Bioinformatics 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 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.

BGI Speeds Genome Analysis with GPUs
Learn how Chinese genomics powerhouse BGI used NVIDIA Tesla GPUs to accelerate DNA sequencing.

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

Learn more about Bioinformatics codes by downloading the Life Science Apps Catalog

Other Relevant Software using CUDA

• MUMmerGPU: High-throughput DNA sequence alignment using CUDA
• 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
• CUSHAW: a CUDA compatible short read aligner to large genomes
• MUMmerGPU: High-through DNA sequence alignment using GPUs
• CUDASW++: Smith-Waterman Sequence Database Searches
• Smith-Waterman sequence alignment using CUDA
• Papers on Bioinformatics 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
• High-performance computational pipeline for metagenomic data analysis
• MrBayes on GPU
• 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

GPU Technology Conference 2012:

• Acceleration of Complex Network Analysis – Newcastle University
• GHOSTM: A GPU-Accelerated Homology Search Tool for Metgenomics – Tokyo Institute of Technology
• Algorithms and Tools for Bioinformatics on GPUs – Nanyang Technological University
• SeqNFind: Application of CUDA GPU Technologies to Sequence Alignment Techniques – Accelerated Technology Labs
• Swift: A GPU-based Smith-Waterman Sequence Alignment Program – St. Jude Children’s Research Hospital
• CUMACH – A Fast GPU-based Genotype Imputation Tool – NVIDIA
• SOAP3: GPU-based Compressed Indexing and Ultra-fast Parallel Alignment of Short Reads – BGI
• Accurate Sequence Alignment Using Distributed Filtering on GPU Clusters – University of Illinois at Urbana-Champaign and UC Berkeley
• Towards Computing the Cure for Cancer – Virginia Tech
• High-Throughput Epistasis Screening Using GPUs – Insilicos LLC
• GPGPU Accelerated Protein Similarity Measures Identifying Biological Relevant Structure – Vanderbilt University
• Dynamic Programming on CUDA: Finding the Most Similar DNA Sequence – IBM Poland
• The Advantage of GPU Computation for Analyzing Complex Traits – Zhejiang University
• GPU Accelerated Bioinformatics Research at BGI - BGI

More presentations are available on GTC On-Demand.

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