bgcflow_AS_hqMAGs

• This repository contains the code required to reproduce the analysis from the manuscript Sánchez-Navarro et al. 2022. Long-Read Metagenome-Assembled Genomes Improve Identification of Novel Complete Biosynthetic Gene Clusters in a Complex Microbial-Activated Sludge Ecosystem.

Preview notebooks

• FIG 1. Overview of the number of BGCs detected in the HQ MAG data set.
• FIG 2. Distribution of BGCs and BGC classes across taxonomic groups.
• FIG 3. BGCs in selected genera functionally relevant and/or abundant in WWTPs with nutrient removal.
• FIG 4. Distribution of the presence of GCFs across the phylogenomic tree of 16 genomes of Nitrospira. TO DO
• FIG 5. Distribution of the presence of GCFs across the phylogenomic tree of 22 genomes within the Polyangiaceae. TO DO
• FIG 6. Comparison of BGC mining studies in HQ MAGs. TO DO

Usage

Follow these steps to reproduce the analysis and data generated in this study.

Clone this repository

Clone this repository to your local machine by:

git clone git@github.com:robertosanchezn/AS_hqMAGs.git
cd AS_hqMAGs

Run the analysis

To generate the figures in the manuscript, run the analysis inside the r_markdown folder or jupyter_notebook folder. Each folder has its own README.md with instructions to run the analysis.

Reproduce the data

1. Install Conda Environments & BGCFlow

• This analysis was done in Microsoft Azure Virtual Machine running on Linux (ubuntu 20.04).
• Get a clone of BGCflow, following the instructions at https://github.com/NBChub/bgcflow :

  git clone git@github.com:NBChub/bgcflow.git
  cd bgcflow

• Switch to branch v0.3.3-alpha (where this study was conducted)

  git checkout v0.3.3-alpha

  • TO DO: attach a zipped archive of the v0.3.3-alpha

• Installing Snakemake using Mamba is advised. In case you don’t use Mambaforge you can always install Mamba into any other Conda-based Python distribution with:

  conda install -n base -c conda-forge mamba

• Install conda environments

  
  # snakemake environment
  mamba create -c conda-forge -c bioconda -n snakemake snakemake=7.6.1

environment to run notebooks

mamba env create -n workflow/envs/bgc_analytics.yaml

### 2. Snakemake configuration set up
- Set up the configuration files by copying the content in `/bgcflow_configuration` folder (replacing the original `config.yaml` in BGCflow)
```shell
cp ../bgcflow_config/* config/. -r

3. Download and prepare data from other studies

• Not all of the genomes are hosted in NCBI, and some fasta files needs cleaning. Run the notebook to grab all custom fasta files to data/raw/fasta.

  
  # run notebook to download genomes from other studies to bgcflow/data/external, will take a while to finish
  conda activate bgc_analytics
  (cd ../jupyter_notebook/notebook2/ && jupyter nbconvert --to html --execute 01_other_MAG_dataset_table.ipynb)
  conda deactivate

generate symlink

ext_dir="data/external" for directory in Bickhart_et_al Chen_et_al_sanitized Liu_et_al Sharrar_et_al_sanitized; do for fna in $ext_dir/$directory/*.fna do (cd data/raw/fasta && ln -s ../../external/$directory/$(basename $fna) $(basename $fna) --verbose) done done

### 4. Run the workflow for each individual study
This will generate antiSMASH results and other downstream processes.
```bash
conda activate snakemake
snakemake --use-conda --cores 8 --keep-going -n
conda deactivate

• PS: remove the args -n to do a real run

5. Run the workflow for all study comparison

This will generate antiSMASH results and other downstream processes.

conda activate snakemake
snakemake --configfile config/config_all_studies.yaml --use-conda --cores 8 --keep-going -n
conda deactivate

• PS: remove the args -n to do a real run

6. Run the workflow for in depth study in Phylum Nitrospirota and Myxococcota

This will generate antiSMASH results and other downstream processes.

conda activate snakemake
snakemake --configfile config/config_in_depth.yaml --use-conda --cores 8 --keep-going -n
conda deactivate

• PS: remove the args -n to do a real run