Our collaborators at Beijing Genomics Institute, Shenzen recently announced the launch of the giant panda genome project. Through the use of next generation sequencing technology, the aim is to complete the panda genome within only six months.
The researchers involved will use the results to answer a number of questions regarding the animals biology. These include, the exact phylogenetic position of the panda and the genetics underlying the pandas extraordinary metabolism. Furthermore, results will be used in the analysis of panda population genetics and conservation biology.
In a press release yesterday, Helicos Biosciences announced that they have shipped their first instrument to the company Expression Analysis in North Carolina. Interesting features of the instrument are off course the high throughput (acclaimed 1.3 billion bases per hour in the near future) and the fact that Helicos claim that they are dedicated to bringing the price of the chemistry down to 1000$ per human genome. However, equally interesting is the absence of an amplification step in the sample preparation which could significantly ease the use of the instrument.
In the new issue of Trends in Genetics, Elaine R. Mardis gives a nice review of the currently operating NGS technologies, their applications and their potential impact on the field of genetics.
As an interesting aside, Mardis quotes her estimate of the cost per mega base of the three commercial systems out:
- 454 - 84.4$
- Solexa - 5.97$
- SOLiD - 5.81$
Mardis ER.
The impact of next-generation sequencing technology on genetics.
Trends in Genetics. 2008 Mar;24(3):133-41
I picked this up via Genome Tehcnology. According to an article at Blomberg.com Church has launched an ambitious project to sequence the coding regions of 100.000 humans, a number that may even increase to a million.
The plan is to tie the genomic information to phenotypic information and health records of the sequenced individuals to create a unique data resource from which novel links between genetic variation and disease can be learned.
Google have been one of the first companies to support the project and apparently have plans of making their Google Health project a front-end to the collected data.
