The CRISPR revolution: Where are the profits?

Usually when an industry gets a booming industry it is largely due to profitability, which garners interest from investors.

But in biotech there is a section of the industry that is gaining investors and various firms chasing a similar goal. However, how that is happening is a mystery. The companies are burning through millions, hasn’t started clinical work on a drug candidate and it will be years, “if ever” before it has something commercializable.

What industry am I referring to? CRISPR-Cas9 technology. We’ve talked on the blog before about the possibilities CRISPR has to offer human health, but over at The Economist here’s a post about whether or not it can be all we dream it to be.

CRISPR

The deadliest animal in the amazon

It’s not an anaconda. Nor is it the piraña.

It’s the golden mussel. No. Seriously.

Invasive species killing local organisms is nothing new. In fact, it’s almost in the definition of “invasive species”. But this mussel has been increasing at an alarming rate int he amazonian waters, and it is killing off existing species and destroying its habitats.

But, combatting this guy, is tougher than one would think. How do you kill the mussel that is destroying the biodiversity of the Amazon without… destroying the biodiversity of the Amazon.

Enter Marcela Uliano da Silva, (PhD student at Federal University of Rio de Janerio) who is finding new ways to target just the golden mussel by using it’s genome.

Read about it over at ZY!

mussels

 

 

 

Being a good bioinformatician

bioinfo3

Or more accurately,  how to avoid being a bad bioinformatician. Over at the blog opiniomics (which is my new favorite name for a blog beside Nothing in Biology), Mick Watson published a lovely post: 5 ways that you may be failing as a bioinformatician.

While the premise behind this post sounds fairly negative (why not 5 practices of productive bioinformaticians?) it is extremely informative. Especially for budding bioinformaticians such as myself.

Essentially it breaks down into:

  • Keep up with the literature
  • Use appropriate software
  • Document your procedures
  • Stop reinvent the wheel

I am definitely guilty of the last bullet, and my better bioinformatics peers get on me about it all the time (“You don’t need to write your own code to collapse a 2d array into a vector/write a sorting algorithm/pick a variable without replacement. Someone has already done that.”). Which ones are you guilty of?

Well worth a read, check it out here!