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.
Usage: samplot.py [options]
Options:
-h, --help show this help message and exit
--marker_size=MARKER_SIZE
Size of marks on pairs and splits (default 3)
-n TITLES Space-delimited list of plot titles. Use quote marks to include spaces (i.e. \"plot 1\" \"plot 2\")"
-r REFERENCE Reference file for CRAM
-z Z Number of stdevs from the mean (default 4)
-b BAMS Bam file names (CSV)
-o OUTPUT_FILE Output file name
-s START Start range
-e END End range
-c CHROM Chromosome range
-w WINDOW Window size (count of bases to include), default(0.5 *
len)
-d MAX_DEPTH Max number of normal pairs to plot
-t SV_TYPE SV type
-T TRANSCRIPT_FILE GFF of transcripts
-A ANNOTATION_FILE Space-delimited list of bed.gz tabixed files of annotations (such as repeats, mappability, etc.)
-a Print commandline arguments
-H PLOT_HEIGHT Plot height
-W PLOT_WIDTH Plot width
-j Create only the json file, not the image plot
--long_read=LONG_READ
Min length of a read to be a long-read (default 1000)
--common_insert_size Set common insert size for all plots
Since samplot runs as a Python script, the only requirements to use it are a working version of Python (2 or 3) and the required Python libraries. Installation of these libraries can be performed easily by using conda:
conda install -y --file https://raw.githubusercontent.com/ryanlayer/samplot/master/requirements.txt
If you have issues with pysam, then you may need to update your conda channels:
conda config --add channels r
conda config --add channels bioconda
All of these libraries are also available from pip.
You can download samplot by cloning the git repository:
git clone https://github.com/ryanlayer/samplot.git
No other installation is required.
Samplot requires either BAM files or CRAM files as primary input. If you use CRAM, you'll also need a reference genome like one used the the 1000 Genomes Project (ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/technical/reference/human_g1k_v37.fasta.gz).
Using data from NA12878, NA12889, and NA12890 in the 1000 Genomes Project, 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/src/samplot.py \
-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 0m9.450s
user 0m9.199s
sys 0m0.217s
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:
The runtime of samplot can be reduced by only plotting a portion of the concordant
pair-end reads (+/- strand orientation, within z s.d. of the mean insert size where z
is a command line option the defaults to 4). If we rerun the prior example, but only plot
a random sampling of 100 normal pairs we get a similar result 3.6X faster.
time samplot/src/samplot.py \
-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.png \
-c chr4 \
-s 115928726 \
-e 115931880 \
-t DEL \
-d 100
real 0m2.621s
user 0m2.466s
sys 0m0.124s
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/src/samplot.py \
-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
real 0m2.784s
user 0m2.633s
sys 0m0.129s
To plot images from structural variant calls in a VCF file, use samplot's
samplot_vcf.py script. This accepts a VCF file and the BAM files of samples
you wish to plot, outputting images and the index for a web page for review.
$ python samplot/src/samplot_vcf.py -h
usage: note that additional arguments are passed through to samplot.py
[-h] [--vcf VCF] [-d OUT_DIR] [--ped PED] [--dn_only]
[--min_call_rate MIN_CALL_RATE] [--filter FILTER]
[-O {png,pdf,eps,jpg}] [--max_hets MAX_HETS]
[--min_entries MIN_ENTRIES] [--max_entries MAX_ENTRIES]
[--max_mb MAX_MB] [--important_regions IMPORTANT_REGIONS] -b BAMS
[BAMS ...]
optional arguments:
-h, --help show this help message and exit
--vcf VCF, -v VCF VCF file containing structural variants
-d OUT_DIR, --out-dir OUT_DIR
path to write output PNGs
--ped PED path ped (or .fam) file
--dn_only plots only putative de novo variants (PED file
required)
--min_call_rate MIN_CALL_RATE
only plot variants with at least this call-rate
--filter FILTER simple filter that samples must meet. Join multiple
filters with '&' and specify --filter multiple times
for 'or' e.g. DHFFC < 0.7 & SVTYPE = 'DEL'
-O {png,pdf,eps,jpg}, --output_type {png,pdf,eps,jpg}
type of output figure
--max_hets MAX_HETS only plot variants with at most this many
heterozygotes
--min_entries MIN_ENTRIES
try to include homref samples as controls to get this
many samples in plot
--max_entries MAX_ENTRIES
only plot at most this many heterozygotes
--max_mb MAX_MB skip variants longer than this many megabases
--important_regions IMPORTANT_REGIONS
only report variants that overlap regions in this bed
file
-b BAMS [BAMS ...], --bams BAMS [BAMS ...]
Space-delimited list of BAM/CRAM file names
samplot_vcf.py 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.
python samplot_vcf.py \
--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. Samplot commands will be printed out for the creation of plots for all samples/variants that pass the above filters, assuming that the samplot.py script is in the same directory as the samplot_vcf.py script.
Filters: The above filters will remove all samples/variants from output except:
DUPvariants with at leastSUof 8INVvariants withSUof 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.py 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:
python samplot_vcf.py \
--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_hetsfilter to remove variants that appear in more thanmax_hetssamples. - Large variants can now be plotted easily by samplot through use of
samplot.py'szoomargument. However, you can still choose to only plot variants larger than a given size using themax_mbargument. Thezoomargument 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_entriesargument. - Arguments that are optional in
samplot.pycan by given as arguments tosamplot_vcf.py. They will be applied to each image generated.
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/src/samplot.py \
-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
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.
Any alignment that is longer than 1000 bp are treated as a longread, 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.
