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:

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

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

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

Activate the Conda environment:

source activate sp_pipeline

Change directory to the cloned repository folder and install:

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:

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:

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

Adjust configuration settings as needed:

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.