Abstract
Early microbiome studies focused on estimating the taxonomic composition of an assemblage of microbes using amplicon sequencing. With improved throughput and decreased cost of sequencing, whole genome shotgun (WGS) sequencing of environmental samples has become a standard procedure in microbial studies. This allows a more detailed analysis of the taxonomic composition and the analysis of the functional potential of a microbiome. Typical metagenomic projects may involve hundreds of samples and billions of reads. Fast sequence alignment tools and powerful analysis methods are an important requirement for any metagenomic study. Here we describe how to efficiently perform functional analysis of large-scale metagenomic datasets using a pipeline consisting of DIAMOND for sequencing alignment, MEGAN 6 for interactive exploration and analysis, and MeganServer for easy access to files.
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References
Beszteri B, Temperton B, Frickenhaus S, Giovannoni SJ (2010) Average genome size: a potential source of bias in comparative metagenomics. ISME J 4:1075–1077
Buchfink B, Xie C, Huson DH (2015) Fast and sensitive protein alignment using diamond. Nat Methods 12:59–60. Published online 17 November 2014
Cummings MP, Bazinet AL (2012) A comparative evaluation of sequence classification programs. BMC Bioinformatics 13:92. PubMed Central PMCID: PMC3428669
Eiler A, Zaremba-Niedzwiedzka K et al (2014) Productivity and salinity structuring of the microplankton revealed by comparative freshwater metagenomics. Environ Microbiol 16(9):2682–2698
Fierer N, Leff J, Adams B et al (2012) Cross-biome metagenomic analysis of soil microbial communities and their functional attributes. PNAS 109(52):21390–21395
Greenblum S, Turnbaugh PJ, Elhanan B (2012) Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease. PNAS 109(2):594–599
Greninger AL, Naccache SN, Federman S et al (2015) Rapid metagenomic identification of viral pathogens in clinical samples by real-time nanopore sequencing analysis. Genome Med 7:99
Hunter S, Corbett M, Denise H, Fraser M, Gonzalez-Beltran A, Hunter C, Jones P, Leinonen R, McAnulla C, Maguire E, Maslen J, Mitchell A, Nuka G, Oisel A, Pesseat S, Radhakrishnan R, Rocca-Serra P, Scheremetjew M, Sterk P, Vaughan D, Cochrane G, Field D, Sansone SA (2014) Ebi metagenomics–a new resource for the analysis and archiving of metagenomic data. Nucleic Acids Res 42(Database issue):D600–D606. doi:10.1093/nar/gkt961
Huson DH, Auch AF, Qi J, Schuster SC (2007) Megan analysis of metagenomic data. Genome Res 17:377–386
Huson DH, Beier S, Flade I, Górska A, El-Hadidi M, Mitra S, Ruscheweyh H-J, Tappu R, Poisot T (2016) MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data. PLOS Comput Biol 12(6):e1004957
Huson DH, Mitra S, Weber N, Ruscheweyh H-J, Schuster SC (2011) Integrative analysis of environmental sequences using megan4. Genome Res 21:1552–1560
Kanehisa M, Goto S (2000) Kegg: kyoto encyclopedia of genes and genomes. Nucleic Acid Res 28(1):27–30
Mitchell A, Chang HY, Daugherty L, Fraser M, Hunter S, Lopez R, McAnulla C, Mc- Menamin C, Nuka G, Pesseat S, Sangrador-Vegas A, Scheremetjew M, Rato C, Yong SY, Bateman A, Punta M, Attwood TK, Sigrist CJ, Redaschi N, Rivoire C, Xenarios I, Kahn D, Guyot D, Bork P, Letunic I, Gough J, Oates M, Haft D, Huang H, Natale DA, Wu CH, Orengo C, Sillitoe I, Mi H, Thomas PD, Finn RD (2015) The InterPro protein families database: the classification resource after 15 years. Nucleic Acids Res 43(Database issue):D213–D221. doi:10.1093/nar/gku1243
Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, Edwards RA, Gerdes S, Parrello B, Shukla M, Vonstein V, Wattam AR, Xia F, Stevens R (2014) The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res 42(Database issue):D206–D214. doi:10.1093/nar/gkt1226
Powell S, Szklarczyk D, Trachana K, Roth A, Kuhn M, Muller J, Arnold R, Rattei T, Letunic I, Doerks T, Jensen LJ, von Mering C, Bork P (2012) eggnog v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic Acids Res 40(D1):D284–D289
Qin J, Li R, Raes J et al (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464:59–65
Willmann M, El-Hadidi M, Huson DH et al (2015) Antibiotic selection pressure determination through sequence-based metagenomics. Antimicrob Agents Chemother 59(12):7335–7345
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Beier, S., Tappu, R., Huson, D.H. (2017). Functional Analysis in Metagenomics Using MEGAN 6. In: Charles, T., Liles, M., Sessitsch, A. (eds) Functional Metagenomics: Tools and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-61510-3_4
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DOI: https://doi.org/10.1007/978-3-319-61510-3_4
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