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MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment.

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  • 1Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea.

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MUGAN significantly accelerates metagenomic error correction by leveraging parallel processing on CPUs and GPUs. This novel tool drastically reduces denoising time and improves the accuracy of operational taxonomic unit (OTU) cataloging.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Metagenomic sequencing generates vast datasets for microbial community analysis.
  • Accurate error correction is vital for reliable gene cataloging and operational taxonomic unit (OTU) identification.
  • Existing error correction tools often present computational bottlenecks due to long processing times.

Purpose of the Study:

  • To develop an efficient computational tool for metagenomic error correction.
  • To address the time-consuming nature of existing denoising methods.
  • To enhance the accuracy of microbial community genetic cataloging.

Main Methods:

  • Developed MUGAN, a tool exploiting data-level parallelism for error correction.
  • Utilized co-processing across multi-core central processing units (CPUs) and multiple graphics processing units (GPUs).
  • Implemented web-based visualization for intuitive result interpretation.

Main Results:

  • Reduced amplicon denoising time from approximately 59 hours to 46 minutes.
  • Decreased overestimation of OTUs by an estimated 6.7 times compared to baseline methods.
  • Provided efficient and convenient error correction with clear result visualization.

Conclusions:

  • MUGAN offers a substantial improvement in computational efficiency for metagenomic error correction.
  • The tool enhances the accuracy of microbial OTU cataloging.
  • MUGAN is expected to significantly facilitate large-scale metagenomics studies.