(BIN_ASSESSMENT_description)=

# <span style="color:#00B050">BIN_ASSESSMENT</span>

<img src="../_static/metafun4_green.png" style="height:200px; width:auto; float:right; margin-left:10px;" />
This module is a part of metaFun pipeline, designed for assessing genome quality and assigning taxonomy to metagenome-assembled genomes (MAGs).

## Overview
The BIN_ASSESSMENT module is the third step in the metaFun pipeline, designed for comprehensive evaluation of metagenome-assembled genomes (MAGs). It performs quality assessment using CheckM2, contamination assessment using GUNC, taxonomic classification using GTDB-Tk, and combines the results for downstream analysis. This module helps identify high-quality genomes suitable for comparative genomic analyses.

## Module Execution

```{code-block} bash
# Basic usage
(metafun) metafun -module BIN_ASSESSMENT

# Specify input directory if you used a custom output path in ASSEMBLY_BINNING
(metafun) metafun -module BIN_ASSESSMENT -i /path/to/bins

# Provide metadata file for combining with quality/taxonomy results (recommended)
(metafun) metafun -module BIN_ASSESSMENT -m your_metadata.csv -c 2

# Specify a quality filter for selecting genomes
(metafun) metafun -module BIN_ASSESSMENT --pass_quality medium_quality.pass
```

:::{admonition} Quality filtering options
:class: note

There are several options for filtering genomes based on quality metrics:

- For basic quality filtering, use `medium_quality.pass`:
```{code-block} bash
:caption: Apply medium-quality filter

(metafun) metafun -module BIN_ASSESSMENT --pass_quality medium_quality.pass
```

- For stricter quality requirements, use `high_quality.pass`:
```{code-block} bash
:caption: Apply high-quality filter

(metafun) metafun -module BIN_ASSESSMENT --pass_quality high_quality.pass
```

- For including genomes that pass both quality and GUNC checks:
```{code-block} bash
:caption: Apply quality and GUNC filter

(metafun) metafun -module BIN_ASSESSMENT --pass_quality QS50_gunc.pass
```

- Available quality filter options:
  - `medium_quality.pass`: Completeness ≥ 50%, Contamination < 10%
  - `high_quality.pass`: Completeness > 90%, Contamination < 5%
  - `medium_quality_gunc.pass`: medium_quality + GUNC pass
  - `high_quality_gunc.pass`: high_quality + GUNC pass
  - `QS50.pass`: QS50 score ≥ 50
  - `QS50_gunc.pass`: QS50 pass + GUNC pass
  - `all`: No quality filtering (include all genomes)
:::

## Module Operation Sequence

This module performs the following steps:

1. Preparation of input genome bins
2. Genome quality assessment using CheckM2
3. Genome contamination assessment using GUNC
4. Combining CheckM2 and GUNC results and filtering genomes based on quality
5. Taxonomic classification of quality-filtered genomes using GTDB-Tk
6. Creating a final report combining quality assessment and taxonomic classification
7. (Optional) Combining results with user-provided metadata

## Parameters
**`${launchDir}` is the directory where you execute metaFun, and utilized as output base directory.** 

| Parameter | Description | Default Value | Note |
|-----------|-------------|---------------|------|
| `-i, --inputDir` | Input directory with genome bins | `${launchDir}/results/metagenome/ASSEMBLY_BINNING/final_bins` | Output from <span style="color:#FF9300">ASSEMBLY_BINNING</span> module or specify your own directory containing genome bins. |
| `-o, --outdir` | Output directory | `${launchDir}/results/metagenome/BIN_ASSESSMENT` | Default recommended for downstream analysis |
| `-m, --metadata` | Path to metadata file | null | Optional but recommended. Used for combining quality/taxonomy results with sample metadata for downstream analysis. |
| `-c, --accession_column` | Column index in metadata | 1 | Specifies which column in the metadata file contains the sample identifiers that match genome bin names. |
| `--pass_quality` | Quality filter for genome selection | `QS50.pass` | Options: `medium_quality.pass`, `high_quality.pass`, `medium_quality_gunc.pass`, `high_quality_gunc.pass`, `QS50.pass`, `QS50_gunc.pass`, `all` |
| `-p, --processors` | Number of CPUs to use | 20 | Adjust based on your system capabilities |
| `--run_id` | Unique run identifier | Timestamp_workflowName | Generated automatically in format "yyyyMMddHHmmss_number" (e.g., "20250306135829_2996"). Can be customized if needed. |

:::{admonition} About parameters for quality filtering and metadata
:class: note

**Quality Filtering Implementation:**

The Nextflow script implements the quality filtering based on the `--pass_quality` parameter. When genomes are processed:

- The parameter selects which genomes will be passed to subsequent steps based on the specified quality criteria.
- If `--pass_quality` is set to `all`, all genome bins are included without filtering.
- For other options (e.g., `QS50.pass`, `medium_quality.pass`), the script filters genomes based on the corresponding column in the combined quality report, including only those marked as "True" for the selected quality criterion.

**Accession Column Example:**

Consider this example of how `--accession_column` works with a metadata file:

Metadata file (sample_metadata.csv):
```
run_accession,sample_id,location,disease_status,age
SRR6915091,P001,USA,Healthy,35
SRR6915092,P002,Canada,Disease,42
SRR6915093,P003,UK,Healthy,28
```

Your genome bin names would be something like:
- SRR6915091_bin.1.fa
- SRR6915091_bin.2.fa
- SRR6915092_bin.1.fa
...

To match these bin names with the metadata, you would use:
```
metafun -module BIN_ASSESSMENT -m sample_metadata.csv -c 1
```

Because column 1 (`run_accession`) contains the sample identifiers (SRR6915091, SRR6915092) that match the prefix of your genome bin names.
:::

## **Inputs and Outputs**

### Inputs
* Genome bins in FASTA format (`.fa`, `.fna`, or `.fasta` extensions)
* These should be the high-quality bins from the <span style="color:#FF9300">ASSEMBLY_BINNING</span> workflow
* Default input directory: `${launchDir}/results/metagenome/ASSEMBLY_BINNING/final_bins`
* Optional: Metadata file in CSV or TSV format with sample information

(BIN_ASSESSMENT_output)=

### Outputs
* CheckM2 quality assessment results
* GUNC contamination assessment results
* GTDB-Tk taxonomic classification
* Quality-filtered genome bins
* Combined quality and taxonomy report
* Optional: Combined metadata with quality/taxonomy information

### Output directory structure

Output directory is at `${launchDir}/results/metagenome/BIN_ASSESSMENT` or your specified directory path with `-o outdir`.

```{admonition} Switching input and output directory.
:class: note

If you define a custom output directory with `-o ${output directory}`, you should modify input parameters in downstream workflows accordingly.
The default output directory is `results/metagenome/BIN_ASSESSMENT` in your ${launchDir}.
```

```{code-block} bash
:caption: Output directory structure

${launchDir}/results/metagenome/BIN_ASSESSMENT/
├── prepared_bins_${run_id}/              # Prepared and renamed input bins
│   └── renamed_bins/
│       ├── ${sample_id}_bin.1.fa
│       ├── ${sample_id}_bin.2.fa
│       └── ...
├── checkm2_${run_id}/                    # CheckM2 quality assessment results
│   └── ${outputDirCheckM2}/
│       ├── quality_report.tsv            # Main quality assessment report
│       ├── protein_files/                # Predicted proteins from genomes
│       │   ├── ${genome_name}.faa
│       │   └── ...
│       └── ...
├── gunc_${run_id}/                       # GUNC contamination assessment results
│   └── ${outputDirGUNC}/
│       ├── GUNC.progenomes_2.1.maxCSS_level.tsv  # Main contamination report
│       └── ...
├── checkm_gunc_combined_${run_id}/       # Combined quality/contamination results
│   └── combined_report.tsv               # Combined CheckM2 and GUNC results
├── bins_quality_passed/                  # Quality-filtered genome bins
│   ├── ${sample_id}_bin.1.fa             # Only bins passing quality filter
│   └── ...
├── gtdb_outdir_${run_id}/                # GTDB-Tk taxonomy classification results
│   ├── gtdbtk.bac120.summary.tsv         # Bacterial genome classifications
│   ├── gtdbtk.ar53.summary.tsv           # Archaeal genome classifications
│   ├── quality_taxonomy_combined.csv     # Combined quality and taxonomy
│   └── ...
└── quality_taxonomy_combined_final.csv   # Final report for downstream analysis
```

Additionally, when metadata is provided:
```{code-block} bash
:caption: Metadata-related output files (in launchDir)

${launchDir}/
├── combined_metadata_quality_taxonomy_${run_id}.csv  # Combined metadata and quality/taxonomy
└── metadata_column_BIN_ASSESSMENT_summary.tsv        # Summary of metadata columns
```

## Execution Examples and Results

### metaFun command line execution example

```{figure} ../images/BIN_ASSESSMENT_command.png
---
width: 100%
figclass: margin-caption
alt: metafun_pipeline
name: BIN_ASSESSMENT_command
align: middle
---
```

::::{admonition} Combining metadata with quality/taxonomy results
:class: note

The combined metadata file (combined_metadata_quality_taxonomy_${run_id}.csv) is essential for downstream analysis in the <span style="color:#00B050">GENOME_</span><span style="color:#4E95D9">SELECTOR</span> module. It links genome quality and taxonomy with sample metadata. The bins selected in this process will be used for further analysis in the <span style="color:#4E95D9">COMPARATIVE_ANNOTATION</span> module.

To work effectively with your metadata files:

1. First, check the metadata column summary to identify the column structure:
```{code-block} bash
:caption: View metadata column summary

cat yourmetadata.csv
```
You need to identify accession column for you metadata


This helps you understand your data's structure before proceeding to genome selection and further analysis.
::::

### Example of Quality Assessment Results

```{code-block} bash
:caption: Example of CheckM2 quality report

$ head -n 5 ${launchDir}/results/metagenome/BIN_ASSESSMENT/checkm2_*/quality_report.tsv

Name    Completeness    Contamination    Completeness_Model_Used  Translation_Table_Used    Coding_Density    Contig_N50    Average_Gene_Length    Genome_Size    GC_Content    Total_Coding_Sequences    Total_Contigs    Max_Contig_Length    Additional_Notes
SRR6915091_bin.1    98.2    1.3    Neural Network (Specific Model)    11    0.877    278453    899.8    2758979    0.474    2698    823    32542    None
SRR6915091_bin.2    87.6    3.2    Neural Network (Specific Model)    11    0.863    129876    891.2    3104325    0.418    3077    945    28754    None
SRR6915091_bin.3    62.4    5.1    Gradient Boost (General Model)    11    0.901    87542    911.5    2198765    0.562    2154    752    18923    None
```

The CheckM2 quality report includes the following key columns:
- **Name**: Genome bin identifier
- **Completeness**: Estimated genome completeness percentage (0-100%)
- **Contamination**: Estimated contamination percentage (0-100%)
- **Completeness_Model_Used**: Model used for estimating completeness (e.g., "Neural Network (Specific Model)", "Gradient Boost (General Model)")
- **Translation_Table_Used**: Genetic code used for translation
- **Coding_Density**: Proportion of the genome that codes for proteins
- **Contig_N50**: N50 value of contigs (length where 50% of the genome is in contigs of this size or larger)
- **Average_Gene_Length**: Average length of predicted genes
- **Genome_Size**: Total size of the genome in base pairs
- **GC_Content**: GC content of the genome (0-1)
- **Total_Coding_Sequences**: Number of predicted coding sequences
- **Total_Contigs**: Number of contigs in the genome assembly
- **Max_Contig_Length**: Length of the largest contig

### Example of Taxonomy Classification Results

```{code-block} bash
:caption: Example of GTDB-Tk taxonomy classification

$ head -n 5 ${launchDir}/results/metagenome/BIN_ASSESSMENT/gtdb_outdir_*/gtdbtk.bac120.summary.tsv

| Genome                | Completeness | Contamination | medium_quality.pass | near_complete.pass | medium_quality_gunc.pass | near_complete_gunc.pass | QS50  | QS50.pass | pass.GUNC | QS50_gunc.pass | classification                                                                                                                                             | Analysis_accession | bioproject_accession | accession_used_in_analysis | country | continent | host_age | host_body_mass_index | host_sex | disease_group | AJCC_stage | age_group |
|-----------------------|--------------|---------------|---------------------|--------------------|--------------------------|-------------------------|-------|-----------|-----------|----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------|----------------------|----------------------------|---------|-----------|----------|----------------------|----------|---------------|------------|-----------|
| SRR6915091_MB2.14     | 91.77        | 0.55          | True                | True               | True                     | True                    | 89.02 | True      | True      | True           | d__Bacteria;p__Bacillota_C;c__Negativicutes;o__Acidaminococcales;f__Acidaminococcaceae;g__Acidaminococcus;s__Acidaminococcus intestini                      | SRR6915091         | PRJNA447983          | SRR6915091                 | Italy   | Europe    | 77.0     | 23.0                 | Male     | CRC           |            | Old       |
| SRR6915091_MB2.16     | 93.32        | 6.58          | True                | False              | False                    | False                   | 60.42 | True      | False     | False          | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola;s__Phocaeicola dorei                                           | SRR6915091         | PRJNA447983          | SRR6915091                 | Italy   | Europe    | 77.0     | 23.0                 | Male     | CRC           |            | Old       |
| SRR6915091_MB2.17     | 70.82        | 2.2           | True                | False              | True                     | False                   | 59.82 | True      | True      | True           | d__Bacteria;p__Bacillota_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Fusicatenibacter;s__Fusicatenibacter saccharivorans                        | SRR6915091         | PRJNA447983          | SRR6915091                 | Italy   | Europe    | 77.0     | 23.0                 | Male     | CRC           |            | Old       |
| SRR6915091_MB2.18_sub | 64.01        | 9.25          | True                | False              | False                    | False                   | 17.76 | True      | False     | False          | d__Bacteria;p__Bacillota_A;c__Clostridia;o__Oscillospirales;f__Oscillospiraceae;g__Flavonifractor;s__Flavonifractor plautii                                  | SRR6915091         | PRJNA447983          | SRR6915091                 | Italy   | Europe    | 77.0     | 23.0                 | Male     | CRC           |            | Old       |


```{code-block} bash
:caption: Example of combined quality and taxonomy report

$ head -n 5 ${launchDir}/results/metagenome/BIN_ASSESSMENT/quality_taxonomy_combined_final.csv

Genome,Completeness,Contamination,medium_quality.pass,high_quality.pass,medium_quality_gunc.pass,high_quality_gunc.pass,QS50,QS50.pass,pass.GUNC,QS50_gunc.pass,classification
SRR6915091_bin.1,98.2,1.3,True,True,True,True,89.02,True,True,True,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Bacteroides;s__Bacteroides vulgatus
SRR6915091_bin.2,87.6,3.2,True,False,True,False,72.76,True,True,True,d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Blautia;s__Blautia obeum
SRR6915091_bin.3,62.4,5.1,True,False,False,False,47.05,False,False,False,d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Oscillospirales;f__Ruminococcaceae;g__Faecalibacterium;s__Faecalibacterium prausnitzii
```

## Nextflow Processes in <span style="color:#00B050">BIN_ASSESSMENT</span> Module

| Process | InputDir | OutputDir | Note |
|---------|----------|-----------|------|
| prepareInputFiles | `${params.inputDir}` | `${params.outdir}/prepared_bins_${params.run_id}` | Prepares and renames input genome bins |
| runCheckM2 | Output from prepareInputFiles | `${params.outdir}/checkm2_${params.run_id}` | CheckM2 quality assessment |
| runGUNC | Output from runCheckM2 | `${params.outdir}/gunc_${params.run_id}` | GUNC contamination assessment |
| combineFiles | Outputs from runCheckM2 and runGUNC | `${params.outdir}/checkm_gunc_combined_${params.run_id}` and `${params.outdir}/bins_quality_passed` | Combines results and filters genomes |
| gtdbtk | Outputs from combineFiles | `${params.outdir}/gtdb_outdir_${params.run_id}` | GTDB-Tk taxonomic classification |
| createFinalFile | Outputs from gtdbtk | `${params.outdir}` | Creates final combined report |
| combineMetadata | Final report and user metadata | `${launchDir}` | Optional: Combines with user metadata |
| create_metadata_summary | Combined metadata file | `${launchDir}` | Creates metadata column summary |

## Descriptions of Processes in <span style="color:#00B050">BIN_ASSESSMENT</span> Workflow

1. **prepareInputFiles**: Prepares input genome bins by standardizing file extensions and handling compressed files.
   - Input: Directory containing genome bins
   - Output: Directory with standardized genome bins in `.fa` format

2. **runCheckM2**: Assesses genome quality using CheckM2, which estimates completeness and contamination based on lineage-specific marker genes.
   - Input: Standardized genome bins
   - Output: CheckM2 quality report and protein files
   - Uses CheckM2 database for marker gene identification

3. **runGUNC**: Assesses genome contamination using GUNC, which detects contamination based on genomic consistency.
   - Input: Protein files from CheckM2
   - Output: GUNC contamination report
   - Uses GUNC database for reference-based contamination detection

4. **combineFiles**: Combines CheckM2 and GUNC results, calculates quality scores, and filters genomes based on quality thresholds.
   - Input: CheckM2 and GUNC reports
   - Output: Combined quality report and filtered genome bins
   - Implements quality filtering based on the selected quality parameter

5. **gtdbtk**: Performs taxonomic classification of quality-filtered genomes using GTDB-Tk, which assigns taxonomy based on the Genome Taxonomy Database.
   - Input: Quality-filtered genome bins
   - Output: GTDB-Tk classification results for bacterial and archaeal genomes
   - Utilizes GTDB-r220 database for classification

6. **createFinalFile**: Creates a comprehensive final report combining quality assessment and taxonomic classification.
   - Input: Combined quality report and GTDB-Tk results
   - Output: Final report with quality metrics and taxonomy
   - Merges results from previous runs if available

7. **combineMetadata**: (Optional) Combines the final quality/taxonomy report with user-provided metadata.
   - Input: Final report and user metadata file
   - Output: Combined metadata file with quality, taxonomy, and sample information
   - Links genomic information with sample metadata for downstream analysis

8. **create_metadata_summary**: Creates a summary of metadata columns to assist in subsequent analyses.
   - Input: Combined metadata file
   - Output: Metadata column summary with indices and names
   - Helps identify relevant columns for downstream analysis

## Tools Used in <span style="color:#00B050">BIN_ASSESSMENT</span>

| Tool | Purpose | Version | Database | Database Version | Default Parameters | Parameters that can be selected |
|------|---------|---------|----------|------------------|---------------------|--------------------------------|
| CheckM2 | Genome quality assessment | 1.0.2 | CheckM2 Database | 1.0.2 | `--threads ${task.cpus} --output-directory ${params.outputDirCheckM2} -x fa` | Output directory name |
| GUNC | Genome contamination assessment | 1.0.6 | GUNC Database | progenomes2.1 | `-g -t ${task.cpus} -o ${params.outputDirGUNC} -e .faa` | Output directory name |
| GTDB-Tk | Genome taxonomy classification | 2.4.0 | GTDB | r220 | `--mash_db mash_220.db.msh --cpus ${task.cpus} --pplacer_cpus ${task.cpus}` | None specific to this workflow |

## Custom Scripts in <span style="color:#00B050">BIN_ASSESSMENT</span>

| Script | Purpose | Input | Output | Note |
|--------|---------|-------|--------|------|
| Checkm2_GUNC_combine_quality_pass.py | Combines CheckM2 and GUNC results | CheckM2 and GUNC reports | Combined quality report | Calculates quality scores and flags |
| GTDB_add2_check2gunc.py | Combines quality and taxonomy data | Quality report and GTDB-Tk results | Combined quality/taxonomy report | Creates comprehensive genome report |
| combine_metadata_WMS_genome.py | Combines genome data with sample metadata | Genome report and user metadata | Combined metadata file | Links genomic data with sample context |

## Usage Notes

- The **<span style="color:#00B050">BIN_ASSESSMENT</span>** workflow is designed to work with the output from the <span style="color:#FF9300">ASSEMBLY_BINNING</span> workflow.
- Providing metadata with the `--metadata` option is highly recommended for downstream analysis in the **<span style="color:#4E95D9">COMPARATIVE_ANNOTATION</span>** module.
- The default quality filter (`QS50.pass`) selects genomes with a QS50 quality score ≥ 50, but you can choose different filters based on your research needs.
- The script checks for the existence and non-emptiness of the input directory before proceeding.
- For parallel processing of multiple samples, you can run the workflow with different `--run_id` values.
- The final quality/taxonomy report and optional combined metadata file are key inputs for the **<span style="color:#4E95D9">COMPARATIVE_ANNOTATION</span>** module.

## Next Steps

After assessing genome quality and taxonomy with this module, proceed to **<span style="color:#00B050">GENOME_</span><span style="color:#4E95D9">SELECTOR</span>** to select genomes of you interest. Then, use the result csv file in  **<span style="color:#4E95D9">COMPARATIVE_ANNOTATION</span>** to:
- Perform gene prediction and annotation
- Conduct comparative genomic analyses
- Generate functional profiles
- Compare genomes across different samples or conditions

(BIN_ASSESSMENT_combine_metadata)=

## Combining Metadata with Quality/Taxonomy Results

:::{admonition} Combining Sample Metadata with Genome Information
:class: note, dropdown

The metadata should contain a common basename of paired-end read metagenomic files in CSV format. 
For example, the base name of a pair of paired-end metagenomic fastq files would be:  
`SRR6915091_1.fastq`, `SRR6915091_2fastq` --> **basename**: `SRR6915091`

### Metadata of Metagenomes

**Example table content of metadata csv file:**
- You need to prepare metadata file in csv format of your own. 
- For your reference, download metadata of bioproject [PRJNA447983](https://www.nature.com/articles/s41591-019-0405-7) in csv format.  
{download}`CRC_Control113_PRJNA447983_metadata.csv </_static/CRC_Control113_PRJNA447983_metadata.txt>`

| bioproject_accession | accession_used_in_analysis | country | continent | host_age | host_body_mass_index | host_sex | disease_group | AJCC_stage | age_group |
|:---------------------|:---------------------------|:--------|:----------|:---------|:---------------------|:---------|:--------------|:-----------|:----------|
| PRJNA447983          | SRR6915092                 | Italy   | Europe    | 60       | 20                   | Female   | Control       | Control    | Old       |
| PRJNA447983          | SRR6915097                 | Italy   | Europe    | 80       | 32                   | Female   | Control       | Control    | Old       |
| PRJNA447983          | SRR6915108                 | Italy   | Europe    | 67       | 20.43816558          | Female   | Control       | Control    | Old       |
| PRJNA447983          | SRR6915113                 | Italy   | Europe    | 77       | NA                   | Female   | Control       | Control    | Old       |

In this table, the second column `accession_used_in_analysis` is the base name of generated MAGs.

### Metadata of MAGs

- Upon successful execution of the `BIN_ASSESSMENT` module, the `quality_taxonomy_combined_final.csv` file is generated with genome quality, taxonomy, and detailed metrics information:

```
Genome,Completeness,Contamination,medium_quality.pass,high_quality.pass,medium_quality_gunc.pass,high_quality_gunc.pass,QS50,QS50.pass,pass.GUNC,QS50_gunc.pass,Completeness_Model_Used,Translation_Table_Used,Coding_Density,Contig_N50,Average_Gene_Length,Genome_Size,GC_Content,Total_Coding_Sequences,Total_Contigs,Max_Contig_Length,classification
ERR1018185qced_headed_MB2.1,62.18,0.38,True,False,True,False,60.28,True,True,True,Neural Network (Specific Model),11,0.886,3309,254.5395019981556,2799002,0.51,3253,900,18216,d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia;s__Escherichia coli
```

### Combined Metadata File

- When using the `--metadata` option with `--accession_column`, the `combined_metadata_quality_taxonomy_${run_id}.csv` file is generated:

| Genome                | Completeness | Contamination | medium_quality.pass | high_quality.pass | classification | bioproject_accession | accession_used_in_analysis | country | host_age | disease_group |
|-----------------------|--------------|---------------|---------------------|-------------------|----------------|----------------------|----------------------------|---------|----------|---------------|
| SRR6915091_bin.1     | 91.77        | 0.55          | True                | True              | d__Bacteria;p__Bacillota_C;c__Negativicutes;o__Acidaminococcales;f__Acidaminococcaceae;g__Acidaminococcus;s__Acidaminococcus intestini | PRJNA447983 | SRR6915091 | Italy | 77 | CRC |
| SRR6915091_bin.2     | 93.32        | 6.58          | True                | False             | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola;s__Phocaeicola dorei | PRJNA447983 | SRR6915091 | Italy | 77 | CRC |

This combined file links genome quality and taxonomy with sample metadata, providing valuable context for downstream analyses in the <span style="color:#7FBDFF">COMPARATIVE_ANNOTATION</span> module.

To generate this combined file, use:
```{code-block} bash
(metafun) metafun -module BIN_ASSESSMENT -m your_metadata.csv -c 2
```

Where `2` refers to the column index in your metadata that contains the sample identifiers matching your genome bin names.

The output file will be named with a timestamp and workflow identifier (e.g., `combined_metadata_quality_taxonomy_20250306135829_2996.csv`), where:
- `20250306135829` is the timestamp in format yyyyMMddHHmmss
- `2996` is a unique workflow identifier
:::

<span style="color:#00B050">BIN_ASSESSMENT</span> is a crucial step that bridges the gap between genome recovery and functional analysis, ensuring that only high-quality genomes are used for downstream comparative studies.