Test data for Binette

Binette is a binning refinement tool that constructs high-quality MAGs from the output of multiple binning tools.

This repository contains test data and instructions for running Binette and comparing the results with expected outcomes.

Test locally

1. Install Binette:

   Make sure you have Binette installed on your system. You can refer to the Binette repository for installation instructions.

2. Clone This Repository:

   Clone this repository to your local machine using Git:

git clone https://github.com/genotoul-bioinfo/Binette_TestData.git

cd Binette_TestData

2. Run Binette:

Execute Binette on the test data using one of the following commands:

• To test Binette with bin2table inputs using the --contig2bin_tables argument:

binette -b binning_results/*.binning --contigs all_contigs.fna --checkm2_db checkm2_tiny_db/checkm2_tiny_db.dmnd -v -o test_results

• To test Binette using bin folders inputs, each containing their bins in fasta files, with the --bin_dirs argument:

binette --bin_dirs binning_results/A/ binning_results/B/ binning_results/C/ --contigs all_contigs.fna --checkm2_db checkm2_tiny_db/checkm2_tiny_db.dmnd -v -o test_results

These commands should complete in just a few seconds.

1. Compare Results:

After running Binette, you can compare the generated final_bins_quality_reports.tsv with the expected results. Some variation in the completeness, contamination, and score columns is expected due to Checkm2's slight variability.

You can perform the comparison manually by using the head command:

head  expected_results/final_bins_quality_reports.tsv test_results/final_bins_quality_reports.tsv

Alternatively, you can use the provided Python script for automated comparison: compare_results.py

python scripts/compare_results.py expected_results/final_bins_quality_reports.tsv test_results/final_bins_quality_reports.tsv