High-Performance Metagenome Assembly with GPU-Accelerated Machine Learning

EasyChair Preprint 14167

Abstract

The rapid expansion of metagenomic sequencing has necessitated the development of advanced computational techniques to manage and analyze the vast amounts of data generated. Traditional methods for metagenome assembly are often hampered by their computational inefficiency and inability to handle the scale and complexity of metagenomic datasets. This study explores the integration of GPU-accelerated machine learning algorithms to enhance the performance and accuracy of metagenome assembly. By leveraging the parallel processing capabilities of GPUs, we aim to significantly reduce the computational time and resource requirements for assembling metagenomic sequences. Our approach involves the application of deep learning models optimized for GPUs to accurately classify, bin, and assemble metagenomic reads. Initial results demonstrate a marked improvement in assembly speed and quality, enabling more precise reconstruction of microbial communities from complex environmental samples. This high-performance framework not only accelerates the metagenome assembly process but also opens new avenues for more detailed and comprehensive analyses in microbiome research, ultimately contributing to advancements in environmental microbiology, clinical diagnostics, and biotechnological applications.

Keyphrases: Graphics Processing Units (GPUs), machine learning, metagenome

@booklet{EasyChair:14167,
  author    = {Abi Litty},
  title     = {High-Performance Metagenome Assembly with GPU-Accelerated Machine Learning},
  howpublished = {EasyChair Preprint 14167},
  year      = {EasyChair, 2024}}