DNA Shuffle
Shuffle DNA sequences while preserving nucleotide composition. Generate randomized control sequences for statistical analysis and hypothesis testing.
What is DNA Shuffle?
DNA Shuffle generates randomized DNA sequences that preserve specific compositional properties of the original sequence. Three shuffling methods are available: mononucleotide shuffling preserves exact nucleotide counts (A, T, C, G), dinucleotide shuffling preserves all 16 dinucleotide frequencies, and k-mer shuffling preserves frequencies of longer subsequences.
Shuffled sequences serve as statistical controls in bioinformatics analyses. When testing whether a sequence property (such as predicted secondary structure stability or regulatory motif enrichment) is significant, comparing against randomized sequences with matching composition provides a null distribution for hypothesis testing.
How to use DNA Shuffle online
ProteinIQ runs DNA shuffling directly in the browser with instant results, no installation or account required.
Input
| Input | Description |
|---|---|
DNA sequences | One or more sequences in FASTA format, or a raw sequence without headers. Only A, T, C, G nucleotides are accepted. |
Settings
Shuffle options
| Setting | Description |
|---|---|
Shuffle method | Algorithm for randomization. Mononucleotide (default) preserves single nucleotide counts. Dinucleotide preserves all 16 dinucleotide frequencies. K-mer preserves frequencies of specified k-mer length. |
K-mer size | Size of k-mers to preserve (2–6), only used with K-mer method. Larger values constrain the shuffle more heavily. |
Number of shuffles | How many randomized sequences to generate per input (1–100). Multiple shuffles provide replicates for statistical analyses. |
Random seed | Seed value for reproducibility (0 = random seed). Setting a specific seed ensures identical output across runs. |
Output formatting
| Setting | Description |
|---|---|
Output case | Uppercase (default) or Lowercase for output sequences. |
Add suffix to headers | Appends _shuffled or _shuffled_N to FASTA headers. Enabled by default. |
Line length | Characters per line in output (0–200, default 80). Set to 0 for no line wrapping. |
Output
FASTA-formatted sequences with shuffled nucleotide order. When generating multiple shuffles per input, each receives a numbered suffix.
How DNA Shuffle works
Mononucleotide shuffling
The simplest method uses the Fisher-Yates algorithm to randomly permute all nucleotides. The result has identical nucleotide counts but completely randomized order, destroying any dinucleotide or higher-order patterns.
Dinucleotide shuffling
Preserving dinucleotide frequencies requires the Altschul-Erickson algorithm, which models the sequence as a directed graph. Each nucleotide (A, T, C, G) becomes a vertex, and each dinucleotide in the sequence becomes a directed edge. The shuffled sequence is reconstructed by finding a random Eulerian path through this graph—a path that traverses each edge exactly once.
Because the graph preserves all dinucleotide transitions from the original sequence, the shuffled output maintains the same dinucleotide composition. This matters for RNA folding analyses where stacking energies depend on adjacent base pairs.
K-mer shuffling
The generalized Euler algorithm extends dinucleotide shuffling to arbitrary k-mer sizes. Instead of single nucleotides as vertices, the graph uses (k-1)-mers. Each k-mer in the original sequence creates an edge between its prefix and suffix (k-1)-mers. Finding an Eulerian path through this graph produces a sequence preserving all k-mer frequencies.
Larger k values impose stronger constraints. With k=6, the shuffled sequence maintains the same hexanucleotide composition as the original, which may be important when codon usage or restriction site patterns need preservation.
Applications
Shuffled sequences commonly serve as negative controls for:
- Motif discovery: Testing whether identified patterns occur more frequently than expected by chance
- RNA structure prediction: Determining if predicted folding stability exceeds that of composition-matched random sequences
- Regulatory element analysis: Validating that putative binding sites show genuine enrichment
- Alignment scoring: Establishing background distributions for sequence similarity statistics
Dinucleotide shuffling is particularly important for RNA analyses because secondary structure free energies depend heavily on stacking interactions between adjacent bases. Mononucleotide-shuffled controls may have systematically different folding energies simply due to altered dinucleotide composition.
Related tools
- Random DNA: Generate synthetic sequences with specified GC content
- DNA Mutator: Introduce point mutations, insertions, or deletions
- GC Content: Analyze nucleotide composition
- Reverse Complement: Generate complementary strand sequences
- RNAfold: Predict RNA secondary structure (uses shuffled controls for significance testing)