BGCflow Tutorial: Glossary

A Snakemake environment is required to run BGCflow

bgcflow_wrapper provides shortcut to commonly used Snakemake commands and other tools integrated in BGCFlow

A project requires a PEP configuration and a .csv file containing a list of genomes to analyse

The project is then defined in the config.yaml file

An example of a project config can be found in config/examples

BGCflow rules can be selected by giving a TRUE value to the rules config

Global rules applied to all projects and defined in the config.yaml

Project specific rules can be given to each project and will override the global rule

Project specific rules are written as a separate .yaml file

BGCflow adopt the cookiecutter data science directory structure

Output files can be found in the data directory, and are splitted into three different stages

The processed directory contained most of the output required for downstream analysis

BGCflow returns an edge table of your BGC query to the top 10 hits of GCF models in the BiG-FAM database

Different BGCflow outputs can be combined to enrich BiG-SLICE query network

For each BGCFlow projects, the result is structured as an interactive Markdown documentation that can be modified

Reports are made using Jupyter notebooks running on Python or R environments

All of the tabular ouputs from BGCFlow are loaded and transformed into an OLAP database (DuckDB)

A business intelligence tools such as Metabase, can be used to interactively explore the database and build SQL queries

BGCFlow can be customized for analyzing fungal genomes using specific configurations

Running BGCFlow with these configurations allows for targeted analysis of fungal genomes