MaxBin2 Specifications
Process Details
- Name:
MaxBin2 - Process UUID:
f931cyf4c7go3q4g1n5caldys7hv0c - Process Group:
Binning
Overview
MaxBin2 is a binning tool designed for generating metagenome-assembled genomes (MAGs) from short-read sequencing data. This process performs automated binning of metagenomic contigs based on tetranucleotide frequency and abundance patterns to recover individual genomes from complex microbial communities.
This process is implemented in Bash, utilizing the MaxBin2 tool (version 2.2.7) for contig binning operations.
Key Functionality
- Contig Binning: Groups assembled contigs into bins representing individual genomes based on composition and abundance patterns
- Coverage Analysis: Utilizes depth coverage information to improve binning accuracy and separate co-occurring species
- Quality Filtering: Applies minimum contig length thresholds to focus on high-quality genomic fragments
- Marker Gene Analysis: Uses single-copy marker genes (107 marker set) to assess bin completeness and contamination
Input/Output Specification
Inputs
Required Inputs
- Input Directory
- Description: Directory containing assembled contigs in FASTA format and corresponding coverage/abundance files prepared for MaxBin2 analysis
- Format: Directory
Outputs
-
Log File
- Description: Detailed log of the MaxBin2 binning process including coverage data sources, processing steps, and output summary
- Format: Log file
-
Output Directory
- Description: Directory containing the generated genomic bins as individual FASTA files, along with the original assembly file
- 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 MaxBin2 to work on. The value should be an integer!
- Default value: 2500
References & Resources
- Tool Documentation: Contact the team for details on
run_MaxBin.pl - Related Papers: Wu, Y.W., Simmons, B.A. & Singer, S.W. MaxBin 2.0: an improved binning method to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605-607 (2016). DOI: 10.1093/bioinformatics/btv638