"We can analyze RNA, agricultural biology, different pipelines for cancer research—all of these have widely varying pipelines and some are just nuanced, but the

"We can analyze RNA, agricultural biology, different pipelines for cancer research—all of these have widely varying pipelines and some are just nuanced, but they all require something different to be loaded into the FPGA before that run.”" Therein lies the rub. I was a sysadmin for a biotech company doing this kind of stuff, and in the end we evaluated fpga/asic and just couldn't justify the costs, not just hardware costs but time and barrier to entry costs, such as having to hire more programmers. I also kept a close eye on GPUblast, but it just wasn't keeping up with official, and due to pipeline specialties, mostly revolving blasting against all kinds of data, it didn't fit the bill. That's the main issue, that people doing blasting and related often have very specialized pipelines and different types of data and would almost be required to spend a lot of money to get such a system to work for them. It much less about the speed of the computation than it is about moving the data around. When you start dealing with 100Tb's of output a day... What we did end up doing was two things, one, we wrote worker system that basically distributed the computation to almost every computer on the network, and two, I figured out that due to how embarrassingly parallel the computation was, AMD's newer opteron chips actually outperformed Intel's, and I build some beast servers, and took computation down from average of 1~3 days to ~4hrs. (think 64 actual core's via quad cpu motherboards... truly some of my favorite servers I have ever built.) Bottom line is that IO is the main bottleneck, so even things like ROCKS clusters or other distributed systems don't work as well as many of the big data people like to think. We also had a very different than normal pipeline mostly revolving around which data to blast against and the methods we used, which were considered the core IP/proprietary, but the real IP was in the analysis. As FPGA's, Asics, and GPU's make it easier to get the data, you still have to understand it and analyze it, and that is much harder than people understand IMHO. I left for personal reasons, and I'm still under a non-compete, but I am very thankful for the opportunities I had to learn the bioinformatic's side under the eccentric genius I did, and perhaps one day I will turn some of my knowledge into a system that is useful for those wanting to do this kind of work. Also, I will never forget it as the first place I ever got asked to build a petabyte system... For example, IO limitations were mainly centered around accessing large amounts of data that wouldn't fit in 128/256Gb of RAM setups, which ended up usually on disks. Even on ZFS/btrfs raid0 ssd's, interface limitations were being hit daily..(sata, backplanes, etc) and the recent PCIE-SSD revolution would really be able to make a big dent in that particular issue.