GPU-Enhanced Bioinformatics: Accelerating Big Data Analysis in Genomics

EasyChair Preprint 13905

Abstract

The burgeoning field of genomics generates vast quantities of data, necessitating robust computational methods to effectively analyze and interpret these datasets. GPU-enhanced bioinformatics represents a transformative approach to addressing the challenges posed by big data in genomics. By leveraging the parallel processing power of Graphics Processing Units (GPUs), researchers can significantly accelerate various computational tasks, from sequence alignment and variant calling to complex simulations and machine learning applications. This acceleration not only reduces the time required for data processing but also enhances the accuracy and scalability of bioinformatics analyses. In this paper, we explore the integration of GPU technology in genomic data analysis, highlighting key advancements and case studies that demonstrate substantial improvements in performance. We also discuss the implications of these enhancements for personalized medicine, evolutionary biology, and other domains within life sciences. Our findings underscore the critical role of GPU-enhanced bioinformatics in advancing genomic research and its potential to catalyze breakthroughs in understanding complex biological systems.

Keyphrases: Accelerated sequence analysis, Bioinformatic algorithms, Computational Proteomics, Computational genomics, Deep learning in bioinformatics, GPU-accelerated machine learning, GPU-based bioinformatics, Genomic data processing, High Performance Computing, Machine learning in computational biology

@booklet{EasyChair:13905,
  author    = {Abey Litty},
  title     = {GPU-Enhanced Bioinformatics: Accelerating Big Data Analysis in Genomics},
  howpublished = {EasyChair Preprint 13905},
  year      = {EasyChair, 2024}}