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.Modern quantitative biology is in some regards no different than previous eras. At its core, it follows the same principles as ever by asking questions of a biological system, and then recording and integrating observations made under varying conditions until some conclusion can be made. Consider, for example, how the very foundation of modern genetics, Mendel's principles of inheritance, was established through an analysis of some 30,000 pea plants and in recognizing the inheritance of certain traits could be explained by a few simple mathematical rules (Mendel 1866). This accomplishment demonstrates the power of quantitative biology, especially considering that these laws were established in advance of modern molecular biology, including deciphering how genes encode for proteins or even that DNA is the modality of inheritance.What is new in the modern era is, unlike Mendel and his successors that collected data by hand, the methods for collecting observations are now largely automated digital sensors. This includes the rise of DNA sequencing instruments, super-resolution digital microscopy, mass spectrometry, magnetic resonance imagery, or even satellite imagery used for studying biological systems with greater throughput and resolution than ever before. Furthermore, although the instruments are now providing great quantities of data, they do not by themselves do any meaningful interpretation of them. Consequently, the second major advance in the field has been the increased importance of computational and analytical techniques used to study biological data. These approaches, including large-scale multicore computing systems, advanced search and indexing algorithms, numerical optimization and modeling techniques, and many others, have primarily originated outside of biology in other quantitative disciplines. This new paradigm, often called "Biological Data Science" acknowledges that computer science, mathematics, physics, statistics, and othe...

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