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.

References:

OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics, 30(23):3310-6. doi: 10.1093/bioinformatics/btu548

• OptiType on GitHub

• Module Name: optitype (see the modules page for more information)
• Environment variables set
• OPTITYPE_HOME
• Default configuration file: $OPTITYPE_HOME/config.ini
  copy and edit this to set the number of threads you want to use, matching $SLURM_CPUS_PER_TASK
• Example files in $OPTITYPE_HOME/test
• Reference data in $OPTITYPE_HOME/data

Allocate an interactive session and run the program.
Sample session (user input in bold):

[user@biowulf]$ sinteractive --cpus-per-task 4 --gres lscratch:10
salloc.exe: Pending job allocation 46116226
salloc.exe: job 46116226 queued and waiting for resources
salloc.exe: job 46116226 has been allocated resources
salloc.exe: Granted job allocation 46116226
salloc.exe: Waiting for resource configuration
salloc.exe: Nodes cn3144 are ready for job

[user@cn3144 ~]$ module load optitype
[+] Loading glpk  4.65 
[+] Loading HDF5  1.10.1 
[+] Loading samtools 1.9  ... 
[+] Loading optitype, version 1.3.2...
[user@cn3144 ~]$ cd /lscratch/$SLURM_JOB_ID
[user@cn3144 46116226]$ cp -a $OPTITYPE_HOME/test . 
[user@cn3144 46116226]$ cp $OPTITYPE_HOME/config.ini . # copy the default configuration and edit it to set the number of threads you want to use.
[user@cn3144 46116226]$ OptiTypePipeline.py -i ./test/rna/CRC_81_N_1_fished.fastq ./test/rna/CRC_81_N_2_fished.fastq --rna -v -o ./test/rna/ -c config.ini

    mapping with 4 threads...

    0:00:03.77 Mapping CRC_81_N_1_fished.fastq to NUC reference...

    0:00:08.84 Mapping CRC_81_N_2_fished.fastq to NUC reference...

    0:00:14.42 Generating binary hit matrix.
    0:00:14.45 Loading ./test/rna/2019_02_01_16_43_08/2019_02_01_16_43_08_1.bam started. Number of HLA reads loaded (updated every thousand):

    0:00:14.60 301 reads loaded. Creating dataframe...
    0:00:14.62 Dataframes created. Shape: 301 x 7339, hits: 67072 (111698), sparsity: 1 in 19.78
    0:00:14.70 Loading ./test/rna/2019_02_01_16_43_08/2019_02_01_16_43_08_2.bam started. Number of HLA reads loaded (updated every thousand):

    0:00:14.89 291 reads loaded. Creating dataframe...
    0:00:14.91 Dataframes created. Shape: 291 x 7339, hits: 59422 (104782), sparsity: 1 in 20.38
    0:00:14.95 Alignment pairing completed. 152 paired, 268 unpaired, 10 discordant 

    0:00:17.07 temporary pruning of identical rows and columns

    0:00:17.11 Size of mtx with unique rows and columns: (124, 250)
    0:00:17.11 determining minimal set of non-overshadowed alleles

    0:00:17.58 Keeping only the minimal number of required alleles (14,)

    0:00:17.58 Creating compact model...

    starting ilp solver with 1 threads...

    0:00:17.60 Initializing OptiType model...
    GLPSOL: GLPK LP/MIP Solver, v4.65
    Parameter(s) specified in the command line:
    --write /tmp/tmpHIKpQd.glpk.raw --wglp /tmp/tmpM6Df7g.glpk.glp --cpxlp /tmp/tmpkcqGUm.pyomo.lp
    Reading problem data from '/tmp/tmpkcqGUm.pyomo.lp'...
    /tmp/tmpkcqGUm.pyomo.lp:715: warning: lower bound of variable 'x1' redefined
    /tmp/tmpkcqGUm.pyomo.lp:715: warning: upper bound of variable 'x1' redefined
    104 rows, 64 columns, 283 non-zeros
    38 integer variables, all of which are binary
    753 lines were read
    Writing problem data to '/tmp/tmpM6Df7g.glpk.glp'...
    632 lines were written
    GLPK Integer Optimizer, v4.65
    104 rows, 64 columns, 283 non-zeros
    38 integer variables, all of which are binary
    Preprocessing...
    14 hidden covering inequaliti(es) were detected
    103 rows, 63 columns, 282 non-zeros
    38 integer variables, all of which are binary
    Scaling...
    A: min|aij| =  1.000e+00  max|aij| =  3.000e+00  ratio =  3.000e+00
    Problem data seem to be well scaled
    Constructing initial basis...
    Size of triangular part is 103
    Solving LP relaxation...
    GLPK Simplex Optimizer, v4.65
    103 rows, 63 columns, 282 non-zeros
    0: obj =  -0.000000000e+00 inf =   3.000e+00 (3)
    3: obj =  -0.000000000e+00 inf =   0.000e+00 (0)
    *    65: obj =   1.284360000e+02 inf =   6.291e-16 (0)
    OPTIMAL LP SOLUTION FOUND
    Integer optimization begins...
    Long-step dual simplex will be used
    +    65: mip =     not found yet <=              +inf        (1; 0)
    +    65: >>>>>   1.284360000e+02 <=   1.284360000e+02   0.0% (1; 0)
    +    65: mip =   1.284360000e+02 <=     tree is empty   0.0% (0; 1)
    INTEGER OPTIMAL SOLUTION FOUND
    Time used:   0.0 secs
    Memory used: 0.2 Mb (193401 bytes)
    Writing MIP solution to '/tmp/tmpHIKpQd.glpk.raw'...
    177 lines were written

    0:00:17.78 Result dataframe has been constructed...
[user@cn3144 46116226]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$

Create a batch input file (e.g. optitype.sh). For example:

#!/bin/bash
set -e
module load optitype
OptiTypePipeline.py -i ./test/rna/CRC_81_N_1_fished.fastq ./test/rna/CRC_81_N_2_fished.fastq --rna -v -o ./test/rna/ -c config.ini

Submit this job using the Slurm sbatch command.

sbatch [--cpus-per-task=#] [--mem=#] optitype.sh

Create a swarmfile (e.g. optitype.swarm). For example:

python OptiTypePipeline.py -i ./test/exome/NA11995_SRR766010_1_fished.fastq ./test/exome/NA11995_SRR766010_2_fished.fastq --dna -v -o ./test/exome/ -c config.ini
OptiTypePipeline.py -i ./test/rna/CRC_81_N_1_fished.fastq ./test/rna/CRC_81_N_2_fished.fastq --rna -v -o ./test/rna/ -c config.ini

Submit this job using the swarm command.

swarm -f optitype.swarm [-g #] [-t #] --module optitype