Integrated ‘-omics’: tools for the future
COLIN FARQUHARSON, CO-EDITOR-IN-CHIEF, JOURNAL OF MOLECULAR ENDOCRINOLOGY | Hot topics
For this anniversary issue, we invited the Editors-in-Chief of the Society's journals to write about a topic of their choice.
Like them or loathe them, ‘-omics’ approaches have transformed how many of us now interrogate our biological samples. Many are available to researchers, and include proteomics, genomics, transcriptomics, lipidomics and metabolomics, amongst others. On their own, they offer an unbiased way of identifying novel regulators, pathways and networks, to advance our knowledge of complex biological processes, including those of an endocrine nature.
In 1920, the botanist Hans Winkler coined the term ‘genome’, as a blend of the words ‘gene’ and ‘chromosome’, to denote the chromosome set as the material foundations of an organism. This was further developed by Thomas Roderick in 1987. He was the first to use the term ‘genomics’. Since then, other ‘-omics’ have emerged and, individually, high throughput ‘-omics’ approaches have allowed scientists to make better sense of the tens of thousands of variables within their biological sample of interest.
On their own, however, the resultant data have their limitations, as proteins, transcription factors, metabolites, etc., do not function in isolation. An integration of the multi-dimensional ‘-omics’ datasets is required to achieve a meaningful holistic insight into the complex biological question under study. Integrated ‘-omics’ provides the researcher with the tools to investigate biological systems, in order to extract meaningful correlations and identify complex interactions.
This approach has been described in a review by Misra et al., published in Journal of Molecular Endocrinology. At present, integrated ‘-omics’ is in its infancy, as the successful integration of more than two ‘-omics’ datasets is currently rare, and many computational and bioinformatic issues are hindering progress. These multi-layered, multifactorial challenges will only grow with the increasing number of datasets now being generated. The review describes these challenges and provides insights into the bioinformatics tools required to develop standard analytical pipelines to make this dream a reality.
Read the full article in Journal of Molecular Endocrinology 62 R21–R45