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Multiomics refers to a biological analysis approach in which the data sets are multiple "omes", such as the genome, proteome, transcriptome, epigenome, and microbiome;[1][2][3] in other words, the use of multiple omics technologies to study life in a concerted way. By combining these omes into a set of omes, scientists can analyze complex biological big data efficiently enough to easily find relevant biomarkers. In so doing, multiomics integrates diverse omics data to find a coherently matching geno-pheno-envirotype relationship or association, even with reduced numbers of samples, although multiomics usually involves 'big' data.

Usually, a very large number of samples is required to detect functional relationships, but, using multiomics, encompassing multiple data types, researchers try to detect such associations with more confidence. A possible example case would be suicide biomarker detection by taking the blood of depressive patients and performing genomic, transcriptomic, and epigenomic sequencing, combining these omes to find significant markers from the case population.


  1. ^ Bersanelli, Matteo; Mosca, Ettore; Remondini, Daniel; Giampieri, Enrico; Sala, Claudia; Castellani, Gastone; Milanesi, Luciano (1 January 2016). "Methods for the integration of multi-omics data: mathematical aspects". BMC Bioinformatics. 17 (2): S15. doi:10.1186/s12859-015-0857-9. ISSN 1471-2105. PMC 4959355. PMID 26821531. Retrieved 31 October 2016.
  2. ^ Bock, Christoph; Farlik, Matthias; Sheffield, Nathan C. (August 2016). "Multi-Omics of Single Cells: Strategies and Applications". Trends in Biotechnology. 34 (8): 605–608. doi:10.1016/j.tibtech.2016.04.004. Retrieved 31 October 2016.
  3. ^ Vilanova, Cristina; Porcar, Manuel (26 July 2016). "Are multi-omics enough?". Nature Microbiology. 1 (8): 16101. doi:10.1038/nmicrobiol.2016.101. Retrieved 31 October 2016.

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