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DNA, RNA & Protein

Codon Frequency and RSCU Comparison Workbench

Count complete codons in one or two declared coding records, calculate per-1,000 frequencies and relative synonymous codon usage, and expose every zero and trailing base.

Biology · experimental measurements

Separate raw codon counts, record-length-normalized frequency and within-amino-acid RSCU instead of collapsing them into one codon score.

Private calculations in your browser · explicit inputs and model boundaries
Example preview · One coding recordMost frequently observed sense codons
AAA1 codons
ATG1 codons
GAA1 codons
GAG1 codons
GCA1 codons
GCC1 codons
GCT1 codons

Bars show raw counts for the most frequent sense codons in record A; the table retains all 61 sense codons including zeros.

  1. 1EnterProvide the known values
  2. 2CalculateResults update automatically
  3. 3VerifyReview the details and units
Try an example

Canonical bases only; complete codons are counted from the selected offset.

Calculation result

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Understand the relationship

The reasoning behind the result

Raw counts and normalized frequencies answer different questions

fᵢ = nᵢ/Nsense × 1,000

Raw count preserves how often a codon occurs in the entered record. Per-1,000 frequency adjusts only for the number of complete sense codons.

Neither value corrects for amino-acid composition.

RSCU normalizes within synonymous families

RSCUᵢ = nᵢ/(Σfamily n/family size)

RSCU of one means equal use within the observed amino-acid family. Values above or below one describe the entered records only.

RSCU is undefined when that amino acid never occurs, and the table says so rather than reporting a false zero.

Frame and code define codon identity

Changing the first complete codon changes every triplet. The workflow uses NCBI standard code table 1 and reports incomplete trailing bases.

Stop codons are counted separately and excluded from sense-codon normalization.

Usage differences are descriptive

A codon-frequency or RSCU difference does not establish expression, translation speed, optimization or organism adaptation.

Reference sets, gene selection, amino-acid composition, tRNA pools and experimental context remain separate.

Follow the numbers

Calculate RSCU for one synonymous family

  1. Select the declared coding frame and divide the record into complete codons.
  2. Count each standard-code sense codon and count stops separately.
  3. For one amino acid, sum all observed synonymous-family codons.
  4. Divide the family total by the number of synonymous codons to obtain equal-use expectation.
  5. Divide each codon's observed count by that expectation; leave the family undefined when its total is zero.

RSCU compares codon choice within an observed amino-acid family; it is not an expression or fitness prediction.

Quick guide

How to use this calculator

  1. Declare whether the records are coding DNA or mRNA and select the actual codon offset.
  2. Use single mode for a complete frequency ledger or comparison mode for signed RSCU changes.
  3. Review stop codons and trailing bases before interpreting normalized values.

Calculation method

Calculation and interpretation

Separate raw codon counts, record-length-normalized frequency and within-amino-acid RSCU instead of collapsing them into one codon score.

Frequency per 1,000 = codon count ÷ sense-codon total × 1,000. RSCU = observed codon count ÷ (amino-acid family count ÷ synonymous-family size).

Worked example

Calculate RSCU for one synonymous family

RSCU compares codon choice within an observed amino-acid family; it is not an expression or fitness prediction.

Frequency per 1,000 = codon count ÷ sense-codon total × 1,000. RSCU = observed codon count ÷ (amino-acid family count ÷ synonymous-family size).

Supported inputs

Precision and limits

Exact canonical coding records

Ambiguity codes, gaps and aligned columns are excluded from exact codon counts.

One selected frame

The workflow does not infer coding start, splicing or a reading frame.

Standard code only

NCBI standard code table 1 is used; alternative genetic codes require a different model.

Descriptive normalization

RSCU and per-1,000 frequencies do not prove codon optimization, expression, speed or adaptation.

No external reference set

Organism databases, highly expressed gene sets and tRNA abundance are not supplied or inferred.

Numerical support

Each record is limited to 200,000 nucleotides and must contain at least one complete sense codon in the selected frame. Cross-record RSCU remains undefined for amino-acid families absent from either record.

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