hammerlab/prohlatype

Name: prohlatype

Owner: Hammer Lab

Description: Probabilistic HLA typing

Created: 2016-04-29 06:22:05.0

Updated: 2018-01-13 00:11:16.0

Pushed: 2018-01-15 06:35:40.0

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Size: 22246

Language: OCaml

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README

Probabilistic HLA Typing

Paper: Prohlatype: A Probabilistic Framework for HLA Typing

This project provides a set of tools to calculate the full posterior distribution of HLA types given read data.

Instead of:

A1      A2      B1      B2      C1      C2      Reads   Objective
A*31:01 A*02:01 B*45:01 B*15:03 C*16:01 C*02:10 538.0   513.79

one can calculate:

| Allele 1 | Allele 2 | Log P | P | |—————|——————-|————-|——-| | A02:05:01:01| A30:114| -23046.81 | 0.5000| | A02:05:01:01| A30:01:01| -23046.81 | 0.5000| | A02:05:01:01| A30:106| -23103.15 | 0.0000| | A02:05:01:02| A30:114| -23146.35 | 0.0000| | … | | | | B07:36| B57:03:01:02| -13717.33 | 0.5000| | B07:36| B57:03:01:01| -13717.33 | 0.5000| | B07:36| B57:03:03| -13804.74 | 0.0000| | B27:157| B57:03:01:02| -13816.17 | 0.0000| | … | | | | C06:103| C18:10| -11936.35 | 0.3338| | C06:103| C18:02| -11936.36 | 0.3331| | C06:103| C18:01| -11936.36 | 0.3331| | C15:102| C18:02| -11951.72 | 0.0000|

How:

There are three options to obtain the software:

1. If you are running on Linux, standalone binaries are available with each release.

2. Use the linked Docker image.

3. Build the software from source:

   a. Install opam.

   b. Make sure that the opam packages are up to date:

     $ opam update
   

   c. Make sure that you're on the relevant compiler:

     $ opam switch 4.05.0
     $ eval `opam config env`
   

   d. Get source:

     $ git clone https://github.com/hammerlab/prohlatype.git prohlatype
     $ cd prohlatype
   

   e. Install the dependent packages:

     $ make setup
   

   f. Build the programs (afterwards they'll be in _build/default/src/apps):

     $ make
   

Make sure that you have IMGT/HLA available:

$ git clone https://github.com/ANHIG/IMGTHLA.git imgthla

“Prohla”-typing:

1. Create an imputed HLA reference sequence via align2fasta. This step makes sure that all alleles have sequence information that spans the entire locus. This way, reads that originate from a region for which we normally do not have sequence information will still align (in the next filtering step), albeit poorly:

     $ align2fasta path-to-imgthla/alignments -o imputed_hla_class_I.fasta
   

   This step needs to be performed only once, per each IMGT version. Run $align2fasta --help for further information.

2. Filter your data against the reference, by first aligning. Ex:

     $ bwa mem imputed_hla_class_I.fasta ${SAMPLE}.fastq | \
         samtools view -F 4 -bT imputed_hla_class_I.fasta -o ${SAMPLE}.bam
   

   While fundamentally, the algorithms here are alignment based. They're too slow to run for all sequences. Sequences that do not originate from the HLA-region would just act as background noice.

3. and then convert aligned reads back to FASTQ:

     $ samtools fastq ${SAMPLE}.bam > ${SAMPLE}_filtered.fastq
   

4. Infer types (see $ multi_par --help for further details):

     $ multi_par path-to-imgthla/aignments ${SAMPLE}_filtered.fastq -o ${SAMPLE}_output.tsv