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
- numpy
- matplotlib
- pysam
- statistics
All of these are available from pip.
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 all structural variants in a VCF file, use samplot's
samplot_vcf.sh
script. This accepts a VCF file and the BAM files of samples
you wish to plot, outputting images and related metadata to a directory of your
choosing.
This script is especially useful as part of the SV-plaudit pipeline and creates metadata files for all images which SV-plaudit requires.
samplot/src/samplot_vcf.sh \
-o output_dir \
-B $HOME/bin/bcftools \
-S samplot/src/samplot.py \
-v samplot/test/data/NA12878.trio.svt.subset.vcf \
samplot/test/data/NA12878_restricted.bam \
samplot/test/data/NA12889_restricted.bam \
samplot/test/data/NA12890_restricted.bam
The arguments used above are:
-o
output directory (make this directory before executing)
-B
Executable file of bcftools
-S
samplot.py script
-v
VCF file with variants to plot
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.