HYMET (Hybrid Metagenomic Tool)

Installation and Configuration

Follow the steps below to install and configure HYMET

1. Installation with Conda (Recommended)

The easiest way to install HYMET is through Bioconda:

conda install -c bioconda hymet

After installation, you will need to download the reference databases as described in the Reference Sketched Databases section.

2. Clone the Repository

Alternatively, you can clone the repository to your local environment:

git clone https://github.com/inesbmartins02/HYMET.git
cd HYMET

3. Installation with Docker

If you prefer using Docker, follow these steps:

1. Build the Docker Image:

   docker build -t hymet .

2. Run the Container:

   docker run -it hymet

3. Inside the Container:

   • The environment will already be set up with all dependencies installed.
   • Run the tool as needed.

4. Installation with Conda Environment File

If you cloned the repository, you can create a Conda environment from the included file:

1. Create the Conda Environment:

   conda env create -f environment.yml

2. Activate the Environment:

   conda activate hymet_env

Input Requirements

Input File Format

The tool expects input files in FASTA format (.fna or .fasta). Each file should contain metagenomic sequences with headers in the following format:

>sequence_id additional_info
SEQUENCE_DATA

• sequence_id: A unique identifier for the sequence.
• additional_info: Optional metadata (e.g., source organism, length).
• SEQUENCE_DATA: The nucleotide sequence.

Input Directory

Place your input files in the directory specified by the $input_dir variable in the main.pl script.

For example, if your input directory contains the following files:

input_dir/
├── sample1.fna
├── sample2.fna
└── sample3.fna

Each file (sample1.fna, sample2.fna, etc.) should follow the FASTA format described above.

Execution

Ensure all scripts have execution permissions:

chmod +x config.pl
chmod +x main.pl
chmod +x scripts/*.sh
chmod +x scripts/*.py

After installation and configuration, first you should run the configuration script to download and prepare the taxonomy files, and define the main paths.

./config.pl

Then, you can run the main tool to perform taxonomic identification:

./main.pl

If installed via Conda, you can use:

hymet-config
hymet

Reference Sketched Databases

The databases required to run the tool are available for download on Google Drive:

• sketch1.msh.gz: Download
• sketch2.msh.gz: Download
• sketch3.msh.gz: Download

Steps to Use the Databases

1. Download the Files:

   • Click on the links above to download the .gz files.

2. Place the Files in the data/ Directory:

   • Move the downloaded files to the data/ directory of the project.

3. Unzip the Files:

   • Use the following command to unzip the .gz files:

     gunzip data/sketch1.msh.gz
     gunzip data/sketch2.msh.gz
     gunzip data/sketch3.msh.gz

   • This will extract the files sketch1.msh, sketch2.msh, and sketch3.msh in the data/ directory.

4. Verify the Files:

   • After unzipping, ensure the files are in the correct format and location:

     ls data/

   • You should see the files sketch1.msh, sketch2.msh, and sketch3.msh.

Project Structure

• config.pl: Configuration script that downloads and prepares taxonomy files.
• main.pl: Main script that runs the taxonomic identification pipeline.
• scripts/: Directory containing helper scripts in Perl, Python, and Bash.
  • mash.sh: Script to run Mash.
  • downloadDB.py: Script to download genomes.
  • minimap.sh: Script to run Minimap2.
  • classification.py: Script for taxonomic classification.
• taxonomy_files/: Directory containing downloaded taxonomy files.
• data/: Directory for storing intermediate data.
  • sketch1.msh
  • sketch2.msh
  • sketch3.msh
  • taxonomy_hierarchy.tsv
• output/: Directory where final results are saved.

Example Output

The tool generates a classified_sequences.tsv file in the output/ directory with the following columns:

• Query: Identifier of the queried sequence.
• Lineage: Identified taxonomic lineage.
• Taxonomic Level: Taxonomic level (e.g., species, genus).
• Confidence: Classification confidence (0 to 1).

Test Dataset

• This folder includes scripts to install and prepare all necessary data to replicate the work using our dataset.
  • Prerequisites:
    • Before running the scripts in this folder, users need to download the assembly files (assembly_files.txt) for each domain from the NCBI FTP site.
  • Scripts:
    • create_database.py: Downloads 10% of the content from each downloaded assembly file and organizes the datasets by domain.
    • extractNC.py: Maps the content of each Genome Collection File (GCF) with its respective sequence identifiers. It generates a CSV file containing this mapping, with one column for the GCF and another column for the sequence identifiers (such as NC, NZ, etc.) present in each GCF.
    • extractTaxonomy.py: Creates a CSV file containing the GCF and its respective taxonomy, among other information.
    • Additional scripts modify the data format and organization, including:
      • Implementing mutations
      • Converting formats (e.g., FASTA to FASTQ)
      • Formatting into paired-end reads
    • GCFtocombinedfasta.py: Combines all GCFs from each domain into a single FASTA file, separating sequences by identifier. This script is used as input for most of the tools.

Support

For questions or issues, please open an issue in the repository.