Better tools, new problems: New technologies help to advance research in the life sciences, but the quantities of data generated are proving hard to manage and interpret.

Abstract

Technological advances have almost always led to accelerated progress in the life sciences. The invention of the light microscope paved the way for the discovery of cells as the building blocks of life, and bacteria as the causes of many infectious diseases. The electron microscope allowed biologists to peer into cells themselves and directly observe viruses. The unravelling of the structure of DNA or proteins was directly enabled by X‐ray crystallo‐graphy. DNA sequencing and PCR became the technological cornerstones of molecular biology, whereas NMR and mass spectro‐metry further improved and accelerated the analysis of proteins and their structures. > As the sequencing of whole genomes becomes faster and cheaper, data production is running ahead of the ability to make sense of it… Many of the inventions themselves have also seen rapid development and improvement. The combination of novel cryological techniques and image analysis algorithms has revived interest in the electron microscope as a tool to visualize the atomic structural details of proteins and cellular structures without the need to create crystals. DNA sequencing has made enormous advances since Fred Sanger's original method based on DNA polymerase followed by gel electrophoresis; today's next‐generation sequencing (NGS) machines can perform millions of reactions in parallel. Light microscopy, one of the oldest tools of biologists, has broken the diffraction limit to let scientists observe the position and movement of individual protein molecules in living cells in real time in three dimensions. As much as all of these technologies help to understand how life works at the molecular level, they create a growing challenge for biologists to make sense of gigabytes or terabytes of data. This is particularly true for next‐generation sequencing, which has become a major challenge for bioinformatics. As the sequencing of whole genomes becomes faster and cheaper, data production is running ahead of the …