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MetaBinner Specifications

Process Details

  • Name: MetaBinner
  • Process UUID: f931qnp0uq3ugz0pm1gpwuoocb1eeu
  • Process Group: Binning

Overview

MetaBinner is a specialized binning tool designed for generating metagenome-assembled genomes (MAGs) from short-read metagenomic sequencing data. This process groups assembled contigs into bins representing individual microbial genomes based on sequence composition and coverage patterns, enabling the reconstruction of complete or near-complete genomes from complex microbial communities.

This process is implemented in Bash, which invokes Python scripts for k-mer analysis and the MetaBinner binning algorithm.

Key Functionality

  • Contig Filtering: Filters input contigs based on minimum length requirements to ensure high-quality binning results
  • Coverage Analysis: Processes coverage information to identify contigs with sufficient sequencing depth for reliable binning
  • K-mer Profiling: Generates tetranucleotide frequency profiles for compositional analysis of contigs
  • Ensemble Binning: Applies MetaBinner's ensemble approach to group contigs into bins representing individual genomes
  • Bin Refinement: Performs quality-based refinement with configurable completeness and contamination thresholds

Input/Output Specification

Inputs

Optional Inputs

  • Input Directory
    • Description: Directory containing prepared assembly files including contigs and coverage data
    • Format: directory

Outputs

  • Log File

    • Description: Detailed execution log containing processing statistics and quality metrics
    • Format: log

  • Output Directory

    • Description: Directory containing binned contigs organized as individual FASTA files representing reconstructed genomes
    • Format: directory

Parameters & Settings

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

  • Minimum Contig Length
    • Description: Write the minimum expected contig length for MetaBinner to work on. The value should be an integer!
    • Default value: 2500

References & Resources

  • Tool Documentation: Contact the team for details on get_kmer.py and run_metabinner.sh
  • Related Papers: Kang, D.D., Li, F., Kirton, E. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019). https://doi.org/10.7717/peerj.7359