Abstract
Nutritional compounds may have an influence on different OMICs levels, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and metagenomics. The integration of OMICs data is challenging but may provide new knowledge to explain the mechanisms involved in the metabolism of nutrients and diseases. Traditional statistical analyses play an important role in description and data association; however, these statistical procedures are not sufficiently enough powered to interpret the large integrated multiple OMICs (multi-OMICS) datasets. Machine learning (ML) approaches can play a major role in the interpretation of multi-OMICS in nutrition research. Specifically, ML can be used for data mining, sample clustering, and classification to produce predictive models and algorithms for integration of multi-OMICs in response to dietary intake. The objective of this review was to investigate the strategies used for the analysis of multi-OMICs data in nutrition studies. Sixteen recent studies aimed to understand the association between dietary intake and multi-OMICs data are summarized. Multivariate analysis in multi-OMICs nutrition studies is used more commonly for analyses. Overall, as nutrition research incorporated multi-OMICs data, the use of novel approaches of analysis such as ML needs to complement the traditional statistical analyses to fully explain the impact of nutrition on health and disease.
Highlights
In 2003, a new era of genomic studies began after the completion of the human genome project (HGP)
The objectives of this review study are 1. to describe the various OMICs techniques; and 2. to examine multi-OMICs analyses in nutrition research, including the supervised and unsupervised
Multi-OMICs studies compared to single OMICs have provided new information to describe the role of nutrients in molecular pathways using together either gene protein, metabolites and/or gut bacteria
Summary
In 2003, a new era of genomic studies began after the completion of the human genome project (HGP). Genomics has affected all areas of health sciences and has enabled us to solve many contradictory studies on human health, including nutrition research [1]. The role of various nutrients in gene expression and regulation is considered a key in nutritional sciences. Nutritional compounds may influence gene expression at different levels including transcription [2], maturing and stability of RNAs, translation process, and post-translational modifications [3,4]. The response to the dietary intake depends on the genetic background of an individual which is known as nutrigenetics [5]. Genome-wide Association Studies (GWAS) have reported the contribution of various single-nucleotide polymorphisms (SNPs) in the interaction with nutrients
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