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HLAQuant - Get HLA allele specific expression

Project description

HLAQuant

Author: Austin Crinklaw

What is it?

HLAQuant is a pipeline that produces fast and accurate allele specific expression for HLA genes. This is done by quantifying on the peptide binding groove domain using Salmon with personalized sequences.

Requirements:

  • Linux OS
  • NCBI Blast+
  • Salmon -- please ensure Salmon is on your PATH!
  • Python 3+
    • Python packages: Pandas, BioPython

How to use:

Installation:

HLAQuant can be downloaded through PyPI using the following pip command.

pip install hlaquant

Input

HLAQuant takes two input files currently.

  • File one (-hla) consists of a sample_id and then a list of alleles corresponding to that sample's typing (tab separated)
  • File two (-fastq) consists of a sample_id and then a list of FASTQ files corresponding to that sample (paired-end or single-end) Examples of these inputs can be found under the 'test_data/' directory

Usage

  • A list of parameters and their descriptions can be found with the -h flag
python -m HLAQuant -h

Output

The output will match that of Salmons. It consists of a tab separated file containing the transcript ID (in this case, a specific HLA allele), as well as the number of reads. TPMs can be ignored as they will be inaccurate since we are only quantifying over a few sequences.

How does it work?

  • We first take the list of alleles and fetch the corresponding sequences from IMGT
  • Next we extract the sequences corresponding to their groove domains from these sequences
  • We build an index for quantification using these G-domain sequences
  • We then perform quantification using this index

The paper outlining this method in detail can be found [....somewhere when it is published]

References:

This pipeline would be unable to work without Salmon

Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017). Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods.

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