Standardize Bracken Specifications
Process Details
- Name:
standardize_bracken - Process UUID:
f9318xpd64g2h2ao0if3yvr6dd07h0 - Process Group:
short_read_taxonomy
Overview
This process converts Bracken taxonomic classification output files to a standardized CSV format that can be consistently used across different taxonomic ranks and downstream analyses. Bracken (Bayesian Reestimation of Abundance with KrakEN) produces species-level abundance estimates from Kraken2 classifications, and this standardization step ensures uniform formatting for comparative analyses and visualization.
This process is implemented in Bash, which invokes a Python script for the data standardization and filtering operations.
Key Functionality
- Format Conversion: Transforms Bracken TSV output files into standardized CSV format
- Abundance Filtering: Applies minimum relative abundance thresholds to filter out low-abundance taxa
- Batch Processing: Processes multiple Bracken output files simultaneously using parallel execution
- Directory Organization: Creates organized output directory structure for standardized results
Input/Output Specification
Inputs
Required Inputs
- Bracken Output Files
- Description: TSV files containing taxonomic abundance estimates generated by Bracken
- Format: TSV (Tab-separated values)
Outputs
- Standardized Taxonomic Data
- Description: Standardized CSV files containing filtered taxonomic abundance data in a consistent format across samples
- Format: CSV (Comma-separated values)
Parameters & Settings
These parameters can be adjusted in the Foundry UI when running this process.
- Minimum Relative Abundance
- Description: Threshold for filtering taxa based on relative abundance to remove low-abundance classifications
- Default value: Configurable via min_rel_abund parameter
References & Resources
- Tool Documentation: Contact the team for details on
standardize_bracken.py - Related Papers: Lu, J., Breitwieser, F.P., Thielen, P., & Salzberg, S.L. (2017). Bracken: estimating species abundance in metagenomics data. PeerJ Computer Science, 3, e104. https://doi.org/10.7717/peerj-cs.104