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.

Accelerating HMMER using GPUs Scalable Informatics	MUMmerGPU: High-through DNA sequence alignment using GPUs Schatz, et al

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• 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

• Medical Imaging
• Molecular Dynamics
• Computational Chemistry
• MATLAB acceleration

• Tesla/CUDA Success stories
• Other Tesla Vertical Solutions
• CUDA Software development tools & libraries