SpecHLA is a software package leveraging reads binning and local assembly to achieve accurate full-resolution HLA typing and loss-of-heterozygosity detection.
First, create the env with conda, and activate the env.
git clone https://github.com/deepomicslab/SpecHLA.git --depth 1
cd SpecHLA/
conda env create --prefix=./spechla_env -f environment.yml
conda activate ./spechla_envSecond, make the softwares in bin/ executable.
chmod +x -R bin/*Third, index the database and install the packages.
unset LD_LIBRARY_PATH && unset LIBRARY_PATH
bash index.shPerform SpecHLA with
bash script/whole/SpecHLA.sh -hWith only long reads, run
python3 script/long_read_typing.py -hNote:
bin/. If not detected, it uses bowtie2 as a replacement automatically.
The License file of Novoalign should be put in the bin/ folder before running bash index.sh. only long-read data, there is no need for the Novoalign license and running bash index.sh.
You need only construct the environment from conda env create --prefix=./spechla_env -f long_only_env.yml.GCC 9.4.0+, see https://github.com/deepomicslab/SpecHLA/issues/42Please go to the example/ folder, run SpecHLA with given scripts, and find results in the output/.
| Scripts | Description |
|---|---|
| script/ExtractHLAread.sh | Extract HLA reads from enrichment-free data. |
| script/whole/SpecHLA.sh | HLA typing with paired-end (PE), PE+long reads, PE+HiC, or PE+10X data. |
| script/long_read_typing.py | HLA typing with only long-read data. |
| script/typing_from_assembly.py | HLA Typing from diploid assemblies. |
| script/cal.hla.copy.pl | Detect HLA LOH events based on SpecHLA's typing results. |
First extract HLA reads with enrichment-free data. Otherwise, HLA typing would be slow. Map reads to hg19 or hg38, then use script/ExtractHLAread.sh to extract HLA-related reads. We use the script of Kourami with minor revision for this step.
Extract HLA-related reads by
USAGE: bash script/ExtractHLAread.sh -s <sample_id> -b <bamfile> -r <refGenome> -o <outdir>
-s : desired sample name (ex: NA12878) [required]
-b : sorted and indexed bam or cram (ex: NA12878.bam) [required]
-r : hg38 or hg19
-o : folder to save extracted reads [required]Full-resolution and exon HLA typing using SpecHLA. With Exome data like WES or RNASeq, only support exon typing. For efficient HLA typing, we strongly recommend utilizing only HLA-related reads. Specifically, for enrichment-free data, we recommend first performing the aforementioned step.
Perform full-resolution HLA typing with paired-end reads by
bash script/whole/SpecHLA.sh -n <sample> -1 <sample.fq.1.gz> -2 <sample.fq.2.gz> -o outdir/Perform exon HLA typing with paired-end reads by
bash script/whole/SpecHLA.sh -n <sample> -1 <sample.fq.1.gz> -2 <sample.fq.2.gz> -o outdir/ -u 1Perform full-resolution HLA typing with paired-end reads and PacBio reads by
bash script/whole/SpecHLA.sh -n <sample> -t <sample.pacbio.fq.gz> -1 <sample.fq.1.gz> -2 <sample.fq.2.gz> -o outdir/ Perform full-resolution HLA typing with paired-end reads and Nanopore reads by
bash script/whole/SpecHLA.sh -n <sample> -e <sample.nanopore.fq.gz> -1 <sample.fq.1.gz> -2 <sample.fq.2.gz> -o outdir/ Perform full-resolution HLA typing with paired-end reads and Hi-C reads by
bash script/whole/SpecHLA.sh -n <sample> -c <sample.hic.fwd.fq.gz> -d <sample.hic.rev.fq.gz> -1 <sample.fq.1.gz> -2 <sample.fq.2.gz> -o outdir/ Perform full-resolution HLA typing with paired-end reads and 10X linked reads by (LongRanger should be installed in system env)
bash script/whole/SpecHLA.sh -n <sample> -x <sample.10x.read.folder> -1 <sample.fq.1.gz> -2 <sample.fq.2.gz> -o outdir/ Consider long Indels and use population information for annotation by
bash script/whole/SpecHLA.sh -n <sample> -v True -p <Asia> -1 <sample.fq.1.gz> -2 <sample.fq.2.gz> -o outdir/ Full arguments can be seen in
SpecHLA: Full-resolution HLA typing from sequencing data.
Note:
1) Use HLA reads only, otherwise, it would be slow. Use ExtractHLAread.sh to extract HLA reads first.
2) WGS, WES, and RNASeq data are supported.
3) With Exome data like WES or RNASeq, must select exon typing (-u 0).
4) Short single-end read data are not supported.
Usage:
bash SpecHLA.sh -n <sample> -1 <sample.fq.1.gz> -2 <sample.fq.2.gz> -o <outdir>
Options:
-n Sample ID. <required>
-1 The first fastq file of paired-end data. <required>
-2 The second fastq file of paired-end data. <required>
-o The output folder to store the typing results. Default is ./output
-u Choose full-length or exon typing [0|1]. 0 indicates full-length, 1 means exon,
default is 0. With Exome or RNA data, must select 1 (i.e., exon typing).
-p The population of the sample [Asian, Black, Caucasian, Unknown, nonuse] for annotation.
Default is Unknown, meaning use mean allele frequency in all populations. nonuse indicates
only adopting mapping score and considering zero-frequency alleles.
-j Number of threads [5].
-t Pacbio fastq file.
-e Nanopore fastq file.
-c fwd hi-c fastq file.
-d rev hi-c fastq file.
-x Path of folder created by 10x demultiplexing. Prefix of the filenames of FASTQs
should be the same as Sample ID. Please install Longranger in the system env.
-w The weight to use allele imbalance info for phasing [0-1]. Default is 0 that means
not use. 1 means only use imbalance info; other values integrate reads and allele imbalance.
-m The maximum mismatch number tolerated in assigning gene-specific reads. Deault
is 2. It should be set larger to infer novel alleles.
-y The minimum different mapping score between the best and second-best aligned genes.
Discard the read if the score is lower than this value. Deault is 0.1.
-v True or False. Consider long InDels if True, else only consider small variants.
Default is False.
-q Minimum variant quality. Default is 0.01. Set it larger in high quality samples.
-s Minimum variant depth. Default is 5.
-a Use this long InDel file if provided.
-r The minimum Minor Allele Frequency (MAF), default is 0.05 for full length and
0.1 for exon typing.
-k The mean depth in a window lower than this value will be masked by N, default is 5.
Set 0 to avoid masking.
-z Whether only mask exon region, True or False, default is False.
-f The trio infromation; child:parent_1:parent_2 [Example: NA12878:NA12891:NA12892]. If provided,
use trio info to improve typing. Note: use it after performing SpecHLA once already.
-b Whether use database for unlinked block phasing [0|1], default is 1 (i.e., use).
-i Location of the IMGT/HLA database folder, default is db.
-l Whether remove all tmp files [0|1], default is 1.
-h Show this message.Perform HLA typing only with long reads by
usage: python3 long_read_typing.py -h
HLA Typing with only long-read data.
Required arguments:
-r Long-read fastq file. PacBio or Nanopore. (default: None)
-n Sample ID (default: None)
-o The output folder to store the typing results. (default: ./output)
Optional arguments:
-p The population of the sample [Asian, Black, Caucasian, Unknown, nonuse] for annotation.
Unknown means use mean allele frequency in all populations. nonuse indicates only adopting
mapping score and considering zero-frequency alleles. (default: Unknown)
-j Number of threads. (default: 5)
-d Minimum score difference to assign a read to a gene. (default: 0.001)
-g Whether use G group resolution annotation [0|1]. (default: 0)
-m 1 represents typing, 0 means only read assignment (default: 1)
-k The mean depth in a window lower than this value will be masked by N, set 0 to avoid masking
(default: 5)
-a Prefix of filtered fastq file. (default: long_read)
-y Read type, [nanopore|pacbio]. (default: pacbio)
--minimap_index Whether build Minimap2 index for the reference [0|1]. Using index can reduce memory usage.
(default: 0)
--db db dir. (default: /home/wangshuai/softwares/SpecHLA/script/../db/)
--strand_bias_pvalue_cutoff
Remove a variant if the allele observations are biased toward one strand (forward or reverse).
Recommand setting 0 to high-depth data. (default: 0.01)
--seed seed to generate random numbers (default: 8)
--max_depth maximum depth for each HLA locus. Downsample if exceed this value. (default: 2000)
-h, --helpusage: python3 typing_from_assembly.py -h
HLA Typing from diploid assemblies.
Required arguments:
-1 Assembly file of the first haplotype in fasta formate (default: None)
-2 Assembly file of the second haplotype in fasta formate (default: None)
-n Sample ID (default: None)
-o The output folder to store the typing results. (default: ./output)
Optional arguments:
-j Number of threads. (default: 10)
-h, --helpAn example of running command is
python SpecHLA/script/typing_from_assembly.py -j 15 -o output -n v12_HG00096 -1 v12_HG00096_hgsvc_pbsq2-clr_1000-flye.h1-un.arrow-p1.fasta -2 v12_HG00096_hgsvc_pbsq2-clr_1000-flye.h2-un.arrow-p1.fastaThe result is stored in <sample>_extracted_HLA_alleles.fasta, please see result description in the below Interpret output section.
After performing HLA typing, we can afford trio information to update the results of child by
bash script/SpecHLA.sh -n <child> -1 <child.fq.1.gz> -2 <child.fq.2.gz> -o outdir/ --trio child:parent1:parent2The fresh results can be found in the trio/ folder inside the original result folder.
Based on the tumor purity and ploidy, we can infer HLA LOH event by
usage: perl cal.hla.copy.pl [Options] -S <samplename> -C <5> -purity <Purity> -ploidy <Ploidy> -F <filelist> -T <hla.result.txt> -O <outdir>
OPTIONS:
-purity [f] the purity of the tumor sample. <required>
-ploidy [f] the ploidy of the tumor sample. <required> Note: tumor purity and ploidy can be inferred by software like ABSOLUTE and ASCAT.
-S [s] sample name <required>
-C [f] the cutoff of heterogeneous snp number. Default is 5.
-F [s] the HLA_*_freq.txt file list. <required> Obtained by "ls typing_result_dir/*_freq.txt >freq.list"
-T [s] the hla typing result file of Spechla <required>
-O [s] the output dir. <required> Need exist before running.
The result file "merge.hla.copy.txt" can be found in the output dir.Use ls typing_result_dir/*_freq.txt >freq.list to generate the file required by -F. The command example is
perl script/cal.hla.copy.pl -purity 0.5 -ploidy 2 -S test -F freq.list -T hla.result.txt -O ./In the denoted outdir, the results of each sample are saved in a folder named as the sample ID.
| In the directory of one specific sample, you will find the below files: | Output | Description |
|---|---|---|
| hla.result.txt | HLA-typing results for all alleles | |
| hla.result.details.txt | All the alleles with the highest mapping score in annotation | |
| hla.result.g.group.txt | G group resolution HLA type | |
| hla.allele.*.HLA_*.fasta | Sequence of each allele (the low-depth region is masked by N) | |
| HLA_*_freq.txt | Haplotype frequencies of each gene | |
| HLA_*.rephase.vcf.gz | Phased vcf file for each gene | |
| hlala.like.results.txt | Report all potential types like HLA*LA, only generated by long-read-only mode |
| If you performed pedigree phasing, you will find below files. | Output | Description |
|---|---|---|
| trio/HLA_*.trio.vcf.gz | Phased vcf file after pedigree phasing | |
| hla.allele.*.HLA_*.fasta | Allele sequence after pedigree phasing |
An example for hla.result.txt is as below:
Sample HLA_A_1 HLA_A_2 HLA_B_1 HLA_B_2 HLA_C_1 HLA_C_2 HLA_DPA1_1 HLA_DPA1_2 HLA_DPB1_1 HLA_DPB1_2 HLA_DQA1_1 HLA_DQA1_2 HLA_DQB1_1 HLA_DQB1_2 HLA_DRB1_1 HLA_DRB1_2
HG00118 A*24:02:01:01 A*02:01:01:01 B*07:02:01:01 B*40:01:02:02 C*03:04:01:01 C*07:02:01:03 DPA1*01:03:01:02 DPA1*01:03:01:02 DPB1*04:01:01:01 DPB1*04:01:01:01 DQA1*01:02:01:01 DQA1*01:02:01:01 DQB1*06:02:01:01 DQB1*06:02:01:01 DRB1*15:01:01:01 DRB1*15:01:01:01Note:
- means low confidence sequence which cannot map to any allele in the database.*An example for `HLA__freq.txt` is as below:**
# Haplotype Frequency
hla.allele.1.HLA_A.fasta 0.532
hla.allele.2.HLA_A.fasta 0.468
# The number of heterozygous variant is 30After typing from diploid assmebly, HLA sequences are in <sample>_extracted_HLA_alleles.fasta, and HLA types can be got by cat <sample>_extracted_HLA_alleles.fasta| grep >, an example is as below:
>v12_HG00096.h1.HLA-A cluster13_contig_98:2073847-2077365 A*29:02:01:01 # version: IPD-IMGT/HLA 3.51.0
>v12_HG00096.h1.HLA-B cluster13_contig_98:657830-661911 B*44:03:01:01 # version: IPD-IMGT/HLA 3.51.0
...
>v12_HG00096.h2.HLA-A cluster13_scaffold_119:2516943-2520446 A*01:01:01:01 # version: IPD-IMGT/HLA 3.51.0
...| Interpret each column in the annotation line as | Column | Description |
|---|---|---|
| first | gene and haplotype, h1 means the first haplotype | |
| second | the region to extract the sequence from the assembly | |
| third | the best matched IMGT allele, i.e., the typing result | |
| four | IMGT version |
An example for HLA LOH result file: merge.hla.copy.txt is as below:
Sample HLA Allele1 Allele2 copyratio KeptHLA LossHLA Freq1 Freq2 Purity Het_num LOH
test A A*24:02:01:01 A*02:01:01:01 1:1 A*24:02:01:01 A*02:01:01:01 0.507 0.493 0.5 100 N
test B B*07:02:01:01 B*40:01:02:01 1:1 B*07:02:01:01 B*40:01:02:01 0.502 0.498 0.5 32 N
test C C*03:04:01:01 C*07:02:01:03 1:1 C*07:02:01:03 C*03:04:01:01 0.433 0.567 0.5 99 N
test DPA1 DPA1*01:03:01:02 DPA1*01:03:01:02 2:0 DPA1*01:03:01:02 homogeneous 1 0 0.5 0 N
test DPB1 DPB1*04:01:01:01 DPB1*04:01:01:01 2:0 DPB1*04:01:01:01 DPB1*04:01:01:01 0.909 0.091 0.5 1 N
test DQA1 DQA1*01:02:01:01 DQA1*01:02:01:01 1:1 DQA1*01:02:01:01 DQA1*01:02:01:01 0.548 0.452 0.5 1 N
test DQB1 DQB1*06:02:01:01 DQB1*06:02:01:01 2:0 DQB1*06:02:01:01 homogeneous 1 0 0.5 0 N
test DRB1 DRB1*15:01:01:01 DRB1*15:01:01:01 2:0 DRB1*15:01:01:01 DRB1*15:01:01:01 0.65 0.35 0.5 2 N| Interpret each column as | Header | Description |
|---|---|---|
| Sample | Sample Name | |
| HLA | HLA locus | |
| Allele1 | HLA type of the first allele | |
| Allele2 | HLA type of the second allele | |
| copyratio | Copy numbers of two alleles | |
| KeptHLA | The remaining allele | |
| LossHLA | The possible lost allele | |
| Freq1 | Frequency of the first allele | |
| Freq2 | Frequency of the second allele | |
| Purity | Tumor purity | |
| Het_num | Number of heterozygous variant | |
| LOH | Whether LOH exists (Y or N) |
To nomenclature the HLA alleles using the latest IMGT/HLA database, please execute the following command:
cd SpecHLA/bin
perl renew_HLA_annotation_db.plThis script will download the latest IMGT/HLA database from Github, and preprocess it for SpecHLA to use.
SpecHLA requires conda 4.12.0+, cmake 3.16.3+, and GCC 9.4.0+ for environment construction and software installation.
SpecHLA enables automatic installation of these third party packages using conda.
Also, we can install the softwares by ourselves, and add their locations to system path (Exception: put bcftools, fermikit, and novoalign to bin/ folder).
After applying for the authority of Novoalign, please put the novoalign.lic (License file) in the bin/ folder.
Should you have any queries, please feel free to contact us, we will reply as soon as possible (swang66-c@my.cityu.edu.hk).