Abstract
Improvements in sequencing technologies and reduced experimental costs have resulted in a vast number of studies generating high-throughput data. Although the number of methods to analyze these “omics” data has also increased, computational complexity and lack of documentation hinder researchers from analyzing their high-throughput data to its true potential. In this chapter we detail our data-driven, transkingdom network (TransNet) analysis protocol to integrate and interrogate multi-omics data. This systems biology approach has allowed us to successfully identify important causal relationships between different taxonomic kingdoms (e.g., mammals and microbes) using diverse types of data.
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References
Schuster SC (2008) Next-generation sequencing transforms today's biology. Nat Methods 5(1):16–18
Metzker ML (2010) Sequencing technologies—the next generation. Nat Rev Genet 11(1):31–46
Goodwin S, McPherson JD, McCombie WR (2016) Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 17(6):333–351
Mardis ER (2008) The impact of next-generation sequencing technology on genetics. Trends Genet 24(3):133–141
Morozova O, Marra MA (2008) Applications of next-generation sequencing technologies in functional genomics. Genomics 92(5):255–264
Erickson AR et al (2012) Integrated metagenomics/metaproteomics reveals human host-microbiota signatures of Crohn's disease. PLoS One 7(11):e49138
Moreno-Risueno MA, Busch W, Benfey PN (2010) Omics meet networks—using systems approaches to infer regulatory networks in plants. Curr Opin Plant Biol 13(2):126–131
Imhann F et al (2016) Interplay of host genetics and gut microbiota underlying the onset and clinical presentation of inflammatory bowel disease. In: Gut
Joyce AR, Palsson BO (2006) The model organism as a system: integrating 'omics' data sets. Nat Rev Mol Cell Biol 7(3):198–210
Gehlenborg N et al (2010) Visualization of omics data for systems biology. Nat Methods 7(3 Suppl):S56–S68
Poirel CL et al (2013) Reconciling differential gene expression data with molecular interaction networks. Bioinformatics 29(5):622–629
Zhang W, Li F, Nie L (2010) Integrating multiple 'omics' analysis for microbial biology: application and methodologies. Microbiology 156(Pt 2):287–301
Greer R et al (2016) Investigating a holobiont: Microbiota perturbations and transkingdom networks. Gut Microbes 7(2):126–135
Greer RL et al (2016) Akkermansia muciniphila mediates negative effects of IFNgamma on glucose metabolism. Nat Commun 7:13329
Morgun A et al (2015) Uncovering effects of antibiotics on the host and microbiota using transkingdom gene networks. Gut 64(11):1732–1743
Mine KL et al (2013) Gene network reconstruction reveals cell cycle and antiviral genes as major drivers of cervical cancer. Nat Commun 4:1806
Schirmer M et al (2016) Linking the Human Gut Microbiome to Inflammatory Cytokine Production Capacity. Cell 167(4):1125–1136 e8
Shulzhenko N et al (2011) Crosstalk between B lymphocytes, microbiota and the intestinal epithelium governs immunity versus metabolism in the gut. Nat Med 17(12):1585–1593
Dong X et al (2015) Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists. Bioinform Biol Insights 9:61–74
Caporaso JG et al (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7(5):335–336
Trapnell C et al (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7(3):562–578
Laird PW (2010) Principles and challenges of genomewide DNA methylation analysis. Nat Rev Genet 11(3):191–203
Krumm N et al (2012) Copy number variation detection and genotyping from exome sequence data. Genome Res 22(8):1525–1532
Perez-Diez A, Morgun A, Shulzhenko N (2007) Microarrays for cancer diagnosis and classification. Adv Exp Med Biol 593:74–85
Zhao S et al (2014) Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PLoS One 9(1):e78644
Schmieder R, Edwards R (2011) Quality control and preprocessing of metagenomic datasets. Bioinformatics 27(6):863–864
Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetJ 17(1):10
Haas BJ et al (2013) De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc 8(8):1494–1512
Mortazavi A et al (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5(7):621–628
Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11(10):R106
McCarthy DJ, Chen Y, Smyth GK (2012) Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res 40(10):4288–4297
Stackebrandt E, Goebel BM (1994) Taxonomic Note: A Place for DNA-DNA Reassociation and 16S rRNA Sequence Analysis in the Present Species Definition in Bacteriology. Int J Syst Evol Microbiol 44(4):846–849
Lane DJ et al (1985) Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc Natl Acad Sci U S A 82(20):6955–6959
Brookman JL et al (2000) Identification and characterization of anaerobic gut fungi using molecular methodologies based on ribosomal ITS1 and 185 rRNA. Microbiology 146(Pt 2):393–403
Schoch CL et al (2012) Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proc Natl Acad Sci U S A 109(16):6241–6246
Sharpton TJ (2014) An introduction to the analysis of shotgun metagenomic data. Front Plant Sci 5:209
Kuczynski J et al (2011) Using QIIME to analyze 16S rRNA gene sequences from microbial communities. Curr Protoc Bioinformatics 10:7 Chapter 10. Unit
Schloss PD et al (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75(23):7537–7541
Paulson JN et al (2013) Differential abundance analysis for microbial marker-gene surveys. Nat Methods 10(12):1200–1202
Meyer F et al (2008) The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinform 9:386
Huson DH, Weber N (2013) Microbial community analysis using MEGAN. Methods Enzymol 531:465–485
Segata N et al (2012) Metagenomic microbial community profiling using unique clade-specific marker genes. Nat Methods 9(8):811–814
Lindgreen S, Adair KL, Gardner PP (2016) An evaluation of the accuracy and speed of metagenome analysis tools. Sci Rep 6:19233
Langmead B et al (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3):R25
Buchfink B, Xie C, Huson DH (2015) Fast and sensitive protein alignment using DIAMOND. Nat Methods 12(1):59–60
Rodrigues RR, Barry CT (2011) Gene pathway analysis of hepatocellular carcinoma genomic expression datasets. J Surg Res 170(1):e85–e92
Morgun A et al (2006) Molecular profiling improves diagnoses of rejection and infection in transplanted organs. Circ Res 98(12):e74–e83
Yambartsev A et al (2016) Unexpected links reflect the noise in networks. Biol Direct 11(1):52
Saccenti E (2017) Correlation patterns in experimental data are affected by normalization procedures: consequences for data analysis and network inference. J Proteome Res 16(2):619–634
Hua YJ et al (2008) Comparison of normalization methods with microRNA microarray. Genomics 92(2):122–128
Li P et al (2015) Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data. BMC Bioinform 16:347
Gautier (2004) L., et al., affy--analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20(3):307–315
Ritchie (2015) M.E., et al., limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43(7):e47
de la Fuente A et al (2004) Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20(18):3565–3574
Weiss S et al (2016) Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J 10(7):1669–1681
Thomas LD et al (2016) Differentially correlated genes in co-expression networks control phenotype transitions. F1000Res 5:2740
Skinner J et al (2011) Construct and Compare Gene Coexpression Networks with DAPfinder and DAPview. BMC Bioinform 12:286
Acknowledgments
The authors thank Karen N. D’Souza, Khiem Lam, and Dr. Xiaoxi Dong for their help in writing the book chapter. This work was supported by the NIH U01 AI109695 (AM) and R01 DK103761 (NS).
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Rodrigues, R.R., Shulzhenko, N., Morgun, A. (2018). Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host–Microbiota Interactions. In: Beiko, R., Hsiao, W., Parkinson, J. (eds) Microbiome Analysis. Methods in Molecular Biology, vol 1849. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8728-3_15
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DOI: https://doi.org/10.1007/978-1-4939-8728-3_15
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