VaPoR is a structural variants (SVs) validator based on long reads from sequencing technology represented by PacBio. VaPoR reads in BED files or VCF files in format 4.1 or newer, and the corresponding long reads in bam format to score each prediction.
python: https://www.python.org/
samtools: http://samtools.sourceforge.net/Usage: vapor [Options] [Parameters]
Options:
vcf - if input structural variants are encoded in vcf format
bed - if input structural variants are encoded in bed formatParameters:
--sv-input: input file in bed or vcf format
--output-path: folder where the recurrence plot will be kept
--output-file: name of output file
--reference: reference genome that pacbio files are aligned against
--pacbio-input: absolute path of input pacbio fileDownload and Install
git clone https://github.com/mills-lab/vapor.git
cd vapor
python setup.py install --user
export PATH=$PATH:$HOME/.local/binplease add 'export PATH=$PATH:$HOME/.local/bin' to your bashrc file
Run VaPoR on a bed file
vapor bed --sv-input ../input.bed --output-path ../vapor_result/ --reference ../reference.fa --pacbio-input ../sample.bamRun VaPoR on a vcf file
vapor vcf --sv-input ../input.vcf --output-path ../vapor_result/ --reference ../reference.fa --pacbio-input ../sample.bamSV input in bed format should be consist of 5 columns, specifying the chromosome , start, end, SVID and description of the variance. The description column should specify the type of the variance (eg. DEL, DUP, INV, INS). For insertions, the description column should have either the sequence of the length of insertion, or both, separated by '_'
Here's an example of the bed input:
chr1 831220 833732 SV_1 DUP
chr1 2093108 2095944 SV_2 DEL
chr1 2994972 2995286 SV_3 DEL
chr1 3935209 3935209 SV_4 INS_191
chr12 28226407 28226407 SV_5 INS_GTAAGTTCCAGTGATCTATTGTATAGCAGGATGACTGTAGTTAATGTACTGTATTCTTTTTTTTTTTTTTCTTTTTTTTTATTTTTTTATTATACTCTAAGTTTTAGGGTACATGTGCACATTGTGCAGGTTACATATGTATACATGTGCCATGCTGGTGCGCTGCACCCACTAACGTGTCATCTAGCATTAGGTATATCTCCCAATGCTATCCCTCCCCCTCCCCCGACCCCACCACAGTCCCAGAGTGTGATATTCCCTTCCTGTGTCCAAGTGTTCTCATTGTTCAATTCCCACCTATGAGTGAGAATATGCGGTGTTTGTTTTTTGTTCTTGCGATAGTTTACTGAGAATGATGGTTTCCAATTTCATCCATGTCCCTACAAAGGACATGAACTCATCATTTTTTATGGCTGCATAGTATTCCATGGTGTATATGTGCCACATTTTCTTAATTCAGTCTATCATTGTTGGACATTTGGGTTGGTTCCAAGTCTTTTGCTATTGTGAATAGTGCCGCAATAAACATACGTGTGCATGTTTCTTTATAGCAGCATGATTTATAGTCCTTTGGGTATATACCCAGTAATGGGATAGCTGGGTCAAATGGTATTTCTAGTTCTAGATCCCTGAGGAATCGCCACACTGACTTCCACAATGGTTGAACTAGTTTACAGTCCCACCAACAGTGTAAAAGTGTTCCTATTTCTCCACATCCTCTCCAGCACCTGTTGTTTCCTGACTTTTTAATGATTGCCATTCTAACTGGTGTGAGATGATATCTCATAGTGGTTTTGATTTGCATTTCTCTGATGGCCAGTGATGATGAGCATTTCTTCATGTGTTTTTTGGCTGCATAAATGTCTTCTTTTGAGAAGTGTCTGTTCATGTCCTTCGCCCACTTTTTGATGGGGTTTTTGTTTTTTTCTTGTAAATTTGTTTGAGTTCATTGTAGATTCTGGATATTAGCCCTTTGTCAGATGAGTAGGTTGCGAAAATTTTTTCCCATGTTGTAGGTTGCCTGTTCACTCTGATGGTAGTTTCTTTTGCTGTGCAGAAGCTCTTTAGTTTAATTAGATCCCATTTGTCAATTTTGGCTTTTGTTGCAATTGCTTTTGGTGTTTTGGACATGAAGTCCTTGCCCACGCCTATGTCCTGAATGGTAATGCCTAGGTTTTCTTCTAGGGTTTTTTATGGTTTTAGGTCTAACGTTTAAATCTTTAATCCATCTTGAATTGATTTTTGTATAAGGTGTAAGGAAGGGATCCAGTTTCAGCTTTCTACATATGGCTAGCCAGTTTTCCCCAGCACATTTATTAAATAGGGAATCCTTTCCCCATTGCTTGTTTTTCTCAGGTTTGTCAAAGATCAGATAGTTGTAGATATGTGGCATTATTTCTGAGGGCTCTGTTCTGTTCCATTGATCTATATCTCTGTTTTGGTACCAGTACCATGCTGTTTTGGTTACTGTAGCCTTGTAGTATAGTTTGAAGTCAGGTAGTGTGATGCCTCCAGCTTTGTTCTTTTGGCTTAGGATTGACTTGGCGATGCGGGCTCTTTTTTGGTTCCATATGAACTTTAAAGTAGTTTTTTCCAATTCTGTGAAGAAAGTCATTGGTAGCTTGATGGGGATGGCATTGAATCTGTAAATTACCTTGGCAGTATGGCCATTTTCACGATATTGATTCTTCCTACCCATGAGCATGGAATGTTCTTCCATTTGTTTGTGTCCTCTTTTATTTCCTTGAGCAGTGGTTTGTAGTTCTCCTTGAAGAGGTCCTTCACATCCCTTGTAAGTTGGATTCCTAGCTATTTTATTCTCTTTGAAGCAATTGTGAATGGGAGTTCACTCATGATTTGGCTCTCTGTTTGTCTGTTGTTGGTGTATAAGAATGCTTTGTGATTTTTGTACATTGATTTTGTATCCTGAGACTTTGCTGAAGTTGCTTATCAGCTTAAGGAGATTTTGGGCTGAGACGATGGGGTTTTCTAGATAAACAATCATGTCGTCTGCAAACAGGGACAATTTGACTTCCTCTTTCCTAATTGAATACCCTTTATTTCCTTCTCCTGCCTGATTGCCCTGGCCAGAACTTCCAACACTATGTTGAATAGTAGCGGTGAGAGAGGGCATCCCTGTCTTGTGadditional columns would be added to the bed format:
VaPoR_qs: highest score of assessed read
VaPoR_gs: proportion of reads supportive of predicted structure versus all reads assessed
VaPoR_GT: genotype of the proposed SV as assessed by VaPoR
VaPoR_GQ: genotype quality of the proposed SV as assessed by VaPoR
VaPoR_Rec: list of scores for all reads assessedIf complex structural variants are encoded within the vcf input, the INFO column should describe the complex event in the similar format as the examples listed below:
chr6 119011725 chr6:119011725:119012294:119012497:119013928 t <DISDUP> 92 PASSSVTYPE=disdup;END=119012294;insert_point=chr6:119013928;Other=abc/abc_abc/abca_chr6:119011725:119012294:119012497:119013928 GT 0/1
chr9 25594104 chr9:25594104:25594327:25594448 c <DEL_INV> 78 PASS SVTYPE=del_inv;END=25594448;del=chr9:25594104-25594327;inv=chr9:25594327-25594448;Other=ab/ab_ab/b^_chr9:25594104:25594327:25594448 GT 0/1
chr3 12913583 chr3:11956093:12913583:12913799 G <DEL_DUP_INV> -24 LowQual SVTYPE=del_dup_inv;END=12913799;del=chr3:11956093-12913583;dup_inv=chr3:12913583-12913799;insert_point=chr3:12913799;Other=ab/ab_a/bb^_chr3:11956093:12913583:12913799 GT 0/1
chr7 40412883 chr7:40412883:40412973:40413111 A <DUP_INV> 98 PASS SVTYPE=dup_inv;END=40412973;insert_point=chr7:40412883;Other=ab/ab_a^ab/abb_chr7:40412883:40412973:40413111 GT 1/0additional information will be added to the info column of the VCF:
##INFO=<ID=VaPoR_GS,Number=1,Type=Float,Description="VaPoR Score, representing the percentage of transverse long reads that support the prediction">'
##INFO=<ID=VaPoR_GT,Number=1,Type=String,Description="Genotype with the highest likelihood as estimated by VaPoR">'
##INFO=<ID=VaPoR_GQ,Number=1,Type=Float,Description="Genotype quality score - likelihood of the second most likely genotype on a -log10 normalized scale"'
##INFO=<ID=VaPoR_REC,Number=.,Type=Float,Description="Similarity scores assigned to each of the reads traversings the predicted SV">'