Bio-Informatics and Life Sciences
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 bio-informatics 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 |
Bio-Informatics Software using CUDA
- GPU HMMER : HMMER on CUDA-enabled GPUs
- MUMmerGPU: High-throughput DNA sequence alignment using CUDA
- CUDASW++ : Protein Sequence Database (Smith-Waterman) Scanning on CUDA-enabled GPUs
- Smith-Waterman Code on GPUs
- Online CUDA Smith-Waterman
- ClustalW on CUDA: Multiple Sequence Alignment using CUDA
- Folding @ home using CUDA-enabled GPUs
- LISSOM: model of human neocortex using CUDA
- CUDA-based AutoDock from Silicon Informatics
- Sequence alignment
- MUMmerGPU: High-through DNA sequence alignment using GPUs
- CUDASW++: Smith-Waterman Sequence Database Searches
- Smith-Waterman sequence alignment using CUDA
- Papers on Bio-informatics using CUDA
- Infernal-GPU: CUDA-accelerated RNA Alignment , Infernal (INFERence of RNA Alignment)
- SWAMP Sequence alignment
- CMatch: Fast protein and gene sequence string matching
- Docking
- 3D Protein Docking
- Accelerating PIPER using CUDA:
- Protein Docking work at University of Wisconsin and video interview of David Dynerman
- Jack Collins from National Cancer Institute talks about GPU computing
- Stochastic simulation algorithm (SSA) for biological systems
- Self-organizing computation models of human visual cortex
- DNA Microarray tool for assaying gene expression by computing pairwise Euclidean distance on GPUs
