Data visualization with Python and SVG

Data visualization with Python and SVG
Plotting an RNA secondary structure
Sukjun Kim
The Baek Research Group of Computational Biology
Seoul National University
April 11th, 2015
Special Lecture at Biospin Group
1

2
Plotting libraries for data visualization
• They have their own language for plotting.
• They should be installed prior to use.
• There are dependencies on upper level libraries.
• They are appropriate for high level graphics.
• We cannot customize a plot at low level.
R matplotlib d3.js
gnuplot Origin PgfPlots
PLplot Pyxplot Grace

3
SVG(Scalable Vector Graphics)
• XML-based vector image format for two-dimensional graphics.
• The SVG specification is an open standard developed by the
World Wide Web Consortium (W3C) since 1999.
• As XML files, SVG images can be created and edited with any
text editor.
• All major modern web browsers – including Mozilla Firefox,
Internet Explorer, Google Chrome, Opera, and Safari – have
at least some degree of SVG rendering support.
(Wikipedia – Scalable Vector Graphics)
Data visualization by writing SVG document
• SVG markup language is open standard and easy to learn.
• Not only python but also any programming language can be used.
• It requires no dependent libraries.
• We can customize graphic elements at low level.

4
Structure of SVG document
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
<svg xmlns="http://www.w3.org/2000/svg"
version="1.1" width="100" height="100">
<circle cx="50" cy="50" r="40" stroke="green"
stroke-width="4" fill="yellow"/>
</svg>
XML tag
declaration of
DOCTYPE
start of SVG tag
end of SVG tag
contents of
SVG document
SVG elements
• SVG has some predefined shape elements.
• rectangle <rect>, circle <circle>, ellipse <ellipse>, line <line>,
polyline <polyline>, polygon <polygon>, path <path>, ...
• group <g>, hyperlink <a>, text <text>, ...
40
(50,50)

RNA secondary structural data
## microRNA structural data
seq = 'CCACCACUUAAACGUGGAUGUACUUGCUUUGAAACUAAAGAAGUAAGUGCUUCCAUGUUUUGGUGAUGG'
dotbr = '(((.((((.(((((((((.(((((((((((.........))))))))))).))))))))).)))).)))'
pairs = [(0,68), (1,67), (2,66), (4,64), (5,63), (6,62), ... , (29,39)]
coor = [(69.515,526.033), (69.515,511.033), ... , (84.515,526.033)]
5
RNAplotRNAfoldseq dotbr, pairs coor
How to generate RNA structural data?
(Vienna RNA package, http://www.tbi.univie.ac.at/RNA/)
• seq: RNA sequence.
• dotbr: dot-bracket notation which is used
to define RNA secondary structure.
• pairs: base-pairing information.
• coor: x and y coordinates for nucleotides.
This is our final
image to plot

Writing a SVG tag in python script
6
out = []
out.append('<svg xmlns="http://www.w3.org/2000/svg" version="1.1">n')
## svg elements here
out.append('</svg>n')
open('rna.svg', 'w').write(''.join(out))
<svg xmlns="http://www.w3.org/2000/svg" version="1.1">
</svg>
rna.py
rna.svg
SVG documents basically requires open and close SVG tags

SVG Polyline
7
<polyline points="10,10 20,10 10,20 20,20"
style="fill:none;stroke:black;stroke-width:3"/>
(10,10) (20,10)
(10,20) (20,20)
fill:none
stroke:black
stroke-width:3

Drawing phosphate backbone
8
points = ' '.join(['%.3f,%.3f'%(x, y) for x, y in coor])
out.append('<polyline points="%s" style="fill:none;
stroke:black; stroke-width:1;"/>n'%(points))
coor = [(69.515,526.033), (69.515,511.033), ... , (84.515,526.033)]
In DNA and RNA, phosphate backbone is regarded as a
skeleton of the molecule. The skeleton will be represented by
SVG <polyline> tag.
We have x and y coordinates of each nucleotide as below.
Using the coordination information, we can specifiy points
attribute of polyline tag.

SVG Line
9
<line x1="0" y1="0" x2="20" y2="20"
style="stroke:red;stroke-width:2"/>
(0,0)
(20,20)
stroke:red
stroke-width:2

Drawing base-pairing
10
for i, j in pairs:
x1, y1 = coor[i]
x2, y2 = coor[j]
out.append('<line x1="%.3f" y1="%.3f" x2="%.3f" y2="%.3f"
style="stroke:black; stroke-width:1;"/>n'%(x1, y1, x2, y2))
pairs = [(0,68), (1,67), (2,66), (4,64), (5,63), (6,62), ... , (29,39)]
coor = [(69.515,526.033), (69.515,511.033), ... , (84.515,526.033)]
Watson-Crick base pairs occur between A and U, and between
C and G. We will use <line> tag to represent the hydrogen
bonds.
In addition to a coordination information, we also have base-
pairing information in the form of tuple carrying the indexes of
two nucleotides.
From two types of data, base-pairing information can be
visualized as a simple line.

SVG Circle
11
<circle cx="50" cy="50" r="20"
style="fill:red;stroke:black;stroke-width:3"/>
(50,50)
fill:red
stroke:black
40
stroke-width:3

SVG Text
12
<text x="0" y="15" font-size="15"
style="fill:blue">I love SVG!</text>
(0,15)
fill:blue
font-size="15"I love SVG!

Drawing nucleotides
13
A
Each nucleotide will be represented by one character text
enclosed with a circle.
coor = [(69.515,526.033), (69.515,511.033), ... , (84.515,526.033)]
<text>
<circle>
for i, base in enumerate(seq):
x, y = coor[i]
out.append('<circle cx="%.3f" cy="%.3f" r="%.3f"
style="fill:white; stroke:black; stroke-width:1"/>n'%(x, y, 5))
out.append('<text x="%.3f" y="%.3f" font-size="6" text-
anchor="middle" style="fill:black">%s</text>n'%(x, y+6*0.35, base))
RNA sequence and a coordination information is required.
<text> tag should be written after the <circle> tag.

Content of the python script
14
## microRNA structural data
dotbr = '(((.((((.(((((((((.(((((((((((.........))))))))))).))))))))).)))).)))'
pairs = [(0, 68), (1, 67), (2, 66), (4, 64), (5, 63), (6, 62), (7, 61), (9, 59), (10, 58), (11, 57), (12, 56), (13, 55), (14,
54), (15, 53), (16, 52), (17, 51), (19, 49), (20, 48), (21, 47), (22, 46), (23, 45), (24, 44), (25, 43), (26, 42), (27, 41),
(28, 40), (29, 39)]
coor =
[(69.515,526.033),(69.515,511.033),(69.515,496.033),(61.778,483.306),(69.515,469.506),(69.515,454.506),(69.515,439.506),(69.
515,424.506),(62.691,412.302),(69.515,400.099),(69.515,385.099),(69.515,370.099),(69.515,355.099),(69.515,340.099),(69.515,3
25.099),(69.515,310.099),(69.515,295.099),(69.515,280.099),(61.778,266.298),(69.515,253.571),(69.515,238.571),(69.515,223.57
1),(69.515,208.571),(69.515,193.571),(69.515,178.571),(69.515,163.571),(69.515,148.571),(69.515,133.571),(69.515,118.571),(6
9.515,103.571),(56.481,95.317),(50.000,81.317),(52.139,66.039),(62.216,54.357),(77.015,50.000),(91.814,54.357),(101.891,66.0
39),(104.030,81.317),(97.549,95.317),(84.515,103.571),(84.515,118.571),(84.515,133.571),(84.515,148.571),(84.515,163.571),(8
4.515,178.571),(84.515,193.571),(84.515,208.571),(84.515,223.571),(84.515,238.571),(84.515,253.571),(92.252,266.298),(84.515
,280.099),(84.515,295.099),(84.515,310.099),(84.515,325.099),(84.515,340.099),(84.515,355.099),(84.515,370.099),(84.515,385.
099),(84.515,400.099),(91.339,412.302),(84.515,424.506),(84.515,439.506),(84.515,454.506),(84.515,469.506),(92.252,483.306),
(84.515,496.033),(84.515,511.033),(84.515,526.033)]
out = []
out.append('<svg xmlns="http://www.w3.org/2000/svg" version="1.1">n')
## [1] phosphate backbone - <polyline> tag
points = ' '.join(['%.3f,%.3f'%(x, y) for x, y in coor])
out.append('<polyline points="%s" style="fill:none; stroke:black; stroke-width:1;"/>n'%(points))
## [2] base-pairing - <line> tag
for i, j in pairs:
x1, y1 = coor[i]
x2, y2 = coor[j]
out.append('<line x1="%.3f" y1="%.3f" x2="%.3f" y2="%.3f" style="stroke:black; stroke-width:1;"/>n'%(x1, y1, x2, y2))
## [3] nucleotide - <circle> and <text> tags
for i, base in enumerate(seq):
x, y = coor[i]
out.append('<circle cx="%.3f" cy="%.3f" r="%.3f" style="fill:white; stroke:black; stroke-width:1"/>n'%(x, y, 5))
out.append('<text x="%.3f" y="%.3f" font-size="6" text-anchor="middle" style="fill:black">%s</text>n'%(x, y+6*0.35,
base))
out.append('</svg>n')
open('rna.svg', 'w').write(''.join(out))

How to use other SVG tags? Go to w3schools.com!

16
Real examples
with Python and SVG

17
reciPlot
<text>
<polygon>
Plot for visualizing
the tissue-specific
expression of genes.

18
escPlot
<line>
<text>
<path>
<circle>
<polyline>
Plot for representing
expression, structure, and
conservation data of RNA
collectively in a single plot.

wheelPlot
19
<circle>
<polyline>
<path> <line>
<rect> <text>
Plot for visualizing
all suboptimal RNA
secondary structures.

Conclusions
20
• There are many graphic tools and libraries for data visualization.
• These software options provide a function limited to high level graphics.
• No dependent libraries or significant time investment are required for
learning a specific language to write SVG documents.
• If you want to plot a noncanonical type of graph and customize it at low
level, writing a SVG document with Python will be the best solution that
meets your purpose.

Thank you!
Have a nice weekend.
21

Data visualization with Python and SVG

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