Biological data sciences in genome research

Abstract

The last 20 years have been a remarkable era for biology and medicine. One of the most significant achievements has been the sequencing of the first human genomes, which has laid the foundation for profound insights into human genetics, the intricacies of regulation and development, and the forces of evolution. Incredibly, as we look into the future over the next 20 years, we see the very real potential for sequencing more than 1 billion genomes, bringing even deeper insight into human genetics as well as the genetics of millions of other species on the planet. Realizing this great potential for medicine and biology, though, will only be achieved through the integration and development of highly scalable computational and quantitative approaches that can keep pace with the rapid improvements to biotechnology. In this perspective, I aim to chart out these future technologies, anticipate the major themes of research, and call out the challenges ahead. One of the largest shifts will be in the training used to prepare the class of 2035 for their highly interdisciplinary world.

Figure 1.

Data science analysis stack. Large-scale projects in quantitative biology must address a multilayer stack of approaches moving toward increasing levels of abstraction. At its base, the experiments begin with the technologies for collecting data and metadata from various biological sensors. The processing then proceeds upward through the input/output (IO) and Compute layers that can support large-scale data processing, statistical and analysis software layers that can summarize and identify trends in the data, until finally biological results can be achieved at the top, leveraging the domain knowledge of the problem.