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<p>The increasing complexity and diversity of multi-omics datasets demand advanced, flexible, and scalable computational tools for revealing the key biogeochemical processes driven by microorganisms within natural ecosystems. Here, we present ‘DiTing v2.0’, an upgraded version of ‘DiTing v1.0’ with a fully modularized workflow developed to support multi-omic analysis. While retaining the key functionalities of v1.0, such as metagenomic assembly, gene prediction, sequence mapping, function annotation, unbiased quantification of biogeochemical pathway abundances, and result visualization, DiTing v2.0 further introduces substantial improvements in flexibility, scalability, and usability. Specifically, the entire workflow was reconstructed using Snakemake and incorporated a fully modular design, enabling a customized, stepwise execution with improved runtime efficiency, streamlined error recovery, and greater extensibility. It automates the resolution of task dependencies, uses a unified YAML configuration file for parameter management, and supports checkpointing and partial reruns. It improved the traditional ‘Hmmsearch’ with ‘KofamScan’ tools used for KEGG function annotation, integrates ‘CD-HIT’ for gene set dereplication, and allows the dynamic allocation of CPU threads for time-consuming steps. Notably, it extends support for genomics, including metagenome-assembled genome (MAG) recovery, quality control, taxonomy, and pathway completeness profiling. The capabilities of DiTing v2.0 were demonstrated by applying it to mangrove sediment samples, reconstructing microbial contributions to biogeochemical cycles, and visualizing the completeness and specific metabolic potential of MAG pathways. With enhanced modularity, faster runtime, and easier maintenance, DiTing v2.0 provides an efficient and extensible platform for investigating the metabolic potential of microbial communities within diverse environments.</p>

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DiTing v2.0: A Snakemake-Optimized Workflow for Multi-Omics-Based Biogeochemical Pathway Analysis

  • 期刊论文

  • 发表日期：

    2026年4月1日

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  • Xing Chen,
  • Heyu Lin,
  • Chunxu Xue,
  • Yi Liu,
  • Jiwen Liu,
  • Min Yu,
  • Xiaolei Wang,
  • Xiaohua Zhang

摘要

The increasing complexity and diversity of multi-omics datasets demand advanced, flexible, and scalable computational tools for revealing the key biogeochemical processes driven by microorganisms within natural ecosystems. Here, we present ‘DiTing v2.0’, an upgraded version of ‘DiTing v1.0’ with a fully modularized workflow developed to support multi-omic analysis. While retaining the key functionalities of v1.0, such as metagenomic assembly, gene prediction, sequence mapping, function annotation, unbiased quantification of biogeochemical pathway abundances, and result visualization, DiTing v2.0 further introduces substantial improvements in flexibility, scalability, and usability. Specifically, the entire workflow was reconstructed using Snakemake and incorporated a fully modular design, enabling a customized, stepwise execution with improved runtime efficiency, streamlined error recovery, and greater extensibility. It automates the resolution of task dependencies, uses a unified YAML configuration file for parameter management, and supports checkpointing and partial reruns. It improved the traditional ‘Hmmsearch’ with ‘KofamScan’ tools used for KEGG function annotation, integrates ‘CD-HIT’ for gene set dereplication, and allows the dynamic allocation of CPU threads for time-consuming steps. Notably, it extends support for genomics, including metagenome-assembled genome (MAG) recovery, quality control, taxonomy, and pathway completeness profiling. The capabilities of DiTing v2.0 were demonstrated by applying it to mangrove sediment samples, reconstructing microbial contributions to biogeochemical cycles, and visualizing the completeness and specific metabolic potential of MAG pathways. With enhanced modularity, faster runtime, and easier maintenance, DiTing v2.0 provides an efficient and extensible platform for investigating the metabolic potential of microbial communities within diverse environments.