Install arcasHLA through bioconda with:
conda install arcas-hla -c bioconda -c conda-forge
Important: Please include channels bioconda and conda-forge as above.
arcasHLA can also be installed through the environment.yml file in this repo:
conda env create -f environment.yml
conda activate arcas-hla
(Update 2023-09-29): The below tests are now implemented as a pytest suite. You can run this locally by building the docker environment and running pytest. From the current directory:
docker build -t <image-name> -f Docker/Dockerfile .
docker run --rm -v /path/to/repo:/app <image-name> pytest
In order to test arcasHLA partial typing, we need to roll back the reference to an earlier version. First, fetch IMGT/HLA database version 3.24.0:
./arcasHLA reference --version 3.24.0
Extract reads:
./arcasHLA extract test/test.bam -o test/output -t 8 -v
Genotyping (no partial alleles):
./arcasHLA genotype test/output/test.extracted.1.fq.gz test/output/test.extracted.2.fq.gz -g A,B,C,DPB1,DQB1,DQA1,DRB1 -o test/output -t 8 -v
Expected output in test/output/test.genotype.json:
{"A": ["A*01:01:01", "A*03:01:01"],
"B": ["B*39:01:01", "B*07:02:01"],
"C": ["C*08:01:01", "C*01:02:01"],
"DPB1": ["DPB1*14:01:01", "DPB1*02:01:02"],
"DQA1": ["DQA1*02:01:01", "DQA1*05:03"],
"DQB1": ["DQB1*02:02:01", "DQB1*06:09:01"],
"DRB1": ["DRB1*10:01:01", "DRB1*14:02:01"]}
Partial typing:
./arcasHLA partial test/output/test.extracted.1.fq.gz test/output/test.extracted.2.fq.gz -g A,B,C,DPB1,DQB1,DQA1,DRB1 -G test/output/test.genotype.json -o test/output -t 8 -v
Expected output in test/output/test.partial_genotype.json:
{"A": ["A*01:01:01", "A*03:01:01"],
"B": ["B*07:02:01", "B*39:39:01"],
"C": ["C*08:01:01", "C*01:02:01"],
"DPB1": ["DPB1*14:01:01", "DPB1*02:01:02"],
"DQA1": ["DQA1*02:01:01", "DQA1*05:03"],
"DQB1": ["DQB1*06:04:01", "DQB1*02:02:01"],
"DRB1": ["DRB1*03:02:01", "DRB1*14:02:01"]}
Remember to update the HLA reference using the following command.
./arcasHLA reference --update
To see the list of available tools, simply enter arcasHLA. To view the required and optional arguments for any of the tools enter arcasHLA [command] -h.
extract: Extracts reads mapped to chromosome 6 and any HLA decoys or chromosome 6 alternates.genotype: Genotypes HLA alleles from extracted reads (no partial alleles).partial: Genotypes partial HLA alleles from extracted reads and output fromgenotype(optional).reference: Update, specify version or force rebuilding of HLA reference.merge: merge genotyping output for multiple samples into a single json file.
arcasHLA takes sorted BAM files and extracts chromosome 6 reads and related HLA sequences. If the BAM file is not indexed, this tool will run samtools index before extracting reads. By default, extract outputs paired FASTQ files; use the --single flag for single-end samples.
arcasHLA extract [options] /path/to/sample.bam
Output: sample.extracted.1.fq.gz, sample.extracted.2.fq.gz
--single: single-end reads (default: False)--unmapped: include unmapped reads, recommended if the aligner used marks multimapping reads as unmapped (default: False)--log FILE: log file for run summary (default: sample.extract.log)--o, --outdir DIR: output directory (default:.)--temp DIR: temp directory (default:/tmp)--keep_files: keep intermediate files (default: False)-t, --threads INT: number of threads (default: 1)-v, --verbose: verbosity (default: False)
To predict the most likely genotype (no partial alleles), input the FASTQs produced by extract or the original FASTQs with all reads (experimental - use with caution).
arcasHLA genotype [options] /path/to/sample.1.fq.gz /path/to/sample.2.fq.gz
Output: sample.alignment.p, sample.em.json, sample.genotype.json
If you have previously run genotype on a sample, you can run genotype again directly from sample.alignment.p to retype without aligning with Kallisto again. This is useful if you want to try different populations, genes and other parameters.
arcasHLA genotype [options] /path/to/sample.alignment.p
{'A': ['A*01:01:01', 'A*29:02:01'],
'B': ['B*08:01:01', 'B*44:03:01'],
'C': ['C*07:01:01', 'C*16:01:01'],
'DQA1': ['DQA1*02:01:01', 'DQA1*05:01:01'],
'DQB1': ['DQB1*02:01:01', 'DQB1*02:02:01'],
'DRB1': ['DRB1*03:01:01', 'DRB1*07:01:01']}
-g, --genes GENES: comma separated list of HLA genes (ex. A,B,C,DQA1,DQB1,DRB1)-p, --population POPULATION: sample population, options are asian_pacific_islander, black, caucasian, hispanic, native_american and prior (default: Prior)--min_count INT: minimum gene read count required for genotyping (default: 75)--tolerance FLOAT: convergence tolerance for transcript quantification (default: 10e-7)--max_iterations INT: maximmum number of iterations for transcript quantification (default: 1000)--drop_iterations INT: number of iterations before dropping low support alleles, a lower number of iterations is recommended for single-end and low read count samples (default: paired - 10, single - 4)--drop_threshold FLOAT: proportion of maximum abundance an allele needs to not be dropped (default: 0.1)--zygosity_threshold FLOAT: threshold for ratio of minor to major allele nonshared count to determine zygosity (default: 0.15)--log FILE: log file for run summary (default:sample.genotype.log)--o, --outdir DIR: output directory (default:.)--temp DIR: temp directory (default:/tmp)--keep_files: keep intermediate files (default: False)-t, --threads INT: number of threads (default: 1)-v, --verbose: verbosity (default: False)--single: Include flag to indicate if single-end FASTQs (paired-end if missing)-l, --avg: Estimated average fragment length for single-end reads (default: 200)-s, --std: Estimated standard deviation of fragment length (default: 20)
Following genotyping, partial alleles can be predicted. This requires aligning the reads to an alternate, partial allele reference. The sample.genotype.json file from the previous step is required.
arcasHLA partial [options] -G /path/to/sample.genotype.json /path/to/sample.1.fq.gz /path/to/sample.2.fq.gz
Output: sample.partial_alignment.p, sample.partial_genotype.json
The options for partial typing are the same as genotype. Partial typing can be run from the intermediate alignment file.
To make analysis easier, this command will merge all jsons produced by genotyping into a single table. All .genotype.json files will be merged into a single run.genotypes.tsv file and all .partial_genotype.json files will be merged into run.partial_genotypes.tsv. In addition, HLA locus read counts and relative abundance produced by alignment will be merged into a single tsv file.
arcasHLA merge [options]
--run RUN: run name--i, --indir DIR: input directory (default:.)--o, --outdir DIR: output directory (default:.)-v, --verbose: toggle verbosity
arcasHLA convert changes alleles in a tsv file from its input form to a specified grouped nomenclature (P-group or G-group) or a specified number of fields (i.e. 1, 2 or 3 fields in resolution). This file can be produced by arcasHLA merge or any tsv following the same structure:
| subject | A1 | A2 | B1 | B2 | C1 | C2 |
|---|---|---|---|---|---|---|
| subject_name | A*01:01:01 | A*01:01:01 | B*07:02:01 | B*07:02:01 | C*04:01:01 | C*04:01:01 |
P-group (alleles sharing the same amino acid sequence in the antigen-binding region) and G-group (alleles sharing the same base sequence in the antigen-binding region) can only be reduced to 1-field resolution as alleles with differing 2nd fields can be in the same group. By the same reasoning, P-group cannot be converted into G-group.
arcasHLA convert --resolution [resolution] genotypes.tsv
-r, --resolution RESOLUTION: output resolution (1, 2, 3) or grouping (g-group, p-group)-o, --outfile FILE: output file (default:./run.resolution.tsv)-f, --force: force conversion for grouped alleles even if it results in loss of resolution-v, --verbose: toggle verbosity
To update the reference to the latest IMGT/HLA version, run
arcasHLA reference --update
If you are running multiple tools to type HLAs, it can be helpful to use the same version of IMGT/HLA. You can select the version you like using the commithash from the IMGT/HLA Github.
arcasHLA reference --version [commithash]
If you suspect there is an issue with the reference files, rebuild the reference with the following command
arcasHLA reference --rebuild
Note: if your reference was built with arcasHLA version <= 0.1.1 and you wish to change your reference to versions >= 3.35.0, it may be necessary to remove the IMGTHLA folder due to the need for Git Large File Storage to properly download hla.dat.
rm -rf dat/IMGTHLA
arcasHLA reference --update
--update: update to latest IMGT/HLA version--version: checkout IMGT/HLA version using commithash--rebuild: rebuild HLA database-v, --verbose: verbosity (default: False)
Customized references can be built from arcasHLA genotype outputs.
./arcasHLA customize genotypes.json -o ~/ref
Customized references can be built from a tab-separated file with the following structure:
| subject | A1 | A2 | B1 | B2 | C1 | C2 |
|---|---|---|---|---|---|---|
| Example | A*01:01 | A*02:01 | B*07:01 | B*52:01 | C*04:01 | C*18:01 |
./arcasHLA customize hla.tsv -o ~/ref
usage: arcasHLA customize [options]
optional arguments:
-h, --help show this help message and exit
-G , --genotype comma-separated list of HLA alleles (e.g. A*01:01,A*11:01,...)
arcasHLA output genotype.json or genotypes.json
or tsv with format specified in README.md
-s , --subject subject name, only required for list of alleles
-g , --genes comma separated list of HLA genes
default: all
options: A, B, C, DMA, DMB, DOA, DOB, DPA1, DPB1, DQA1,
DQB1, DRA, DRB1, DRB3, DRB5, E, F, G, H, J, K, L
--transcriptome TRANSCRIPTOME
transcripts to include besides input HLAs
options: full, chr6, none
default: full
--resolution RESOLUTION
genotype resolution, only use >2 when typing performed with assay or Sanger sequencing
default: 2
--grouping GROUPING type/number of transcripts to include per allele
single - one 3-field resolution transcript per allele (e.g. A*01:01:01)
g-group - all transcripts with identical binding regions
default: protein group - all transcripts with identical protein types (2 fields the same)
-o , --outdir out directory
--temp temp directory
--keep_files keep intermediate files
-t , --threads
-v, --verbose
Note: if the reference was built with the --chr6 flag, you should run quant with extracted chromosome 6 FASTQs (see extract).
./arcasHLA quant --ref /path/to/ref/sample FASTQ
Example:
./arcasHLA quant --ref ~/ref/Pt23 -t 8 -o /Volumes/quant/ /Volumes/fastq/Pt23_pre.1.fq.gz /Volumes/fastq/Pt23_pre.2.fq.gz
usage: arcasHLA quant [options] FASTQs
positional arguments:
file list of fastq files
optional arguments:
-h, --help show this help message and exit
--sample SAMPLE sample name
--ref arcasHLA quant_ref path (e.g. "/path/to/ref/sample")
-o , --outdir out directory
--temp temp directory
--keep_files keep intermediate files
-l AVG, --avg AVG Estimated average fragment length for single-end reads
default: 200
-s STD, --std STD Estimated standard deviation of fragment length for single-end reads
default: 20
--single Include flag if single-end reads. Default is paired-end.
-t , --threads
-v, --verbose
- Orenbuch R, Filip I, Comito D, et al (2019) arcasHLA: high resolution HLA typing from RNA seq. Bioinformatics doi:10.1093/bioinformatics/btz474
- Orenbuch R, Filip I, Rabadan R (2020) HLA Typing from RNA Sequencing and Applications to Cancer. Methods Mol. Biol. doi: 10.1007/978-1-0716-0327-7_5 (https://link.springer.com/protocol/10.1007%2F978-1-0716-0327-7_5)
- Filip, I., Wang, A., Kravets, O. et al. Pervasiveness of HLA allele-specific expression loss across tumor types. Genome Med 15, 8 (2023). https://doi.org/10.1186/s13073-023-01154-x