Sequencing and protein docking are very compute-intensive tasks that see a large performance benefit by using a CUDA-enabled GPU. There is quite a bit of ongoing work on using GPUs for a range of bioinformatics and life sciences codes.
With the introduction of NVIDIA Tesla Bio Workbench, it provides bio-physicists and computational chemists the tools to push the boundaries of bio-chemical research, optimizing the scientific workflow and accelerating the pace of research. Learn more.
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| Accelerating HMMER using GPUs Scalable Informatics |
MUMmerGPU: High-through DNA sequence alignment using GPUs Schatz, et al |
Key Bioinformatics ISVs and Applications using CUDA
| ISV/Application | Supported Features | Expected Speed up* | Release Status |
| GPU-Blast | Protein alignment, multiple protein queries. | 10x | Released |
| PIPER Protein Docking | Molecule docking | 17x | Released |
| SeqNFind | Smith-Waterman | 60x | Released |
| UGene | Smith Waterman, Short DNA sequence aligner | 9x | Released |
| CUDASW++ | Smith-Waterman | 10x-50x | Released |
*Expected Speed Up vs a quad-core x64 CPU based system. Speed-ups as per NVIDIA in house testing or application provider documentation.
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