https://github.com/cameronmartino/hs-bacteria-covid19
Bacterial modification of the host glycosaminoglycan heparan sulfate modulates SARS-CoV-2 infectivity
https://github.com/cameronmartino/hs-bacteria-covid19
Last synced: 3 months ago
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Bacterial modification of the host glycosaminoglycan heparan sulfate modulates SARS-CoV-2 infectivity
- Host: GitHub
- URL: https://github.com/cameronmartino/hs-bacteria-covid19
- Owner: cameronmartino
- Created: 2020-08-05T05:59:42.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2021-12-22T18:06:46.000Z (over 3 years ago)
- Last Synced: 2025-01-19T20:39:40.622Z (4 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 29.9 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# SARS-CoV-2 infectivity is modulated through bacterial grooming of the glycocalyx
This repository includes all the source code, tests and notebooks to generate the figures used in the (Martino, Kellman, Sandoval, & Clausen et al. 2021). Each analysis step is outlined here.
## Notes to the reader
We do not provide all of the data in this repository only tables. All American Gut Project sequence data and de-identified participant responses can be found in EBI under project PRJEB11419 and Qiita (https://qiita.ucsd.edu/) study ID 10317. The COVID-19 patient data is available through EBI under accession ERP124721 associated feature tables are publicly available in Qiita ([https://qiita.ucsd.edu/](https://qiita.ucsd.edu/)) under study ID 13092. In particular the FINRISK data that support the findings of this study are available from the THL Biobank based on a written application and following relevant Finnish legislation. Details of the application process are described in the web-site of the Biobank: [https://thl.fi/en/web/thl-biobank/for-researchers](https://thl.fi/en/web/thl-biobank/for-researchers).
## Repository Structure
* code
This directory contains the code (as notebooks) for each step in a numerically organized format. More descriptions can be found within the README file for that directory.
* data
This directory contains the input data, along with some output from computationally expensive tools.
* results
The main and extended figures and tables from the paper which can be reproduced by running the notebooks within the code directory.
# Notebooks
## Setup
All jupyter notebooks (.ipynb) were run in a [qiime2-2019.11](https://data.qiime2.org/distro/core/qiime2-2021.11-py38-osx-conda.yml) conda environment - these are also located in the `environments` directory.