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 | DESCRIPTION | GPU ADVANTAGE |
| Protein sequence database scanning using NCBI BLASTP | 10x speed-up: from minutes on CPUs to seconds on GPUs | |
| Protein sequence database (Smith-Waterman) scanning | 10x-50x speed-up: achieving up to 30 GCUPs on query lengths over 5000 | |
| HMMER accelerated on CUDA | 60-100x speed-up: from hours on CPUs to minutes on GPUs | |
| Protein-Protein interaction modeling application | 6x speed-up: superior to FPGA-based system for protein-protein docking | |
| Molecule shape comparison application | 170x speed-up: faster screening for drug discovery (see slide 43) |
Other Relevant Software using CUDA
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