Metagenomics, metatranscriptomics, and metabolomics approaches for microbiome analysis

Evolutionary Bioinformatics

Volumn 12, Issue , 2016, Pages 5-16

  Aguiar Pulido, Vanessa ^a   Huang, Wenrui ^a   Suarez Ulloa, Victoria ^a   Cickovski, Trevor ^a,b   Mathee, Kalai ^a,c   Narasimhan, Giri ^a  

^a FLORIDA INTERNATIONAL UNIVERSITY   (United States)

^b ECKERD COLLEGE   (United States)

^c FLORIDA INTERNATIONAL UNIVERSITY   (United States)

Author keywords

Metabolomics; Metagenomics; Metatranscriptomics; Microbiome; Networks

Indexed keywords

DNA 16S; VITAMIN;

AIR; BIOINFORMATICS; CLASSIFICATION; CONSTRUCTED ENVIRONMENT; DIGESTION; ECOLOGICAL NICHE; FRESHWATER ENVIRONMENT; GENE SEQUENCE; GUT MICROBIOME; HEALTH; HOST PATHOGEN INTERACTION; HUMAN; IMMUNE SYSTEM; INCIDENCE; INFLAMMATORY BOWEL DISEASE; INFORMATION; MARINE ENVIRONMENT; METABOLOMICS; METAGENOMICS; METATRANSCRIPTOMICS; MICROBIAL COMMUNITY; MICROBIAL DIVERSITY; MICROBIAL GENETICS; MICROBIOME; NEXT GENERATION SEQUENCING; NONHUMAN; OBESITY; PHYLOGENY; POLYMERASE CHAIN REACTION; POPULATION ABUNDANCE; PROBLEM SOLVING; REVIEW; SEDIMENT; SPECIES RICHNESS; TAXONOMY; TERRESTIAL ENVIRONMENT; TRANSCRIPTOMICS; WHOLE METAGENOME SEQUENCING;

EID: 84975282763     PISSN: None     EISSN: 11769343     Source Type: Journal    
DOI: 10.4137/EBO.S36436     Document Type: Review

References (165)

1. Ley RE, Peterson DA, Gordon JI. Ecological and evolutionary forces shaping microbial diversity in the human intestine. Cell. 2006;124(4):837-48.
2. Costello EK, Lauber CL, Hamady M, et al. Bacterial community variation in human body habitats across space and time. Science. 2009;326:1694-7.
3. Hibbing ME, Fuqua C, Parsek MR, Peterson SB. Bacterial competition: surviving and thriving in the microbial jungle. Nat Rev Microbiol. 2010;8:15-25.
4. Liu L, Chen X, Skogerb G, et al. The human microbiome: a hot spot of microbial horizontal gene transfer. Genomics. 2012;100(5):265-70.
5. Proft T. Microbial Toxins: Current Research and Future Trends. Caister Academic Press, Norfolk, UK; 2009.
6. Sharon G, Garg N, Debelius J, Knight R, Dorrestein PC, Mazmanian SK. Specialized metabolites from the microbiome in health and disease. Cell Metab. 2014;20(5):719-30.
7. Kau AL, Ahern PP, Griffin NW, Goodman AL, Gordon JI. Human nutrition. Nature. 2011;474(7351):327-36.
8. Foxman B, Martin ET. Use of the microbiome in the practice of epidemiology: a primer on -omic technologies. Am J Epidemiol. 2015;182(1):1-8.
9. Betts K. A study in balance: how microbiomes are changing the shape of environmental health. Environ Health Perspect. 2011;119(8):340-6.
10. Whipps JM, Lewis K, Cooke RC. Mycoparasitism and Plant Disease Control. Manchester University Press, Manchester, UK; 1988:161-87.
11. Segata N, Boernigen D, Tickle TL, Morgan XC, Garrett WS, Huttenhower C. Computational metaomics for microbial community studies. Mol Syst Biol. 2013;9(1):666-80.
12. Franzosa EA, Hsu T, Sirota-Madi A, et al. Sequencing and beyond: integrating molecular ‘omics’ for microbial community profiling. Nat Rev Microbiol. 2015;13(6):360-72.
13. Barberan A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore cooccurrence patterns in soil microbial communities. ISME J. 2011;6:343-51.
14. Faust K, Raes J. Microbial interactions: from networks to models. Nat Rev Microbiol. 2012;10:538-50.
15. Fernandez M, Riveros JD, Campos M, Mathee K, Narasimhan G. Microbial “Social Networks”. BMC Genomics. 2015;16(suppl 11):S6.
16. Fernandez M, Aguiar-Pulido V, Huang W, et al. Microbiome analysis: state-of-the-art and future trends. In: Mandoiu I, Zelikovsky A, eds. Computational Methods for Next Generation Sequencing Data Analysis. Wiley, Hoboken, NJ;2015:333-51.
17. Peterson J, Garges S, Giovanni M, et al. The NIH Human Microbiome Project. Genome Res. 2009;19:2317-23.
18. Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486:207-14.
19. Human Microbiome Project Consortium. A framework for human microbiome research. Nature. 2012;486(7402):215-21.
20. Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R, Gordon JI. The human microbiome project. Nature. 2007;449:804-10.
21. Turnbaugh PJ, Gordon JI. The core gut microbiome, energy balance and obesity. J Physiol (Lond). 2009;587:4153-8.
22. Marrazzo JM, Martin DH, Watts DH, et al. Bacterial vaginosis: identifying research gaps proceedings of a workshop sponsored by DHHS/NIH/NIAID. Sex Transm Dis. 2010;37:732-44.
23. Qin J, Li R, Raes J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59-65.
24. Ackerman J. The ultimate social network. Sci Am. 2012;306(6):36-43.
25. Brown K, DeCoffe D, Molcan E, Gibson DL. Diet-induced dysbiosis of the intestinal micro-biota and the effects on immunity and disease. Nutrients. 2012;4(8):1095.
26. Cho I, Blaser MJ. The human microbiome: at the interface of health and disease. Nat Rev Genet. 2012;13:260-70.
27. Integrative HMP (iHMP) Research Network Consortium. 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):276-89.
28. Human Microbiome Project Consortium. HMSCP - Shotgun Community Profiling. Available at: http://hmpdacc.org/HMSCP/. Last accessed: Jan. 2016.
29. Ehrlich SD, MetaHIT Consortium. Metagenomics of the intestinal microbiota: potential applications. Gastroenterol Clin Biol. 2010;34:S23-8.
30. Goedert JJ, Hua X, Yu G, Shi J. Diversity and composition of the adult fecal microbiome associated with history of cesarean birth or appendectomy: analysis of the American gut project. EBioMedicine. 2014;1(2):167-72.
31. Gilbert JA, Jansson JK, Knight R. The earth microbiome project: successes and aspirations. BMC Biol. 2014;12(1):69.
32. Venter JC, Remington K, Heidelberg JF, et al. Environmental genome shotgun sequencing of the Sargasso sea. Science. 2004;304(5667):66-74.
33. Nealson KH, Venter JC. Metagenomics and the global ocean survey: what’s in it for us, and why should we care? ISME J. 2007;1(3):185.
34. Lima-Mendez G, Faust K, Henry N, et al. Determinants of community structure in the global plankton interactome. Science. 2015;348(6237):1262073.
35. Karsenti E, Acinas SG, Bork P, et al. A holistic approach to marine eco-systems biology. PLoS Biol. 2011;9(10):e1001177.
36. Sunagawa S, Coelho LP, Chaffron S, et al. Structure and function of the global ocean microbiome. Science. 2015;348(6237):1261359.
37. Marchesi JR, Ravel J. The vocabulary of microbiome research: a proposal. Microbiome. 2015;3:31.
38. Chaffron S, Rehrauer H, Pernthaler J, von Mering C. A global network of coexisting microbes from environmental and whole-genome sequence data. Genome Res. 2010;20:947-59.
39. Gonzalez A, Knight R. Advancing analytical algorithms and pipelines for billions of microbial sequences. Curr Opin Biotechnol. 2012;23:64-71.
40. Freilich S, Kreimer A, Meilijson I, Gophna U, Sharan R, Ruppin E. The large-scale organization of the bacterial network of ecological co-occurrence interactions. Nucleic Acids Res. 2010;38(12):3857-68.
41. Kuczynski J, Liu Z, Lozupone C, McDonald D, Fierer N, and Knight R. Microbial community resemblance methods differ in their ability to detect biologically relevant patterns. Nat Methods. 2010;7:813-9.
42. Faust K, Sathirapongsasuti JF, Izard J, et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput Biol. 2012;8(7):e1002606.
43. Cole JR, Wang Q, Fish JA, et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42:D633-42.
44. 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):7188-96.
45. DeSantis TZ, Hugenholtz P, Larsen N, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069-72.
46. Sharpton TJ. An introduction to the analysis of shotgun metagenomic data. Front Plant Sci. 2014;5:209.
47. Nelson KE, Weinstock GM, Highlander SK, et al. A catalog of reference genomes from the human microbiome. Science. 2010;328:994-9.
48. Frias-Lopez J, Shi Y, Tyson GW, et al. Microbial community gene expression in ocean surface waters. Proc Natl Acad Sci (PNAS). 2008;105(10):3805-10.
49. Chain PS, Grafham DV, Fulton RS, et al. Genome project standards in a new era of sequencing. Science. 2009;326:236-7.
50. Kim M, Lee K-H, Yoon S-W, Kim B-S, Chun J, and Yi H. Analytical tools and databases for metagenomics in the next-generation sequencing era. Genomics Inform. 2013;11(3):102-13.
51. Gallopoulos E, Houstis E, Rice J. Computer as thinker/doer: problem-solving environments for computational science. Comput Sci Eng IEEE. 1994;1:11-23.
52. Goecks J, Nekrutenko A, Taylor J. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 2010;11:R86.
53. Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335-6.
54. Schloss PD, Westcott SL, 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):7537-41.
55. Hong C, Manimaran S, Shen Y, et al. PathoScope 2.0: a complete computational framework for strain identification in environmental or clinical sequencing samples. Microbiome. 2014;2(1):33.
56. Chen W, Zhang CK, Cheng Y, Zhang S, Zhao H. A comparison of methods for clustering 16s rRNA sequences into OTUs. PLoS One. 2013;8(8):e70837.
57. Mande SS, Mohammed MH, Ghosh TS. Classification of metagenomic sequences: methods and challenges. Brief Bioinform. 2012;13(6):669-81.
58. Colwell R. Estimates, Version 7.5: Statistical Estimation of Species Richness and Shared Species from Samples (Software and Users Guide); 2005. Available at: http://viceroy.eeb.uconn.edu/estimates
59. Colwell RK. Biodiversity: concepts, patterns, and measurement. In: Levin SA, ed. The Princeton Guide to Ecology. Princeton University Press, Princeton, NJ;2009:257-63.
60. Ondov BD, Bergman NH, Phillippy AM. Interactive metagenomic visualization in a web browser. BMC Bioinformatics. 2011;12(1):385.
61. Huse SM, Welch DBM, Voorhis A, et al. VAMPS: a website for visualization and analysis of microbial population structures. BMC Bioinformatics. 2014; 15(1):41.
62. Wu M, Scott AJ. Phylogenomic analysis of bacterial and archaeal sequences with amphora2. Bioinformatics. 2012;28(7):1033-4.
63. Matsen F IV, Evans SN. Edge principal components and squash clustering: using the special structure of phylogenetic placement data for sample comparison. PLoS One. 2013;8:3.
64. Brady A, Salzberg SL. Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models. Nat Methods. 2009;6:673-6.
65. Brady A, Salzberg S. Phymmbl expanded: confidence scores, custom databases, parallelization and more. Nat Methods. 2011;8(5):367-7.
66. Matsen FA, Kodner RB, Armbrust EV. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics. 2010;11(1):538.
67. 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(1):386.
68. Stark M, Berger SA, Stamatakis A, von Mering C. Mltreemap-accurate maximum likelihood placement of environmental DNA sequences into taxonomic and functional reference phylogenies. BMC Genomics. 2010;11(1):461.
69. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25-9.
70. Gene Ontology Consortium. Gene ontology consortium: going forward. Nucleic Acids Res. 2015;43(D1):D1049-56.
71. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27-30.
72. Kotera M, Moriya Y, Tokimatsu T, Goto S. Kegg and genomenet, new developments, metagenomic analysis. In: Nelson KE, ed. Encyclopedia of Metagenomics. Springer, New York;2015:329-39.
73. Tatusov RL, Koonin EV, Lipman DJ. A genomic perspective on protein families. Science. 1997;278:631-7.
74. Tatusov RL, Galperin MY, Natale DA, Koonin EV. The cog database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000;28(1):33-6.
75. Abubucker S, Segata N, Goll J, et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput Biol. 2012;8(6):e1002358.
76. Markowitz VM, Chen I-MM, Palaniappan K, et al. IMG: the integrated microbial genomes database and comparative analysis system. Nucleic Acids Res. 2012;40:D115-22.
77. Seshadri R, Kravitz SA, Smarr L, et al. Camera: a community resource for metagenomics. PLoS Biol. 2007;5(3):e75.
78. Goll J, Rusch DB, Tanenbaum DM, et al. METAREP: JCVI metagenomics reports - an open source tool for high-performance comparative metagenomics. Bioinformatics. 2010;26(20):2631-2.
79. Huson DH, Mitra S, Ruscheweyh H-J, Schuster SC. Integrative analysis of environmental sequences using MEGAN4. Genome Res. 2011;21(9):1552-60.
80. Lingner T, Aßhauer KP, Schreiber F, Meinicke P. Comet - a web server for comparative functional profiling of metagenomes. Nucleic Acids Res. 2011;39(Web Server issue):W518-23.
81. Langille MG, Zaneveld J, Caporaso JG, et al. Predictive functional profiling of microbial communities using 16 s rrna marker gene sequences. Nat Biotechnol. 2013;31(9):814-21.
82. Eisen J. Environmental shotgun sequencing: its potential and challenges for studying the hidden world of microbes. PLoS Biol. 2007;5(3):e82.
83. Moran MA. Metatranscriptomics: eavesdropping on complex microbial communities. Microbiome. 2009;4(7):329-34.
84. Poretsky RS, Bano N, Buchan A, et al. Analysis of microbial gene transcripts in environmental samples. Appl Environ Microbiol. 2005;71(7):4121-6.
85. Botero LM, D’imperio S, Burr M, McDermott TR, Young M, Hassett DJ. Poly (a) polymerase modification and reverse transcriptase PCR amplification of environmental RNA. Appl Environ Microbiol. 2005;71(3):1267-75.
86. Carvalhais LC, Dennis PG, Tyson GW, Schenk PM. Application of metatranscriptomics to soil environments. J Microbiol Methods. 2012;91(2):246-51.
87. Gilbert JA, Field D, Huang Y, et al. Detection of large numbers of novel sequences in the metatranscriptomes of complex marine microbial communities. PLoS One. 2008;3(8):e3042.
88. Leimena MM, Ramiro-Garcia J, Davids M, et al. A comprehensive metatranscriptome analysis pipeline and its validation using human small intestine microbiota datasets. BMC Genomics. 2013;14(1):530.
89. Yost S, Duran-Pinedo AE, Teles R, Krishnan K, Frias-Lopez J. Functional signatures of oral dysbiosis during periodontitis progression revealed by microbial metatranscriptome analysis. Genome Med. 2015;7(1):27.
90. Duran-Pinedo AE, Chen T, Teles R, et al. Community-wide transcriptome of the oral microbiome in subjects with and without periodontitis. ISME J. 2014;8(8):1659-72.
91. Jorth P, Turner KH, Gumus P, Nizam N, Buduneli N, Whiteley M. Metatranscriptomics of the human oral microbiome during health and disease. MBio. 2014;5(2):e1012-4.
92. Xiong X, Frank DN, Robertson CE, et al. Generation and analysis of a mouse intestinal meta-transcriptome through illumina based RNA-sequencing. PLoS One. 2012;7(4):e36009.
93. Dumont MG, Pommerenke B, Casper P. Using stable isotope probing to obtain a targeted metatranscriptome of aerobic methanotrophs in lake sediment. Environ Microbiol Rep. 2013;5(5):757-64.
94. Celaj A, Markle J, Danska J, Parkinson J. Comparison of assembly algorithms for improving rate of metatranscriptomic functional annotation. Microbiome. 2014;2(1):39.
95. Grabherr MG, Haas BJ, Yassour M, et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat Biotechnol. 2011;29(7): 644-52.
96. Namiki T, Hachiya T, Tanaka H, Sakakibara Y. Metavelvet: an extension of velvet assembler to de novo metagenome assembly from short sequence reads. Nucleic Acids Res. 2012;40(20):e155.
97. Schulz MH, Zerbino DR, Vingron M, Birney E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics. 2012;28(8):1086-92.
98. Birol I, Jackman SD, Nielsen CB, et al. De novo transcriptome assembly with abyss. Bioinformatics. 2009;25(21):2872-7.
99. Li R, Zhu H, Ruan J, et al. De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 2010;20:265-72.
100. Robertson G, Schein J, Chiu R, et al. De novo assembly and analysis of RNA-seq data. Nat Methods. 2010;7(11):909-12.
101. Guttman M, Garber M, Levin JZ, et al. Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincrnas. Nat Biotechnol. 2010;28(5):503-10.
102. Trapnell C, Williams BA, Pertea G, et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28(5):511-5.
103. Li B, Dewey CN. Rsem: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics. 2011;12(1):323.
104. Haas BJ, Papanicolaou A, Yassour M, et al. De novo transcript sequence reconstruction from RNA-seq using the trinity platform for reference generation and analysis. Nat Protoc. 2013;8(8):1494-512.
105. De Bona F, Ossowski S, Schneeberger K, Rätsch G. Optimal spliced alignments of short sequence reads. BMC Bioinformatics. 2008;9(suppl 10):i174-80.
106. Cao HX, Schmutzer T, Scholz U, Pecinka A, Schubert I, Vu GTH. Metatranscriptome analysis reveals host-microbiome interactions in traps of carnivorous genlisea species. Front Microbiol. 2015;6:526.
107. Peano C, Pietrelli A, Consolandi C, et al. An efficient rRNA removal method for RNA sequencing in GC-rich bacteria. Microb Inform Exp. 2013;3(1):1.
108. Perez-Losada M, Castro-Nallar E, Bendall ML, Freishtat RJ, Crandall KA. Dual transcriptomic profiling of host and microbiota during health and disease in pediatric asthma. PLoS One. 2015;10:e0131819.
109. Fiehn O. Metabolomics - the link between genotypes and phenotypes. Plant Mol Biol. 2002;48(1-2):155-71.
110. Bernini P, Bertini I, Luchinat C, et al. Individual human phenotypes in metabolic space and time. J Proteome Res. 2009;8(9):4264-71.
111. Krumsiek J, Mittelstrass K, Do KT, et al. Gender-specific pathway differences in the human serum metabolome. Metabolomics. 2015;11(6):1815-33.
112. Mastrangelo A, Armitage EG, García A, Barbas C. Metabolomics as a tool for drug discovery and personalised medicine. A review. Curr Top Med Chem. 2014;14(23):2627-36.
113. Xu J, Mahowald MA, Ley RE, et al. Evolution of symbiotic bacteria in the distal human intestine. PLoS Biol. 2007;5(7):e156.
114. Manor O, Levy R, Borenstein E. Mapping the inner workings of the microbiome: genomic-and metagenomic-based study of metabolism and metabolic interactions in the human microbiome. Cell Metab. 2014;20(5):742-52.
115. Wu GD, Compher C, Chen EZ, et al. Comparative metabolomics in vegans and omnivores reveal constraints on diet-dependent gut microbiota metabolite production. Gut. 2014;65(1):63-72.
116. Lankadurai BP, Nagato EG, Simpson MJ. Environmental metabolomics: an emerging approach to study organism responses to environmental stressors. Environ Rev. 2013;21(3):180-205.
117. Kimes NE, Callaghan AV, Aktas DF, et al. Metagenomic analysis and metabolite profiling of deep-sea sediments from the gulf of Mexico following the deepwater horizon oil spill. Front Microbiol. 2013;4:50.
118. Bassler BL, Greenberg EP, Stevens AM. Cross-species induction of luminescence in the quorum-sensing bacterium Vibrio harveyi. J Bacteriol. 1997;179(12):1943-5.
119. Miller MB, Bassler BL. Quorum sensing in bacteria. Ann Rev Microbiol. 2001;55(1): 165-99.
120. Bassler BL. Small talk: cell-to-cell communication in bacteria. Cell. 2002;109(4): 421-4.
121. Henke JM, Bassler BL. Three parallel quorum-sensing systems regulate gene expression in Vibrio harveyi. J Bacteriol. 2004;186(20):6902-14.
122. Waters CM, Bassler BL. Quorum sensing: cell-to-cell communication in bacteria. Annu Rev Cell Dev Biol. 2005;21:319-46.
123. Camilli A, Bassler BL. Bacterial small-molecule signaling pathways. Science. 2006;311(5764):1113-6.
124. Aldridge BB, Rhee KY. Microbial metabolomics: innovation, application, insight. Curr Opin Microbiol. 2014;19:90-6.
125. Smolinska A, Blanchet L, Buydens LM, Wijmenga SS. NMR and pattern recognition methods in metabolomics: from data acquisition to biomarker discovery: a review. Anal Chim Acta. 2012;750:82-97.
126. Wishart DS, Tzur D, Knox C, et al. HMDB: the human metabolome database. Nucleic Acids Res. 2007;35(suppl 1):D521-6.
127. Wishart DS, Knox C, Guo AC, et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 2009;37(suppl 1):D603-10.
128. Wishart DS, Jewison T, Guo AC, et al. HMDB 3.0-the human metabolome database in 2013. Nucleic Acids Res. 2012;41(Database issue):D801-7.
129. Ulrich EL, Akutsu H, Doreleijers JF, et al. Biomagresbank. Nucleic Acids Res. 2008;36(suppl 1):D402-8.
130. Cui Q, Lewis IA, Hegeman AD, et al. Metabolite identification via the Madison metabolomics consortium database. Nat Biotechnol. 2008;26(2):162-4.
131. Horai H, Arita M, Kanaya S, et al. Massbank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom. 2010;45(7):703-14.
132. Kopka J, Schauer N, Krueger S, et al. Gmdcsb.db: the Golm metabolome database. Bioinformatics. 2005;21(8):1635-8.
133. Smith CA, O’Maille G, Want EJ, et al. Metlin: a metabolite mass spectral database. Ther Drug Monit. 2005;27(6):747-51.
134. Reigstad CS, Kashyap PC. Beyond phylotyping: understanding the impact of gut microbiota on host biology. Neurogastroenterol Motil. 2013;25(5):358-72.
135. Aw W, Fukuda S. Toward the comprehensive understanding of the gut ecosystem via metabolomics-based integrated omics approach. Semin Immunopathol. 2015;37(1):5-16.
136. Mason OU, Hazen TC, Borglin S, et al. Metagenome, metatranscriptome and single-cell sequencing reveal microbial response to deepwater horizon oil spill. ISME J. 2012;6(9):1715-27.
137. McNulty NP, Yatsunenko T, Hsiao A, et al. The impact of a consortium of fermented milk strains on the gut microbiome of gnotobiotic mice and monozygotic twins. Sci Transl Med. 2011;3(106):106ra106.
138. Maurice CF, Haiser HJ, Turnbaugh PJ. Xenobiotics shape the physiology and gene expression of the active human gut microbiome. Cell. 2013;152(1):39-50.
139. Verberkmoes NC, Russell AL, Shah M, et al. Shotgun metaproteomics of the human distal gut microbiota. ISME J. 2009;3(2):179-89.
140. Weir TL, Manter DK, Sheflin AM, Barnett BA, Heuberger AL, Ryan EP. Stool microbiome and metabolome differences between colorectal cancer patients and healthy adults. PLoS One. 2013;8(8):e70803.
141. Wang Z, Klipfell E, Bennett BJ, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature. 2011;472(7341):57-63.
142. Koeth RA, Wang Z, Levison BS, et al. Intestinal microbiota metabolism of l-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med. 2013;19(5):576-85.
143. Kaddurah-Daouk R, Baillie RA, Zhu H, et al. Enteric microbiome metabolites correlate with response to simvastatin treatment. PLoS One. 2011;6(10): e25482.
144. Haiser HJ, Gootenberg DB, Chatman K, Sirasani G, Balskus EP, Turnbaugh PJ. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science. 2013;341(6143):295-8.
145. Franzosa EA, Morgan XC, Segata N, et al. Relating the metatranscriptome and metagenome of the human gut. Proc Natl Acad Sci. 2014;111(22):E2329-38.
146. Shi Y, Tyson GW, Eppley JM, DeLong EF. Integrated metatranscriptomic and metagenomic analyses of stratified microbial assemblages in the open ocean. ISME J. 2011;5(6):999-1013.
147. Turnbaugh PJ, Gordon JI. An invitation to the marriage of metagenomics and metabolomics. Cell. 2008;134(5):708-13.
148. Lu K, Abo RP, Schlieper KA, et al. Arsenic exposure perturbs the gut microbiome and its metabolic profile in mice: an integrated metagenomics and metabolomics analysis. Environ Health Perspect. 2014;122(3):284-91.
149. Zhang Y, Zhao F, Deng Y, Zhao Y, Ren H. Metagenomic and metabolomic analysis of the toxic effects of trichloroacetamide-induced gut microbiome and urine metabolome perturbations in mice. J Proteome Res. 2015;14(4):1752-61.
150. Narayanasamy S, Muller EE, Sheik AR, Wilmes P. Integrated omics for the identification of key functionalities in biological wastewater treatment microbial communities. Microb Biotechnol. 2015;8(3):363-8.
151. Muller EE, Glaab E, May P, Vlassis N, Wilmes P. Condensing the omics fog of microbial communities. Trends Microbiol. 2013;21(7):325-33.
152. Abram F. Systems-based approaches to unravel multi-species microbial community functioning. Comput Struct Biotechnol J. 2015;13:24-32.
153. Levy R, Borenstein E. Reverse ecology: from systems to environments and back. In: Soyer OS, ed. Evolutionary Systems Biology. Springer, New York;2012: 329-45.
154. Borenstein E, Kupiec M, Feldman MW, Ruppin E. Large-scale reconstruction and phylogenetic analysis of metabolic environments. Proc Natl Acad Sci. 2008;105(38):14482-7.
155. Ebenhöh O, Handorf T, Heinrich R. Structural analysis of expanding metabolic networks. Genome Inform. 2004;15(1):35-45.
156. Bachmaier C, Brandes U, Schreiber F. Chapter 20: Biological networks. In: Tamassia R, ed. Handbook of Graph Drawing and Visualization. CRC Press, Boca Raton, FL; 2013:621-51.
157. Wuchty S, Ravasz E, Barabási A-L. The architecture of biological networks. In: Deisboek TS, Kresh JY, eds. Complex Systems Science in Biomedicine. Springer, New York; 2006:165-81.
158. Barabási A-L, Oltvai ZN, Wuchty S. Characteristics of biological networks. In: Ben-Naim E, Frauenfelder H, Tonoczkai Z, eds. Complex Networks. Springer-Verlag, Berlin; 2004:443-57.
159. Pawson T, Nash P. Protein-protein interactions define specificity in signal transduction. Genes Dev. 2000;9:1027-47.
160. Dutkowski J, Kramer M, Surma MA, et al. A gene ontology inferred from molecular networks. Nat Biotechnol. 2013;31:38-45.
161. Demchak B, Hull T, Reich M, et al. Cytoscape: the network visualization tool for genomespace workflows. F1000Res. 2014;2014(3):151-63.
162. Friedman J, Alm EJ. Inferring correlation networks from genomic survey data. PLoS Comput Biol. 2012;8(9):e1002687.
163. Srivas R, Hannum G, Ruscheinski J, et al. Assembling global maps of cellular function through integrative analysis of physical and genetic networks. Nat Protoc. 2011;6(9):1308-23.
164. Amar D, Shamir R. Constructing module maps for integrated analysis of heterogeneous biological networks. Nucleic Acids Res. 2014;42(7):4208-19.
165. van Dam JC, Schaap PJ, dos Santos VAM, Suárez-Diez M. Integration of heterogeneous molecular networks to unravel gene-regulation in mycobacterium tuberculosis. BMC Syst Biol. 2014;8(1):111.