BGCFlow

BGCFlow is a systematic workflow for the analysis of biosynthetic gene clusters across large collections of genomes (pangenomes) from internal & public datasets.

At present, BGCFlow is only tested and confirmed to work on Linux systems with conda / mamba package manager.

Publication

Matin Nuhamunada, Omkar S Mohite, Patrick V Phaneuf, Bernhard O Palsson, Tilmann Weber, BGCFlow: systematic pangenome workflow for the analysis of biosynthetic gene clusters across large genomic datasets, Nucleic Acids Research, 2024;, gkae314, https://doi.org/10.1093/nar/gkae314

Pre-requisites

BGCFlow requires gcc and the conda/mamba package manager. See installation instruction for details.

Please use the latest version of BGCFlow available.

Quick Start

A quick and easy way to use BGCFlow using the command line interface wrapper:

1. Create a conda environment and install the BGCFlow python wrapper :

```bash

create and activate a new conda environment

mamba create -n bgcflow -c conda-forge python=3.11 pip openjdk -y # also install java for metabase conda activate bgcflow

install BGCFlow wrapper

pip install bgcflow_wrapper==0.4.0

make sure to use bgcflow_wrapper version >= 0.2.7

bgcflow --version ```

1. Additional pre-requisites: With the environment activated, install or setup this configurations:

   • Set conda channel priorities to flexible bash conda config --set channel_priority disabled conda config --describe channel_priority

2. Deploy and run BGCFlow, change your_bgcflow_directory variable accordingly: ```bash

   Deploy and run BGCFlow

   bgcflow clone bgcflow # clone BGCFlow a directory named bgcflow cd bgcflow # move to bgcflow directory bgcflow init # initiate BGCFlow config and examples from template bgcflow run -n # do a dry run, remove the flag "-n" to run the example dataset ```

3. Build and serve interactive report (after bgcflow run finished). The report will be served in http://localhost:8001/. A demo of the report is available here:

   

```bash

build a report

bgcflow build report

show available projects

bgcflow serve

serve interactive report

bgcflow serve --project Lactobacillus_delbrueckii ```

• For detailed usage and configurations, have a look at the WIKI:

• Read more about bgcflow_wrapper for a detailed overview of the command line interface.

Workflow overview

The main Snakefile workflow comprises various pipelines for data selection, functional annotation, phylogenetic analysis, genome mining, and comparative genomics for Prokaryotic datasets.

Available pipelines in the main Snakefile can be checked using the following command: bash bgcflow pipelines

List of Available Pipelines

Here you can find pipeline keywords that you can run using the main Snakefile of BGCflow.

| | Keyword | Description | Links | |---:|:------------------|:-------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------| | 0 | eggnog | Annotate samples with eggNOG database (http://eggnog5.embl.de) | eggnog-mapper | | 1 | mash | Calculate distance estimation for all samples using MinHash. | Mash | | 2 | fastani | Do pairwise Average Nucleotide Identity (ANI) calculation across all samples. | FastANI | | 3 | automlst-wrapper | Simplified Tree building using autoMLST | automlst-simplified-wrapper | | 4 | roary | Build pangenome using Roary. | Roary | | 5 | eggnog-roary | Annotate Roary output using eggNOG mapper | eggnog-mapper | | 6 | seqfu | Calculate sequence statistics using SeqFu. | seqfu2 | | 7 | bigslice | Cluster BGCs using BiG-SLiCE (https://github.com/medema-group/bigslice) | bigslice | | 8 | query-bigslice | Map BGCs to BiG-FAM database (https://bigfam.bioinformatics.nl/) | bigfam.bioinformatics.nl | | 9 | checkm | Assess genome quality with CheckM. | CheckM | | 10 | gtdbtk | Taxonomic placement with GTDB-Tk | GTDBTk | | 11 | prokka-gbk | Copy annotated genbank results. | prokka | | 12 | antismash | Summarizes antiSMASH result. | antismash | | 13 | arts | Run Antibiotic Resistant Target Seeker (ARTS) on samples. | arts | | 14 | deeptfactor | Use deep learning to find Transcription Factors. | deeptfactor | | 15 | deeptfactor-roary | Use DeepTFactor on Roary outputs. | Roary | | 16 | cblaster-genome | Build diamond database of genomes for cblaster search. | cblaster | | 17 | cblaster-bgc | Build diamond database of BGCs for cblaster search. | cblaster | | 18 | bigscape | Cluster BGCs using BiG-SCAPE | BiG-SCAPE | | 19 | gecco | GEne Cluster prediction with COnditional random fields. | GECCO

Development & Funding

The development of BGCFlow commenced within the Natural Products Genome Mining research group at the Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark (DTU Biosustain). BGCFlow development was/is made possible through the generous support of various funding organizations:

• Novo Nordisk Foundation: BGCFlow development was supported by grants from the Novo Nordisk Foundation, specifically [NNF20CC0035580] and [NNF16OC0021746]. Matin Nuhamunada received support from the NNF Copenhagen Bioscience PhD Program: , grant [NNF20SA0035588].

• Danish National Research Foundation: Additional funding was provided by the Danish National Research Foundation for the Center for Microbial Secondary Metabolites (CeMiSt), under the grant [DNRF137].

References

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