankton. structure and function of the global ocean microbiome. Science, 2015, 348(6237):1261359.

Mukherjee S, Stamatis D, Bertsch J, et al. Genomes OnLine Database (GOLD) v.6:data updates and feature enhancements. Nucleic Acids Res, 2017, 45(D1):D446-D456.

Chen I A, Markowitz V M, Chu K, et al. IMG/M:integrated genome and metagenome comparative data analysis system. Nucleic Acids Res, 2017, 45(D1):D507-D516.

Meyer F, Paarmann D, D' Souza M, et al. The metagenomics RAST server-a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics, 2008, 9:386.

Felippes F F, Wang J W, Weigel D. MIGS:miRNA-induced gene silencing. Plant J, 2012, 70(3):541-547.

Field D, Garrity G, Gray T, et al. The minimum information about a genome sequence (MIGS) specification. Nat Biotechnol, 2008, 26(5):541-547.

Ten Hoopen P, Pesant S, Kottmann R, et al. Marine microbial biodiversity, bioinformatics and biotechnology (M2B3) data reporting and service standards. Stand Genomic Sci, 2015, 10: 20.

Vandamme P, Pot B, Gillis M, et al. Polyphasic taxonomy, a consensus approach to bacterial systematics. Microbiol Rev, 1996, 60(2):407-438.

Ramasamy D, Mishra A K, Lagier J C, et al. A polyphasic strategy incorporating genomic data for the taxonomic description of novel bacterial species. Int J Syst Evol Microbiol, 2014, 64(Pt 2):384-391.

Soucy S M, Huang J, Gogarten J P. Horizontal gene transfer: building the web of life. Nat Rev Genet, 2015, 16(8):472-482.

Vandamme P, Peeters C. Time to revisit polyphasic taxonomy. Antonie Van Leeuwenhoek, 2014, 106(1):57-65.

Cole J R, Wang Q, Fish J A, et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res, 2014, 42(Database issue):D633-642.

McDonald D, Price M N, Goodrich J, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J, 2012, 6(3):610-618.

Pruesse E, Quast C, Knittel K, et al. SILVA:a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res, 2007, 35(21):7188-7196.

Yarza P, Richter M, Peplies J, et al. The all-species living Tree project:a 16S rRNA-based phylogenetic tree of all sequenced type strains. Syst Appl Microbiol, 2008, 31(4):241-250.

Mou X, Sun S, Edwards R A, et al. Bacterial carbon processing by generalist species in the coastal ocean. Nature, 2008, 451(7179):708-711.

Warnecke F, Luginbühl P, Ivanova N, et al. Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite. Nature, 2007, 450(7169):560-565.

Schloss P D, Westcott S L, Ryabin T, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol, 2009, 75(23):7537-7541.

Zhou Q, Su X, Jing G, et al. Meta-QC-Chain:comprehensive and fast quality control method for metagenomic data. Genomics Proteomics Bioinformatics, 2014, 12(1):52-56.

Zhou Q, Su X, Wang A, et al. QC-Chain:fast and holistic quality control method for next-generation sequencing data. Plos One, 2013, 8(4):e60234.

Finotello F, Mastrorilli E, Di Camillo B. Measuring the diversity of the human microbiota with targeted next-generation sequencing. Brief Bioinform, 2016.

Huson D H, Auch A F, Qi J, et al. MEGAN analysis of metagenomic data. Genome Res, 2007, 17(3):377-386.

Caporaso J G, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods, 2010, 7(5):335-336.

Su X, Xu J, Ning K. Parallel-META:efficient metagenomic data analysis based on high-performance computation. BMC Systems Biology, 2012, 6(Suppl 1):S16.

Skinnider M A, Dejong C A, Rees P N, et al. Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM). Nucleic acids research, 2015, 43(20):9645-9662.

Dejong C A, Chen G M, Li H, et al. Polyketide and nonribosomal peptide retro-biosynthesis and global gene cluster matching. Nature chemical biology, 2016, 12(12):1007-1014.

Klementz D, Doring K, Lucas X, et al. StreptomeDB 2.0-an extended resource of natural products produced by streptomycetes. Nucleic acids research, 2016, 44(D1):D509-514.

Blin K, Medema M H, Kottmann R, et al. The antiSMASH database, a comprehensive database of microbial secondary metabolite biosynthetic gene clusters. Nucleic Acids Res, 2017, 45(D1):D555-D559.

Placzek S, Schomburg I, Chang A, et al. BRENDA in 2017:new perspectives and new tools in BRENDA. Nucleic Acids Res, 2017, 45(D1):D380-D388.

Morgat A, Lombardot T, Axelsen K B, et al. Updates in Rhea-an expert curated resource of biochemical reactions. Nucleic Acids Res, 2017, 45(D1):D415-D418.

Kanehisa M, Furumichi M, Tanabe M, et al. KEGG:new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res, 2017, 45(D1):D353-D361.

Hu Q N, Deng Z, Hu H, et al. RxnFinder:biochemical reaction search engines using molecular structures, molecular fragments and reaction similarity. Bioinformatics, 2011, 27(17):2465-2467.

Hatzimanikatis V, Li C, Ionita J A, et al. Exploring the diversity of complex metabolic networks. Bioinformatics, 2005, 21(8): 1603-1609.

Moriya Y, Shigemizu D, Hattori M, et al. PathPred:an enzymecatalyzed metabolic pathway prediction server. Nucleic Acids Res, 2010, 38(Web Server issue):W138-143.

Pitkanen E, Jouhten P, Rousu J. Inferring branching pathways in genome-scale metabolic networks. BMC Syst Biol, 2009, 3:103.

Tu W, Zhang H, Liu J, et al. BioSynther:a customized biosynthetic potential explorer. Bioinformatics, 2016, 32(3): 472-473.

King Z A, Lloyd C J, Feist A M, et al. Next-generation genomescale models for metabolic engineering. Curr Opin Biotechnol, 2015, 35:23-29.

Chou C H, Chang W C, Chiu C M, et al. FMM:a web server for metabolic pathway reconstruction and comparative analysis. Nucleic Acids Res, 2009, 37(Web Server issue):W129-134.

Rahman S A, Advani P, Schunk R, et al. Metabolic pathway analysis web service (Pathway Hunter Tool at CUBIC). Bioinformatics, 2005, 21(7):1189-1193.

Kuwahara H, Alazmi M, Cui X, et al. MRE:a web tool to suggest foreign enzymes for the biosynthesis pathway design with competing endogenous reactions in mind. Nucleic Acids Res, 2016, 44(W1):W217-225.

Thiele I, Palsson B O. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc, 2010, 5(1): 93-121.

Narihiro T, Sekiguchi Y. Microbial communities in anaerobic digestion processes for waste and wastewater treatment:a microbiological update. Current opinion in biotechnology, 2007, 18(3):273-278.

Tong X, Xu H, Zou L, et al. High diversity of airborne fungi in the hospital environment as revealed by meta-sequencing-based microbiome analysis. Sci Rep, 2017, 7:39606.

Tringe S G, Zhang T, Liu X, et al. The airborne metagenome in an indoor urban environment. Plos one, 2008, 3(4):e1862.

Gupta R, Beg Q K, Lorenz P. Bacterial alkaline proteases: molecular approaches and industrial applications. Appl Microbiol Biotechnol, 2002, 59(1):15-32.

Cephas K D, Kim J, Mathai R A, et al. Comparative analysis of salivary bacterial microbiome diversity in edentulous infants and their mothers or primary care givers using pyrosequencing. Plos one, 2011, 6(8):e23503.

Yang F, Zeng X, Ning K, et al. Saliva microbiomes distinguish caries-active from healthy human populations. The ISME Journal, 2012, 6(1):1-10.

Jumpertz R, Le D S, Turnbaugh P J, et al. Energy-balance studies reveal associations between gut microbes, caloric load, and nutrient absorption in humans. The American Journal of Clinical nutrition, 2011, 94(1):58-65.

Kong H H. Skin microbiome:genomics-based insights into the diversity and role of skin microbes. Trends Mol Med, 2011, 17(6):320-328.

Vasudevan D, Richter H, Angenent L T. Upgrading dilute ethanol from syngas fermentation to n-caproate with reactor microbiomes. Bioresour Technol, 2014, 151:378-382.

Shi W, Moon C D, Leahy S C, et al. Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome. Genome Res, 2014, 24(9):1517-1525.

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