samplot is a command line tool for rapid, multi-sample structural variant visualization. samplot takes SV coordinates and bam files and produces high-quality images that highlight any alignment and depth signals that substantiate the SV.

If you use samplot, please cite https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02380-5

Usage

Installing

Samplot is available from bioconda and is installable via the conda package manager:

conda install -c bioconda samplot 

Examples:

Samplot requires either BAM files or CRAM files as primary input. If you use CRAM, you'll also need a reference genome. You can easily acquire a reference genome file with GGD, which is also available from conda.

Basic use case

Using data from NA12878, NA12889, and NA12890 in the 1000 Genomes Project (available in the test/data directory of samplot), we will inspect a possible deletion in NA12878 at 4:115928726-115931880 with respect to that same region in two unrelated samples NA12889 and NA12890.

The following command will create an image of that region:

time samplot plot \
    -n NA12878 NA12889 NA12890 \
    -b samplot/test/data/NA12878_restricted.bam \
      samplot/test/data/NA12889_restricted.bam \
      samplot/test/data/NA12890_restricted.bam \
    -o 4_115928726_115931880.png \
    -c chr4 \
    -s 115928726 \
    -e 115931880 \
    -t DEL

real    0m3.882s
user    0m3.831s
sys 0m0.328s

The arguments used above are:

-n The names to be shown for each sample in the plot

-b The BAM/CRAM files of the samples (space-delimited)

-o The name of the output file containing the plot

-c The chromosome of the region of interest

-s The start location of the region of interest

-e The end location of the region of interest

-t The type of the variant of interest

This will create an image file named 4_115928726_115931880.png, shown below:

Gene and other genomic feature annotations

Gene annotations (tabixed, gff3 file) and genome features (tabixed, bgzipped, bed file) can be included in the plots.

Get the gene annotations:

wget ftp://ftp.ensembl.org/pub/grch37/release-84/gff3/homo_sapiens/Homo_sapiens.GRCh37.82.gff3.gz
bedtools sort -i Homo_sapiens.GRCh37.82.gff3.gz \
| bgzip -c > Homo_sapiens.GRCh37.82.sort.gff3.gz
tabix Homo_sapiens.GRCh37.82.sort.gff3.gz

Get genome annotations, in this case Repeat Masker tracks and a mappability track:

wget http://hgdownload.cse.ucsc.edu/goldenpath/hg19/encodeDCC/wgEncodeMapability/wgEncodeDukeMapabilityUniqueness35bp.bigWig
bigWigToBedGraph wgEncodeDukeMapabilityUniqueness35bp.bigWig wgEncodeDukeMapabilityUniqueness35bp.bed
bgzip wgEncodeDukeMapabilityUniqueness35bp.bed
tabix wgEncodeDukeMapabilityUniqueness35bp.bed.gz

curl http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/rmsk.txt.gz \
| bgzip -d -c \
| cut -f 6,7,8,13 \
| bedtools sort -i stdin \
| bgzip -c > rmsk.bed.gz
tabix rmsk.bed.gz

Plot:

samplot plot \
    -n NA12878 NA12889 NA12890 \
    -b samplot/test/data/NA12878_restricted.bam \
      samplot/test/data/NA12889_restricted.bam \
      samplot/test/data/NA12890_restricted.bam \
    -o 4_115928726_115931880.d100.genes_reps_map.png \
    -c chr4 \
    -s 115928726 \
    -e 115931880 \
    -t DEL \
    -d 100 \
    -T Homo_sapiens.GRCh37.82.sort.gff3.gz \
    -A rmsk.bed.gz wgEncodeDukeMapabilityUniqueness35bp.bed.gz

Generating images from a VCF file

To plot images from structural variant calls in a VCF file, use samplot's vcf subcommand. This accepts a VCF file and the BAM files of samples you wish to plot, outputting images and an index.html page for review.

Usage

samplot vcf can be used to quickly apply some basic filters to variants. Filters are applied via the --filter argument, which may be repeated as many times as desired. Each expression specified with the --filter option is applied separately in an OR fashion, which & characters may be used within a statement for AND operations.

Example:

samplot vcf \
    --filter "SVTYPE == 'DEL' & SU >= 8" \
    --filter "SVTYPE == 'INV' & SU >= 5" \
    --vcf example.vcf\
    -d test/\
    -O png\
    --important_regions regions.bed\
    -b example.bam > samplot_commands.sh

This example will create a directory named test (in the current working directory). A file named index.html will be created inside that directory to explore the images created.

Filters: The above filters will remove all samples/variants from output except:

• DUP variants with at least SU of 8
• INV variants with SU of at least 5

The specific FORMAT fields available in your VCF file may be different. I recommend SV VCF annotation with duphold by brentp.

For more complex expression-based VCF filtering, try brentp's slivar, which provides similar but more broad options for filter expressions.

Region restriction. Variants can also be filtered by overlap with a set of region (for example, gene coordinates for genes correlated with a disease). The important_regions argument provides a BED file of such regions for this example.

Filtering for de novo SVs Using a PED file with samplot vcf allows filtering for variants that may be spontaneous/de novo variants. This filter is a simple Mendelian violation test. If a sample 1) has valid parent IDs in the PED file, 2) has a non-homref genotype (1/0, 0/1, or 1/1 in VCF), 3) passes filters, and 4) both parents have homref genotypes (0/0 in VCF), the sample may have a de novo variant. Filter parameters are not applied to the parents. The sample is plotted along with both parents, which are labeled as father and mother in the image.

Example call with the addition of a PED file:

samplot vcf \
    --filter "SVTYPE == 'DEL' & SU >= 8" \
    --filter "SVTYPE == 'INV' & SU >= 5" \
    --vcf example.vcf\
    -d test/\
    -O png\
    --ped family.ped\
    --important_regions regions.bed\
    -b example.bam > samplot_commands.sh

Additional notes.

• Variants where fewer than 95% of samples have a call (whether reference or alternate) will be excluded by default. This can be altered via the command-line argument min_call_rate.
• If you're primarily interested in rare variants, you can use the max_hets filter to remove variants that appear in more than max_hets samples.
• Large variants can now be plotted easily by samplot through use of samplot plot's zoom argument. However, you can still choose to only plot variants larger than a given size using the max_mb argument. The zoom argument takes an integer parameter and shows only the intervals within +/- that parameter on either side of the breakpoints. A dotted line connects the ends of the variant call bar at the top of the window, showing that the region between breakpoint intervals is not shown.
• By default, if fewer than 6 samples have a variant and additional homref samples are given, control samples will be added from the homref group to reach a total of 6 samples in the plot. This number may be altered using the min_entries argument.
• Arguments that are optional in samplot plot can by given as arguments to samplot vcf. They will be applied to each image generated.

CRAM inputs

Samplot also support CRAM input, which requires a reference fasta file for reading as noted above. Notice that the reference file is not included in this repository due to size. This time we'll plot an interesting duplication at X:101055330-101067156.

samplot plot \
    -n NA12878 NA12889 NA12890 \
    -b samplot/test/data/NA12878_restricted.cram \
      samplot/test/data/NA12889_restricted.cram \
      samplot/test/data/NA12890_restricted.cram \
    -o cramX_101055330_101067156.png 
    -c chrX \
    -s 101055330 \
    -e 101067156 \
    -t DUP \
    -r hg19.fa

The arguments used above are the same as those used for the basic use case, with the addition of the following:

-r The reference file used for reading CRAM files

Plotting without the SV

Samplot can also plot genomic regions that are unrelated to an SV. If you do not pass the SV type option (-t) then the top SV bar will go away and only the region that is given by -c -s and -e will be displayed.

Long read (Oxford nanopore and PacBio) and linked read support

Any alignment that is longer than 1000 bp is treated as a long read, and the plot design will focus on aligned regions and gaps. Aligned regions are in orange, and gaps follow the same DEL/DUP/INV color code used for short reads. The height of the alignment is based on the size of its largest gap.

If the bam file has an MI tag, then the reads will be treated as linked reads. The plots will be similar to short read plots, but all alignments with the same MI is plotted at the same height according to alignment with the largest gap in the group. A green line connects all alignments in a group.