GLAMR: The Great Lakes Atlas of Multi-omics Research

This repository contains the pipleines that power the GLAMR database.

GLAMR is designed to be a centralized resource housing Great Lakes omics datasets analyzed with standardized pipelines and integrated with environmental data.

Folder structure

GLAMR
├── code (scripts)
├── pypelib (Python modules)
├── config
│   ├── conda_yaml (for installing software dependencies)
│   └── profiles (for snakemake)
├── data
│   ├── omics [primary data directory]
│   │   └── {sample_type}
|   |       └── {sample_directories}
│   ├── projects [links to directories in the data/omics folder]
|   |   └── project_name
|   |       └── {sample_type}
|   |           └── {symlinks to sample_directories}
│   ├── reference
│   │   ├── amplicons
│   │   ├── genomes
│   │   ├── MAGs
│   │   └── UMRAD
│   ├── sample_metadata
|   └──environment
├── ReadMe.md
└── Snakefile

Snakemake

Snakemake is used extensively to manage the workflows used in this database. A conda environment including dependencies required to execute the pipeline can be found at config/conda_yaml/snakemake.yaml and can be installed with:

conda env create -f config/conda_yaml/snakemake.yaml

And activated with:

conda activate snakemake

Individual rule dependencies are managed with conda and singularity (primarily for R code). To use snakemake's dependency management, it must be run with --sdm conda apptainer. Running snakemake with profiles that can be used to specify a commonly used set of otions like this is encouraged, examples can be found here.

Pipeline overview

To Add:

• Instructions for importing samples
• Instructions for running individual pipelines and all pipelines together