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In Silico Metagenomics Analysis

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Gut Microbiome-Related Diseases and Therapies

Part of the book series: The Microbiomes of Humans, Animals, Plants, and the Environment ((MHAPE,volume 1))

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Abstract

The field of metagenomics (study of a system’s microbiome) comes with various questions researchers are called to answer. Questions about the microbiota’s identity, the interactions of the participating bacteria, fungi, and viruses and their associations with health and disease. Nowadays, the answers to these questions are revealed via next-generation sequencing (NGS) and bioinformatics pipelines. NGS has allowed us to study even the unculturable microbiota whereas the development of appropriate in silico methodologies has made analyzing them fast, accurate, and accessible.

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References

  • Allard G, et al. SPINGO: a rapid species-classifier for microbial amplicon sequences. BMC Bioinformatics. 2015;16(1):324.

    Article  Google Scholar 

  • Alneberg J, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11(11):1144–6.

    Article  CAS  Google Scholar 

  • Bolyen E, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):852–7.

    Article  CAS  Google Scholar 

  • D’Amore R, et al. A comprehensive benchmarking study of protocols and sequencing platforms for 16S rRNA community profiling. BMC Genomics. 2016;17(1):55.

    Article  Google Scholar 

  • Davenport M, et al. Metabolic alterations to the mucosal microbiota in inflammatory bowel disease. Inflamm Bowel Dis. 2014;20(4):723–31.

    Article  Google Scholar 

  • DeSantis TZ, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72(7):5069–72.

    Article  CAS  Google Scholar 

  • Dhariwal A, et al. MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 2017;45(W1):W180–8.

    Article  CAS  Google Scholar 

  • Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10(10):996–8.

    Article  CAS  Google Scholar 

  • Flygare S, et al. Taxonomer: an interactive metagenomics analysis portal for universal pathogen detection and host mRNA expression profiling. Genome Biol. 2016;17(1):111.

    Article  Google Scholar 

  • Fu L, et al. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28(23):3150–2.

    Article  CAS  Google Scholar 

  • Huson DH, Weber N. Microbial community analysis using MEGAN. Methods Enzymol. 2012;531:465–85.

    Article  Google Scholar 

  • Iwai S, et al. Piphillin: improved prediction of metagenomic content by direct inference from human microbiomes. PLoS One. 2016;11(11):e0166104.

    Article  Google Scholar 

  • Kang DD, et al. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165.

    Article  Google Scholar 

  • Le Cao K-A, et al. mixMC: a multivariate statistical framework to gain insight into Microbial Communities. PLoS One. 2016;11(8):e0160169.

    Article  Google Scholar 

  • Mendes-Soares H, et al. MMinte: an application for predicting metabolic interactions among the microbial species in a community. BMC Bioinformatics. 2016;17(1):343.

    Article  Google Scholar 

  • Minich JJ, et al. High-throughput miniaturized 16S rRNA amplicon library preparation reduces costs while preserving microbiome integrity. MSystems. 2018;3(6):e00166–18.

    Article  CAS  Google Scholar 

  • Petersen TN, et al. MGmapper: reference based mapping and taxonomy annotation of metagenomics sequence reads. PLoS One. 2017;12(5):e0176469.

    Article  Google Scholar 

  • Pruesse E, 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.

    Article  CAS  Google Scholar 

  • Pruitt KD, Tatusova T, Maglott DR. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2007;35(suppl_1):D61–5.

    Article  CAS  Google Scholar 

  • Rintala A, et al. Gut microbiota analysis results are highly dependent on the 16S rRNA gene target region, whereas the impact of DNA extraction is minor. J Biomol Technol. 2017;28(1):19.

    Article  Google Scholar 

  • Salipante SJ, et al. Performance comparison of Illumina and ion torrent next-generation sequencing platforms for 16S rRNA-based bacterial community profiling. Appl Environ Microbiol. 2014;80(24):7583–91.

    Article  Google Scholar 

  • Schloss PD, 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.

    Article  CAS  Google Scholar 

  • Segata N, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60.

    Article  Google Scholar 

  • Segata N, et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat Methods. 2012;9(8):811–4.

    Article  CAS  Google Scholar 

  • Shannon P, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.

    Article  CAS  Google Scholar 

  • Ter Braak CJ. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology. 1986;67(5):1167–79.

    Article  Google Scholar 

  • Ulyantsev VI, et al. MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data. Bioinformatics. 2016;32:btw312.

    Article  Google Scholar 

  • Yang Y, Chen N, Chen T. Inference of environmental factor-microbe and microbe-microbe associations from metagenomic data using a hierarchical Bayesian statistical model. Cell Syst. 2017;4(1):129–137.e5.

    Article  CAS  Google Scholar 

  • Zakrzewski M, et al. Calypso: a user-friendly web-server for mining and visualizing microbiome–environment interactions. Bioinformatics. 2017;33(5):782–3.

    CAS  PubMed  Google Scholar 

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Correspondence to Nikolas Dovrolis .

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Dovrolis, N. (2021). In Silico Metagenomics Analysis. In: Gazouli, M., Theodoropoulos, G. (eds) Gut Microbiome-Related Diseases and Therapies. The Microbiomes of Humans, Animals, Plants, and the Environment, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-59642-2_2

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