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Checkm2 Specifications

Process Details

  • Name: checkm2
  • Process UUID: f9312bipxs5vjn93lt2dir0pm1wg5f
  • Process Group: Binning

Overview

This process assesses the quality of metagenome-assembled genomes (MAGs), checking for contamination and completeness using the CheckM2 tool. CheckM2 is a rapid method for assessing genome quality that provides estimates of completeness and contamination for microbial genomes, making it essential for evaluating the quality of assembled genomes from metagenomic data.

This process is implemented in Bash, which invokes the CheckM2 tool for genome quality assessment.

Key Functionality

  • Quality Assessment: Evaluates genome completeness and contamination levels using CheckM2's machine learning approach
  • Batch Processing: Processes multiple genome files efficiently with multi-threading support
  • Diamond Database Integration: Utilizes pre-built Diamond databases for rapid protein sequence comparison
  • Standardized Output: Generates consistent TSV format results for downstream analysis and filtering

Input/Output Specification

Inputs

Required Inputs

  • Fasta File Set

    • Description: Collection of genome assembly files (MAGs) to be assessed for quality
    • Format: FASTA

  • Diamond Database

    • Description: Pre-built Diamond database containing reference protein sequences for CheckM2 analysis
    • Format: DMND

Outputs

  • Quality Assessment Results
    • Description: Tab-separated values file containing completeness and contamination estimates for each input genome
    • Format: TSV

Parameters & Settings

These parameters can be adjusted in the Foundry UI when running this process.

  • Diamond Database Path
    • Description: Path to the Diamond database file for CheckM2 analysis
    • Default value: (empty string)

References & Resources

  • Tool Documentation: Contact the team for details on CheckM2 configuration and usage
  • Related Papers: Chklovski, A., Imelfort, M., Soucy, S.M. et al. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat Methods 20, 1203–1212 (2023). https://doi.org/10.1038/s41592-023-01940-w