Session

DescriptionThis minisymposium aims to address the critical challenges faced in the design and implementation of systems for omics research. As the field of omics, encompassing various disciplines such as genomics, proteomics, and metabolomics, continues to expand rapidly, there is an increasing demand for hardware-software co-design and robust computational systems that can handle large datasets, provide accurate analyses, and facilitate meaningful biological insights. The enormous data growth continuously shifts the life sciences from model-driven towards data-driven science driving the adoption of deep neural network models, massively parallel accelerators such as GPUs, and vendor-independent portability frameworks. This session will bring together experts from both computational and life sciences to discuss innovative approaches to systems design that meet the unique needs of omics workloads. Topics will include advanced algorithms for data processing in genomics and proteomics, novel data representations that achieve superior memory efficiency, and hardware-software co-design to improve performance and energy efficiency. Mechanisms that enable real-time analysis of genomic data by analyzing electrical signals as raw sequencing data, lessons learned from GPU acceleration of computations in widely used bioinformatics tools, and an outlook on future software and hardware trends that will likely impact computational biology will be shared.