YMS 1.2.14

YOLO Microbiome Analysis System

YaMAS (YOLO Microbiome Analysis System)

YaMAS is a package designed to easily download DNA datasets from the NCBI SRA,ENA and qiita websites. It is developed by the YOLO lab team, and is designed to be simple, efficient, and easy to use for non-programmers.

Dependencies

Before proceeding with the installation and execution of YaMAS, please ensure that you have a clean environment set up on your system, with all dependencies installed. To create one, follow the steps below:

1. Create a new qiime2 environment using conda. Make sure you name it 'qiime2'.
2. Download the SRA-toolkit and Entrez packages to the environment.
3. Download the metaphlan package. Make sure the database works properly before proceeding.
4. Exporting a 16S project requires a downloaded classifier file.
5. Get YaMAS ready.

You are now ready to run and install YaMAS in the newly created and activated qiime2 environment.

Installation

To install YaMAS, you can use pip:

pip install YMS

Getting Started

YaMAS provides an easy-to-use interface in the terminal.
First, get the dependencies ready.
Then, follow the steps below.

Get YaMAS ready

yamas --ready <operating_system_type> 

Arguments:

• operating_system_type: Ubuntu/CentOS

Pay attention to the output of the command.
If the environment is ready, you will need to run one more command.
If not, follow the output guidelines.

To download a project from NCBI SRA or from ENA, qiita, use the one of the following templates:

Downloading a project

Download from NCBI SRA

yamas --download <dataset_id> --type <data_type>

Arguments:

• dataset_id: the dataset id from the NCBI SRA website. For example: PRJEB01234
• data_type: choose one of the following types: 16S / 18S / Shotgun

Continue data downloading

1. Continue downloading project after downloading SRA before converting to .fastq.
   Use the following command:

yamas --continue_from_fastq <dataset_id> <project_path> <data_type>

Arguments:

• dataset_id: the dataset id from the NCBI SRA website. For example: PRJEB01234
• project_path: path to the project directory (created by YaMAS, if you started downloading data in the past).
• data_type: choose one of the following types: 16S / 18S / Shotgun

2. Continue downloading project after downloading SRA and after converting them to .fastq.
   Use the following command:

yamas --continue_from <dataset_id> <project_path> <data_type>

Arguments:

• dataset_id: the dataset id from the NCBI SRA website. For example: PRJEB01234
• project_path: path to the project directory (created by YaMAS, if you started downloading data in the past).
• data_type: choose one of the following types: 16S / 18S / Shotgun

Download from ENA

yamas --qiita <preprocessed_fastq_path> <metadata_path> <data_type>

Arguments: All can be found in https://qiita.ucsd.edu/

• data_type : choose one of the following types: 16S / 18S
• Where preprocessed fastq can be found?
  Click the study description --> in the graph click on 'demultiplexed' --> scroll down and download 'preprocessed fastq' --> rename the file to be: "forward.fastq.gz"
• Where metadata can be found? Click the study description --> download 'Prep info' --> rename the file to be: "metadata.tsv"
• The new data will be created in the folder of the fastq and metadata, so it is recommended to be organized.

Download using fastq files

yamas --fastq <preprocessed_fastq_path> <barcode_path> <metadata_path> <data_type>

Arguments:

• preprocessed_fastq_path: path to the preprocessed fastq file. rename the file to be: "preprocessed_fastq_path"
• barcode_path: path to the barcode file. rename the file to be: "barcodes.fastq.gz"
• metadata_path: path to the metadata file. rename the file to be: "metadata.tsv". The metadata should contains column names: "barcode".
• data_type: choose one of the following types: 16S / 18S / Shotgun

Exporting a project

To export an OTU (Operational Taxonomic Unit), taxonomy, phylogeny tree and a tree.nwk for a single project, use the following command:

yamas --export <project_path> <data_type> <start> <end> <classifier_file> <threads>

Arguments:

• project_path: path to the project directory (created by YaMAS in the previous step).
• data_type: choose one of the following types: 16S / 18S / Shotgun
• classifier_file: path to the trained classifier file.
• start & end: choose graph edges.
• threads: specifies the number of threads to use for parallel processing, which can speed up the export process (default is 12).

Arguments and configurations

1. config: You can add a configuration file in order to save the data in a different folder, and change other configurations.
2. verbose: To get more information about a downloading process, use the verbose option (this is highly recommended).
3. Listing more than one project will download them one by one into different folders.

Cite us

If you are using our package, YaMAS for any purpose, please cite us; Shtossel Oshrit, Sondra Turjeman, Alona Riumin, Michael R. Goldberg, Arnon Elizur, Yarin Bekor, Hadar Mor, Omry Koren, and Yoram Louzoun. "Recipient-independent, high-accuracy FMT-response prediction and optimization in mice and humans." Microbiome 11, no. 1 (2023): 181. https://link.springer.com/article/10.1186/s40168-023-01623-w