GC Content Distribution Plot
Plot the GC percentage across a DNA sequence with a sliding window to find GC-rich and AT-rich regions.
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What it does
The GC content distribution plot slides a window along a DNA sequence and charts the GC percentage of each window against position, so local composition is visible instead of a single average. Below the chart it summarizes the overall GC content, the mean across windows, and the minimum and maximum window values. It is how you find GC-rich stretches such as CpG island promoters and AT-rich stretches such as replication origins and regulatory regions.
How it works
For a window of size w starting at position i, the plotted value is GC%(i) = (G + C within the window) / w x 100, and the window advances one base at a time, so a sequence of length L yields L - w + 1 windows, each plotted at its midpoint. Window size sets the trade-off between resolution and noise: small windows respond to short features but fluctuate wildly, since with w = 20 a single base changes the value by 5 percentage points, while large windows give a smooth trace that can average a short feature away. The default of 100 base pairs is a reasonable compromise for gene-scale sequences, and values above 50% mark GC-rich regions. Interpreting peaks is easier with published criteria in mind, for example CpG islands are conventionally regions longer than 200 base pairs with GC above 50% and an observed to expected CpG ratio above 0.6.
Worked example
Take a 1000 base pair sequence spanning a promoter and the start of a gene, analysed with the default 100 base pair window: this gives 1000 - 100 + 1 = 901 windows. If the overall GC content is 48%, the trace might still peak at 72% over a 200 base pair stretch just upstream of the transcription start site, where 72 of the 100 bases in the window are G or C, and drop to 32% over an AT-rich segment further downstream. The 40 point spread between minimum and maximum window values is the signal that a single average of 48% would have hidden completely.
When to use it
Before designing bisulfite primers or a methylation assay, plot the region around a promoter to locate the CpG island and place primers at its edges rather than in the middle of the GC-rich block. When a PCR repeatedly fails over one part of a template, plotting GC content often reveals a window above 70% where the polymerase stalls, indicating a need for DMSO, betaine or a higher denaturation temperature. When inspecting a newly assembled contig, a segment whose GC content departs sharply from the rest of the sequence can flag a horizontally transferred island, a mobile element, or contamination from another organism.
FAQ
- What window size should I use for a GC content plot?
- Match the window to the feature you are looking for: 50 to 100 base pairs for promoters, CpG islands and other local features, and several hundred base pairs when you want the broad landscape of a long sequence. Too small a window produces a noisy trace where single bases dominate, while too large a window smooths short features into the background, so it is worth comparing two sizes.
- What does a GC-rich region mean?
- A local excess of G and C usually indicates functional structure rather than chance. GC-rich regions include CpG island promoters, exons, and structured elements that form stable secondary structures, and because G-C pairs are held by three hydrogen bonds these regions denature at higher temperatures, which is why they are harder to amplify and sequence.
- How do I find CpG islands from a GC plot?
- Look for sustained peaks above 50% GC that extend for at least a couple of hundred base pairs, typically just upstream of a transcription start site. A GC plot alone is suggestive rather than conclusive, since the conventional definition also requires the observed to expected ratio of CpG dinucleotides to exceed about 0.6, which counts CG pairs rather than G and C separately.
- Why is my GC plot flat or extremely noisy?
- A flat trace usually means the window is large relative to the sequence, so every window overlaps almost the same bases; reduce the window size or use a longer sequence. A very jagged trace means the opposite, that the window is too small for the sequence length, and each new base swings the value, so increase the window until real features stand out from the fluctuation.