Circos > Documentation > Tutorials > Quick Start > Links And Rules
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Circos at the EMBO NGS workshop in Tunis, Sept 15–25.

Use the latest version of Circos and read Circos best practices—these list recent important changes and identify sources of common problems.
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Don't know what question to ask? Read Points of View: Visualizing Biological Data by Bang Wong, myself and invited authors from the Points of View series.

1 — Quick Start

4. Links & Rules

The first data track we will add are links. Links represent an association between two genomic positions (e.g. similarity, difference, fusion, etc) by curves or straight lines.

Links can be visually formatted using rules, which compose a decision tree of expressions that are evaluated for every link. Rules test links based on data value, and can therefore dynamically change how a link appears based on its position, size, or other parameter.

But before adding links to the image from the previous tutorial, I want to show you another way of changing the colors of ideograms that is more helpful if you want to reuse the custom colors for other elements in the image.

custom colors

If you look in the human karyotype file linked to above, you'll see that each chromosome's color is chrN where N is the number of the chromosome. Thus, chromosome hs1 has color chr1, hs2 has color chr2 and so on. For convenience, a color can be referenced using chr and hs prefixes (chr1 and hs1 are the same color).

Because the color and chromosome name is the same for human genome data (or any data set in which you use chr or hs as the chromosome prefix), you can color a data point by using its chromosome name.

In the previous tutorial, I used chromosomes_colors to change the color of the ideograms. This approach works well when the only thing you want to do is change the color of the segments, because although the color of hs1 on the image has changed, the definition of color hs1 remains the same.

chromosomes_color = hs1=red,hs2=orange,hs3=green,hs4=blue

To change the color of the ideogram and color of the same name, you need to redefine the value of the color in the <colors> block. This block is included below from the colors_fonts_patterns.conf file, which contains all the default definitions. To overwrite colors, use a * suffix and provide a new value, which can be a lookup to another color.

<colors>
chr1* = red
chr2* = orange
chr3* = green
chr4* = blue
endblock(colors>

links

Links are defined in <link> blocks enclosed in a <links> block. The links start at a radial position defined by radius and have their control point (adjusts curvature) at the radial position defined by bezier_radius. In this example, I use the segmental duplication data set, which connects regions of similar sequence (90%+ similarity, at least 1kb in size).

<links>

<link>
file          = data/5/segdup.txt
radius        = 0.8r
bezier_radius = 0r
color         = black_a4
thickness     = 2
</link>

</links>

rules

Rule blocks can be added to any or block and form a decision chain that changes how data points (e.g. links, histogram bins, scatter plot glyphs, etc) are formatted.

The decision chain is composed of one or more <rule> blocks enclosed by a <rules> block.

<rules>

<rule>
...
</rule>

<rule>
...
</rule>

...

</rules>

Each rule has a condition, formatting statements and an optional flow statement. If the condition is true, the rule is applied to the data point and no further rules are checked (unless flow=continue). If the condition is false, the next rule is checked.

var(X) referrs to the value of variable X for the data point. Here intrachr means intra-chromosomal.

<rule>
condition     = var(intrachr)
# Any links that are intra-chromosomal will not be shown. Further rules are not tested.
show          = no
</rule>

A rule with condition=1 is applied to all remaining links, since its condition is always true.

The color of the link is set to the 2nd chromosome in the link coordinate (link's end). Here eval() is required so that the expression var(chr2) is evaluated (we want the result of var(chr2), not the color named "var(chr2)"). Note that for conditions, evaluation is automatic, but required for all other parameters.

<rule>
condition     = 1
color         = eval(var(chr2))
# After this rule is applied, the rule chain continues.
flow          = continue
</rule>

The remaining two rules each change the radius of the start and end of a link, respectively, if it starts or ends on hs1.

<rule>
# If the link's start is on hs1...
condition     = from(hs1)
# ...set the radial position of the link's start to be close to the ideogram.
radius1       = 0.99r
</rule>

<rule>
# Same as the rule above, but applies to the end of the link.
condition     = to(hs1)
# 'radius2' (like chr2, start2, end2) refers to the variable 'radius' of the end of the link.
radius2       = 0.99r
</rule>

more on rules

Rules are evaluated in order of appearance. You can move a rule up in the decision tree by either moving its <rule> block up, or adding the importance parameter. Rules with this parameter will be tested first, in descending value of the parameter, followed by rules without the parameter.

<rules>

<rule>
# 2nd
</rule>

<rule>
# 3rd
</rule>

<rule>
# 1st 
importance = 10
</rule>

</rules>

If the condition of a rule is true, the rule is applied and the rule chain stops for the current data point. If you want to change this behaviour, use the flow parameter. Optionally, you can make the flow control dependent on the outcome of the condition by using if true or if false.

To force the testing to continue,

flow = continue {if true|false}

The default behaviour is

flow = continue if false

To force the testing to stop,

flow = stop {if true|false}

You can restart the testing (only once though, to avoid endless loops)

flow = restart {if true|false}

Finally, you can assign a tag to a rule and then jump to it from another.

<rule>
flow = goto special_rule if true
...
</rule>

<rule>
tag = special_rule
...
</rule>