Reveal sequence bias using clear codon usage statistics. Compare frames, strands, and genetic codes easily. Download CSV or PDF outputs for fast sharing today.
Codon usage describes how often each synonymous nucleotide triplet appears in a coding sequence. Multiple codons can encode the same amino acid, yet biological systems often prefer some codons over others. In quantitative modeling, this bias is treated like a distribution over discrete symbols, enabling comparisons between genes, strains, or design variants.
This analyzer reports raw codon counts and converts them into frequency (%) and codons per 1000. Per‑1000 rates help compare sequences of different lengths without losing interpretability. When you align these metrics with amino‑acid totals, you can separate protein composition effects from true synonymous selection.
GC% summarizes the fraction of G and C across the cleaned sequence, while GC3% focuses only on third codon positions. GC3 often changes rapidly under mutational pressure and can dominate synonymous patterns. In simulation workflows, GC and GC3 can be treated as constraints when generating synthetic sequences or evaluating null models.
Relative Synonymous Codon Usage (RSCU) normalizes each codon against the expectation under equal use among synonyms for the same amino acid. An RSCU near 1 indicates neutral usage; values above 1 indicate preference. Because it controls for amino‑acid totals, RSCU is well suited for cross‑gene comparisons and clustering analyses.
Codons depend on reading frame, so shifting frame changes every triplet boundary and can transform the statistical signature. Strand selection is equally important: antisense analysis uses the reverse‑complement and may be useful for validation or when sequences are provided in an opposite orientation. Always analyze the biologically relevant coding frame to avoid misleading bias patterns.
Different organisms and organelles decode codons differently. Selecting an appropriate genetic code table ensures correct amino‑acid mapping and stop identification, which directly affects amino‑acid totals and RSCU expectations. For mitochondrial sequences, using a mitochondrial code can change STOP assignments and recast interpretation of apparent anomalies.
Codon preference can correlate with tRNA availability, translation speed, and error rates. In applied contexts, codon optimization balances expression goals against constraints such as GC3, motif avoidance, and secondary‑structure propensity. For computational studies, these outputs support hypothesis testing by comparing observed distributions against randomized sequences matched on amino‑acid content or GC.
Exporting tables to CSV supports downstream statistics, while PDF export is useful for lab notebooks, reports, and peer review. For reproducible workflows, record the input sequence source, frame, strand, and code table. When comparing datasets, standardize these settings so differences reflect biology or design choices rather than analysis configuration.
Paste plain DNA/RNA or FASTA. Headers starting with “>” are ignored, and non‑nucleotide characters are removed. The cleaned sequence preview shows what was actually analyzed.
RNA input is accepted. U is converted to T internally so codons can be mapped consistently. Your results still represent the same triplets, just expressed in the standard DNA alphabet.
Stops are part of the genetic code and may appear in short sequences, incomplete CDS, or incorrect frames. They are counted for frequency, but RSCU is not reported for stop codons.
Frames shift triplet boundaries. A one‑base offset produces a completely different codon series and usually introduces premature stops. Use the coding frame defined by your annotation or ORF.
Use it if your sequence is provided in antisense orientation or you want to verify orientation assumptions. For typical coding sequences already in the correct direction, keep the sense option.
It means the codon is used about twice as often as expected under equal usage among synonymous codons for that amino acid, given the amino‑acid total in your sequence.
Longer coding sequences are better. Very short inputs can be dominated by chance, especially for rare codons. For organism‑level profiles, aggregate many CDS or a full gene set.
| Example sequence (DNA) | Frame | Expected notes |
|---|---|---|
| ATGGCTGCTGCTGAACTGCTGCTTAA | 1 | Starts with ATG (M). Ends with TAA stop. High GCT/GAA usage in this short sample. |
| AUGGCU... (RNA input allowed) | 1 | U is converted to T for analysis. Codons are computed after cleaning. |
Tip: For realistic codon usage, analyze longer coding sequences (CDS) from the same gene set or organism.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.