So, if any of you out in the blogosphere don’t already regularly review what comes out of Genomics Law Report – you should.

Today – John Conley – clarified many of the points in the supreme court decision I was wondering about and ties it back to existing case law in a way that just helps the whole thing make sense to me.

Basically- simply isolating a gene sequence, gDNA, is not patentable, but isolating and purifying the gene sequence into cDNA is allowed to be patented.

Still pondering how this will shake up the biotech world, but the decision is pretty clear.

Thanks Genomics Law Report for again taking what is complex and making it easier to comprehend.

Human genes can’t be patented!

I’m sure you’ve heard the news from the supreme court today. I’m still processing the implications, but thought I’d put this out there for everyone to see.

Justice Clarence Thomas wrote that the U.S. Court of Appeals for the Federal Circuit was wrong to find that both isolated human DNA and cDNA were both patent eligible.

“We hold that a naturally occurring DNA segment is a product of nature and not patent eligible merely because it has been isolated,” Thomas wrote.

“Myriad did not create anything. To be sure, it found an important and useful gene, but separating that gene from its surrounding genetic material is not an act of invention.”

As a semi-compromise synthetic DNA can be patented. However, the fallout from this decision is just beginning.

A more in depth look at the ruling is here:

There is something I’ve wanted to talk about for a while. I call it the computation/biology gap.

While talking with various clients, customers, researchers, etc. I often hear complaints from different sides of the biology spectrum.

If I’m talking with someone on the bioinformatics end of the spectrum it’s something like “I’m afraid the researchers I share this data with are going to interpret it incorrectly and see variants that aren’t there. They don’t understand how this algorithm/analysis works…”

If I’m talking with someone who is more on the wet lab side of the spectrum it’s something like “My bioinformatics resource is always overloaded. Plus all they ever give me are spreadsheets. They just get frustrated when I ask them to explain something. Isn’t there something that I can use to really dig into my data?”

Admittedly, this is a tough one to answer, but it’s something I wanted to post here anyway. Maybe we can get a conversation started around this.

Here’s my take. Biology, especially the -omics disciplines that generate large volumes of data have become, like it or not, and information science by the very fact that there is such a large volume of data generated and the analysis of said data is complex. This complexity and volume of data requires computers to analyze. So, without bioinformatics researchers out on the forefront, thinking about how to reliably break this data down into usable information, most of the -omics disciplines would grind to a halt.  Next time you see a comp bio or informatics specialist realize that it’s a tough and often thankless job; without a lot of credit given in major journals.

Conversely, bench scientists, are the guys generating the data for the informatics people to analyze. They’re the researchers who are asking the biologically relevant questions – like is this variant disease related, etc. Bench scientists are the reason there is such a discipline as computational biology – because if you take away the biology it’s just comp sci.

Yes, I’m glossing over large areas here and oversimplifying, but in my opinion the research community would be a happier place if bench scientists and bioinformatics scientists tried to see the world a bit through each others eyes.

Now, I’m off my soap box and I’ll get on with the point of this blog.

Bench scientists and bioinformaticists need different types of software, and this is an okay thing. If you’re king or queen of the command line, and can bend data to your very will by elegant code, this is great! Just understand that the researcher who asked for the analysis might not like command line, might get frustrated with excel sheets, and is looking for something they can interact with to help them frame the questions and find the answers they’re after.

We’re trying to bridge the gap – and deliver simple but powerful tools to enable bench scientists to really dig into their data. I think this is a healthy philosophy, and I believe there is plenty of room, and the necessity, to further bridge the gap. The idea for us is to enable both bench scientists and bioinformatics scientists to work more smoothly to move the whole genomics discipline forward.

Just my 2 cents – what do you think?