Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
Next-generation sequencing technologies are beginning to facilitate genome sequencing. But in addition, new applications and new assay concepts have emerged that are vastly increasing our ability to understand genome function.
A new generation of non-Sanger-based sequencing technologies has delivered on its promise of sequencing DNA at unprecedented speed, thereby enabling impressive scientific achievements and novel biological applications. However, before stepping into the limelight, next-generation sequencing had to overcome the inertia of a field that relied on Sanger-sequencing for 30 years.
Mass spectrometry is more than ever at the forefront of functional proteomics research. The technology has come a long way, but what does the future hold? Nathan Blow gets perspectives, predictions and wishes from key developers.
When generating novel tailor-made proteins, protein engineers routinely apply the principles of 'Darwinian' evolution. However, laboratory evolution of proteins also has the potential to test evolutionary theories and reproduce evolutionary scenarios, thus reconstructing putative protein intermediates and providing a glimpse of 'protein fossils'. This commentary describes research at the interface of applied and fundamental molecular evolution, and provides a personal view of how synergy between fundamental and applied experiments indicates novel and more efficient ways of generating new proteins in the laboratory.
To characterize the contributions of individual amino acids to the structure or function of a protein, researchers have adopted directed evolution approaches, which use iterated cycles of mutagenesis and selection or screening to search vast areas of sequence space for sets of mutations that provide insights into the protein of interest.