Abstract
The success of a synthetic biology project is heavily dependent on omics studies, while multiomics data integration and data mining techniques have served as the foundation for rational synthetic biology work. Multiomics combines multiple types of omics data, including genomics, transcriptomics, proteomics, epigenomics, and microbiomics. It is foreseeable that multiomics research will be widely used in many biological problems to reveal more profound omics patterns. In this chapter, we have described and explained the basics of multiomics data integration and data mining techniques, which could be helpful for conducting synthetic biology studies, especially those that focus on the utilization of microbes.
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References
Keshava, R., et al.: Chapter 4—Synthetic biology: overview and applications. In: Barh, D., Azevedo, V. (eds.) Omics Technologies and Bio-Engineering, pp. 63–93. Academic Press (2018)
Osier, N.D., et al.: Symptom science: repurposing existing omics data. Biol. Res. Nurs. 19(1), 18–27 (2017)
Manzoni, C., et al.: Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Brief. Bioinform. 19(2), 286–302 (2018)
Sun, Y.V., Hu, Y.-J.: Chapter three - integrative analysis of multi-omics data for discovery and functional studies of complex human diseases. In: Friedmann, T., Dunlap, J.C., Goodwin, S.F. (eds.) Advances in Genetics, pp. 147–190. Academic Press (2016)
Chung, R.-H., Kang, C.-Y.: A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification. GigaScience. 8, 5 (2019)
Dong, Z., Chen, Y.: Transcriptomics: advances and approaches. Sci. China Life Sci. 56(10), 960–967 (2013)
Marchesi, J.R., Ravel, J.: The vocabulary of microbiome research: a proposal. Microbiome. 3(1), 31 (2015)
Kumar, P.S.: Microbiomics: were we all wrong before? Periodontology.85(1), 8–11 (2021)
Chakraborty, S.A.-O.X., et al.: Onco-Multi-OMICS approach: a new frontier in cancer research. Biomed. Res. Int.2018, 9836256 (2018)
Portin, P.: The birth and development of the DNA theory of inheritance: sixty years since the discovery of the structure of DNA. J. Genet.93(1), 293–302 (2014)
Heather, J.M., Chain, B.: The sequence of sequencers: the history of sequencing DNA. Genomics. 107(1), 1–8 (2016)
GarcÚa-Quesada, A.A.-O., et al.: Seroprevalence and prevalence of Babesia vogeli in clinically healthy dogs and their ticks in Costa Rica. J. Genet.93, 293–302 (2014)
Fraser, C.M., Rappuoli, R.: Application of microbial genomic science to advanced therapeutics. Annu. Rev. Med. 56(1), 459–474 (2004)
Zhang, M.Q.: Promoter analysis of co-regulated genes in the yeast genome. Comput. Chem. 23(3), 233–250 (1999)
Degtyarenko, K., et al.: ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res.36(Database issue), D344–D350 (2008)
Benson, D.A., et al.: GenBank. Nucleic Acids Res. 46(D1), D41–D47 (2018)
Wishart, D.S., et al.: HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 46(D1), D608–D617 (2017)
Kanehisa, M., et al.: KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45(D1), D353–D361 (2016)
Gluth, S., Rieskamp, C., Fau-Bﺴchel, J., Bﺴchel, C.: Deciding not to decide: computational and neural evidence for hidden behavior in sequential choice. PLoS Comput. Biol.9(10), e1003309 (2013)
Shen, R., Olshen, M., Fau-Ladanyi, A., Ladanyi, M.: Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics.25(22), 2906–2912 (2009)
Pierre-Jean, M., et al.: Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration. Brief. Bioinform.21(6), 2011–2030 (2020)
LRAcluster. http://lifeome.net/software/lracluster/
Hasin, Y., Seldin, M., Lusis, A.: Multi-omics approaches to disease. Genome Biol. 18(1), 83 (2017)
Palazzotto, E., Weber, T.: Omics and multi-omics approaches to study the biosynthesis of secondary metabolites in microorganisms. Curr. Opin. Microbiol.45, 109–116 (2018)
Hasin, Y., Seldin, M., Lusis, A.: Multi-omics approaches to disease. Genome Biol.18(1), 83 (2017)
Garrett-Bakelman Francine, E., et al.: The NASA twins study: a multidimensional analysis of a year-long human spaceflight. Science. 364(6436), eaau8650 (2019)
Pang, Y.J., et al.: A multi-omics approach to investigate the etiology of non-communicable diseases: recent advance and applications. Zhonghua Liu **ng Bing Xue Za Zhi. 42(1), 1–9 (2021)
Zhou, W., et al.: Longitudinal multi-omics of host–microbe dynamics in prediabetes. Nature. 569(7758), 663–671 (2019)
Schüssler-Fiorenza Rose, S.M., et al.: A longitudinal big data approach for precision health. Nat. Med. 25(5), 792–804 (2019)
Liu, J., et al.: Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug–metabolite atlas. Nat. Med. 26(1), 110–117 (2020)
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Ning, K., Li, Y. (2023). Synthetic Biology-Related Multiomics Data Integration and Data Mining Techniques. In: Ning, K., Zhan, Y. (eds) Synthetic Biology and iGEM: Techniques, Development and Safety Concerns. Springer, Singapore. https://doi.org/10.1007/978-981-99-2460-8_3
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