Over the past decade, the rapid evolution of next generation DNA sequencing technologies has inundated the field of Bioinformatics with sequence data. Although DNA sequencing costs have decreased significantly over this period, the analytical component of this process has remained costly. The best way to address this may depend on how much data you have to contend with and your data-security comfort level.
Within the field of Bioinformatics, those with occasional data analysis needs can employ the use of Cloud Computing resources. There are significant concerns with this approach however including slow data transfer rates when moving data to and from the cloud and data security issues. Cost is also an issue if you have a heavy, persistent workload. You wouldn't hire a taxi cab if you needed a car every day and using the cloud for heavy data analysis is a lot like that.
Within Life Sciences Research and Bioinformatics, Graphics Processing Unit (GPU) card usage has been pushed recently by the big GPU card manufacturers. These cards provide an opportunity to parallelize certain algorithmic components but for biological sequence comparison algorithms, their usage is somewhat limited. This is principally because of poor performance for Heuristic algorithms (things like BLAST or HMMER3). For Dynamic Programming algorithms (Smith-Waterman) there is some opportunity for GPU-based acceleration however the marketplace favors Heuristic algorithms for their improved performance. And furthermore, if you need to accelerate Smith-Waterman, FPGA cards provide a much better opportunity for parallelization and consequently, much more bang for your buck.
Searching the web for 'GPU life sciences' will highlight the types of applications GPU cards are most commonly used for, namely Smith-Waterman based alignment algorithms or Computational Chemistry / Molecular Dynamics algorithms.
General assemblies of standard multi-core CPU servers are commonplace within the field of Bioinformatics because of their general purpose utility. Typically however, application specific performance suffers at the expense of that flexibility. For that reason, most of our TimeLogic customers also have CPU-clusters that are used in conjunction with their TimeLogic hardware. The ability to offload specific applications onto one of our DeCypher® servers frees up CPU-cluster resources, which can in turn extend their lives. And upgrading or expanding one of our DeCypher® solutions is a far simpler process than upgrading a CPU-cluster.
J-Series FPGA Hardware
Our J-Series Similarity Search Engine (SSE) was engineered to handle the explosion of data generated by next-generation sequencing platforms. With an eye towards Heuristic algorithms in particular, this new circuitboard design pushes the limits of the latest Xilinx® FPGA chips and the PCIe data bus. By augmenting available memory, we've been able to deliver huge improvements to Tera-BLAST performance and provide an update to our popular DeCypherHMM algorithm that is based on the popular HMMER3 package. Our new VelociMapper algorithm effectively runs at the limits at which the host server can read in a FASTQ data file and write out a sorted BAM file -- no other accelerators come close to this level of performance. And you can find out for yourself by having us run custom benchmark tests using your own data.
In addition to hardware performance upgrades, our DeCypher software also includes job queueing and distribution, administrative tools, and application specific support from our team of experienced bioinformaticians. For additional details regarding DeCypher® host-server options, please see our product FAQs page.