Use the latest version of Circos and read Circos best practices—these list recent important changes and identify sources of common problems.
If you are having trouble, post your issue to the Circos Google Group and include all files and detailed error logs. Please do not email me directly unless it is urgent—you are much more likely to receive a timely reply from the group.
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.
3 — Drawing Ideograms
2. Karyotypes
The karyotype file defines the axes. In biological context, these are typically chromosomes, sequence contigs or clones.
Each axis (e.g. chromosome) is defined by unique identifier (referenced in data files), label (text tag for the ideogram seen in the image), size and color.
In addition to chromosomes, the karyotype file is used to define position, identity and color of cytogenetic bands. For some genomes these band data are available.
designing the Circos image
The most difficult part of creating a Circos image—any visualization for that matter—is deciding what data to show. Chances are your data is too complex to show (e.g. its native format doesn't have a trivial visual encoding, such as a scatter plot).
Mapping data onto a Circos figure requires that you identify what patterns in your data are (a) likely to be important and (b) likely to be present, and create a figure that exposes such patterns. Remember, if the pattern exists, you can't afford to miss it. If it doesn't exist, you can't afford to be fooled into thinking that it's there, or left wondering whether it's occluded by other data.
If you don't know where to start when creating Circos images from genomic and non-genomic data, look through published examples from the literature. Find images whose patterns map onto your data types.
Don't think necessarily from the point of view of how to construct input files. First, identify what you want to show and how. Make a sketch of the kind of figure you want to make. This is the hard part.
defining chromosomes
Chromosome definitions are formatted as follows
chr - ID LABEL START END COLOR
The first two fields are always "chr", indicating that the line defines a chromosome, and "-". The second field defines the parent structure and is used only for band definitions.
The id is the identifier used in data files whereas the LABEL is
the text that will appear next to the ideogram on the image. If you
are working with multiple species, I suggest prefixing the chromosome
number with a species identifier (e.g., hs = Homo sapiens, mm = Mus
musculus, etc). Even when working with only one species, prefixing the
chromosome with a species code is highly recommended — this will
greatly help in creating more transparent configuration and data files.
The start and end values define the size of the chromosome. The
karyotype file should store the entire chromsome size, not just the
region you wish to draw. Other configuration file parameters control
the drawable regions.
The color of the ideogram will be used, if you decide not to
show the cytogenetic bands and select the fill option. The color can
be useful to distinguish between species and chromosomes. Consider
using the conventional chromosome color scheme as defined in the
etc/color.conf configuration file. Colors are defined for each
human chromosome and are named similiarly: chr1, chr2,
... chrx, chry, chrun. Colors must be in lowercase.
The karyotype file is specified in the configuration file using
karyotype = data/karyotype/karyotype.human.txt
For example, the human karyotype for assembly GRCh37 (hg19, Feb 2009) is composed of 24 chromosomes
chr - hs1 1 0 249250621 chr1 chr - hs2 2 0 243199373 chr2 chr - hs3 3 0 198022430 chr3 ... chr - hs22 22 0 51304566 chr22 chr - hsX x 0 155270560 chrx chr - hsY y 0 59373566 chry
together with 862 bands
band hs1 p36.33 p36.33 0 2300000 gneg band hs1 p36.32 p36.32 2300000 5400000 gpos25 band hs1 p36.31 p36.31 5400000 7200000 gneg ... band hsY q11.223 q11.223 22100000 26200000 gpos50 band hsY q11.23 q11.23 26200000 28800000 gneg band hsY q12 q12 28800000 59373566 gvar
cytogenetic bands
Bands are defined in the same manner as chromosomes, but the first two fields are
now band and the id of the parent chromosome.
band hs1 p36.33 p36.33 0 2300000 gneg band hs1 p36.32 p36.32 2300000 5300000 gpos25 band hs1 p36.31 p36.31 5300000 7100000 gneg ... band hs2 p25.3 p25.3 0 4300000 gneg band hs2 p25.2 p25.2 4300000 7000000 gpos50 band hs2 p25.1 p25.1 7000000 12800000 gneg ...
You can obtain the karyotype structure from UCSC Genome Viewer Table Browser (Mapping and Sequencing Tracks > Chromosome Band) . Not all genomes have these data, however. For example, mouse (mm9) and rat (rn4) have band information, but not dog (canfam2) or cow (bostau3)
band transparency
Cytogenetic band structure is drawn on top of the ideograms. The ideogram itself can have an associated color—this is defined in the karotype file—and when band transparency is turned on, this color shows through.
<ideogram> show_bands = yes fill_bands = yes band_transparency = 4 ... </ideogram>
The value for band_transparency can be 1..auto_alpha_steps,
where auto_alpha_steps is the number of automatically generated
transparency steps for each color (see etc/image.conf).
When band_transparency=1, the bands are least
transparent. Conversely, when
band_transparency=auto_alpha_steps, the bands are most
transparent. For a given band_transparency value, the opacity is
given by band_transparency/(auto_alpha_steps+1) (e.g. 2/6 when
band_transparency=2 and auto_alpha_steps=5).
when to use bands
The cytogenetic bands feature is meant ... for cytogenetic bands. These ideogram annotations have two specific properties: they cover the entire ideogram and they don't overlap.
If you want to use the band feature to show other ideogram annotations, the data must meet these two conditions. It's likely that unless your data are cytogenetic banding patterns, they won't (e.g. gene regions, repeat regions, etc). In these case, use the highlight plot block and draw the highlights within the ideograms by setting
r0 = dims(ideogram,radius_inner) r1 = dims(ideogram,radius_outer)
defining multiple species
If you would like to draw ideograms from multiple species, list their karyotype files in the karyotype parameter
karyotype = data/karyotype/karyotype.human.txt,data/karyotype/karyotype.rat.txt
# data/karyotype/karyotype.human.txt chr - hs1 1 0 249250621 chr1 chr - hs2 2 0 243199373 chr2 chr - hs3 3 0 198022430 chr3 ... # data/karyotype/karyotype.rat.txt chr - rn1 1 0 267910886 chr1 chr - rn2 2 0 258207540 chr2 chr - rn3 3 0 171063335 chr3 ...
non-genomic karyotype files
Circos was designed to draw genomic data, but this isn't a limitation. If you have any positional data that would benefit from a circular composition, you can define abstract "chromosomes" to act as data domains.
For example, consider this "Naming Names" NYT graphic of a presidential debate. This image could be generated with Circos. In this case, the karyotype would define each candidate as a "chromosome", and a segment of speech as a "band". The NYT illustration is a wonderful analogy to comparative genomics - each candidate (a genome) makes verbal reference to (shows synteny) to another candidate (another genome).
If you have two integer ranges 0-1000 over which you wanted to display data, you might define
chr - axis1 1 0 1000 green chr - axis2 2 0 1000 red
Furthermore, you could use the band functionality to display a checkered grid within each domain to indicate length.
band axis1 band1 band1 0 99 grey band axis1 band2 band2 100 199 white band axis1 band1 band1 200 299 grey band axis1 band2 band2 300 399 white ...
The definitions in the karyotype file need not correspond to physical structures. Among other things, you can use them to define contigs, such as sequence or map contigs.