Paste one or many DNA or RNA sequences and get GC content, AT content, GC skew, base composition, and a sliding-window GC plot. FASTA input and multi-sequence analysis supported, with CSV export.
GC content
57.5%
AT content
42.5%
Length (A/C/G/T)
106
GC skew (G−C)/(G+C)
0.0492
GC content is deceptively simple to compute yet informs decisions across the whole research pipeline, because the three hydrogen bonds of a G-C pair versus the two of an A-T pair change how a sequence behaves physically. This single difference in bonding is why GC-rich duplexes melt at higher temperatures, a relationship formalized by Marmur and Doty (1962) and still used to estimate melting temperatures today.
At the genome scale, GC content is not uniform. Bernardi's isochore theory (1985) described long stretches of relatively homogeneous GC content, with GC-rich isochores being gene-dense and associated with CpG islands near active promoters. Comparing organisms, the range is enormous: the malaria parasite is roughly 19% GC while some actinobacteria exceed 70%, which is why GC content is a standard first-pass fingerprint in metagenomics and contamination screening.
For primer and probe design, GC content in the 40 to 60% band is the usual target because it anneals stably without requiring impractically high denaturation temperatures. The sliding-window GC plot in this tool reveals local GC-rich clamps and AT-rich troughs, which matter when you place primers or interpret regions that resist amplification. Pair it with the primer melting temperature calculator to turn composition into a precise annealing temperature.
GC content also explains sequencing artefacts. Both very GC-rich and very AT-rich fragments amplify inefficiently during library preparation, producing the coverage bias described by Benjamini and Speed (2012). Knowing the GC profile of a region helps you decide whether uneven read depth is biological or technical. To inspect coding regions further, the DNA to protein translation tool reads open reading frames, and the reverse complement tool reconstructs the opposite strand.
One sequence, or many in FASTA format. Spaces, numbers, and line breaks are ignored.
GC content, AT content, GC skew, and the count and share of each base, with ambiguous characters excluded.
The sliding-window plot shows how GC content varies along a single sequence, exposing GC-rich and AT-rich regions.
Copy the summary or download a CSV of per-sequence GC statistics for supplementary materials.
Next step
Sequence-composition profiling, statistical comparison across groups, and publication-ready figures, handled by a PhD statistician.
Our promise: Free pipeline re-run and figure revisions if reviewers push back.
Timeline
Most projects deliver in under 2 weeks. We confirm an exact date in your quote.
If reviewers push back
If reviewers question the pipeline, parameters, or figures, we re-run the analysis and revise free.
Confidentiality
NDA available on request before any project discussion. Your data, study design, and manuscript stay private either way.
Want a PhD methodologist to handle the whole project?
Get a full sequence-composition and genomics statistics workup by a PhD statistician. Free pipeline re-run and figure revisions if reviewers push back. Pay only after you approve your quote.
GC content is the percentage of bases in a nucleotide sequence that are guanine (G) or cytosine (C). It is calculated as (G + C) divided by the total number of unambiguous bases (A, C, G, and T or U), multiplied by 100. GC content is a fundamental descriptor of a sequence because G-C base pairs form three hydrogen bonds while A-T pairs form only two, so higher GC content means a more thermally stable, tightly bound duplex.
Count the number of G and C bases, divide by the total count of A, C, G, and T (or U for RNA), and multiply by 100. For the sequence GGCCAATT, there are four G or C bases out of eight total, so the GC content is 50%. This calculator does the count for you, excludes ambiguous bases like N from the denominator (the EMBOSS convention), and also reports the value for each sequence when you paste a multi-sequence FASTA file.
It depends entirely on the organism and region. Human genomic DNA averages around 41% GC, but this varies widely across isochores; many bacteria range from 25% to 75%; and Plasmodium falciparum is famously AT-rich at about 19% GC. There is no universally good value. For PCR primers, a GC content of roughly 40% to 60% is usually targeted because it balances stable annealing against ease of denaturation.
GC content predicts duplex melting temperature, which matters for primer design, probe hybridization, and PCR annealing conditions. It also correlates with genome features: GC-rich regions tend to be gene-dense and are associated with CpG islands near promoters, while AT-rich regions often mark isochore boundaries and origins of replication. In sequencing, extreme GC content causes coverage bias, so knowing it helps you interpret uneven read depth.
GC skew is (G − C) / (G + C), a measure of the strand asymmetry between guanine and cytosine. In bacterial genomes the sign of GC skew typically flips at the origin and terminus of replication, because the leading and lagging strands accumulate mutations differently, so a cumulative GC skew plot is used to locate the replication origin. This tool reports GC skew for each sequence alongside GC content.
Because each G-C pair contributes three hydrogen bonds versus two for an A-T pair, a higher GC content raises the melting temperature of the duplex. Simple estimators such as the Wallace rule weight G and C twice as heavily as A and T, and the more accurate nearest-neighbor model uses GC-dependent thermodynamic parameters. To compute a precise primer melting temperature, use the primer melting temperature calculator rather than GC content alone.
To turn composition into a precise annealing temperature, the primer melting temperature calculator uses the nearest-neighbor model. To reconstruct the opposite strand, use the reverse complement tool, and to read coding sequences in protein space across six frames, the DNA to protein translation tool handles the genetic code. For the full analysis of biological data, the bioinformatics analysis service covers everything from composition profiling to differential expression.
Reviewed by
Dr. Sarah Mitchell holds a PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health and has over 15 years of experience in systematic review methodology and meta-analysis. She has authored or co-authored 40+ peer-reviewed publications in journals including the Journal of Clinical Epidemiology, BMC Medical Research Methodology, and Research Synthesis Methods. A former Cochrane Review Group statistician and current editorial board member of Systematic Reviews, Dr. Mitchell has supervised 200+ evidence synthesis projects across clinical medicine, public health, and social sciences. She reviews all Research Gold tools to ensure statistical accuracy and compliance with Cochrane Handbook and PRISMA 2020 standards.
Our PhD statisticians run the complete pipeline: differential expression with multiple-testing correction, survival modelling, dimensionality reduction, and publication-ready figures with a reproducible methods section. Constant pricing, most projects delivered in under two weeks.
Our promise: Free pipeline re-run and figure revisions if reviewers push back.
Your project is led by a named PhD methodologist with real credentials and published work.
4.9 / 5 across 1,194+ delivered projects