ARD reduction for HLA with Python
py-ard
works with Python 3.8 and higher.
pip install py-ard
python3 -m venv venv
source venv/bin/activate
python setup.py install
To run behavior-driven development (BDD) tests locally via the behave framework, you'll need to set up a virtual environment. See Install from source.
# Install test dependencies
pip install --upgrade pip
pip install -r test-requirements.txt
# Running Behave and all BDD tests
behave
# Run unit-tests
python -m unittest tests.test_pyard
py-ard
can be used in a program to reduce/expand HLA GL String representation. If pyard discovers an invalid Allele, it'll throw an Invalid Exception, not silently return an empty result.
Import pyard
package.
import pyard
The cache size of pre-computed reductions can be changed from the default of 1000 (not working: will be fixed in a later release.)
pyard.max_cache_size = 1_000_000
Initialize ARD
object with a version of IMGT HLA database
ard = pyard.ARD(3290)
You can specify a different directory for the cached data.
ard = pyard.ARD('3290', data_dir='/tmp/py-ard')
You can choose to refresh the MAC code for current IMGT HLA database version
ard.refresh_mac_codes()
The default initialization is to use the latest IMGT HLA database
ard = pyard.ARD()
Reduce a single locus HLA Typing.
allele = "A*01:01:01"
ard.redux_gl(allele, 'G')
# >>> 'A*01:01:01G'
ard.redux_gl(allele, 'lg')
# >>> 'A*01:01g'
ard.redux_gl(allele, 'lgx')
# >>> 'A*01:01'
Reduce an ambiguous GL String
# Reduce GL String
#
ard.redux_gl("A*01:01/A*01:01N+A*02:AB^B*07:02+B*07:AB", "G")
# 'B*07:02:01G+B*07:02:01G^A*01:01:01G+A*02:01:01G/A*02:02'
You can also reduce serology based typings.
ard.redux_gl('B14', 'lg')
# >>> 'B*14:01g/B*14:02g/B*14:03g/B*14:04g/B*14:05g/B*14:06g/B*14:08g/B*14:09g/B*14:10g/B*14:11g/B*14:12g/B*14:13g/B*14:14g/B*14:15g/B*14:16g/B*14:17g/B*14:18g/B*14:19g/B*14:20g/B*14:21g/B*14:22g/B*14:23g/B*14:24g/B*14:25g/B*14:26g/B*14:27g/B*14:28g/B*14:29g/B*14:30g/B*14:31g/B*14:32g/B*14:33g/B*14:34g/B*14:35g/B*14:36g/B*14:37g/B*14:38g/B*14:39g/B*14:40g/B*14:42g/B*14:43g/B*14:44g/B*14:45g/B*14:46g/B*14:47g/B*14:48g/B*14:49g/B*14:50g/B*14:51g/B*14:52g/B*14:53g/B*14:54g/B*14:55g/B*14:56g/B*14:57g/B*14:58g/B*14:59g/B*14:60g/B*14:62g/B*14:63g/B*14:65g/B*14:66g/B*14:68g/B*14:70Qg/B*14:71g/B*14:73g/B*14:74g/B*14:75g/B*14:77g/B*14:82g/B*14:83g/B*14:86g/B*14:87g/B*14:88g/B*14:90g/B*14:93g/B*14:94g/B*14:95g/B*14:96g/B*14:97g/B*14:99g/B*14:102g'
Reduction Type | Description |
---|---|
G |
Reduce to G Group Level |
lg |
Reduce to 2 field ARD level (append g ) |
lgx |
Reduce to 2 field ARD level |
W |
Reduce/Expand to 3 field WHO nomenclature level |
exon |
Reduce/Expand to exon level |
U2 |
Reduce to 2 field unambiguous level |
import pyard
pyard.dr_blender(drb1='HLA-DRB1*03:01+DRB1*04:01', drb3='DRB3*01:01', drb4='DRB4*01:03')
# >>> 'DRB3*01:01+DRB4*01:03'
$ pyard-import
Created Latest py-ard database
$ pyard-import --db-version 3.29.0
Created py-ard version 3290 database
Import particular version of IMGT database and replace the v2 to v3 mapping table from a CSV file.
$ pyard-import --db-version 3.29.0 --v2-to-v3-mapping map2to3.csv
Created py-ard version 3290 database
Updated v2_mapping table with 'map2to3.csv' mapping file.
pyard-import --db-version 3340 --re-install
$ pyard-import --v2-to-v3-mapping map2to3.csv
$ pyard-import --db-version 3450 --refresh-mac
$ pyard-status
$ pyard --gl 'A*01:AB' -r lgx
A*01:01/A*01:02
$ pyard --gl 'DRB1*08:XX' -r G
DRB1*08:01:01G/DRB1*08:02:01G/DRB1*08:03:02G/DRB1*08:04:01G/DRB1*08:05/ ...
$ pyard -v 3290 --gl 'A1' -r lgx # For a particular version of DB
A*01:01/A*01:02/A*01:03/A*01:06/A*01:07/A*01:08/A*01:09/A*01:10/A*01:12/ ...
pyard-csv-reduce
can be used to batch process a CSV file with HLA typings. See documentation for instructions on how to configure and run.
Run py-ard
as a service so that it can be accessed as a REST service endpoint.
Build the docker image:
make docker-build
Build the docker and run it with:
make docker
The endpoint should then be available at localhost:8080