The main steps of the workflow involve:
- Mapping each barcode to insertion location in the genome.
- Profiling barcode abundances across samples.
- Mutant fitness analyses.
- Exploratory analysis using mBARq web app
- Clone the repository and create and activate the conda environment
git clone https://github.com/MicrobiologyETHZ/mbarq.git
cd mbarq
#conda env create -f mbarq_environment.yaml
mamba env create -f mbarq_environment.yaml
conda activate mbarq
pip install -e .
mbarq --help
- Map each barcode to the insertion location in the genome
mbarq map -f <library_R1.fastq.gz> -g <host.fasta> -a <host.gff> -l 100 \
-n LibraryName -tn B17N13GTGTATAAGAGACAG
- Profile barcode abundances for each sample
mbarq count -f <sample.fastq.gz> -m <library_mapping_file.csv> \
-n ExperimentName -tn B17N13GTGTATAAGAGACAG
- Merge barcode counts from multiple samples into the final table
mbarq merge -d <directory_with_count_files> -a locus_tag -n ExperimentName -o .
- Identify enriched/depleted genes between treatments and control
mbarq analyze -i <count_file> -s <sample_data_file> -c <control_file> --treatment_column treatment \
--batch_column batch --baseline control
Please see mBARq documentation for detailed instructions and tutorials.
If you use mBARq, please cite:
mBARq: a versatile and user-friendly framework for the analysis of DNA barcodes from transposon insertion libraries, knockout mutants, and isogenic strain populations
Anna Sintsova, Hans-Joachim Ruscheweyh, Christopher M Field, Lilith Feer, Bidong D Nguyen, Benjamin Daniel, Wolf-Dietrich Hardt, Julia A Vorholt, Shinichi Sunagawa#
Bioinformatics (2024)
- Use the instructions below to run the jupyter notebook used to produce figures for the mbarq manuscript
git clone --branch manuscript https://github.com/MicrobiologyETHZ/mbarq.git
# or git checkout manuscript
cd mbarq/manuscript
tar -xvzf manuscript_files.tar.gz
mamba env create -f manuscript_environment.yaml
conda activate mbarq_manuscript
mkdir figures
jupyter notebook