lofreq - NVIDIA Docs

Call variants with high sensitivity, predicting variants below the average base-call quality (Original Lofreq Project). The call part is accelerated.

Quick Start

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            $ pbrun lofreq \
    --ref Ref/Homo_sapiens_assembly38.fasta \
    --in-tumor-bam  tumor.bam \
    --in-normal-bam  normal.bam \
    --output-dir   output

Note that the value for --output-dir will be used as a prefix for all the output filenames. If you want the output files to be placed in a folder called output, use --output-dir output/ (note the trailing slash). You'll also need to create the directory before running lofreq.

Compatible CPU Command

The command below is the original CPU counterpart of the Parabricks command above. The output from this command will be identical to the output from the above command. See the Output Comparison page for comparing the results.

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            lofreq somatic -n normal.bam -t tumor.bam \
-o output -f /data/Ref/GRCh38.d1.vd1.fa  \
--baq-off --no-src-qual --call-rlx-extra-args "@d 2147483647"

lofreq Reference

Call variants with high sensitivity, predicting variants below the average base-call quality.

Input/Output file options

--ref REF

Path to the reference file. (default: None)

Option is required.

--in-tumor-bam IN_TUMOR_BAM

Path of BAM/CRAM file for tumor reads. This option is required. (default: None)

Option is required.

--in-normal-bam IN_NORMAL_BAM

Path of BAM/CRAM file for normal reads. This option is required. (default: None)

Option is required.

--in-dbsnp-file IN_DBSNP_FILE

Path to an input dbsnp file containing known germline variants. Must be in vcf.gz format with its tabix index. (default: None)

--ignore-vcf IGNORE_VCF

Path to an input VCF file containing variants that will be ignored for source quality computation in tumor. If this option is not used, stringently filtered predictions in normal sample will be used by default. (default: None)

--output-dir OUTPUT_DIR

Path to the directory that will contain all of the generated files. (default: None)

Option is required.

Options specific to this tool

--out-prefix OUT_PREFIX: Prefix filename for output data. (default: None)
--num-threads NUM_THREADS: Number of threads per GPU for each call. (default: 4)
--tumor-mtc TUMOR_MTC: Type of multiple testing correction for tumor. Possible values are {bonf,holm-bonf,fdr}. Default value is bonf. (default: bonf)
--tumor-mtc-alpha TUMOR_MTC_ALPHA: Multiple testing correction alpha for tumor. Default value is 1.000000. (default: 1.0)
--min-cov MIN_COV: Minimum coverage for somatic calls. Default value is 7. (default: 7)
--germline: Also list germline calls in separate file. (default: None)
--use-orphan: Use orphaned samples. (default: None)
--baq-on: Use BAQ (base-call quality correction) in all samples. (default: None)
--src-qual: Use source quality in tumor. (default: None)

Common options:

--logfile LOGFILE: Path to the log file. If not specified, messages will only be written to the standard error output. (default: None)
--tmp-dir TMP_DIR: Full path to the directory where temporary files will be stored.
--with-petagene-dir WITH_PETAGENE_DIR: Full path to the PetaGene installation directory. By default, this should have been installed at /opt/petagene. Use of this option also requires that the PetaLink library has been preloaded by setting the LD_PRELOAD environment variable. Optionally set the PETASUITE_REFPATH and PGCLOUD_CREDPATH environment variables that are used for data and credentials (default: None)
--keep-tmp: Do not delete the directory storing temporary files after completion.
--license-file LICENSE_FILE: Path to license file license.bin if not in the installation directory.
--no-seccomp-override: Do not override seccomp options for docker (default: None).
--version: View compatible software versions.

GPU options:

--num-gpus NUM_GPUS: Number of GPUs to use for a run. GPUs 0..(NUM_GPUS-1) will be used.
--gpu-devices GPU_DEVICES: GPU devices to use for a run. By default, all GPU devices will be used. To use specific GPU devices, enter a comma-separated list of GPU device numbers. Possible device numbers can be found by examining the output of the nvidia-smi command. For example, using --gpu-devices 0,1 would only use the first two GPUs.