To install vamos, g++ (>= 8.3.0), htslib, abpoa, edlib and alglib are required. htslib and abpoa can be installed through bioconda. Static libraries libalglib.a and libedlib.a are distributed along with vamos.
Install required libraries through conda
conda create --name vamos python=3.10
conda activate vamos
conda install -c bioconda --file requirements.txt
Download the latest release
wget https://github.com/ChaissonLab/vamos/archive/refs/tags/vamos-v1.2.0.tar.gz
tar -zxvf vamos-v1.2.0.tar.gz && cd vamos-v1.2.0
Or download the latest code from github
git clone https://github.com/ChaissonLab/vamos.git
Make from source and run with test data:
cd vamos*/src/ && make
For running vamos on a haplotype-resolved assembly:
vamos --contig -b assembly.hap1.mapped_to_grch38.bam -r emotifs.d10.64h.bed -s sample_name -o assembly.hap1.vcf -t 8
vamos --contig -b assembly.hap2.mapped_to_grch38.bam -r emotifs.d10.64h.bed -s sample_name -o assembly.hap2.vcf -t 8
For running vamos on aligned reads (phased or unphased):
vamos --read -b ../example/demo.aln.bam -r ../example/region_motif.bed -s NA24385_CCS_h1 -o reads.vcf -t 8
Note, if the reads are pre-phased (e.g. by HapCut or WhatsHap) and have the HA SAM tag, the phasing heuristic will not be applied.
For downloading efficient motif set selected at a level of Delta=20:
vamos -m q20
Vamos is a tool to perform run-length encoding of VNTR sequences using a set of selected motifs from all motifs observed at that locus. Vamos guarantees that the encoding sequence is winthin a bounded edit distance of the original sequence. For example, a VNTR sequence ACGGT|ACTGT|ACGT may be encoded to a more compact representation: ACGGT|ACGGT|ACGT using efficient motif set [ACGGT, ACGT]. The edit distance between the original VNTR sequence and encoding sequence is 1.
Vamos can generate annotation for haplotype-resolved assembly at each VNTR locus, given a set of motifs at that VNTR locus. Vamos can generate annotation for aligned reads (phased or unphased) at each VNTR locus.
We defined VNTR loci and motifs using a collection of 32 haplotype-resolved LRS genomes constructed by Human Genome Structural Variation Consortium. 692,882 loci of simple repeating sequences on the GRCh38 assembly were obtained from the table browser tool of the UCSC Genome Browser. For each assembly, VNTR sequences were lifted-over and decomposed into motifs by Tandem Repeats Finder (TRF). Post-filtering step leaves 467104 well-resolved VNTR loci. We propose efficient motif set as a smallest set of motifs, such that the string decompositions of the assembly alleles are bounded by a given edit distance. snakefile/configs/vntr_region_motifs.e.bed.gz provides efficent motifs for 467104 VNTR loci. snakefile/configs/vntr_region_motifs.o.bed.gz provides original motifs for 467104 VNTR loci.
You can build vamos from source files. Make sure to install the required libraries: g++ (>= 8.3.0), htslib, abpoa, edlib and alglib before compiling.
Install required libraries
conda create --name vamos python=3.10
conda activate vamos
conda install -c bioconda --file requirements.txt
Download the latest release
wget https://github.com/ChaissonLab/vamos/archive/refs/tags/vamos-v1.2.0.tar.gz
tar -zxvf vamos-v1.2.0.tar.gz
cd vamos-v1.2.0/src; make
Or, you can use git clone command to download the source code.
This gives you the latest version of vamos, which might be still under development.
git clone https://github.com/ChaissonLab/vamos.git
cd vamos/src; make
If you meet any compiling issue, please try the pre-built binary file:
wget https://github.com/ChaissonLab/vamos/releases/download/vamos-v1.2.0/vamos-v1.2.0_x64-linux
tar -zxvf vamos-v1.2.0_x64-linux
vamos --read -b ../example/demo.aln.bam -r ../example/region_motif.bed -s NA24385_CCS_h1 -o reads.vcf -t 8
vamos --contig -b assembly.hap1.mapped_to_grch38.bam -r emotifs.d10.64h.bed -s sample_name -o assembly.hap1.vcf -t 8
vamos --contig -b assembly.hap2.mapped_to_grch38.bam -r emotifs.d10.64h.bed -s sample_name -o assembly.hap2.vcf -t 8
Usage: vamos [subcommand] [options] [-b in.bam] [-r vntrs_region_motifs.bed] [-o output.vcf] [-s sample_name] [-t threads]
Version: v1.2.0
subcommand:
vamos --contig [-b in.bam] [-r vntrs_region_motifs.bed] [-o output.vcf] [-s sample_name] [-t threads]
vamos --read [-b in.bam] [-r vntrs_region_motifs.bed] [-o output.vcf] [-s sample_name] [-t threads] [-p phase_flank]
vamos -m [verison of efficient motif set]
Input:
-b FILE Input indexed bam file.
-r FILE File containing region coordinate and motifs of each VNTR locus.
The file format: columns `chrom,start,end,motifs` are tab-delimited.
Column `motifs` is a comma-separated (no spaces) list of motifs for this VNTR.
-s CHAR Sample name.
Output:
-o FILE Output vcf file.
Dynamic Programming:
-d DOUBLE Penalty of indel in dynamic programming (double) DEFAULT: 1.0.
-c DOUBLE Penalty of mismatch in dynamic programming (double) DEFAULT: 1.0.
-a DOUBLE Global accuracy of the reads. DEFAULT: 0.98.
--naive Specify the naive version of code to do the annotation, DEFAULT: faster implementation.
Phase reads:
-p INT Range of flanking sequences which is used in the phasing step. DEFAULT: 3000 bps.
Downloading motifs:
-m MOTIF Prints a command to download a particular motif set. Current supported motif set is: q20.
This motif set is selected at a level of Delta=20 from 64 haplotype-resolvd assemblies (Ebert et al., 2021)
This may be copied and pasted in the command line, or executed as: vamos -m q20
Others:
-t INT Number of threads, DEFAULT: 1.
--debug Print out debug information.
-h Print out help message.
Vamos generates annotation for each VNTR locus in VCF file under --contig and --read modes.
The following shows an example of the VCF file
##fileformat=VCFv4.1
##source=vamos_v1.1.0
##contig=<ID=chr1,length=248956422>
##INFO=<ID=END,Number=1,Type=Integer,Description="End position of the variant">
##INFO=<ID=RU,Number=1,Type=String,Description="Comma separated motif sequences list in the reference orientation">
##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">
##INFO=<ID=ALTANNO_H1,Number=1,Type=String,Description=""Motif representation for the h1 alternate allele>
##INFO=<ID=ALTANNO_H2,Number=1,Type=String,Description=""Motif representation for the h2 alternate allele>
##INFO=<ID=LEN_H1,Number=1,Type=Integer,Description=""Length of the motif annotation for the h1 alternate allele>
##INFO=<ID=LEN_H2,Number=1,Type=Integer,Description=""Length of the motif annotation for the h2 alternate allele>
##FILTER=<ID=PASS,Description="All filters passed">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##ALT=<ID=VNTR,Description="Allele comprised of VNTR repeat units">
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA24385_CCS_h1
chr1 191351 . N <VNTR> . PASS END=191386;RU=CACCACAGAAAACAGAG,CACCACAGAAAACAGAGC;SVTYPE=VNTR;ALTANNO_H1=1,0;LEN_H1=2; GT 1/1
A python script snakefile/pyscript/combine_vcf.py is developed that combines vcfs output by vamos into multi-sample vcf.
python combine_vcf.py -i vcf.list -o combine.vcf
The input vcf.list is simply a list of all vcfs to be combined, one line for each vcf. The combined vcf records all unique alleles of the input samples in the info field and records the genotype of each sample accordingly.
