Command line Usage
Example
The following code is used to generate the example images.
trackplot \
-e chr1:1270656-1284730:+ \
-r example/example.sorted.gtf.gz \
--density example/density_list.tsv \
--show-site \
--focus 1272656-1272656:1275656-1277656 \
--stroke 1275656-1277656:1277856-1278656@blue \
--sites 1271656,1271656,1272656 \
--line example/line_list.tsv \
-o example/example.png \
--dpi 300 \
--width 10 \
--height 1 \
--barcode example/barcode_list.tsv \
--heatmap example/heatmap_list.tsv \
--interval example/interval_list.tsv \
--customized-junction example/junctions.tsv
Command line arguments
List all the parameters
python main.py --help
# Or
trackplot --help
Parameters:
Usage: main.py [OPTIONS]
Welcome to use trackplot
Options:
--version Show the version and exit.
--verbose enable debug level log
--logfile PATH save log info into file
-e, --event TEXT Event range eg: chr1:100-200:+ [required]
Common input files configuration:
--color-factor INTEGER RANGE Index of column with color levels (1-based);
NOTE: LUAD|red -> LUAD while be labeled in
plots and red while be the fill color
[default: 1; x>=1]
--barcode PATH Path to barcode list file, At list two
columns were required, - 1st The name of bam
file, not the alias of bam;
- 2nd the barcode;
- 3rd The group label, optional;
- 4th The color of each cell type, default
using the color of corresponding bam file.
--barcode-tag TEXT The default cell barcode tag label
[default: CB]
--umi-tag TEXT The default UMI barcode tag label [default:
UB]
-p, --process INTEGER RANGE How many cpu to use [1<=x<=12]
--group-by-cell Group by cell types in density/line plot
--remove-duplicate-umi Drop duplicated UMIs by barcode
--normalize-format [count|cpm|rpkm]
The normalize format for bam file [default:
count]
Output settings:
-o, --output PATH Path to output graph file
-d, --dpi INTEGER RANGE The resolution of output file [default:
300; x>=1]
--raster The would convert heatmap and site plot to
raster image (speed up rendering and produce
smaller files), only affects pdf, svg and PS
--height FLOAT The height of single subplot, default adjust
image height by content [default: 1]
--width INTEGER RANGE The width of output file, default adjust
image width by content [default: 10; x>=0]
--backend TEXT Recommended backend [default: Agg]
Reference settings:
-r, --annotation PATH Path to gtf file, both transcript and exon
tags are necessary
--interval PATH Path to list of interval files in bed
format, 1st column is path to file, 2nd
column is the label [optional]
--show-id Whether show gene id or gene name
--show-exon-id Whether show gene id or gene name
--no-gene Do not show gene id next to transcript id
--domain Add domain information into annotation track
--proxy TEXT The http or https proxy for EBI/Uniprot
requests,if `--domain` is True, eg:
http://127.0.0.1:1080
--timeout INTEGER RANGE The requests timeout when `--domain` is
True. [default: 10; x>=1]
--local-domain TEXT Load local domain folder and load into
annotation track, download from https://hgdow
nload.soe.ucsc.edu/gbdb/hg38/uniprot/
--domain-include TEXT Which domain will be included in annotation
plot
--domain-exclude TEXT Which domain will be excluded in annotation
plot
--remove-empty Whether to plot empty transcript
--transcripts-to-show TEXT Which transcript to show, transcript name or
id in gtf file, eg: transcript1,transcript2
--choose-primary Whether choose primary transcript to plot.
--ref-color TEXT The color of exons [default: black]
--intron-scale FLOAT The scale of intron [default: 0.5]
--exon-scale FLOAT The scale of exon [default: 1]
Density plot settings:
--density PATH The path to list of input files, a tab
separated text file,
- 1st column is path to input file,
- 2nd column is the file category,
- 3rd column is input file alias (optional),
- 4th column is color of input files
(optional),
- 5th column is the library of input file
(optional, only required by bam file),
- 6th column is the number of total reads
(optional, only required by bam file).
--customized-junction TEXT Path to junction table column name needs to
be bam name or bam alias.
--only-customized-junction Only used customized junctions.
-t, --threshold INTEGER RANGE
Threshold to filter low abundance junctions
[default: 0; x>=0]
--density-by-strand Whether to draw density plot by strand
--show-site Whether to draw additional site plot
--site-strand [all|+|-] Which strand kept for site plot, default use
all [default: all]
--included-junctions TEXT The junction id for including, chr1:1-100
--show-junction-num Whether to show the number of junctions
--fill-step [pre|post|mid] Define step if the filling should be a step
function, i.e. constant in between x. The
value determines where the step will occur:
- pre: The y value is continued constantly
to the left from every x position, i.e. the
interval (x[i-1], x[i]] has the value y[i].
- post: The y value is continued constantly
to the right from every x position, i.e.
the interval [x[i], x[i+1]) has the value
y[i].
- mid: Steps occur half-way between the x
positions." [default: post]
--smooth-bin INTEGER The bin size used to smooth ATAC fragments.
[default: 20]
--sc-density-height-ratio FLOAT
The relative height of single cell density
plots [default: 1]
Line plot settings:
--line PATH The path to list of input files, a tab
separated text file,
- 1st column is path to input file,
- 2nd column is the file category,
- 3rd column is input file group (optional),
- 4th column is input file alias (optional),
- 5th column is color platte of
corresponding group (optional). - 6th column
is the number of total reads (optional, only
required by bam file).
--hide-legend Whether to hide legend
--legend-position TEXT The legend position
--legend-ncol INTEGER RANGE The number of columns of legend [x>=0]
Heatmap plot settings:
--heatmap PATH The path to list of input files, a tab
separated text file,
- 1st column is path to input file,
- 2nd column is the file category,
- 3rd column is input file group (optional),
- 4th column is color platte of
corresponding group. - 5th column is the
number of total reads (optional, only
required by bam file).
--clustering Enable clustering of the heatmap
--clustering-method [single|complete|average|weighted|centroid|median|ward]
The clustering method for heatmap [default:
ward]
--distance-metric [braycurtis|canberra|chebyshev|cityblock|correlation|cosine|dice|euclidean|hamming|jaccard|jensenshannon|kulsinski|kulczynski1|mahalanobis|matching|minkowski|rogerstanimoto|russellrao|seuclidean|sokalmichener|sokalsneath|sqeuclidean|yule]
The distance metric for heatmap [default:
euclidean]
--heatmap-scale Do scale on heatmap matrix.
--heatmap-vmin INTEGER Minimum value to anchor the colormap,
otherwise they are inferred from the data.
--heatmap-vmax INTEGER Maximum value to anchor the colormap,
otherwise they are inferred from the data.
--show-row-names Show row names of heatmap
--sc-heatmap-height-ratio FLOAT
The relative height of single cell heatmap
plots [default: 0.2]
IGV settings:
--igv PATH The path to list of input files, a tab
separated text file,
- 1st column is path to input file,
- 2nd column is the file category,
- 3rd column is input file alias (optional),
- 4th column is color of input files
(optional),
- 5th column is exon_id for sorting the
reads (optional).
--m6a TEXT trackplot will load location information
from the given tags and then highlight the
RNA m6a modification cite at individual
reads. If there are multiple m6a
modification site, please add tag as follow,
234423,234450
--polya TEXT trackplot will load length of poly(A) from
the given tags and then visualize the
poly(A) part at end of each individual
reads.
--rs TEXT trackplot will load real strand information
of each reads from the given tags and
the strand information is necessary for
visualizing poly(A) part.
--del-ratio-ignore FLOAT RANGE
Ignore the deletion gap in nanopore or
pacbio reads.
if a deletion region was smaller than
(alignment length) * (del_ratio_ignore),
then the deletion gap will be filled.
currently the del_ratio_ignore was 1.0.
[0.0<=x<=1.0]
HiC settings:
--hic PATH The path to list of input files, a tab
separated text file,
- 1st column is path to input file,
- 2nd column is the file category,
- 3rd column is input file alias (optional),
- 4th column is color of input files
(optional),
- 5th column is data transform for HiC
matrix, eg 0, 2, 10 (optional). Same to
`--log`
Additional annotation:
-f, --genome PATH Path to genome fasta
--sites TEXT Where to plot additional indicator lines,
comma separated int
--stroke TEXT The stroke regions:
start1-end1:start2-end2@color-label, draw a
stroke line at bottom, default color is red
--link TEXT The link: start1-end1:start2-end2@color,
draw a link between two site at bottom,
default color is blue
--focus TEXT The highlight regions: 100-200:300-400
Motif settings:
--motif PATH The path to customized bedGraph file, first
three columns is chrom, start and end site,
the following 4 columns is the weight of
ATCG.
--motif-region TEXT The region of motif to plot in start-end
format
--motif-width FLOAT The width of ATCG characters [default: 0.8]
Layout settings:
--n-y-ticks INTEGER RANGE The number of ticks of y-axis [x>=0]
--distance-ratio FLOAT The distance between transcript label and
transcript line [default: 0.1]
--annotation-scale FLOAT The size of annotation plot in final plot
[default: 0.25]
--stroke-scale FLOAT The size of stroke plot in final image
[default: 0.25]
Overall settings:
--font-size INTEGER RANGE The font size of x, y-axis and so on [x>=1]
--reverse-minus Whether to reverse strand of bam/annotation
file
--hide-y-label Whether hide y-axis label
--same-y Whether different density/line plots shared
same y-axis boundaries
--same-y-sc Similar with --same-y, but only shared same
y-axis boundaries between same single cell
files
--log [0|2|10|zscore] y axis log transformed, 0 -> not log
transform;2 -> log2;10 -> log10
--title TEXT Title
--font TEXT Fonts
Web settings:
--start-server Start web ui instead of running in cmd mode
--host TEXT The ip address binding to
--port INTEGER The port to listen on
--plots PATH The path to directory where to save the
backend plot data and logs, required while
using appImage.
--data PATH The path to directory contains all necessary
data files.
-h, --help Show this message and exit.
Detailed description
Common options
1.--color-factor: the index of column to set colors
- basic usage: the input file list as follows,
path/to/bam1 LUAD
path/to/bam2 LUSC
Then the --color-factor 2 means trackplot assign two colors to LUAD and LUSC separately automatically.
- advanced usage:
path/to/bam1 LUAD|red
path/to/bam2 LUSC|#000000
Then the --color-factor 2 means trackplot assign red color to LUAD and "#000000" to LUSC separately.
Output options
1.-o, --output: the path to output file, the common image format such as pdf, png, jpg and svg are supported.
2.--backend: the backend is used to switch matplotlib plotting backend,
**known issues: **
- the
Agg,PDF, etc. backends may cause the small protein domains missing in final output image. - the
Cairobackend requiredcairocffipackages, which may have difficulty to install for some users, if then please try to use our docker image or use an alternative backend likeAgg/PDF.
Note: Cairo backend may disable the heatmap rasterisation, so use as appropriate.
The recommended combination of backend and image formats please check matplotlib backend
Annotation plot
1.--domain: fetch domain information from uniprot and ensemble, then map amino acid coordinate into genomic coordinate.
For each transcript, trackplot firstly get the uniprot id from uniprot website and check whether the length of protein is one third of CDS length. If yes, then fetch the uniprot information from ebi.
The trackplot will present these domains from ['DOMAIN_AND_SITES', 'MOLECULE_PROCESSING', 'PTM', 'STRUCTURAL', 'TOPOLOGY'] in default, and please refer this for the detail of each category.
2. Include or exclude the domain based on category or type
--domain-include: the domain will be included and a domain which is not in the list will not be presented
--domain-exclude: the domain will be excluded
For example,
# The protein information from PTM, DOMAIN_AND_SITES and helix from STRUCTURAL will be presented.
--include PTM:DOMAIN_AND_SITES:STRUCTURAL,helix
# The protein information from PTM, DOMAIN_AND_SITES and helix from STRUCTURAL will be excluded.
--exclude PTM:DOMAIN_AND_SITES:STRUCTURAL,helix
The Category and type of domain refer to here.
here is the command of the tutorial,
trackplot \
-e 7:107830089-107832297 \
-r example/Article_figures/FigS1/A/Homo_sapiens.GRCh37.87.gtf.sorted.gz \
-o domain.raw.pdf \
--dpi 300 \
--width 6 \
--height 1 --show-junction-num -t 10 --domain
# domain include test
trackplot \
-e 7:107830089-107832297 \
-r example/Article_figures/FigS1/A/Homo_sapiens.GRCh37.87.gtf.sorted.gz \
-o domain.include.pdf \
--dpi 300 \
--width 6 \
--height 1 --show-junction-num -t 10 --domain --domain-include MOLECULE_PROCESSING:PTM,CARBOHYD
# domain exclude test
trackplot \
-e 7:107830089-107832297 \
-r example/Article_figures/FigS1/A/Homo_sapiens.GRCh37.87.gtf.sorted.gz \
-o domain.exclude.pdf \
--dpi 300 \
--width 6 \
--height 1 --show-junction-num -t 10 --domain --domain-exclude PTM:TOPOLOGY
3.--local-domain: load domain information from a folder that contains bigbed files which download from UCSC
In order to facilitate these people from poor network regions, Sashimi also provides a local mode for domain visualization. First, the user must download the corresponding annotation from UCSC, and collect all bigbed file into a folder which could pass to trackplot with --local-domain.
But the bigbed file from UCSC didn't provide a transcript or uniprot id, Sashimi couldn't map the protein information into the corresponding transcript id.
4.--interval: add additional feature track into annotation.
In addition to fetch genomic feature from GTF or GFF file, Sashimi also provides a flexible way to load other features into annotation track. And user could prepare and record custom annotation information into a config file, like this
$ cat example/interval_list.tsv
#file_location label_of_file
example/PolyASite.chr1.atlas.clusters.2.0.GRCh38.96.simple.bed.gz polyAS
Then Sashimi receive the custom annotation file with parameter --interval, the additional genomic features will be visualized at the annotation track.
Density plot
Density plot takes bam, bigwig or bgzipped depth file generated using samtools depth as input.
The input file list as follows
# filepath file_category label color
example/bams/1.bam bam
example/bams/2.bam bam 2bam
example/bams/3.bam bam 3bam blue
example/bws/2.bw bw bw green
example/bams/sc.bam bam sc
1.--customized-junction
This parameter is used to add the custom junctions from users
# junctions corresponding_input_file
junctions 2bam 3bam
chr1:1000-20000 100 200
- The first row is title of each column, first column is the junction id
- Each row means the different junctions except the first column.
- Each column means the counts of the custom junction
** Example: --customized-junction **
trackplot \
-e chr1:1270656-1284730:+ \
-r example/example.sorted.gtf.gz \
--density example/density_list.tsv \
--customized-junction example/custome_junction.tsv \
--output example.pdf
2.--show-site and --show-strand
These two parameters were used to show the density of reads starts by forward and reverse strand separately.
**Example: --show-site, --focus and --sites **
trackplot \
-e chr1:1270656-1284730:+ \
-r example/example.sorted.gtf.gz \
--density example/density_list.tsv \
--show-site \
--focus 1272656-1272656:1275656-1277656 \
--sites 1271656,1272656 \
--output example.pdf
3. Sample preparetion
Firstly, prepare the example datas
# annotation gtf file
wget -c https://ftp.ensembl.org/pub/release-101/gtf/homo_sapiens/Homo_sapiens.GRCh38.101.chr.gtf.gz
# bam files
aria2c -x 16 https://www.encodeproject.org/files/ENCFF125RUG/@@download/ENCFF125RUG.bam
aria2c -x 16 https://www.encodeproject.org/files/ENCFF854PFR/@@download/ENCFF854PFR.bam
aria2c -x 16 https://www.encodeproject.org/files/ENCFF709LHN/@@download/ENCFF709LHN.bam
aria2c -x 16 https://www.encodeproject.org/files/ENCFF613CGT/@@download/ENCFF613CGT.bam
# bigWig files
aria2c -x 16 https://www.encodeproject.org/files/ENCFF936SHU/@@download/ENCFF936SHU.bigWig
aria2c -x 16 https://www.encodeproject.org/files/ENCFF363UDO/@@download/ENCFF363UDO.bigWig
aria2c -x 16 https://www.encodeproject.org/files/ENCFF051PIE/@@download/ENCFF051PIE.bigWig
aria2c -x 16 https://www.encodeproject.org/files/ENCFF245YUN/@@download/ENCFF245YUN.bigWig
aria2c -x 16 https://www.encodeproject.org/files/ENCFF556EQK/@@download/ENCFF556EQK.bigWig
aria2c -x 16 https://www.encodeproject.org/files/ENCFF476HFB/@@download/ENCFF476HFB.bigWig
aria2c -x 16 http://hgdownload.cse.ucsc.edu/goldenpath/hg38/phastCons470way/hg38.phastCons470way.bw%
Here is the configure file bam.tsv for next section 4, 5, 6,
# we use third column to group the different data to aggregate datasets.
ENCFF125RUG.bam bam PTBP1_KD_agg #0084d1 frf
ENCFF854PFR.bam bam PTBP1_KD_agg #0084d1 frf
ENCFF854PFR.bam bam PTBP1_KD_2 #0084d1 frf
4. Filter junction counts and include the specific junction
User could also filter the minimum number of reads supporting the junction, and show the specific junction in plot.
Here is the command,
# panel A, show all junctions
trackplot \
-e chr9:112296343-112335026 \
-r Homo_sapiens.GRCh38.101.chr.gtf.gz \
--density bam.tsv \
-o PTBP3.raw.pdf \
--dpi 300 \
--width 6 \
--height 1 --show-junction-num
# panel B, show the junctions with more 10 counts.
trackplot \
-e chr9:112296343-112335026 \
-r Homo_sapiens.GRCh38.101.chr.gtf.gz \
--density bam.tsv \
-o PTBP3.filter.pdf \
--dpi 300 \
--width 6 \
--height 1 --show-junction-num -t 10
# panel C, show the specific junctions.
trackplot \
-e chr9:112296343-112335026 \
-r Homo_sapiens.GRCh38.101.chr.gtf.gz \
--density bam.tsv \
-o PTBP3.include.pdf \
--dpi 300 \
--width 6 \
--height 1 --show-junction-num \
--included-junctions chr9:112297917-112330441:-,chr9:112297917-112333470:-,chr9:112330475-112333470:-
5. Sample aggregation
User could aggregate multiple files into one track, like scRNAseq from smart-seq2 or splice-QTL datasets.
trackplot \
-e chr9:112296343-112335026 \
-r Homo_sapiens.GRCh38.101.chr.gtf.gz \
--density bam.tsv \
-o PTBP3.agg.pdf \
--dpi 300 \
--width 6 \
--height 1 --show-junction-num \
--included-junctions chr9:112297917-112330441:-,chr9:112297917-112333470:-,chr9:112330475-112333470:-
The PTBP1_KD_agg is the mean of ENCFF854PFR.bam and ENCFF125RUG.bam
6. The intron shrinkage
The shrinkage of intron
# Without shrinkage of the intron (shrinkage_scale: 1)
trackplot \
-e chr9:112296343-112335026 \
-r Homo_sapiens.GRCh38.101.chr.gtf.gz \
--density bam.tsv \
-o PTBP3.1.pdf \
--dpi 300 \
--width 6 \
--height 1 --show-junction-num \
--included-junctions chr9:112297917-112330441:-,chr9:112297917-112333470:-,chr9:112330475-112333470:- \
--intron-scale 1
# with shrinkage of the intron (shrinkage_scale: 0.001)
trackplot \
-e chr9:112296343-112335026 \
-r Homo_sapiens.GRCh38.101.chr.gtf.gz \
--density bam.tsv \
-o PTBP3.001.pdf \
--dpi 300 \
--width 6 \
--height 1 --show-junction-num \
--included-junctions chr9:112297917-112330441:-,chr9:112297917-112333470:-,chr9:112330475-112333470:- \
--intron-scale .01
7. Visualize coverage by cpm or rpkm
We also support to visualize the coverage by normalized values.
Inspired by rpkm_per_region from MISO
here is the configuration bam.tsv file,
# get https://www.encodeproject.org/files/ENCFF854PFR/@@download/ENCFF854PFR.bam
ENCFF854PFR.bam bam PTBP1_KD_2 #0084d1 frf 6830944
# No any normalize
trackplot \
-e chr9:112296343-112335026 \
-r Homo_sapiens.GRCh38.101.chr.gtf.gz \
--density bam.tsv \
-o PTBP3.count.pdf \
--dpi 300 \
--width 6 \
--height 1 --show-junction-num \
--included-junctions chr9:112297917-112330441:-,chr9:112297917-112333470:-,chr9:112330475-112333470:- \
--intron-scale .01 --normalize-format count
# Normalize by rpkm
trackplot \
-e chr9:112296343-112335026 \
-r Homo_sapiens.GRCh38.101.chr.gtf.gz \
--density bam.tsv \
-o PTBP3.rpkm.pdf \
--dpi 300 \
--width 6 \
--height 1 --show-junction-num \
--included-junctions chr9:112297917-112330441:-,chr9:112297917-112333470:-,chr9:112330475-112333470:- \
--intron-scale .01 --normalize-format rpkm
# Normalize by cpm
trackplot \
-e chr9:112296343-112335026 \
-r Homo_sapiens.GRCh38.101.chr.gtf.gz \
--density bam.tsv \
-o PTBP3.cpm.pdf \
--dpi 300 \
--width 6 \
--height 1 --show-junction-num \
--included-junctions chr9:112297917-112330441:-,chr9:112297917-112333470:-,chr9:112330475-112333470:- \
--intron-scale .01 --normalize-format cpm
Single cell bam related parameters
1.--barcode
Provide a manually curated barcode list to separate bam files by cell types or other groups.
This barcode list as follows:
#bam barcode cell_type(optional) cell_type(optional)
sc AAACCTGCACCTCGTT-1 AT2 #A6DCC2
sc AAAGATGTCCGAATGT-1 AT2 #A6DCC2
sc AAAGCAATCGTACGGC-1 AT2 #A6DCC2
2.--barocde-tag and --umi-tag
3.The tag to extract barcode and umi from each reads record, here we take the 10x Genomics bam format as default.
4.--group-by-cell
Group by cell types in density/line plot.
Example: --barcode and --group-by-cell
trackplot \
-e chr1:1270656-1284730:+ \
-r example/example.sorted.gtf.gz \
--density example/density_list.tsv \
--barcode example/barcode_list.tsv \
--focus 1272656-1272656:1275656-1277656 \
--sites 1271656,1271656,1272656 \
--group-by-cell \
--output example.pdf
Line plot
The line plot is simply another format of density plots.
The input file list as same as density plots
1.--hide-legend, --legend-position and --legend-ncol
These three parameters were used to disable legend, modify legend position and the columns of legend separately.
By default, the position of legend and columns of legend were determined by matplotlib, and the further detailed legend configuration please check matplotlib legend.
**Example: --hide-legend **
trackplot \
-e chr1:1270656-1284730:+ \
-o example_without_legend.pdf \
--dpi 300 \
--width 10 \
--height 1 \
--barcode example/barcode_list.tsv \
--raster --density-by-strand \
-r example/example.sorted.gtf.gz \
--line example/line_list.tsv \
--hide-legend
Example: --legend-position and --legend-ncol
trackplot \
-e chr1:1270656-1284730:+ \
-o example_with_legend.pdf \
--dpi 300 \
--width 10 \
--height 1 \
-t 100000 \
--barcode example/barcode_list.tsv \
--raster --density-by-strand \
-r example/example.sorted.gtf.gz \
--line example/line_list.tsv \
--legend-position right \
--legend-ncol 2
single cell bam related parameters
Please check the documentation in density plot.
Heatmap plot
Heatmap plot takes bam or bigwig as input.
The input file list as follows
# filepath file_category group color
example/bams/1.bam bam bam
example/bams/2.bam bam bam
example/bams/3.bam bam bam
example/bams/4.bam bam bam
example/bams/0.bam bam bam
example/bws/1.bw bw bw YlOrBr
example/bws/2.bw bw bw YlOrBr
example/bws/3.bw bw bw YlOrBr
example/bws/4.bw bw bw YlOrBr
example/bws/0.bw bw bw YlOrBr
Example: --heatmap-scale
Raw value
Scaled value
**Example: --heatmap-vmin and --heatmap-vmax to uniform color map **
Read-by-read plot
1.Sashimi.igv module support different format file as input.
A read-by-read plot provides a landscape of aligned reads in a straight and convenient way.
User could pass bed and bam file into Sashimi, and the input config file list as follows
#filepath file_category label color
example/SRX9697989.corrected_reads.bed.gz igv bed12 blue
example/SRX9697989.corrected_reads.6.bed.gz igv bed6
example/SRX9697989.corrected_reads.3.bed.gz igv bed3 red
example/bams/0.bam igv bam
here is the plotting command line
trackplot \
-e 1:10024601-10038168:+ \
-r example/example.sorted.gtf.gz \
--igv example/igv.tsv \
-o igv.1.pdf \
--dpi 300 \
--width 10 \
--height 1
2.Sashimi.igv module load and visualize features from bam tags.
In this topic, Sashimi.igv could load m6A modification (tag, ma:i) and length of polyA (tag, pa:f) tag from bam file, and then present it on each reads.
Here is the subset bam information,
SRR12503063.3513161 16 1 14362 1 .. * 0 0 .. .. NM:i:197 ms:i:1177 AS:i:977 nn:i:0 ts:Z:+ tp:Z:P cm:i:191 s1:i:837 s2:i:861 de:f:0.089 rl:i:41 pa:f:55.6556 rs:Z:- ma:i:14368
SRR12503063.4180535 0 1 14363 48 .. * 0 0 .. .. NM:i:117 ms:i:636 AS:i:1093 nn:i:0 ts:Z:- tp:Z:P cm:i:245 s1:i:1045 s2:i:1023 de:f:0.0588 rl:i:0 pa:f:90.4319 rs:Z:- ma:i:14372
SRR12503063.4171820 16 1 14363 51 .. * 0 0 .. .. NM:i:132 ms:i:700 AS:i:1191 nn:i:0 ts:Z:+ tp:Z:P cm:i:257 s1:i:1182 s2:i:1152 de:f:0.0555 rl:i:0 pa:f:99.63 rs:Z:- ma:i:14367
SRR12503063.8305358 16 1 14364 23 .. * 0 0 .. .. NM:i:285 ms:i:890 AS:i:716 nn:i:0 ts:Z:+ tp:Z:P cm:i:99 s1:i:487 s2:i:466 de:f:0.1315 rl:i:33 pa:f:78.8698 rs:Z:- ma:i:14368
SRR12503063.89603 16 1 14394 2 .. * 0 0 .. .. NM:i:220 ms:i:1064 AS:i:891 nn:i:0 ts:Z:+ tp:Z:P cm:i:163 s1:i:740 s2:i:741 de:f:0.0992 rl:i:30 pa:f:66.8433 rs:Z:- ma:i:14401
......
here is the command line,
trackplot \
-e chr1:13362-29900:+ \
--igv example/igv.m6a.tsv \
-o igv.m6a.pdf \
--dpi 300 \
--width 5 \
--height 1 --rs rs --polya pa --m6a ma
In this picture, the red track and blue dot represents the length of poly(A) and m6a modification respectively,
3.Sashimi.igv module also allow sort these reads by specific alternative exon
User could modify the config file as follows,
#filepath file_category label color focus_exon
example/SRX8994511.example.bed.gz igv bed12 blue 43100453-43100519,43101366-43101432
here is the command line for plotting
wget -c https://ftp.ensembl.org/pub/release-101/gtf/homo_sapiens/Homo_sapiens.GRCh38.101.chr.gtf.gz
trackplot \
-r example/Homo_sapiens.GRCh38.101.chr.gtf.gz \
-e 21:43092956-43107570:+ \
--igv example/igv.3.tsv \
--focus 43100453-43100519:43101366-43101432 \
-o test_igv_plot.3.pdf \
--dpi 300 \
--width 6 \
--height 1
HiC heatmap
HiC toy example 1
Sashimi also support HiC track, and user could prepare Li_et_al_2015.h5 into a config file, then pass to --hic. here is an example config file,
Here is the command line for generating domain
hicFindTADs -m example/Li_et_al_2015.h5 \
--outPrefix example/Li_et_al_2015_thres0.05_delta0.01_fdr \
--thresholdComparisons 0.05 \
--delta 0.01 \
--correctForMultipleTesting fdr \
-p 12
The format of configuration file,
# filepath file_category label color transform (2,10 and e means log2, log10 and ln.) depth domaininfor
example/Li_et_al_2015.h5 hic Li_hic RdYlBu_r 2 50000 example/Li_et_al_2015_thres0.05_delta0.01_fdr_domains.bed.gz
here is the plotting command line
trackplot \
-e X:1500000-2500000:+ \
--hic example/hic.tsv \
-o example.hic.png \
--dpi 300 \
--width 10 \
--height 1
for each hic track, a bigger depth means a higher y-axis.
HiC toy example 2
Because Li_et_al_2015.h5 doesn't contain chromosome 1, user could download a new toy dataset and add into example picture.
1.download hic file and convert into h5 format
wget https://encode-public.s3.amazonaws.com/2016/12/01/a241cba5-df2e-45fb-9a8f-5af5587fb02a/ENCFF121YPY.hic
hicConvertFormat -m ENCFF121YPY.hic --inputFormat hic --outputFormat cool -o ENCFF121YPY.cool --resolutions 1000
hicConvertFormat -m ENCFF121YPY_1000.cool --inputFormat cool --outputFormat h5 -o ENCFF121YPY.h5
If you met UnicodeDecodeError when converting hic into h5 format, you can download pairs format file from ENCODE.
Here is an example,
wget https://www.encodeproject.org/files/ENCFF931NQV/@@download/ENCFF931NQV.pairs.gz
wget http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes
pairix ENCFF931NQV.pairs.gz
cooler cload pairix -p 16 hg38.chrom.sizes:1000 ENCFF931NQV.pairs.gz ENCFF931NQV_1kb.cool
hicConvertFormat -m ENCFF931NQV_1kb.cool --inputFormat cool --outputFormat h5 -o ENCFF931NQV.h5
2.prepare the config file
# filepath file_category label color transform depth
example/ENCFF931NQV.h5 hic ENCFF931NQV RdYlBu_r log2 30000
3.run trackplot
trackplot \
-e chr19:35600000-35800000:+ \
-r example/example.sorted.2.gtf.gz \
--hic example/hic.2.tsv \
-o hic.2.png \
--dpi 300 \
--width 10 \
--height 1
here is the result. 
circRNA plot
The linear and circRNA raw data were downloaded from PRJNA541935.
The command for generating a circRNA coverage plot with highlight the back-splice junction
wget -c https://ftp.ensembl.org/pub/release-101/gtf/homo_sapiens/Homo_sapiens.GRCh38.101.chr.gtf.gz
trackplot \
-e chr1:925421-944308:+ \
--density example/circRNA.tsv \
--stroke 937113-937713@blue \
-o circRNA.pdf \
--dpi 300 \
--width 10 \
--height 1 \
-t 10 \
-r Homo_sapiens.GRCh38.101.chr.gtf.gz \
--link 925921-943808
Motif plot
trackplot \
-e chr1:1270656-1270776:+ \
--density example/density_list.tsv \
-o motif.pdf \
--dpi 300 \
--width 10 \
--height 1 \
--raster \
-r example/example.sorted.gtf.gz \
--motif example/motif.bed.gz \
--motif-region 1270756-1270760
The motif weight matrix should be customized bedGraph format as follows:
# chromosome start end A_weight T_weight C_weight G_weight
chr1 100 101 0.1 0.2 -0.3 -0.4
Then, bgzipped && tabix indexed
here is the result. 
Additional annotation
We also provide multiple annotations, including indicator lines, focus, stroke and sequence.
Example: --focus, --sites and --stroke
trackplot \
-e chr1:1270656-1284730:+ \
--focus 1272656-1272656:1275656-1277656 \
--stroke 1275656-1277656:1277856-1278656@blue \
--sites 1271656,1271656,1272656 \
-o additional.pdf \
--dpi 300 \
--width 10 \
--height 1 \
--raster \
-r example/example.sorted.gtf.gz \
--line example/line_list.tsv
