HLA-VBSeq is software to estimate the most likely HLA types from high-throughput sequencing data.
Read data (FASTQ), or read data aligned to GRCh37/hg19 in BAM format. (e.g. NA12878.sorted.bam)
HLA types estimated from sequencing data
HLA v2 database based on IMGT/HLA database Release 3.31.0 and Japanese HLA reference dataset (Mimori et al Pharmacogenomics J. 2018) New 2018/10/12
HLA v1 database based on IMGT/HLA database, Release 3.15.0
Please align read data (FASTQ file) to GRCh37/hg19 Please convert sam file into bam file, and then sort the bam file by genomic coordinate
Extract a list of read name that were aligned to HLA loci (HLA-A, B, C, DM, DO, DP, DQ, DR, E, F, G, H, J, K, L, P, V, MIC, and TAP) samtools view NA12878.sorted.bam chr6:29907037-29915661 chr6:31319649-31326989 chr6:31234526-31241863 chr6:32914391-32922899 chr6:32900406-32910847 chr6:32969960-32979389 chr6:32778540-32786825 chr6:33030346-33050555 chr6:33041703-33059473 chr6:32603183-32613429 chr6:32707163-32716664 chr6:32625241-32636466 chr6:32721875-32733330 chr6:32405619-32414826 chr6:32544547-32559613 chr6:32518778-32554154 chr6:32483154-32559613 chr6:30455183-30463982 chr6:29689117-29699106 chr6:29792756-29800899 chr6:29793613-29978954 chr6:29855105-29979733 chr6:29892236-29899009 chr6:30225339-30236728 chr6:31369356-31385092 chr6:31460658-31480901 chr6:29766192-29772202 chr6:32810986-32823755 chr6:32779544-32808599 chr6:29756731-29767588 | awk '{print $$1}' | sort | uniq > NA12878_partial_reads.txt
Build read name index and search read pairs and their sequences on HLA loci java -jar -Xmx32g -Xms32g bamNameIndex.jar index NA12878.sorted.bam --indexFile NA12878.sorted.bam.idx java -jar bamNameIndex.jar search NA12878.sorted.bam --name NA12878_partial_reads.txt --output NA12878_partial.sam java -jar SamToFastq.jar I=NA12878_partial.sam F=NA12878_partial_1.fastq F2=NA12878_partial_2.fastq If for some reason bamNameIndex.jar doesn't work, please use bedtools to extract reads from bam files: http://bedtools.readthedocs.org/en/latest/content/tools/bamtofastq.html Or, alternatively, below is a python script that uses pysam to extract reads by read name from a bam file: http://timoast.github.io/2015/10/12/ExtractReads/ Or, please try samgrep: https://github.com/lindenb/jvarkit/wiki/SamGrep
Extract unmapped reads samtools view -bh -f 12 NA12878.sorted.bam > NA12878.sorted_unmapped.bam java -jar SamToFastq.jar I=NA12878.sorted_unmapped.bam F=NA12878_unmapped_1.fastq F2=NA12878_unmapped_2.fastq
Combine reads in FASTQ format cat NA12878_partial_1.fastq NA12878_unmapped_1.fastq > NA12878_part_1.fastq cat NA12878_partial_2.fastq NA12878_unmapped_2.fastq > NA12878_part_2.fastq
For analysis with v2 HLA database:
Alignment by BWA-MEM allowing multiple alignments for each read bwa index hla_all_v2.fasta bwa mem -t 8 -P -L 10000 -a hla_all_v2.fasta NA12878_part_1.fastq NA12878_part_2.fastq > NA12878_part.sam
Estimation of HLA types by HLA-VBSeq For paired-end read data: java -jar HLAVBSeq.jar hla_all_v2.fasta NA12878_part.sam NA12878_result.txt --alpha_zero 0.01 --is_paired For single-end read data: java -jar HLAVBSeq.jar hla_all_v2.fasta NA12878_part.sam NA12878_result.txt --alpha_zero 0.01 Here, alpha_zero is a hyperparameter as described in the paper and we recommend to use 0.01.
For analysis with v1 HLA database (old database):
Alignment by BWA-MEM allowing multiple alignments for each read bwa index hla_all.fasta bwa mem -t 8 -P -L 10000 -a hla_all.fasta NA12878_part_1.fastq NA12878_part_2.fastq > NA12878_part.sam
Estimation of HLA types by HLA-VBSeq For paired-end read data: java -jar HLAVBSeq.jar hla_all.fasta NA12878_part.sam NA12878_result.txt --alpha_zero 0.01 --is_paired For single-end read data: java -jar HLAVBSeq.jar hla_all.fasta NA12878_part.sam NA12878_result.txt --alpha_zero 0.01 Here, alpha_zero is a hyperparameter as described in the paper and we recommend to use 0.01.
Output file format 1st column: HLA allele ID (e.g. HLA00001) 2nd column: genomic locus length (in bp) in fasta format (e.g. 3503 bp) 3rd column: Z, the number of reads that the algorithm assigned to the HLA allele (e.g. 300 reads) 4th column: Normalized number of reads (Fragments Per Kilobase per Million mapped fragments) 5th column: Relative abundance, theta, which is Z divided by the number of total mapped reads.
How HLA typing result looks like (e.g. HLA-A) For v2 HLA database:
./parse_result.pl Allelelist_v2.txt NA12878_result.txt | grep "^A\*" | sort -k2 -n -r > HLA_A.txt less HLA_A.txtFor v1 HLA database (old database):
./parse_result.pl Allelelist.txt NA12878_result.txt | grep "^A\*" | sort -k2 -n -r > HLA_A.txt less HLA_A.txt A*01:01:01:01 17.4022266628604 A*11:01:01 12.0376819868684 1st column: HLA allele name 2nd column: Average depth of coverage Here, the perl script, parse_result.pl, calculates the average depth of coverage for each HLA allele. Please modify "parse_result.pl" according to your data. This perl script assumes 100bp x 2 data as in the paper.
$(PYTHON2.7) call_hla_digits.py -v NA12878_result.txt -a Allelelist.txt -r 90 -d 4 --ispaired > NA12878_report.d4.txt
-v xxxxx_result.txt : Need to set the output file from the HLA-VBSeq
-a Allelelist.txt : IMGT HLA Allelelist
-r 90 : mean single read length (mean_rlen)
-d 4 : HLA call resolutioni4 or 6 or 8j
--ispaired : if set, twice the mean rlen for depth calculation (need to specify when the sequenced data is paired-end protocol)
Any published works where HLA-VBSeq has been used in data analysis should include a citation:
HLA-VBSeq: accurate HLA typing at full resolution from whole-genome sequencing data
Naoki Nariai, Kaname Kojima, Sakae Saito, Takahiro Mimori, Yukuto Sato, Yosuke Kawai, Yumi Yamaguchi-Kabata, Jun Yasuda and Masao Nagasaki
BMC Genomics 2015, 16(Suppl 2):S7
To use v2 please cite the following manuscript as a reference:
Construction of full-length Japanese reference panel of class I HLA genes with single-molecule, real-time sequencing, Mimori T, Yasuda J, Kuroki Y, Shibata TF, Katsuoka F, Saito S, Nariai N, Ono A, Nakai-Inagaki N, Misawa K, Tateno K, Kawai Y, Fuse N, Hozawa A, Kuriyama S, Sugawara J, Minegishi N, Suzuki K, Kinoshita K, Nagasaki M and Yamamoto M Pharmacogenomics J. 2018
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