New journal tackles “Grand challenges in bioinformatics and computational biology”
|September 23, 2011||Posted by News under Genomics, News|
Here’s a new journal to watch:
Invention of automated sequencer in early 1980s, formal establishment of Human Genome Project in 1990, and announcement of the completion of human genome draft in 2000 are the recognized milestones of biology. The day of July 25, 1995, however, is a good candidate for the Day 1 of the Heroic Age of the Era of Complete Genomes. On that day, the first complete genome sequence of a cellular organism, H. influenzae, was reported in the Science magazine (Fleischmann et al., 1995). With several more genome sequences of bacteria and, later, archaea, the approaches of bioinformatics and computational biology could be applied at the genomic scale. And, although our understanding of the organisms with completely sequenced genomes continues to be far from perfect, a significant amount of new information could be teased out of the very first genomes in a matter of few months – the concern that the genome sequences for a long time will remain an enigma, similar to Linear A script of Cretan archeologists, proved to be unfounded.
Also in the 1980–1990s, the quiet revolutions occurred in many other fields of scientific instrumentation development, by now allowing us not only to sequence genomes completely, but also to profile quantitatively the amounts, activities, spatial locations, and movements of many molecules inside the cells, as well as register multiple parameters describing the whole cells and cell populations. Thus, in addition to the strings of symbols representing the genetic information encoded in the genomes, we have another genome-scale data type – the vectors of numeric measurements associated with every genetic element and every other molecule in the cells of different types, as well as with the cells and supracellular structures. In this case too, we are not completely clueless, as the existing algorithmic approaches and methods of multidimensional statistics help to discern biologically significant patterns in these data, and, on the other hand, the properties of these data motivate the development of new methods. It does not hurt that, as biologists come up with new platforms for data acquisition, the cost of high-performance computing and terabyte-scale data storage continues to go down.
Which could upend truisms en masse.
Hat tip: Pos-Darwinista