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MetaflowX: a scalable and resource-efficient workflow for multi-strategy metagenomic analysis.

Yan Xia1,2,3, Lifeng Liang1,2, Xiaokai Wang1,2

  • 101Life Institute, Shenzhen 518100, China.

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MetaflowX is a new workflow for analyzing microbiome data, offering faster processing and reduced disk usage. It improves the identification and quality of microbial genomes from large datasets.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Microbiomes are vital across environmental, agricultural, and health sectors.
  • Large-scale metagenomic data analysis faces computational and resource hurdles.
  • Current pipelines lack efficiency for processing extensive datasets.

Purpose of the Study:

  • Introduce MetaflowX, an open-resource workflow for enhanced metagenomic analysis.
  • Integrate both reference-based and reference-free approaches for comprehensive investigations.
  • Address the need for efficient, scalable metagenomic data processing.

Main Methods:

  • Developed MetaflowX, a modular framework for metagenomic analysis.
  • Integrated modules for quality control, microbial profiling, assembly, binning, and MAG identification.
  • Included bin refinement, reassembly, and functional annotation capabilities.

Main Results:

  • MetaflowX achieved up to 14-fold faster analysis and 38% less disk usage compared to existing workflows.
  • Recovered a higher number of high-quality, taxonomically diverse metagenome-assembled genomes (MAGs).
  • A dedicated reassembly module improved MAG completeness by 5.6% and reduced contamination by 53%.

Conclusions:

  • MetaflowX offers an efficient and extensible solution for large-scale metagenomic research.
  • The workflow enhances the speed, resource efficiency, and quality of MAG recovery.
  • Enables detailed functional analysis, including virulence and antibiotic resistance gene detection.