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Microbiome Data Analysis and Interpretation: Correlation Inference and Dynamic Pattern Discovery

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Methodologies of Multi-Omics Data Integration and Data Mining

Part of the book series: Translational Bioinformatics ((TRBIO,volume 19))

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Abstract

Microbial communities are everywhere within our bodies and in the environments (Byrd et al. n.d.), which play a key role in human health and all critical nutrient cycles on earth. The microbiome refers to the entire micro-environment, including microorganisms, genomes, and the surrounding environment. With the development of high-throughput sequencing (HTS) technology and data analysis methods, the role of the microbiome in humans, animals, plants, and the environment has become increasingly clear in recent years.

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References

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

    Article  CAS  Google Scholar 

  • Aßhauer KP, et al. Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data. Bioinformatics. 2015;31(17):2882–4.

    Article  Google Scholar 

  • Bankevich A, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19(5):455–77.

    Article  CAS  Google Scholar 

  • de Sena Brandine G, Smith AD. Falco: high-speed FastQC emulation for quality control of sequencing data. F1000Res. 2019b;8:1874.

    Article  Google Scholar 

  • Blin K, et al. antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 2019;47(W1):W81–7.

    Article  CAS  Google Scholar 

  • Boisvert S, et al. Ray Meta: scalable de novo metagenome assembly and profiling. Genome Biol. 2012;13(12):R122.

    Article  Google Scholar 

  • Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20.

    Article  CAS  Google Scholar 

  • Bray NL, et al. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016;34(5):525–7.

    Article  CAS  Google Scholar 

  • Breitwieser FP, Lu J, Salzberg SL. A review of methods and databases for metagenomic classification and assembly. Brief Bioinform. 2019;20(4):1125–36.

    Article  CAS  Google Scholar 

  • Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12(1):59–60.

    Article  CAS  Google Scholar 

  • Byrd AL, Belkaid Y, Segre JA. The human skin microbiome. Nat Rev Microbiol. 2018;16(3):143–55.

    Article  CAS  Google Scholar 

  • Callahan BJ, et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581–3.

    Article  CAS  Google Scholar 

  • Camacho C, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10(1):421.

    Article  Google Scholar 

  • Caporaso JG, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335–6.

    Article  CAS  Google Scholar 

  • Chen C, et al. Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods. PLoS One. 2011;6(2):–e17238.

    Google Scholar 

  • Chiu CY, Miller SA. Clinical metagenomics. Nat Rev Genet. 2019;20(6):341–55.

    Article  CAS  Google Scholar 

  • Crusoe MR, et al. The khmer software package: enabling efficient nucleotide sequence analysis. F1000Res. 2015;4:900.

    Article  Google Scholar 

  • Di Bella JM, et al. High throughput sequencing methods and analysis for microbiome research. J Microbiol Methods. 2013;95(3):401–14.

    Article  Google Scholar 

  • Douglas GM, Beiko RG, Langille MGI. Predicting the functional potential of the microbiome from marker genes using PICRUSt. In: Beiko RG, Hsiao W, Parkinson J, editors. Microbiome analysis: methods and protocols. New York, NY: Springer New York; 2018. p. 169–77.

    Chapter  Google Scholar 

  • Douglas GM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38(6):685–8.

    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 

  • Franzosa EA, et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat Microbiol. 2019;4(2):293–305.

    Article  CAS  Google Scholar 

  • Franzosa EA, et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods. 2018;15(11):962–8.

    Article  CAS  Google Scholar 

  • Gleeson M, et al. The anti-inflammatory effects of exercise: mechanisms and implications for the prevention and treatment of disease. Nat Rev Immunol. 2011;11(9):607–15.

    Article  CAS  Google Scholar 

  • Huson DH, et al. MEGAN analysis of metagenomic data. Genome Res. 2007;17(3):377–86.

    Article  CAS  Google Scholar 

  • Institute., D.J.G. BBDuk guide, 2021.

    Google Scholar 

  • Keegan KP, Glass EM, Meyer F. MG-RAST, a metagenomics service for analysis of microbial community structure and function. Methods Mol Biol. 2016;1399:207–33.

    Article  CAS  Google Scholar 

  • Kim D, et al. Optimizing methods and dodging pitfalls in microbiome research. Microbiome. 2017;5(1):52.

    Article  Google Scholar 

  • Kishikawa T, et al. Metagenome-wide association study of gut microbiome revealed novel aetiology of rheumatoid arthritis in the Japanese population. Ann Rheum Dis. 2020;79(1):103–11.

    Article  CAS  Google Scholar 

  • Knight R, et al. Best practices for analysing microbiomes. Nat Rev Microbiol. 2018;16(7):410–22.

    Article  CAS  Google Scholar 

  • Kuczynski J, et al. Using QIIME to analyze 16S rRNA gene sequences from microbial communities. Curr Protoc Microbiol. 2012;27(1):1E.5.1–1E.5.20.

    Article  Google Scholar 

  • Kultima JR, et al. MOCAT: a metagenomics assembly and gene prediction toolkit. PLoS One. 2012;7(10):e47656.

    Article  Google Scholar 

  • Kultima JR, et al. MOCAT2: a metagenomic assembly, annotation and profiling framework. Bioinformatics. 2016;32(16):2520–3.

    Article  CAS  Google Scholar 

  • Langille MGI, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31(9):814–21.

    Article  CAS  Google Scholar 

  • Liu Y, et al. Methods and applications for microbiome data analysis. Yi chuan = Hereditas. 2019;41(9):845–62.

    Google Scholar 

  • Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71(12):8228–35.

    Article  CAS  Google Scholar 

  • Lu YY, et al. COCACOLA: binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment and paired-end read LinkAge. Bioinformatics (Oxford, England). 2017;33(6):791–8.

    CAS  Google Scholar 

  • Luo J, et al. A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data. Pharmacogenomics J. 2010;10(4):278–91.

    Article  CAS  Google Scholar 

  • Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal. 2011.

    Google Scholar 

  • Medema MH, et al. antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences. Nucleic Acids Res. 2011;39(suppl_2):W339–46.

    Article  CAS  Google Scholar 

  • Merelli I, Viti F, Milanesi L. IBDsite: a Galaxy-interacting, integrative database for supporting inflammatory bowel disease high throughput data analysis. BMC Bioinformatics. 2012;13(14):S5.

    Article  Google Scholar 

  • Monzoorul Haque M, et al. SOrt-ITEMS: sequence orthology based approach for improved taxonomic estimation of metagenomic sequences. Bioinformatics (Oxford, England). 2009;25(14):1722–30.

    Article  CAS  Google Scholar 

  • Nowrotek M, et al. Culturomics and metagenomics: in understanding of environmental resistome. Front Environ Sci Eng. 2019;13(3):40.

    Article  Google Scholar 

  • Nurk S, et al. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27(5):824–34.

    Article  CAS  Google Scholar 

  • Oulas A, et al. Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies. Bioinf Biol Insights. 2015;9:BBI.S12462.

    Article  Google Scholar 

  • Ounit R, Lonardi S. Higher classification sensitivity of short metagenomic reads with CLARK-S. Bioinformatics. 2016;32(24):3823–5.

    Article  CAS  Google Scholar 

  • Papageorgiou L, et al. Genomic big data hitting the storage bottleneck. EMBnetjournal. 2018;24:e910.

    Google Scholar 

  • Peng Y, et al. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics. 2012;28(11):1420–8.

    Article  CAS  Google Scholar 

  • Qin J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464(7285):59–65.

    Article  CAS  Google Scholar 

  • Rognes T, et al. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.

    Article  Google Scholar 

  • Ruairi Robertson P. 16S rRNA Gene Sequencing vs. Shotgun Metagenomic Sequencing https://blog.microbiomeinsights.com/16s-rrna-sequencing-vs-shotgun-metagenomic-sequencing. 2020.7.20.

  • Sangwan N, Xia F, Gilbert JA. Recovering complete and draft population genomes from metagenome datasets. Microbiome. 2016;4(1):8.

    Article  Google Scholar 

  • Schloss PD. Reintroducing mothur: 10 Years Later. Appl Environ Microbiol. 2020;86(2).

    Google Scholar 

  • Schuler CJ, Fau-Hirsch M, et al. Learning to Deblur, 2015.

    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 

  • Sharon G, et al. Human gut microbiota from autism spectrum disorder promote behavioral symptoms in mice. Cell. 2019;177(6):1600–18.e17

    Article  CAS  Google Scholar 

  • Sunagawa S, et al. Metagenomic species profiling using universal phylogenetic marker genes. Nat Methods. 2013;10(12):1196–9.

    Article  CAS  Google Scholar 

  • Tedersoo L, et al. High-throughput identification and diagnostics of pathogens and pests: overview and practical recommendations. Mol Ecol Resour. 2019;19(1):47–76.

    Article  Google Scholar 

  • Treangen TJ, et al. MetAMOS: a modular and open source metagenomic assembly and analysis pipeline. Genome Biol. 2013;14(1):R2.

    Article  Google Scholar 

  • Truong DT, et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods. 2015;12(10):902–3.

    Article  CAS  Google Scholar 

  • Vakhlu J, Sudan AK, Johri BN. Metagenomics: future of microbial gene mining. Indian J Microbiol. 2008;48(2):202–15.

    Article  CAS  Google Scholar 

  • Wang Z, et al. Time-course relationship between environmental factors and microbial diversity in tobacco soil. Sci Rep. 2019;9(1):19969.

    Article  CAS  Google Scholar 

  • Wingett SW, Andrews S. FastQ Screen: a tool for multi-genome mapping and quality control. F1000Res. 2018;7:1338.

    Article  Google Scholar 

  • Wood DE, Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 2014;15(3):R46.

    Article  Google Scholar 

  • Wosinska L, et al. The potential impact of probiotics on the gut microbiome of athletes. Nutrients. 2019;11(10):2270.

    Article  CAS  Google Scholar 

  • Zhang X, et al. Age-related compositional changes and correlations of gut microbiome, serum metabolome, and immune factor in rats. GeroScience. 2021;43(2):709–25.

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China

    Kang Ning & Yuxue Li

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  1. Kang Ning
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  2. Yuxue Li
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Correspondence to Kang Ning .

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Editors and Affiliations

  1. Department of Bioinformatics and Systems Biology, Center of AI biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China

    Kang Ning

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Ning, K., Li, Y. (2023). Microbiome Data Analysis and Interpretation: Correlation Inference and Dynamic Pattern Discovery. In: Ning, K. (eds) Methodologies of Multi-Omics Data Integration and Data Mining. Translational Bioinformatics, vol 19. Springer, Singapore. https://doi.org/10.1007/978-981-19-8210-1_7

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