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saccharopolyspora_manuscript

https://github.com/matinnuhamunada/saccharopolyspora_manuscript

Science Score: 49.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 12 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.4%) to scientific vocabulary
Last synced: 6 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: matinnuhamunada
  • License: mit
  • Language: HTML
  • Default Branch: main
  • Size: 12.2 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 2
  • Open Issues: 3
  • Releases: 1
Created almost 3 years ago · Last pushed about 2 years ago
Metadata Files
Readme License Citation

README.md

README

This repository contains the scripts (in jupyter notebooks) to generate the figure in the manuscript "BGCFlow: Systematic pangenome workflow for the analysis of biosynthetic gene clusters across large genomic datasets".

USAGE

1. Clone this repository

bash git clone https://github.com/matinnuhamunada/saccharopolyspora_manuscript.git

2. Set up BGCFlow

```bash

create and activate new conda environment

conda create -n bgcflow pip -y conda activate bgcflow

install BGCFlow wrapper

pip install git+https://github.com/NBChub/bgcflow_wrapper.git

clone BGCFlow to "bgcflow" folder

bgcflow clone bgcflow ```

2. Download the dataset

  • Donwload the dataset containing the BGCFlow runs from Zenodo

```bash

move to bgcflow dir

cd bgcflow

download and extract dataset

wget https://zenodo.org/record/8018055/files/saccharopolysporadataset.zip unzip saccharopolysporadataset.zip ```

3. Set configurations

```bash

go back to the manuscript dir

cd ../saccharopolyspora_manuscript/

edit the location of the bgcflow dir to the right directory

nano config.yaml ```

4. Setting up Conda Environments

Install these conda environments: bash mamba env create -f python_notebook.yaml mamba env create -f r_notebook.yaml mamba env create -f <bgcflow_dir>/workflow/envs/cblaster.yaml

5. Run the notebooks

  • There are two kind of notebooks, R (.R.ipynb) and python (.python.ipynb)
  • Run the notebook using the corresponding conda environment: python_notebook or r_notebook
  • Start jupyter session bash # for python conda activate python_notebook jupyter lab bash # for R conda activate r_notebook jupyter lab
  • Run the notebooks in order

Citation

Matin Nuhamunada, Omkar S. Mohite, Patrick V. Phaneuf, Bernhard O. Palsson, and Tilmann Weber. (2023). BGCFlow: Systematic pangenome workflow for the analysis of biosynthetic gene clusters across large genomic datasets. bioRxiv 2023.06.14.545018; doi: https://doi.org/10.1101/2023.06.14.545018

Nuhamunada, Matin, & Mohite, Omkar Satyavan. (2023). BGCFlow Analysis of Saccharopolyspora Genomes (0.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8018055

Owner

  • Name: Matin Nuhamunada
  • Login: matinnuhamunada
  • Kind: user
  • Location: Copenhagen

PhD Student at DTU Biosustain | Natural Products Genome Mining

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