# Sequence Processing Pipeline

A Jupyter notebook to assist wet lab shotgun pipeline. A packaged Python-based implementation of Knight Lab's sequencing process pipeline.

## Installation

To install this package, first clone this repository from GitHub:

`bash git clone https://github.com/biocore/mg-scripts.git `

Create a Python3 Conda environment in which to run the notebook:

`bash conda create -n sp_pipeline 'python==3.9' numpy pandas click scipy matplotlib fastq-pair `

Activate the Conda environment:

`bash source activate sp_pipeline `

Change directory to the cloned repository folder and install:

`bash cd mg-scripts pip install -e . `

This will automatically install https://github.com/biocore/metagenomics_pooling_notebook.git, a dependency of mg-scripts and the sequence_processing_pipeline.

## Running Unittests

Change directory to the downloaded repository folder:

`bash cd mg-scripts nosetests --with-coverage --cover-inclusive --cover-package sequence_processing_pipeline `

## Getting Started

Review Pipeline.py and main.py to learn how to import and access package functionality:

`bash cd mg-scripts/sequence_processing_pipeline more Pipeline.py more main.py `

Adjust configuration settings as needed:

`bash cd mg-scripts/sequence_processing_pipeline vi configuration.json `

Please note that the setting 'minimap2_databases' is expected to be a list of paths to individual .mmi files for QCJob. For NuQCJob, minimap2_databases is expected to be the path to a directory containing two subdirectories: 'metagenomic' and 'metatranscriptomic'. Each directory should contain or symlink to the appropriate .mmi files needed for that Assay type.