HLA Annotation Pipeline

Background
Tools
- Python Package
- HLA Annotation Pipeline
Docker Deployment

Background ⤴

In many cases, modern HLA nomenclature cannot fully describe the various genetic polymorphisms between both primary and reference data, as well as between recipients and stem cell sources. This tool addresses this issue by integrating sequence data with HLA nomenclature to facilitate research into some of these less-accessible genetic polymorphisms. Additionally, various mismatching models (HLA-B leader and HLA-DPB1 expression/TCE, for instance) have been built-in to provide an aggregating hub for aiding in clinical decision-making. This operating procedure details the usage and deployment of this tool.

Tools ⤴

Python Package ⤴

Installation

pip install .

Setup

from hlann.hlann import HLAnn
hlann = HLAnn(db_version='3470', verbose=True)

Usage

Allotype

Command

hlann.annotate_allotype('DPB1*01:AETTG')

Result

{'name': 'DPB1*01:AETTG',
 'tce': '3',
 'resolution': 'intermediate',
 'matched': False,
 'expr_level': '~high',
 'expr_annot_type': '~experimental',
 'alleles': [{'allele_expr': 'DPB1*01:01',
   'CIWD_TOTAL': 'C',
   'exon3_motif_ref': 'ACCACTC',
   'utr3_motif_ref': 'G',
   'tce': '3',
   'resolution': 'high',
   'expr_level': 'high',
   'experimental': True},
  {'allele_expr': 'DPB1*162:01',
   'CIWD_TOTAL': 'WD',
   'exon3_motif_ref': 'GTTGTCT',
   'utr3_motif_ref': 'A',
   'tce': '3',
   'resolution': 'high',
   'expr_level': 'low',
   'experimental': False},
  {'allele_expr': 'DPB1*417:01',
   'CIWD_TOTAL': 'WD',
   'exon3_motif_ref': 'ACCACTC',
   'utr3_motif_ref': 'unknown',
   'tce': '3',
   'resolution': 'high',
   'expr_level': 'high',
   'experimental': False}]}

Genotype

Command

hlann.annotate_genotype('DPB1*01:AETTA+DPB1*04:AETTB')

Result

{
  "allele_one": {
    "alleles": [
      {
        "CIWD": "C",
        "allele_expr": "DPB1*01:01",
        "exon3_motif_ref": "ACCACTC",
        "experimental": true,
        "expr_level": "high",
        "resolution": "high",
        "tce": "3",
        "utr3_motif_ref": "G"
      },
      {
        "CIWD": "WD",
        "allele_expr": "DPB1*417:01",
        "exon3_motif_ref": "ACCACTC",
        "experimental": false,
        "expr_level": "high",
        "resolution": "high",
        "tce": "3",
        "utr3_motif_ref": "unknown"
      }
    ],
    "expr_annot_type": "experimental",
    "expr_level": "high",
    "matched": false,
    "name": "DPB1*01:AETTA",
    "resolution": "intermediate",
    "tce": "3"
  },
  "allele_two": {
    "alleles": [
      {
        "CIWD": "C",
        "allele_expr": "DPB1*04:01",
        "exon3_motif_ref": "GTTGTCT/GTTGCCT/GTTATCT",
        "experimental": true,
        "expr_level": "low",
        "resolution": "high",
        "tce": "3/0",
        "utr3_motif_ref": "A"
      },
      {
        "CIWD": "C",
        "allele_expr": "DPB1*126:01",
        "exon3_motif_ref": "GTTGTCT",
        "experimental": false,
        "expr_level": "low",
        "resolution": "high",
        "tce": "3",
        "utr3_motif_ref": "A"
      },
      {
        "CIWD": "I",
        "allele_expr": "DPB1*350:01",
        "exon3_motif_ref": "ACCACTC",
        "experimental": false,
        "expr_level": "high",
        "resolution": "allelic",
        "tce": "3",
        "utr3_motif_ref": "unknown"
      },
      {
        "CIWD": "WD",
        "allele_expr": "DPB1*415:01",
        "exon3_motif_ref": "GTTGTCT",
        "experimental": false,
        "expr_level": "low",
        "resolution": "allelic",
        "tce": "3",
        "utr3_motif_ref": "A"
      }
    ],
    "expr_annot_type": "~experimental",
    "expr_level": "~low",
    "matched": false,
    "name": "DPB1*04:AETTB",
    "resolution": "intermediate",
    "tce": "3"
  },
  "genotype": "DPB1*01:AETTA+DPB1*04:AETTB"
}

Matches

Command

hlann.annotate_match('DPB1*04:01+DPB1*40:01', 'DPB1*40:01+DPB1*40:01')

Result

{
  "directionality": "GvH",
  "genotype_donor": {
    "allele_one": {
      "alleles": null,
      "expr_annot_type": "experimental",
      "expr_level": "low",
      "matched": true,
      "name": "DPB1*40:01",
      "resolution": "high",
      "tce": "3"
    },
    "allele_two": {
      "alleles": null,
      "expr_annot_type": "experimental",
      "expr_level": "low",
      "matched": true,
      "name": "DPB1*40:01",
      "resolution": "high",
      "tce": "3"
    },
    "genotype": "DPB1*40:01+DPB1*40:01"
  },
  "genotype_recipient": {
    "allele_one": {
      "alleles": null,
      "expr_annot_type": "experimental",
      "expr_level": "low",
      "matched": false,
      "name": "DPB1*04:01",
      "resolution": "high",
      "tce": "3"
    },
    "allele_two": {
      "alleles": null,
      "expr_annot_type": "experimental",
      "expr_level": "low",
      "matched": true,
      "name": "DPB1*40:01",
      "resolution": "high",
      "tce": "3"
    },
    "genotype": "DPB1*04:01+DPB1*40:01"
  },
  "grade": "MA",
  "matched_alleles_don": [
    "DPB1*40:01",
    "DPB1*40:01"
  ],
  "matched_alleles_pat": [
    "DPB1*40:01"
  ],
  "mismatched_allele_pat_expr_level": "low",
  "mismatched_alleles_don": [],
  "mismatched_alleles_pat": [
    "DPB1*04:01"
  ],
  "tce_match": "Permissive"
}

HLA Annotation Pipeline ⤴

Bootstrapping

To begin, ensure that you have Python3 (3.9 was used for this project) installed. To check, issue this command to verify your python version:

python --version

If Python3 is not installed, please download it from here.

If Python3 is readily available, set up your virtual environment by running these commands:

python3 -m venv venv
source venv/bin/activate

You can also use Anaconda for the specific python version.

conda create -n "venv10" python=3.10
conda activate venv10

Pip is the package installer for Python. It comes pre-packaged with Python. This will be used to install our requirements as such:

pip install --upgrade pip
pip install -r requirements.txt

Once installed, behave will be available for testing.

Initialization

Initialize the web service via this command:

streamlit run webapp.py

Once initialized, you may access the tool at http://localhost:8501.

Testing

Running all the tests in this repository is as simple as running this command:

behave

The results of your BDD tests can sometimes be difficult to view in the terminal. To view the tests results in the browser, we can use allure-behave, which was installed by pip during the bootstrapping process.

You will first need to specify behave to generate formatted allure results

behave -f allure_behave.formatter:AllureFormatter -o tests/results/ Finally, to view these formatted results in the browser, enter this command:

allure serve tests/results

Docker Deployment ⤴

To build the docker image the following command may be executed in the project root directory:

docker build -t agg-match-tool .

To deploy the app now we can use the following command. The application should be available in your domain, i.e. http://host:80/

docker run -d -p 80:80 -t agg-match-tool .

To stop the app container, We have to obtain the container id by executing

docker ps -a

Then we have to execute

docker stop $CONTAINER_ID

Release files for hlann 0.0.5

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for hlann 0.0.5
File	Interpreter	ABI	Platform	Reset
hlann-0.0.5-py3-none-any.whl 35.8 MB Jun 8, 2023	Python 3	none	any	Details

Release files / hlann-0.0.5-py3-none-any.whl

Download URL	hlann-0.0.5-py3-none-any.whl
Size	35.8 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`fca5d4a7b4fdea1d6c7a4a82edcbad5f320585cd84bb0a30b36fa68d4fe09358`
BLAKE2b-256 checksum How to use checksums	`deba3d10eea3cc41d91f29b688d43f92831859858730e8fc41030478129903fe`
Upload date	Jun 8, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
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