PNNL

Abstract

There comes a time in every field of science when things suddenly change. While it might not be immediately apparent that things are different, a tipping point has occurred. Biology is now at such a point. The reason is the introduction of high-throughput genomics-based technologies. I am not talking about the consequences of the sequencing of the human genome (and every other genome within reach). The change is due to new technologies that generate an enormous amount of data about the molecular composition of cells. These include proteomics, transcriptional profiling by sequencing, and the ability to globally measure microRNAs and post-translational modifications of proteins. These mountains of digital data can be mapped to a common frame of reference: the organism’s genome. With the new high-throughput technologies, we can generate tens of thousands of data points from each sample. Data are now measured in terabytes and the time necessary to analyze data can now require years. Obviously, we can’t wait to interpret the data fully before the next experiment. In fact, we might never be able to even look at all of it, much less understand it. This volume of data requires sophisticated computational and statistical methods for its analysis and is forcing biologists to approach data interpretation as a collaborative venture.