Popular opinion is that two big shifts have changed the game for bioinformatics. The first is a technology development—single cell ‘omics—that began just over 10 years ago, the other is a more recent move to a new model of drug discovery that looks to leverage the vast amounts of human genetic data now being generated in support of precision medicine.
Over the past 5 decades or so, the growing understanding that RNA can influence protein function through routes other than direct translation has opened the prospect of discovering small molecules for tackling diseases in novel ways.
Precision medicine is one of the most talked about trends in healthcare. The promise is improved health outcomes and patient satisfaction, gained through an understanding of individual variability in genes, environment, and lifestyle. Recent commentators suggest that many of the technologies required to realize this goal already exist—and predict much
Single-cell technologies have positioned themselves at the forefront of biomedical research. These platforms allow researchers to bypass the uncertainties of bulk data and instead interrogate biological systems at a level of detail that was previously unreachable. But the value of these systems risks being impaired by bottlenecks in data analysis
