SciEngines in Life-Sciences / Bioinformatics
Already for a couple of years, and accelerated by the proliferation of Next-Generation-Sequencers, the volume of sequence databases has grown faster than improvements in computer speed. The computation time for sequence analysis has thus become a notable issue in the workflow of life science research, leading to significant delays or use of less-than-optimal tools to avoid such delays. As a solution to this problem, special purpose computers based on the technology of Field Programmable Gate Arrays (FPGA) can offer significantly better performance at lower cost than standard architectures due to the data-types and calculation methods commonly employed in bioinformatics. With SciEngines' products, one can expect small data-center processing power at a fraction of the cost.
Additionally, also other application fields in life-sciences, e.g. all types of imaging or molecular modeling, can strongly benefit from the technology. Therefore, SciEngines is happy to provide its FPGA-based massively parallel computer RIVYERA, which enables unprecedented levels of speed and quality for life-science related calculations. Some implemented applications are described below as examples, but please don't hesitate to
This e-mail address is being protected from spambots. You need JavaScript enabled to view it
to receive a free assessment of your application of interest.
For more information, please refer to the subsections (click):
Sequence Alignment (Algorithm: Smith-Waterman)
|
A major time factor in the common workflow of a molecular biologist is to compare sequences to each other and find the best fitting alignment. High-quality alignment algorithms such as Smith-Waterman alignment have almost been replaced by "average-quality yet fast" heuristic approaches such as BLAST because of the overwhelming lack of performance.
Given SciEngines RIVYERA, it is now possible to revive non-heuristic alignment algorithms and to give biologists back the possibility of high-quality alignment results within minimal time-frames. But, also for heuristic approaches, the RIVYERA provides a benefit as new levels of analysis can be reached. The common constraint "it's going to take too long to calculate" simply doesn't apply anymore with a FPGA cluster that provides thousands of PC cores performance as can be seen in below performance comparison of different technologies.
|
Motif Search (Algorithm: BMA)
|
The discovery of regulatory sequences in DNA - called motif-finding - is one of the most computationally challenging problems in the field of bioinformatics. In fact, there are problem instances of motif-finding which are unsolvable by current approaches. There are two reasons that make this problem so difficult:
- First, the parameters of a given problem instance (like sequence length, motif length, grade of mutation) can make it impossible to trace the motif
in the background noise of the DNA even if you had all the time you wanted to compute.
- Second, it is computationally expensive. So, a precise algorithm can fail to discover the motif in a given sequence because its execution time exceeds rational means.
We address both problems with an approach to motif finding that makes use of our massively parallel RIVYERA FPGA cluster to speed up the execution time of an algorithm applying Boolean Matrices. Less than 20 minutes are needed for a search of all motive instances in a Bacillus Subtilis genome file (5.9 Mbp). Please
This e-mail address is being protected from spambots. You need JavaScript enabled to view it
if you are interested in motif search approaches allowing a new level of science.
|
|
