GC sliding window

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Calculate GC% across DNA or RNA sequences using sliding windows. Supports FASTA or plain sequence input, custom window and step size, per-sequence plots, CSV export, and SVG/PNG/PDF plot downloads.

Input
Accepts FASTA or plain sequence text.
Plain-text FASTA or sequence text. Processed synchronously.
Counted alphabet
Window settings
Limits: up to 10 MB (uncompressed) • up to 50,000 records

About this tool

GC sliding window calculates GC% across sequence positions instead of reporting one global value. Ambiguous or non-standard characters are excluded from each window denominator and reported separately. For a single overall GC%, use the GC content calculator. For broader dataset metrics, use Sequence stats. For nucleotide alphabets and ambiguity codes, see the sequence alphabets reference.

For larger datasets, multi-file runs, or more involved workflows, this can be executed separately as a custom analysis.

Tool guarantees
  • ✓ No hidden transformations
  • ✓ Input processed only for this request
  • ✓ FASTA structure preserved in output

Results

Submit input to see results here.
Running analyses at scale?
We execute bioinformatics analyses and data-processing workflows on your data.
Useful for larger datasets, multiple files, or tasks that are not convenient to run locally.
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  • Window size: number of bases included in each GC% calculation.
  • Step size: number of bases between consecutive window starts.
  • Coordinates: start and end positions are 1-based and inclusive.
  • GC%: G and C divided by counted bases in the selected DNA or RNA alphabet.
  • Ambiguous bases: characters such as N are excluded from the denominator.

  • Scan sequences for local GC-rich or GC-poor regions.
  • Compare composition patterns across FASTA records.
  • Prepare positional GC% tables for plotting or downstream analysis.
  • Check synthetic sequences or contigs for abrupt composition changes.

A sliding-window GC calculation reports local sequence composition rather than one global GC%. Smaller windows show finer positional changes, while larger windows smooth local variation.