BamQuery: a proteogenomic tool to explore the immunopeptidome and prioritize actionable tumor antigens
BamQuery is a computational pipeline that counts all RNA-seq reads that can code for a given peptide (8 to 11 residues) in chosen RNA-seq samples (single or bulk). The strength of BamQuery lies in its ability to quantify RNA expression from any genomic region: protein-coding exons or non-coding genomic regions, annotated or not. Briefly, it reverse-translates peptides into all possible coding sequences, aligns these sequences on the genome (human or mouse) with STAR, retains only regions (spliced or not) that have perfect alignments with the coding sequence, and queries (grep) the coding sequences at their respective coding regions in the RNA-seq sample (bam file) of interest. All primary reads (samtools view -F 256) that can code for the peptide are counted, normalized on the total primary read number of the sample, and listed in a detailed report.
BamQuery supports only genomically templated peptides, not proteasome-recombined peptides. Any non-mutated peptide (located on splicing sites or not) is supported. BamQuery supports single nucleotide mutated peptides by optionally including the full dbSNP database in the genomic alignment step. Mutated peptides that derive from indels or from regions not listed in dbSNP can be analyzed with the manual mode of BamQuery by providing the genomic region of origin of the peptide. Finally, BamQuery uses an expectation-maximization algorithm to annotate the most-likely biotype of each peptide analyzed based on the annotations of their genomic regions of origin and the number of reads present at each of these regions.
BamQuery can be installed as described here or tested with our online portal. The portal will quantify any peptide in medullary thymic epithelial cells and dendritic cells to evaluate their probability of being expressed by normal tissues. A prediction of their immunogenicity will also be provided based on these expression measures. Therefore, BamQuery can be used to evaluate the specificity and immunogenicity of tumor antigens.
Read our article in Genome Biology
For detailed usage instructions, see the documentation on our web page: https://bamquery.iric.ca/
BamQuery was designed and developed by Gregory Ehx (GIGA Institute, University of Liege) and Maria Virginia Ruiz Cuevas (Institute for Research in Immunology and Cancer (IRIC), University of Montreal).
See the user manual for a detailed description of usage.
export INSTALLDIR=./opt/bamquery
mkdir $INSTALLDIR
cd $INSTALLDIR
git clone https://github.com/lemieux-lab/BamQuery.git
wget https://bamquery.iric.ca/download/lib_essentials.tar.gz
tar vxzf lib_essentials.tar.gz
BamQuery supports three different versions of the human genome (v26_88 / v33_99 / v38_104) and two versions of the mouse genome (GRCm38 and GRCm39, respectively: M24 / M30).
You need to download the human or mouse genome version you wish to use to:
cd lib/genome_versions
And use the command below to download any human genome version: v26_88 or v33_99 or v38_104.
Change SET_VERSION for any of the genome versions.
wget https://bamquery.iric.ca/download/genome_SET_VERSION.tar.gz
or to download any mouse genome version: m24, m30.
wget https://bamquery.iric.ca/download/genome_mouse_SET_VERSION.tar.gz
Finally, you need to:
tar vxzf GENOME_VERSION.tar.gz
Change GENOME_VERSION by the name of the genome version that was downloaded. Example: genome_v26_88.tar.gz
BamQuery supports three different versions of dbSNPs of the human genome (149/151/155) and two versions of dbSNPs of the mouse genome (snps_GRCm38 and snps_GRCm39, respectively: M24 / M30).
You can download the annotated snps you need to (by default BamQuery does not use snps):
cd lib/snps
And use the command below to download any dbSNP corresponding to human genome releases 149 or 151 or 155.
wget https://bamquery.iric.ca/download/dbsnps_SET_RELEASE.tar.gz
or to download any dbSNP corresponding to mouse genome releases GRCm38 or GRCm39.
wget https://bamquery.iric.ca/download/snps_mouse_SET_RELEASE.tar.gz
Finally, you need to:
tar vxzf SNPS_RELEASE.tar.gz
For users having no administrator privileges, we recommend installing BamQuery with conda.
-
First create a conda environment and activate it:
conda create -n BQ conda activate BQ -
Then install all dependencies:
conda install -y -c bioconda pysam conda install -y -c anaconda pandas conda install -y -c conda-forge pathos conda install -y -c conda-forge xlsxwriter conda install -y -c anaconda seaborn conda install -y -c conda-forge billiard conda install -y -c conda-forge biopython conda install -y -c anaconda scipy conda install -y -c bioconda bedtools conda install -y -c bioconda star=2.7.9a conda install -y -c conda-forge mamba mamba install -y -c conda-forge r-ggplot2 mamba install -y -c conda-forge r-data.table -
Launch the analysis:
conda activate BQ python3 ${INSTALLDIR}/BamQuery/BamQuery.py path_to_input_folder name_exp genome_version
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Download Python 3 and create a virtual environment. Python: https://www.python.org/
python3 -m venv bamquery-venv source ${INSTALLDIR}/env/bin/activate -
Install Python packages in the virtual environment
pip install --upgrade pip pip install pandas pip install pysam pip install pathos pip install xlsxwriter pip install seaborn pip install billiard pip install numpy pip install scipy pip install biopython -
Install external dependencies so that their binaries are available in your $PATH:
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STAR 2.7.9a: https://github.com/alexdobin/STAR
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R: https://www.r-project.org/, required R packages: ggplot2, data.table
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Launch the analysis
python3 ${INSTALLDIR}/BamQuery/BamQuery.py path_to_input_folder name_exp genome_version
A docker container is also available to provide a self contained working environment.
export INSTALLDIR=/opt/bamquery
mkdir $INSTALLDIR
cd $INSTALLDIR
wget https://bamquery.iric.ca/download/bamquery-2023-07-03.tar.gz
gunzip bamquery-2023-07-03.gz
sudo docker load --input bamquery-2023-07-03
Please, follow the instructions in step 2 enumerated above.
sudo docker run -i -t \
--user $(id -u):$(id -g) \
-v $INSTALLDIR/lib:/opt/bamquery/lib \
-v $DATAFOLDER:$DATAFOLDER \
-v $PWD:$PWD \
iric/bamquery:0.2 python3 /opt/bamquery/BamQuery/BamQuery.py path_to_input_folder name_exp genome_version
making sure to map any required folder mentioned in the input files (BAM locations, input folder) so that these paths may be available from within the container.
This is done with multiple arguments -v $DATAFOLDER:$DATAFOLDER (where $DATAFOLDER is to be replaced by an actual folder name) and -v $PWD:$PWD if needed.
Note also that we force the application to run with user permissions instead of root using the --user $(id -u):$(id -g) argument.
For more information on configuration, see: https://bamquery.iric.ca/documentation/configuration.html
Note: BamQuery requires a specific folder structure to work.
Once BamQuery is installed, check that the structure looks as follows:
.
├── BamQuery
│ ├── BamQuery.py
│ ├── genomics
│ ├── plotting
│ ├── readers
│ ├── README.md
│ └── utils
└── lib
├── coefficients.dic
├── Cosmic_info.dic
├── ERE_info.dic
├── ERE_info_mouse.dic
├── EREs_souris.bed
├── genome_versions
│ ├── genome_mouse_m24
│ ├── genome_mouse_m30
│ ├── genome_v26_88
│ ├── genome_v33_99
│ └── genome_v38_104
├── hg38_ucsc_repeatmasker.gtf
├── README.txt
└── snps
├── snps_dics_149
├── snps_dics_149_common
├── snps_dics_151
├── snps_dics_151_common
├── snps_dics_155
└── snps_dics_155_common