With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges.
Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events and diseases through various computational and informatics frameworks. Commonly used approaches are currently limited by the 3 i’s – integration, interpretation and insights. The ultimate goal is toward the holistic realization of a ‘systems biology’ understanding of the biological question.
Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics or shortened to just ‘omics’, the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing and data archiving. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis.
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