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Melon: metagenomic long-read-based taxonomic identification and quantification using marker genes

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# Melon
Melon: **me**tagenomic **lon**g-read-based taxonomic identification and quantification using marker genes

## Quick start
### Installation
<a href="https://anaconda.org/bioconda/melon"> <img src="https://anaconda.org/bioconda/melon/badges/version.svg" /></a>
<a href="https://anaconda.org/bioconda/melon"> <img src="https://anaconda.org/bioconda/melon/badges/latest_release_date.svg" /></a>

Create a new conda environment and install:
```bash
conda create -n melon -c conda-forge -c bioconda melon
conda activate melon
```

### Database setup
> [!NOTE]
> Database released on or after 2025-04-16 is not compatible with version `<0.3.0`. Instructions for building the GTDB database can be found at https://github.com/xinehc/melon-supplementary.

Download either the [NCBI](https://doi.org/10.5281/zenodo.12571187) or the [GTDB](https://doi.org/10.5281/zenodo.12571470) database:
| Taxonomy | Latest version | Species | Genome | Update |
| :---: | :---: | :---: | :---: | :---: |
| NCBI | RefSeq R229 | 63,660 | 423,988 | 2025 |
| GTDB | GTDB R232 | 199,923 | 900,691 | 2026 |

```bash
## NCBI
# wget -qN --show-progress https://zenodo.org/records/15231351/files/database.tar.gz
# tar -zxvf database.tar.gz

## GTDB
wget -qN --show-progress https://zenodo.org/records/19924976/files/database.tar.gz
tar -zxvf database.tar.gz
```

Index the files: 
```bash
## if you encounter memory issue consider manually lowering cpu_count or setting cpu_count=1
cpu_count=$(python -c 'import os; print(os.cpu_count())')

diamond makedb --in database/prot.fa --db database/prot --quiet
ls database/nucl.*.fa | sort | xargs -P $cpu_count -I {} bash -c '
    filename=$(basename ${1} .fa);
    minimap2 -x map-ont -d database/$filename.mmi ${1} 2> /dev/null;
    echo "Indexed <database/$filename.fa>.";
' - {}

## remove unnecessary files to save space
rm -rf database/*.fa
```

### Run Melon
> [!NOTE]
> Melon takes ***quality-controlled*** long reads as input. We suggest removing low-quality raw reads before running Melon with e.g., `nanoq -q 10 -l 1000` (min. quality score 10; min. read length 1,000 bp). If your sample is known to have a large proportion of human DNAs or other eukaryotes/viruses and you want to estimate the ***mean genome size*** of prokaryotes, please consider removing them via proper mapping, or enabling the simple pre-filtering module. See [Run Melon with pre-filtering of non-prokaryotic reads](#run-melon-with-pre-filtering-of-non-prokaryotic-reads) for more details.

We provide an example file comprising 10,000 quality-controlled (processed with `Porechop` and `nanoq`) prokaryotic reads (fungal and other reads removed with `minimap2`), randomly selected from the R10.3 mock sample of [Loman Lab Mock Community Experiments](https://lomanlab.github.io/mockcommunity/r10.html).

```bash
wget -qN --show-progress https://zenodo.org/records/12571849/files/example.fa.gz
melon example.fa.gz -d database -o .
```

You should see:
```
INFO: Estimating genome copies ...
INFO: ... found 27.75 copies of genomes (bacteria: 27.75; archaea: 0).
INFO: Assigning taxonomy ...
INFO: Reassigning taxonomy ...
INFO: ... found 8 unique species (bacteria: 8; archaea: 0).
INFO: Done.
```

The output file `*.tsv` contains the estimated genome copies for individual species, their corresponding relative abundances and gap-compressed/gap-uncompressed ANI (average nucleotide identity between marker-gene-containing reads and reference genome clusters) values:
```
...    species                          copy     abundance       identity
...    Pseudomonas aeruginosa           2.125    7.657658e-02    0.9573/0.9474
...    Bacillus spizizenii              2.750    9.909910e-02    0.9612/0.9530
...    Salmonella enterica              3.125    1.126126e-01    0.9524/0.9433
...    Enterococcus faecalis            3.500    1.261261e-01    0.9600/0.9515
...    Listeria monocytogenes_B         3.500    1.261261e-01    0.9623/0.9547
...    Escherichia coli                 3.625    1.306306e-01    0.9569/0.9477
...    Staphylococcus aureus            4.125    1.486486e-01    0.9594/0.9513
...    Limosilactobacillus fermentum    5.000    1.801802e-01    0.9651/0.9574

```

The output file `*.json` contains the lineage and remark of each processed read.
```
{
    "002617ff-697a-4cd5-8a97-1e136a792228": {
        "remark": "marker-gene-containing",
        "lineage": "Bacteria;Bacillota;Bacilli;Lactobacillales;Enterococcaceae;Enterococcus;Enterococcus faecalis"
    },
    ...
    "ffe73d61-55eb-4ad8-9519-e38c364fc11d": {
        "remark": "marker-gene-containing",
        "lineage": "Bacteria;Pseudomonadota;Gammaproteobacteria;Enterobacterales;Enterobacteriaceae;Salmonella;Salmonella enterica"
    }
}
```

### Run Melon with pre-filtering of non-prokaryotic reads
To enable the pre-filtering module, you need to download a database of Kraken2 that includes at least human and fungi (PlusPF, PlusPFP, or their capped versions). Using the PlusPF-8 (ver. 2023-06-05, capped at 8 GB) as an example:

```bash
## https://benlangmead.github.io/aws-indexes/k2
mkdir database_kraken
wget -q --show-progress https://genome-idx.s3.amazonaws.com/kraken/k2_pluspf_08gb_20230605.tar.gz -O database_kraken/db.tar.gz
tar -zxvf database_kraken/db.tar.gz -C database_kraken

## remove temporary files to save space
rm -rf database_kraken/db.tar.gz
```

Run with argument `-k/-db-kraken`:
```bash
melon *.fa -d database -o . -k database_kraken
```

## Miscellaneous
### Statistics of influent/effluent samples with/without pre-filter
Both samples were collected from the Shatin wastewater treatment plant, sequenced with ONT SQK-NBD114 & R10.4.1 on PromethION, basecalled with Guppy v6.5.7.

<table>
   <thead>
      <tr>
         <th></th>
         <th></th>
         <th>no pre-filter</th>
         <th>PlusPF-8</th>
         <th>PlusPF-16</th>
         <th>PlusPF</th>
      </tr>
   </thead>
   <tbody>
      <tr>
         <td rowspan="6">influent
            <div>
               <ul>
                  <li>read: 1,091,772</li>
                  <li>bp: 7,815,976,467</li>
                  <li>n50: 9,500</li>
                  <li>mean quality: 18.3</li>
               </ul>
            </div>
         </td>
         <td>genome copy</td>
         <td>1,809</td>
         <td>1,801</td>
         <td>1,800</td>
         <td>1,797</td>
      </tr>
      <tr>
         <td>species richness</td>
         <td>2,101</td>
         <td>2,098</td>
         <td>2,099</td>
         <td>2,097</td>
      </tr>
      <tr>
         <td>number of filtered reads</td>
         <td>-</td>
         <td>8,312</td>
         <td>10,212</td>
         <td>14,988</td>
      </tr>
      <tr>
         <td>mean genome size</td>
         <td>4.322</td>
         <td>4.304</td>
         <td>4.300</td>
         <td>4.292</td>
      </tr>
      <tr>
         <td>real time (sec)</td>
         <td>2,056</td>
         <td>2,271</td>
         <td>2,279</td>
         <td>-</td>
      </tr>
      <tr>
         <td>peak resident set size (GB)</td>
         <td>10.629</td>
         <td>10.665</td>
         <td>17.294</td>
         <td>-</td>
      </tr>
      <tr>
         <td rowspan="6">effluent
            <ul>
               <li>read: 1,118,502</li>
               <li>bp: 5,157,996,045</li>
               <li>n50: 6,577</li>
               <li>mean quality: 18.2</li>
            </ul>
         </td>
         <td>genome copy</td>
         <td>1,348</td>
         <td>1,336</td>
         <td>1,331</td>
         <td>1,315</td>
      </tr>
      <tr>
         <td>species richness</td>
         <td>1,704</td>
         <td>1,697</td>
         <td>1,700</td>
         <td>1,696</td>
      </tr>
      <tr>
         <td>number of filtered reads</td>
         <td>-</td>
         <td>16,602</td>
         <td>29,300</td>
         <td>54,774</td>
      </tr>
      <tr>
         <td>mean genome size</td>
         <td>3.826</td>
         <td>3.789</td>
         <td>3.757</td>
         <td>3.715</td>
      </tr>
      <tr>
         <td>real time (sec)</td>
         <td>1,341</td>
         <td>1,496</td>
         <td>1,495</td>
         <td>-</td>
      </tr>
      <tr>
         <td>peak resident set size (GB)</td>
         <td>10.893</td>
         <td>10.902</td>
         <td>17.067</td>
         <td>-</td>
      </tr>
   </tbody>
</table>

Tested on MacBook Pro 2021, Apple M1 Max, 64 GB memory, macOS Sonoma. Melon v0.1.0, NCBI database (ver. RefSeq R219) and Kraken database (ver. 2023-06-05). Mean genome size is in unit of Mb. Real time and peak resident set size are measured with `time`.

## Citation
Chen, X., Yin, X., Shi, X., Yan, W., Yang, Y., Liu, L., & Zhang, T. (2024). Melon: metagenomic long-read-based taxonomic identification and quantification using marker genes. *Genome Biology*, 25(1), 226. https://doi.org/10.1186/s13059-024-03363-y

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