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COVID-19 CG (CoV Genetics)

Preprint now up on bioRxiv: https://www.biorxiv.org/content/10.1101/2020.09.23.310565v2

Table of Contents

Requirements

  • Get a conda distribution of python, we recommend miniconda3.
  • curl
  • node.js > 8.0.0
  • npm

Installation

git clone https://github.com/vector-engineering/covidcg.git

Install dependencies

conda env create -n covidcg -f environment.yml

Javascript

This app was built from the react-slingshot example app.

  1. Install Node 8.0.0 or greater

    Need to run multiple versions of Node? Use nvm.

  2. Install Git.

  3. Disable safe write in your editor to assure hot reloading works properly.

  4. Complete the steps below for your operating system:

    macOS

    • Install watchman via brew install watchman to avoid this issue which occurs if your macOS has no appropriate file watching service installed.

    Linux

    • Run this to increase the limit on the number of files Linux will watch. Here's why.

      echo fs.inotify.max_user_watches=524288 | sudo tee -a /etc/sysctl.conf && sudo sysctl -p.

  5. Install NPM packages

    npm install

  6. Run the example app

    CONFIGFILE=config/config_[workflow].yaml npm start -s

    This will run the automated build process, start up a webserver, and open the application in your default browser. When doing development with this kit, this command will continue watching all your files. Every time you hit save the code is rebuilt, linting runs, and tests run automatically. Note: The -s flag is optional. It enables silent mode which suppresses unnecessary messages during the build.

Analysis Pipeline

Data analysis is run with Snakemake, Python scripts, and bioinformatics tools such as bowtie2. Please ensure that the conda environment is configured correctly (See Installation).

Data analysis is broken up into two snakemake pipelines: 1) ingestion and 2) main. The ingestion pipeline downloads, chunks, and prepares metadata for the main analysis, and the main pipeline analyzes sequences, extracts SNVs, and compiles data for display in the web application.

Configuration of the pipeline is defined in the config/config_[workflow].yaml files.

Ingestion

Three ingestion workflows are currently available, workflow_genbank_ingest, workflow_custom_ingest, and workflow_gisaid_ingest.

NOTE: While the GISAID ingestion pipeline is provided as open-source, it is intended only for internal use.

Both workflow_genbank_ingest and workflow_gisaid_ingest are designed to automatically download and process data from their respective data source. The workflow_custom_ingest can be used for analyzing and visualizing your own in-house SARS-CoV-2 data. More details are available in README files within each ingestion pipeline's folder. Each ingestion workflow is parametrized by its own config file . i.e., config/config_genbank.yaml for the GenBank workflow.

For example, you can run the GenBank ingestion pipeline with:

cd workflow_genbank_ingest
snakemake --use-conda

Both workflow_genbank_ingest and workflow_gisaid_ingest are designed to be run regularly, and attempt to chunk data in a way that minimizes expensive reprocessing/realignment in the downstream main analysis step. The workflow_custom_ingest pipeline does not attempt to chunk data to minimize expensive reprocessing but this can be accomplished outside of covidcg by dividing up your sequence data into separate FASTA files.

Main Analysis

The main data analysis pipeline is located in workflow_main. It requires data, in a data folder, from the ingestion pipeline. The data folder is defined in the config/config_[workflow].yaml file. The path to the config file is required for the main workflow, as it needs to know what kind of data to expect (as described in the config files).

For example, if you ingested data from GenBank, run the main analysis pipeline with:

cd workflow_main
snakemake --configfile ../config/config_genbank.yaml

This pipeline will align sequences to the reference sequence with bowtie2, extract SNVs on both the NT and AA level, and combine all metadata and SNV information into one file: data_package.json.gz.

NOTE: bowtie2, the sequence aligner we use, usually uses anywhere from 8 – 10 GB of RAM per CPU during the alignment step. If the pipeline includes the alignment step, then only use as many cores as you have RAM / 10. i.e., if your machine has 128 GB RAM, then you can run at most 128 / 10 ~= 12 cores.

To pass this data onto the front-end application, host the data_package.json.gz file on an accessible endpoint, then specify that endpoint in the data_package_url field in the config/config_[workflow] file that you are using.

About the project

This project is developed by the Vector Engineering Lab:

  • Albert Tian Chen (Broad Institute)
  • Kevin Altschuler
  • Shing Hei Zhan, PhD (University of British Columbia)
  • Alina Yujia Chan, PhD (Broad Institute)
  • Ben Deverman, PhD (Broad Institute)

The manuscript for this project is currently being prepared.

Contact the authors by email: [email protected]

Python/snakemake scripts were run and tested on MacOS 10.15.4 (8 threads, 16 GB RAM), Google Cloud Debian 10 (buster), (64 threads, 412 GB RAM), and Windows 10/Ubuntu 20.04 via. WSL2 (48 threads, 128 GB RAM)

Data enabling COVID CG

We are extremely grateful to the GISAID Initiative and all its data contributors, i.e. the Authors from the Originating laboratories responsible for obtaining the speciments and the Submitting laboratories where genetic sequence data were generated and shared via the GISAID Initiative, on which this research is based.

Elbe, S., and Buckland-Merrett, G. (2017) Data, disease and diplomacy: GISAID’s innovative contribution to global health. Global Challenges, 1:33-46. DOI:10.1002/gch2.1018 PMCID: 31565258

Citing COVID CG

Users are encouraged to share, download, and further analyze data from this site. Plots can be downloaded as PNG or SVG files, and the data powering the plots and tables can be downloaded as well. Please attribute any data/images to covidcg.org, or cite our manuscript:

Chen AT, Altschuler K, Zhan SH, Chan YA, Deverman BE. COVID-19 CG: Tracking SARS-CoV-2 mutations by locations and dates of interest. bioRxiv (2020), doi: https://doi.org/10.1101/2020.09.23.310565

Note: When using results from these analyses in your manuscript, ensure that you acknowledge the contributors of data, i.e. We gratefully acknowledge all the Authors from the Originating laboratories responsible for obtaining the speciments and the Submitting laboratories where genetic sequence data were generated and shared via the GISAID Initiative, on which this research is based.

and cite the following reference(s):

Shu, Y., McCauley, J. (2017) GISAID: Global initiative on sharing all influenza data – from vision to reality. EuroSurveillance, 22(13) DOI:10.2807/1560-7917.ES.2017.22.13.30494 PMCID: PMC5388101

License

COVID-19 CG is distributed by an MIT license.

Contributing

Please feel free to contribute to this project by opening an issue or pull request in the GitHub repository.