Graphics processing units in bioinformatics, computational biology and systems biology

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology
• Life Sciences

Background:

• Computational methods in life sciences often require significant Central Processing Unit (CPU) resources.
• Complex biological system modeling, from molecular interactions to genome-wide networks, is computationally intensive.
• Traditional CPU-bound software limits the scope and depth of biological system investigations.

Purpose of the Study:

• To review recently developed Graphics Processing Unit (GPU) tools for computational analyses in life sciences.
• To highlight the advantages and disadvantages of using GPU parallel architectures in biological research.
• To provide a resource for researchers seeking to leverage GPU acceleration for biological modeling and data analysis.

Main Methods:

• Review of existing literature and software repositories for GPU-accelerated tools in bioinformatics, computational biology, and systems biology.
• Categorization and analysis of identified GPU tools based on their application areas and computational approaches.
• Evaluation of the performance benefits and limitations associated with employing GPUs for biological modeling.

Main Results:

• A collection of GPU-powered tools for various computational life science tasks has been identified.
• GPUs offer significant speedups compared to traditional CPU-based methods for complex biological simulations and analyses.
• The adoption of GPU technology enables more intensive and comprehensive investigations of biological systems.

Conclusions:

• Graphics Processing Units (GPUs) represent a powerful parallel computing architecture for advancing life science research.
• The reviewed GPU tools provide viable alternatives to CPU-intensive methods, enhancing research efficiency and scope.
• Further exploration and adoption of GPU computing are recommended for researchers in bioinformatics, computational biology, and systems biology.