Multiomics

Multiomics means a new biological analyses approach where the data sets are multiple omes such as genome, proteome, transcriptome, epigenome, and microbiome.[1][2][3] By combining these omes into a set of omes, one can analyze the complex big data efficiently enough to find biomarkers easily. The use of multiple omics technologies to study life in a concerted way. It means, it integrates diverse omics data to find a coherently matching geno-pheno-envirotype relationship or association even with reduced numbers of samples, although multiomics usually involve large 'big' data.

Usually, a very large number of samples are necessary to detect functional relationships, but using multi-omics, trespassing multiple data types, researchers try to detect such associations with more confidence. An example case is to find suicide marker detection by taking the blood of depression patients and perform genome, transcriptome, and epigenome sequencing to combine those omes and find a commonly found significant markers from the case population.

References

  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. ISSN 1471-2105. PMC 4959355 Freely accessible. PMID 26821531. doi:10.1186/s12859-015-0857-9. 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.

See also

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