Precision HLA typing from next-generation sequencing data
Authors: András Szolek, Benjamin Schubert, Christopher Mohr
Date: April 2014
License: OptiType is released under a three-clause BSD license
OptiType is a novel HLA genotyping algorithm based on integer linear programming, capable of producing accurate 4-digit HLA genotyping predictions from NGS data by simultaneously selecting all major and minor HLA Class I alleles.
OptiType uses the following software and libraries:
- Python 2.7
- RazerS 3.4
- SAMtools 1.2
- HDF5 1.8.15
- CPLEX 12.5 or other Pyomo-supported ILP solver (GLPK, CBC, ...)
And the following Python modules:
- NumPy 1.9.3
- Pyomo 4.2
- PyTables 3.2.2
- Pandas 0.16.2
- Pysam 0.8.3
- Matplotlib 1.4.3
- Future 0.15.2
Note: CPLEX has a proprietary license but is free for academic use. See IBM's academic initiative.
Installation via Docker
Install Docker on your computer and make sure it works.
docker pull fred2/optitypewhich will download the Docker image.
You can use the image as followes:
docker run -v /path/to/data/folder:/data/ -t fred2/optitype -i input1 [input2] (-r|-d) -o /data/
OptiType uses the CBC-Solver and RazerS3 internally with one thread if no other configuration file is provided. RazerS3's binary can be found at
/usr/local/bin within the Docker image.
Installation from Source
Install all required software and libraries from the first list.
Include SAMtools and your ILP solver in your
PATHenvironment variable. They both have to be globally accessible every time you run OptiType, so make them permanent (put in in your
.bashrcor similar shell startup script).
libdirectory to your
LD_LIBRARY_PATH. Make sure it's permanent too.
Create and activate a Python virtual environment with the package virtualenv. This will automatically install the package manager
pipwhich you will need for the next steps. Always run OptiType from this virtual environment.
Install NumPy, Pyomo, Pysam and Matplotlib with the following commands:
pip install numpy pip install pyomo pip install pysam pip install matplotlib
Create a new environment variable containing the path to your HDF5 installation. It doesn't have to be permanent, but it has to be accessible when you install PyTables. On the bash shell it would be
Install PyTables, Pandas and Future with
pip install tables pip install pandas pip install future
Create a configuration file following
config.ini.example. You will find all necessary instructions in there. OptiType will look for the configuration file at
config.iniin the same directory by default, but you can put it anywhere and pass it with the
-coption when running OptiType.
Optional step zero: you might want to filter your sequencing data for HLA reads. Should you have to re-run OptiType multiple times on the same sample (different settings, etc.) it could save you time if instead of giving OptiType the full, multi-gigabyte sequencing data multiple times, you would rather give it the relevant reads only, on the order of megabytes.
You can use any read mapper to do this step, although we suggest you use RazerS3. Its only drawback is that due to way RazerS3 was designed, it loads all reads into memory, which could be a problem on older, low-memory computing nodes.
Make sure to filter your files correctly depending on whether you have DNA
(exome, WGS) or RNA-Seq data. The reference fasta files are
Below is an example for DNA sequencing data:
>razers3 -i 95 -m 1 -dr 0 -o fished_1.bam /path/to/OptiType/data/hla_reference_dna.fasta sample_1.fastq >samtools bam2fq fished_1.bam > sample_1_fished.fastq >rm fished_1.bam
If you have paired-end data, repeat this with the second ends' fastq file as well. Note: it's important that you filter the two ends individually. Don't use the read mapper's paired-end capabilities.
After the optional filtering, OptiType can be called as follows:
>python /path/to/OptiTypePipeline.py -i sample_fished_1.fastq [sample_fished_2.fastq] (--rna | --dna) [--beta BETA] [--enumerate N] [-c CONFIG] [--verbose] --outdir /path/to/out_dir/
This will produce a time-stamped directory inside the specified output directory containing a CSV file with the predicted optimal (and if enumerated, sub-optimal) HLA genotype, and a pdf file containing a coverage plot of the predicted alleles for diagnostic purposes.
>python OptiTypePipeline.py --help usage: OptiType [-h] --input FQ [FQ] (--rna | --dna) [--beta B] [--enumerate N] --outdir OUTDIR [--verbose] [--config CONFIG] OptiType: 4-digit HLA typer optional arguments: -h, --help show this help message and exit --input FQ [FQ], -i FQ [FQ] .fastq file(s) (fished or raw) or .bam files stored for re-use, generated by an earlier OptiType run. One file: single-end mode, two files: paired-end mode. --rna, -r Use with RNA sequencing data. --dna, -d Use with DNA sequencing data. --beta B, -b B The beta value for for homozygosity detection (see paper). Default: 0.009. Handle with care. --enumerate N, -e N Number of enumerations. OptiType will output the optimal solution and the top N-1 suboptimal solutions in the results CSV. Default: 1 --outdir OUTDIR, -o OUTDIR Specifies the out directory to which all files should be written. --verbose, -v Set verbose mode on. --config CONFIG, -c CONFIG Path to config file. Default: config.ini in the same directory as this script
Furthermore, depending on your settings in
config.ini you can choose to keep
the bam files OptiType produces when all-mapping reads against the reference:
these will be stored in the output directory of your current run.
Then, if you want to re-run OptiType on the same sample, you can give it those
.bam files as input instead of
.fastq files, and spare on the
mapping part of the pipeline. Note: these
.bam files have nothing to do with
those produced during the filtering/fishing step.
DNA data (paired end):
python OptiTypePipeline.py -i ./test/exome/NA11995_SRR766010_1_fished.fastq ./test/exome/NA11995_SRR766010_2_fished.fastq --dna -v -o ./test/exome/
RNA data (paired end):
python OptiTypePipeline.py -i ./test/rna/CRC_81_N_1_fished.fastq ./test/rna/CRC_81_N_2_fished.fastq --rna -v -o ./test/rna/
University of Tübingen, Applied Bioinformatics,
Center for Bioinformatics, Quantitative Biology Center,
and Dept. of Computer Science,
Sand 14, 72076 Tübingen, Germany
Szolek, A, Schubert, B, Mohr, C, Sturm, M, Feldhahn, M, and Kohlbacher, O (2014). OptiType: precision HLA typing from next-generation sequencing data Bioinformatics, 30(23):3310-6.