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https://github.com/bcgsc/hlaminer

⛏ HLA predictions from NGS shotgun data
https://github.com/bcgsc/hlaminer

alignments hla-prediction hla-sequence oxford-nanopore shotgun-sequence-datasets targeted-assemblies

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⛏ HLA predictions from NGS shotgun data

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README 

          
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## HLAminer (c) 2011-present



Derivation of HLA (Human Leukocyte Antigen) class I and II predictions from DNA/RNA sequencing datasets



*This manual assumes that you have a working knowledge of Unix, and some shell and perl scripting experience



### CONTENTS

--------

1. [SYNOPSIS](#synopsis)

2. [LICENSE](#license)

3. [OVERVIEW](#overview)

4. [DESCRIPTION](#description)

5. [INSTALL](#install)

6. [COMMANDS AND OPTIONS](#commands)

7. [PREDICTING FROM LONG (NANOPORE/PACBIO) READS](#nanopore)

8. [REFERENCE SEQUENCE FOR LONG-READ ALIGNMENTS](#reference)

9. [DATABASES](#databases)

10. [AUTHORS](#authors)

11. [CITING](#citing)

12. [FULL LICENSE](#full)

--------

### SYNOPSIS 

--------



  HLAminer is a pipeline for predicting Human Leukocyte Antigen (HLA) signatures from shotgun sequence data (ie. whole genome, whole transcriptome/RNA-Seq, exome), at the group and allele resolution.

  It supports predictions from a variety of DNA sequencing technologies including those from Illumina, MGI, PacBio and Oxford Nanopore.   

  Predictions are either derived from targeted sequence assembly, or direct sequence alignments.



  For quick tests on Illumina RNA-seq data:

  1. Copy ./test-demo/    eg. cp -rf test-demo foo

  2. In folder "foo", edit the patient.fof file to point to your NGS RNAseq data.  Ensure all paths are ok.

  3. For HLA Predictions by Targeted Assembly of Shotgun Reads: execute ./HLAminer/foo/HPTASRrnaseq.sh 

     For HLA Predictions by Read Alignment: execute ./HLAminer/foo/HPRArnaseq.sh



### LICENSE 

--------



  HLAminer Copyright (c) 2011-present Canada's Michael Smith Genome Science Centre.  All rights reserved.

  TASR Copyright   (c) 2010-present Canada's Michael Smith Genome Science Centre.  All rights reserved.

  SSAKE Copyright  (c) 2006-present Canada's Michael Smith Genome Science Centre.  All rights reserved.

   

  Due to the clinical implications of HLAminer, the code is now released

  under the BC Cancer Agency software license agreement (academic use).

  Details of the license can be accessed at:

  and at the bottom of this readme file



  For commercial licensing options, please contact Patrick Rebstein prebstein@bccancer.bc.ca



  Software components of HLAminer (eg. TASR) are still distributed under the

  terms of the GNU General Public License



### OVERVIEW 

--------



Derivation of HLA class I and class II predictions from shotgun sequence datasets (HLAminer) by:

1) Targeted Assembly of Shotgun Reads (HPTASR)

2) Read Alignment (HPRA)



BEST SHORT READ RESULTS ARE OBTAINED WITH HPTASR WITH READS 100bp AND UP (IDEALLY 150bp).

IT WILL WORK WITH SHORTER READS (50bp) BUT 4-digit HLA ALLELE PREDICTIONS MAY BE AMBIGUOUS 



This clip summarizes the pipeline:

https://www.youtube.com/watch?v=j-g8Geh5ST8&list=LL&index=110



### DESCRIPTION 

--------



The HLA prediction by targeted assembly of short sequence reads (HPTASR), performs targeted de novo assembly of HLA NGS reads and align the resulting contigs to reference HLA alleles from the IMGT/HLA sequence repository using commodity hardware with standard specifications (<2GB RAM, 2GHz).  Putative HLA types are inferred by mining and scoring the contig alignments and an expect value is determined for each.  The method is accurate, simple and fast to execute and, for transcriptome data, requires low depth of coverage. Known HLA class I/class II reference sequences available from the IMGT/HLA public repository are read by TASR using default options (Warren and Holt 2011) to create a hash table of all possible 15 nt words (k-mers) from these reference sequences. Note that this parameter is customizable and larger k values will yield predictions with increased specificity (at the possible expense of sensitivity). Subsequently, NGS data sets are interrogated for the presence of one of these kmers (on either strand) at the 5’ or 3’ start. Whenever an HLA word is identified, the read is recruited as a candidate for de novo assembly. Upon de novo assembly of all recruited reads, a set of contigs is generated.  Only sequence contigs equal or larger than 200nt in length are considered for further analysis, as longer contigs better resolve HLA allelic variants.  Reciprocal BLASTN alignments are performed between the contigs and all HLA allelic reference sequences. HPTASR mines the alignments, scoring each possible HLA allele identified, computing and reporting an expect value (E-value) based on the chance of contigs characterizing given HLA alleles and, reciprocally, the chance of reference HLA alleles aligning best to certain assembled contig sequences



The HLA prediction from direct read alignment (HPRA) method is conceptually simpler and faster to execute, since paired reads are aligned up-front to reference HLA alleles.  Alignments from the HPTASR and HPRA methods are processed by the same software (HLAminer.pl) to derive HLA-I predictions by scoring and evaluating the probability of each candidate bearing alignments.



### What's new in version 1.4?

--------



Ability to stream the (.sam) output of modern read  aligners, directly into HLAminer.

Initial support for predicting HLA types from long nanopore reads such as those from Oxford Nanopore Technologies.

Better information/sub-routine/date tracking in hlaminer 



### What's new in version 1.3?

--------



A more concise HLA allele summary in HLAminer_HPTASR.csv and HLAminer_HPRA.csv (associated .log is unchanged and lists all predictions)

Keeps top two [highest-scoring by HLA group] predictions per gene and only the 'P' designated allele when the summary include HLA Sequences reported to have the same antigen binding domain.

For the original output, refer to the HLAminer_v1-2.pl included in the ./bin directory

A prediction example from MCF-7 PacBio RNA-seq reads is also provided



### What's new in version 1.2?

--------



Updated all HLA sequence databases

Corrected shell script that download HLA sequences to reflect change of location at EBI (ie. fasta sub folder) 

Added support for predictions from direct alignment of single-end reads



### INSTALL 

--------


1. Download and decompress the tar ball

gunzip HLAminer_v1-4.tar.gz 

tar -xvf HLAminer_v1-4.tar

2. Make sure you see the following directories:

./bin

./databases

./docs

./test-demo



3. Read the docs in the ./docs/ folder

4. Change/Add/Adjust the perl shebang line of each .pl and .sh script in the ./bin/ folder as needed



From direct Read Alignment (HPRA, faster but less accurate):

HPRArnaseq_classI.sh

HPRArnaseq_classI-II.sh

HPRAwgs_classI.sh

HPRAwgs_classI-II.sh

-and for single end reads-

HPRArnaseq_classI_SE.sh

HPRArnaseq_classI-II_SE.sh

HPRAwgs_classI_SE.sh

HPRAwgs_classI-II_SE.sh



From Targeted Assembly (HPTASR, longer but more accurate):

HPTASRrnaseq_classI.sh

HPTASRrnaseq_classI-II.sh

HPTASRwgs_classI.sh

HPTASRwgs_classI-II.sh



*Running HPTASRwgs(rnaseq)_classI-II.sh will take longer than HPTASRwgs(rnaseq)_classI.sh, due to the reciprocal BLAST step.  You may remove this step from the former (and HLAminer.pl command) to speed things up.  However, this step is helpful in weeding out spurious alignments to HLA references.  That said, if you're solely interested in HLA-I, you have the option to run the latter set of scripts [HPTASRwgs(rnaseq)_classI.sh].



Also, in the ncbiBlastConfig2-2-XX.txt files (bin and test-demo directories), you may adjust the number of threads and number of reported alignments to speed things up. The options have different name depending on the blast version, refer to the blast manual

eg.

v2.2.22

option:description

-a:threads

-v:number of descriptions

-b:number of alignments



v2.2.28

-num_threads:threads

-max_target_seqs:number of hit sequences to report (when output is 5/xml)



In our hands, a few tests show that blast 2.2.22 may be faster than blast+ (2.2.28) while

producing accurate results - HLAminer (Warren et al. 2012) was thoroughly tested with 2.2.22



NCBI blast may be downloaded from:

ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/

-or-

ftp://ftp.ncbi.nlm.nih.gov/blast/executables/release/



HLAminer.pl

parseXMLblast.pl

[![link](https://img.shields.io/badge/TASR-github-yellow)](https://github.com/warrenlr/TASR)



5. You must install perl module Bio::SearchIO to use HPTASR

6. Edit the fullpath location of bwa and other software dependencies in the shell scripts in the ./bin/ folder, as needed

7. For your convenience, ncbi blastall and formatdb have been placed in the ./bin/ folder and executed from the following shell scripts:



NAME,PROCESS,NGS DATA TYPE,PREDICTIONS

HPRArnaseq_classI.sh,Paired read alignment,RNAseq (transcriptome),HLA-I A,B,C genes

HPRArnaseq_classI-II.sh,Paired read alignment,RNAseq (transcriptome),HLA-I A,B,C and HLA-II DP,DQ,DR genes



HPRAwgs_classI.sh,Paired read alignment,Exon capture (exome) and WGS (genome),HLA-I A,B,C genes

HPRAwgs_classI-II.sh,Paired read alignment,Exon capture (exome) and WGS (genome),HLA-I A,B,C and HLA-II DP,DQ,DR genes



HPTASRrnaseq_classI.sh,Targeted assembly of sequence reads,RNAseq (transcriptome),HLA-I A,B,C genes

HPTASRrnaseq_classI-II.sh,Targeted assembly of sequence reads,RNAseq (transcriptome),HLA-I A,B,C and HLA-II DP,DQ,DR genes



HPTASRwgs_classI.sh,Targeted assembly of sequence reads,Exon capture (exome) and WGS (genome),HLA-I A,B,C genes

HPTASRwgs_classI-II.sh,Targeted assembly of sequence reads,Exon capture (exome) and WGS (genome),HLA-I A,B,C and HLA-II DP,DQ,DR genes



Run those scripts by specifying the relative path ../bin/blastall or ../bin/formatdb in the shell scripts AND config file "ncbiBlastConfig.txt".  Make sure HPRA and HPTASR are running in same-level directories to ../bin/ (eg. ../test-demo/)



8. Before running on your data, inspect the ./test-demo/ folder and familiarize yourself with the files and the execution.  

9. When you are ready and the demo works well, place the fullpath location of your short read fastq or fasta files in the "patient.fof" file.

10. Make sure that the following files are in your working directory:



patient.fof

ncbiBlastConfig.txt (specific to the version of blast you are using, see in

../bin and ../test-demo directories



### ADDITIONAL INSTALL NOTES ON MAC OSX



#### install bioperl

---------------

sudo perl -MCPAN -e shell

install CJFIELDS/BioPerl-1.6.923.tar.gz

change shebang line in all PERL (.pl) scripts and location of bioperl on your system in HLAminer.pl



#### install ncbi blast

------------------

download and install blast-2.2.22-universal-macosx.tar.gz

change path to blast in ncbiconfig.txt



#### install homebrew

----------------


ruby -e "$(curl -fsSL

https://raw.githubusercontent.com/Homebrew/install/master/install)"



Since databases were indexed with older

version, had to re-index:

bwa index -a is HLA_ABC_CDS.fasta




change path to bwa in HPRA* shell scripts



TEST YOUR INSTALL BY RUNNING THE SCRIPT IN test-demo. CONSULT THE README-FIRST.txt FILE



### COMMANDS AND OPTIONS 

--------






Usage: ../bin/HLAminer.pl [v1.4]

Derivation of HLA class I and II predictions from shotgun sequence datasets

--------------------------------------------------------------------

HPTASR (HLA Predictions by Targeted Assembly of Shotgun Reads):

-b blastn alignments.........................

-r reciprocal blastn.........................

-c contig fasta file.........................

-z minimum contig size.......................<200>

------------------------------- OR ---------------------------------

HPRA (HLA Predictions by Read Alignment):

-a sam alignments............................< -a ngs_vs_hla.sam > or < -a stream >

-e single-end reads used (1=yes/0=no)........<0>

--------------------------------------------------------------------

-h hla fasta file............................

-p P-designation file........................

-i minimum % sequence identity...............<99>

-q minimum log10 (phred-like) expect value...<30>

-s minimum score.............................<1000>

-n consider null alleles (1=yes/0=no)........<0>

-l label (run name) -optional-



The shell scripts are set to filter out short (<200) contigs that could blur HPTASR predictions.  Feel free to adjust as you see fit.



Likewise, HLAminer.pl runs with the set defaults:

-z minimum contig size.......................<200> (HPTASR)

-i minimum % sequence identity...............<99>  (HPTASR / HPRA)

-q minimum log10 (phred-like) expect value...<30>  (HPTASR / HPRA)

-s minimum score.............................<1000> (HPTASR / HPRA)

-n consider null alleles (1=yes/0=no)........<0> (HPTASR / HPRA)




The minimum sequence identity applies to the short read paired alignment or blast alignment, depending on the choice made.  HLA predictions with a phred-like expect value lower than -q or a score lower than -s will not be diplayed.  Because IMGT/HLA reports numerous null alleles, an option exist to consider or not these unexpressed alleles. 



Likewise, TASR-based (Targeted Assembly of Short Reads) predictions could be

improved by using larger k values for assembly (-k). Experimentation for

choosing the ideal k to use depends on the input read length and is warranted. 



### PREDICTING FROM LONG (NANOPORE/PACBIO) READS 

--------



HLAminer v1.4 provides initial support for HLA prediction from raw uncorrected shotgun nanopore long reads (such as those from Oxford Nanopore Technologies).

HLAminer v1.4 implements a streaming approach to reading .sam alignment files, supporting the alignment of GB worth of read data in a few hours and predictions within seconds of alignment completion, without saving costly .sam files to disk.



*More information here:* 

Warren RL, Birol I. 2025. Streaming long-read sequence alignments for HLA

predictions using HLAminer. Current Protocols. https://doi.org/10.1002/cpz1.70124



We tested the software on the NA19240 WGS promethion dataset (2018, older chemistry).

https://gigabaseorgigabyte.wordpress.com/2018/05/24/promethion-human-genome-na19240/

with data available from ENA at this location:

https://www.ebi.ac.uk/ena/data/view/PRJEB26791



1) First, we downloaded read files (ERR2585112 and ERR2585115 from the ENA)


nohup wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR258/005/ERR2585115/ERR2585115.fastq.gz &

nohup wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR258/002/ERR2585112/ERR2585112.fastq.gz &



2) Then, we predicted HLA by running minimap2 and HLAminer v1.4:


/usr/bin/time -v -o minimap_hlaminerERR2585115-1mod.time minimap2 -t 60 -ax map-ont --MD ../database/GCA_000001405.15_GRCh38_genomic.chr-only-noChr6-HLA-I_II_GEN.fa.gz ERR2585115.fastq.gz | ./HLAminer.pl -h ../database/HLA-I_II_GEN.fasta -s 500 -q 1 -i 1 -p ../database/hla_nom_p.txt -a stream



-or-



/usr/bin/time -v -o minimap_hlaminerERR2585115-2mod.time minimap2 -t 60 -ax map-ont --MD ../database/GCA_000001405.15_GRCh38_genomic.chr-only-chr6hlamasked-HLA-I_II_GEN.fa.gz ERR2585115.fastq.gz | ./HLAminer.pl -h ../database/HLA-I_II_GEN.fasta -s 500 -q 1 -i 1 -p ../database/hla_nom_p.txt -a stream





A test is provided at ./test-demo/HPRAwgs_ONTclassI-IIdemo.sh



The output files:

HLAminer_HPRA_ERR2585115.csv

-and-

HLAminer_HPRA_ERR2585115.log



you generate should be comparable* to:

HLAminer_HPRA_ERR2585115_test.csv

-and-

HLAminer_HPRA_ERR2585115_test.log



*exact scores are not to be expected, because they vary based on the HLA sequence database used, which differ from different release of the code (and your own updating of HLA sequence databases, which is highly recommended to do before you use HLAminer).



Results below demonstrate HLAminer's ability to fairly accurately predict HLA-I and -II types from direct [and streamed] nanopore sequencing reads [old chemistry] alignments 




HLAminer PREDICTIONS (top 2 per HLA allele group, by high-score):

--------------------



A*30:106/A*68:02P

B*35:01P/B*57:01P ex. B*57:03P

C*04:01P/C*18:01P

F*01:01P

G*01:01P



DQA1*01:02P/DQA1*05:01P

DQB1*05:02P/DQB1*03:03P ex. DQB1*03:01P

DRB1*12:01P/DRB1*16:02P

DPA1*02:02P/DPA1*01:03P

DPB1*90:01P/DPB1*01:01P

DRA*01:01P



REPORTED TYPES (extracted from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5804087/#MOESM1 Supp file 1 13059_2018_1388_MOESM1_ESM.xlsx Tab S6, NA19240 (Child)):

--------------

 

A*30:01:01G/A*68:02:01G

B*35:01:01G/B*57:03:01G

C*04:01:01G/C*18:01:01G

F*01:01:01:08;F*01:01:01:09;F*01:02;F*01:01:01:10;F*01:03:01:01;F*01:01:01:11;F*01:03:01:02;F*01:01:01:12;F*01:01:02:01;F*01:01:02:02;F*01:01:02:03;F*01:01:02:04;F*01:01:02:05;F*01:01:02:06;F*01:01:01:01;F*01:01:01:02;F*01:01:01:03;F*01:01:01:04;F*01:01:01:05;F*01:01:01:06;F*01:01:01:07/F*01:01:01:08;F*01:01:01:09;F*01:02;F*01:01:01:10;F*01:03:01:01;F*01:01:01:11;F*01:03:01:02;F*01:01:01:12;F*01:01:02:01;F*01:01:02:02;F*01:01:02:03;F*01:01:02:04;F*01:01:02:05;F*01:01:02:06;F*01:01:01:01;F*01:01:01:02;F*01:01:01:03;F*01:01:01:04;F*01:01:01:05;F*01:01:01:06;F*01:01:01:07 

G*01:06;G*01:01:02:01;G*01:01:02:02;G*01:18;G*01:08:01;G*01:01:18;G*01:01:19/G*01:05N

H*02:05/H*02:05         

J*02:01;J*01:01:01:05;J*01:01:01:04;J*01:01:01:03;J*01:01:01:02;J*01:01:01:01/J*02:01;J*01:01:01:05;J*01:01:01:04;J*01:01:01:03;J*01:01:01:02;J*01:01:01:01     L*01:01:02;L*01:01:01:01;L*01:01:01:02;L*01:01:01:03/L*01:02



DQA1*01:02:01G/DQA1*05:01:01G

DQB1*05:02:01G/DQB1*03:01:01G

DRB1*12:01:01G/DRB1*16:02:01G

DPA1*02:01:01G/DPA1*02:02:02G

DPB1*01:01:01G/DPB1*01:01:01G

DOA*01:01:05/DOA*01:01:02:03;DOA*01:01:02:02;DOA*01:01:02:01;DOA*01:01:04:02;DOA*01:01:04:01    DMA*01:01:01:02;DMA*01:01:01:01;DMA*01:04;DMA*01:03;DMA*01:02;DMA*01:01:01:04;DMA*01:01:01:03/DMA*01:01:01:02;DMA*01:01:01:01;DMA*01:04;DMA*01:03;DMA*01:02;DMA*01:01:01:04;DMA*01:01:01:03

DMB*01:02;DMB*01:01:01:04;DMB*01:01:01:03;DMB*01:01:01:02;DMB*01:03:01:04;DMB*01:01:01:01;DMB*01:03:01:03;DMB*01:03:01:02;DMB*01:03:01:01;DMB*01:05;DMB*01:04/DMB*01:02;DMB*01:01:01:04;DMB*01:01:01:03;DMB*01:01:01:02;DMB*01:03:01:04;DMB*01:01:01:01;DMB*01:03:01:03;DMB*01:03:01:02;DMB*01:03:01:01;DMB*01:05;DMB*01:04

DRA*01:01:01:01;DRA*01:02:01;DRA*01:01:01:02;DRA*01:02:02;DRA*01:01:01:03;DRA*01:02:03;DRA*01:01:02/DRA*01:01:01:01;DRA*01:02:01;DRA*01:01:01:02;DRA*01:02:02;DRA*01:01:01:03;DRA*01:02:03;DRA*01:01:02




For more information, please refer to:

[![link](https://img.shields.io/badge/HLAminer-preprint-brightgreen)](https://doi.org/10.48550/arXiv.2209.09155)

[![link](https://img.shields.io/badge/HLAminer-protocol-brightgreen)](https://doi.org/10.1002/cpz1.70124)



### REFERENCE SEQUENCE FOR LONG-READ ALIGNMENTS 

--------

#### Use of HG38 reference for long-read alignments with HLAminer



Here’s why we originally used HG38 without chr6 for alignments with long reads:



✅ 1. Excluding chr6 and replacing with complete HLA alleles improves allele resolution

The classical HLA loci (HLA-A, -B, -C, -DRB1, etc.) on chr6 are highly polymorphic and not well-represented in a linear reference.

Mapping short or long reads to the limited HLA representation in GRCh38 often results in ambiguous or incorrect alignment, especially for novel or rare alleles.

By using all known HLA alleles (from IPD-IMGT/HLA) in a dedicated contig, you're allowing your mapper (e.g. minimap2) to consider each allele as an independent path — effectively emulating a graph reference without the overhead.



✅ 2. Keeping the rest of GRCh38 intact ensures genomic compatibility

Most reads outside of the MHC region still align correctly to the standard GRCh38 chromosomes.


GCA_000001405.15_GRCh38_genomic.chr-only-noChr6-HLA-I_II_GEN.fa.gz




Improvements to the reference sequence used with long-read alignments (April 2025):



✅ 1. Masking of chr6 HLA locus (HG38 coordinates 28510120-33480577). This range is the standard extended (Class I, II, and III regions and flanking genes+regulatory regions relevant to immune function) MHC region definition from 1000 Genomes and GRC and it's masked in many genomics pipelines to improve mapping accuracy and downstream interpretation. https://www.ncbi.nlm.nih.gov/grc/human/regions/MHC?asm=GRCh38


GCA_000001405.15_GRCh38_genomic.chr-only-chr6hlamasked-HLA-I_II_GEN.fa.gz




Notes:


-Custom references are better suited to HLA inference than any static genome, including hs38DH, and the approach aligns with best practices in immunogenomics.

-It's crucial for you to keep the HLA allele set up-to-date, and update+document+date those files frequently (see the database folder README and the DATABASES section below for details):




GCA_000001405.15_GRCh38_genomic.chr-only-noChr6-HLA-I_II_GEN.fa.gz

GCA_000001405.15_GRCh38_genomic.chr-only-chr6hlamasked-HLA-I_II_GEN.fa.gz




### DATABASES 

--------



Follow these instructions to download updated HLA sequences from ebi/imgt (shell scripts to automatically download and format the databases exist in ./database/) and refer to README.txt in the ./database directory:



1) Coding HLA sequences



HLA CDS sequences from:


wget ftp://ftp.ebi.ac.uk/pub/databases/imgt/mhc/hla/fasta/A_nuc.fasta

wget ftp://ftp.ebi.ac.uk/pub/databases/imgt/mhc/hla/fasta/B_nuc.fasta

wget ftp://ftp.ebi.ac.uk/pub/databases/imgt/mhc/hla/fasta/C_nuc.fasta

cat A_nuc.fasta B_nuc.fasta C_nuc.fasta | perl -ne 'chomp;if(/\>\S+\s+(\S+)/){print ">$1\n";}else{print "$_\n";}' > HLA_ABC_CDS.fasta

../bin/formatdb -p F -i HLA_ABC_CDS.fasta

/home/pubseq/BioSw/bwa/bwa-0.5.9/bwa index -a is HLA_ABC_CDS.fasta




2) HLA genomic sequences 



To make the HLA genomic sequence database, execute these unix commands:


wget ftp://ftp.ebi.ac.uk/pub/databases/imgt/mhc/hla/fasta/A_gen.fasta

wget ftp://ftp.ebi.ac.uk/pub/databases/imgt/mhc/hla/fasta/B_gen.fasta

wget ftp://ftp.ebi.ac.uk/pub/databases/imgt/mhc/hla/fasta/C_gen.fasta

cat A_gen.fasta B_gen.fasta C_gen.fasta | perl -ne 'chomp;if(/\>\S+\s+(\S+)/){print ">$1\n";}else{print "$_\n";}' > HLA_ABC_GEN.fasta

../bin/formatdb -p F -i HLA_ABC_GEN.fasta

/home/pubseq/BioSw/bwa/bwa-0.5.9/bwa index -a is HLA_ABC_GEN.fasta




FOR YOUR CONVENIENCE, A SINGLE SHELL SCRIPT CAN BE RUN FROM ../database TO

UPDATE ALL IMGT-HLA SEQUENCE DATABASES AND CREATE BWA/BLAST INDEXES. JUST KEEP

IN MIND THAT THIS IS DONE WITH A SPECIFIC VERSION OF THESE TOOLS, IF YOU

UPGRADE, YOU WILL HAVE TO REGENERATE THE INDEXES

******************************

***../database/updateAll.sh***

******************************



Note: For alignment of nanopore genomic/transcriptomic reads, we recommend aligning to a file comprised of human genome chromosomes and HLA-I and -II alleles to reduce the noise in alignments (especially when running minimap2). If you are predicting from direct ONT (nanopore) or PacBio long read alignments, please update the genome files (refer to the above section "Use of HG38 reference for long-read alignments with HLAminer"):



Genome file (without Chromosome 6):


https://www.bcgsc.ca/downloads/btl/hlaminer/GCA_000001405.15_GRCh38_genomic.chr-only-noChr6.fa.gz



then:


cd database

cat GCA_000001405.15_GRCh38_genomic.chr-only-noChr6.fa HLA-I_II_GEN.fasta | pigz - > GCA_000001405.15_GRCh38_genomic.chr-only-noChr6-HLA-I_II_GEN.fa.gz

cat GCA_000001405.15_GRCh38_genomic.chr-only-noChr6.fa HLA-I_II_CDS.fasta | pigz - > GCA_000001405.15_GRCh38_genomic.chr-only-noChr6-HLA-I_II_CDS.fa.gz




Genome file (without HLA region on Chromosome 6):


https://www.bcgsc.ca/downloads/btl/hlaminer/GCA_000001405.15_GRCh38_genomic.chr-only-chr6hlamasked.fa.gz



then:


cd database

cat GCA_000001405.15_GRCh38_genomic.chr-only-chr6hlamasked.fa HLA-I_II_GEN.fasta | pigz - > GCA_000001405.15_GRCh38_genomic.chr-only-chr6hlamasked-HLA-I_II_GEN.fa.gz

cat GCA_000001405.15_GRCh38_genomic.chr-only-chr6hlamasked.fa HLA-I_II_CDS.fasta | pigz - > GCA_000001405.15_GRCh38_genomic.chr-only-chr6hlamasked-HLA-I_II_CDS.fa.gz




or download those files (JAN/APR 2025 update) directly from:

https://www.bcgsc.ca/downloads/btl/hlaminer/



3) P designation files



Upgrade the P designation info from:



info:

http://hla.alleles.org/wmda/index.html



file:

http://hla.alleles.org/wmda/hla_nom_p.txt



### OUTPUT FILES 

--------



HLA predictions from read pair alignments:


HLAminer_HPRA.log

HLAminer_HPRA.csv



HLA predictions from targeted assemblies:


HLAminer_HPTASR.log

HLAminer_HPTASR.csv



The .log file tracks the process of HLA mining. It contains the following information:

-HLAminer command and parameters utilized

-Contig/read pair alignment output and best HLA hit for each

-Initial gene summary, score and expect value

-Final summary, listing all predictions by highest score (more likely).



The .csv file contains HLAminer predictions.  Predictions are listed by HLA

gene and ranked by highest score.  Predictions 1) and 2) are expected to

represent the two alleles for each.



eg.


----------------------------------------------------------------------

SUMMARY

MOST LIKELY HLA-I ALLELES (Confidence (-10 * log10(Eval)) >= 30, Score >= 500)

Allele,Score,Expect (Eval) value,Confidence (-10 * log10(Eval))

----------------------------------------------------------------------



HLA-A

Prediction #1 - A*26

        A*26:33,4179,5.22e-124,1232.8



Prediction #2 - A*33

        A*33:24,1791,2.41e-75,746.2



Prediction #3 - A*68

        A*68:05,597,1.85e-10,97.3




From these predictions, the individual is expected to be heterozygous with HLA-I A alleles A*26 and

A*33. The chance, expect value and confidence are different representations of the same

metric. The Confidence represents the Expect value - Eval - as a score in a

manner analoguous to the phred score employed in sequencing to quickly assess

the likelihood of a base being correct. 



Predictions/read pair are ambiguous when there are multiple predicted allele groups and/or protein coding alleles with the same score.



### AUTHORS 

--------




Rene Warren




### CITING 

--------



Thank you for your [![Stars](https://img.shields.io/github/stars/warrenlr/HLAminer.svg)](https://github.com/warrenlr/HLAminer/stargazers) and for using, developing and promoting this free software!



If you use HLAminer for you research, please cite:




Warren RL, Choe G, Freeman DJ, Castellarin M, Munro S, Moore R, Holt RA. (2012)

Derivation of HLA types from shotgun sequence datasets

Genome Med. 4, 95. https://doi.org/10.1186/gm396



[![link](https://img.shields.io/badge/HLAminer-manuscript-brightgreen)](https://doi.org/10.1186/gm396)



and




Warren RL, Birol I. (2025)

Streaming long-read sequence alignments for HLA predictions using HLAminer

Current Protocols. 5, e70124. https://doi.org/10.1002/cpz1.70124



[![link](https://img.shields.io/badge/HLAminer-protocol-brightgreen)](https://doi.org/10.1002/cpz1.70124)



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