Advances in DNA sequencing technology have contributed to the accumulation of molecular sequence data at an unprecedented pace, since whole genomes can be sequenced rapidly, accurately, and cost effectively. When methods and tools are not specifically designed to handle big volumes of data efficiently, large-scale analyses practically become infeasible due to the explosion in processing and memory requirements. Bioinformatics algorithms frequently rely on approximations and heuristics to yield computationally tractable implementations, at the cost of performing less thorough analyses. Hence, performance- and memory-aware solutions are required to ensure that future computing systems will be able to keep up with the molecular data avalanche.
The field of Bioinformatics is dominated by resource demanding kernels. This has attracted the attention of the computer engineering community to such a great extent that the well-known Smith-Waterman pairwise sequence alignment algorithm frequently serves as one of the test applications to demonstrate new engineering concepts in accelerator platforms. Yet, most performance-driven innovations for Bioinformatics problems frequently remain at the basic-research level. This collection of articles aims to foster collaborations that can lead to high-performance techniques and hardware accelerators being deployed in the field. The goal of this Research Topic is a) to uncover compute- and/or data-intensive problems that arise from all areas of computational life sciences, in an effort to direct future performance-driven optimizations accordingly, and b) present high-performance and/or memory-aware solutions that span the entire computing spectrum, from novel methods and tools to custom computer architectures and accelerators, in an effort to demonstrate their potential in processing future large-scale datasets efficiently.
This Research Topic aspires to connect computational problems in the fields of Bioinformatics and Computational Biology with software and hardware solutions from the fields of Computer Science and Computer Engineering. We encourage submissions of both Original Research and Review articles that address existing challenges and present solutions.
Computational life sciences areas of interest include (but are not limited to):
• Sequence alignment, Phylogenetics, Population Genetics, Omics tools and databases, Microbes and microbiomes, Computational Epidemiology, Computational Neuroscience
Computer science and engineering areas of interest include (but are not limited to):
• Parallel and distributed algorithms, Data-aware and out-of-core techniques, Parallel computer architectures (multicore, manycore, GPU, FPGA, SoC)
Advances in DNA sequencing technology have contributed to the accumulation of molecular sequence data at an unprecedented pace, since whole genomes can be sequenced rapidly, accurately, and cost effectively. When methods and tools are not specifically designed to handle big volumes of data efficiently, large-scale analyses practically become infeasible due to the explosion in processing and memory requirements. Bioinformatics algorithms frequently rely on approximations and heuristics to yield computationally tractable implementations, at the cost of performing less thorough analyses. Hence, performance- and memory-aware solutions are required to ensure that future computing systems will be able to keep up with the molecular data avalanche.
The field of Bioinformatics is dominated by resource demanding kernels. This has attracted the attention of the computer engineering community to such a great extent that the well-known Smith-Waterman pairwise sequence alignment algorithm frequently serves as one of the test applications to demonstrate new engineering concepts in accelerator platforms. Yet, most performance-driven innovations for Bioinformatics problems frequently remain at the basic-research level. This collection of articles aims to foster collaborations that can lead to high-performance techniques and hardware accelerators being deployed in the field. The goal of this Research Topic is a) to uncover compute- and/or data-intensive problems that arise from all areas of computational life sciences, in an effort to direct future performance-driven optimizations accordingly, and b) present high-performance and/or memory-aware solutions that span the entire computing spectrum, from novel methods and tools to custom computer architectures and accelerators, in an effort to demonstrate their potential in processing future large-scale datasets efficiently.
This Research Topic aspires to connect computational problems in the fields of Bioinformatics and Computational Biology with software and hardware solutions from the fields of Computer Science and Computer Engineering. We encourage submissions of both Original Research and Review articles that address existing challenges and present solutions.
Computational life sciences areas of interest include (but are not limited to):
• Sequence alignment, Phylogenetics, Population Genetics, Omics tools and databases, Microbes and microbiomes, Computational Epidemiology, Computational Neuroscience
Computer science and engineering areas of interest include (but are not limited to):
• Parallel and distributed algorithms, Data-aware and out-of-core techniques, Parallel computer architectures (multicore, manycore, GPU, FPGA, SoC)
