Generate random RNA sequences with configurable RNA types, GC content, UTRs, and structural features.
mRNA
miRNA
tRNA
Generate batches of mutated DNA variants from one or more FASTA sequences. Create substitution, insertion, deletion, or mixed variant libraries with reproducible settings.
Generate random DNA sequences with customizable length, GC content, and restriction sites for molecular cloning and testing purposes.
Generate random protein sequences with customizable length, composition, and amino acid properties
Shuffle DNA sequences while preserving nucleotide, dinucleotide, or k-mer composition for generating randomized control sequences
Convert CSV and TSV files containing sequence data to FASTA format with flexible column mapping and automatic delimiter detection
Convert DNA sequences to RNA (transcription) - replaces T with U
Split large FASTA files into smaller chunks. Divide by sequence count or create individual files for each sequence.
Convert FASTA sequence files to FASTQ format with mock quality scores
Convert FASTQ sequence files to FASTA format
Extract sequence features (CDS, mRNA, gene, etc.) from GenBank files in FASTA format with support for spliced features
RNAGenIQ is a random RNA sequence generator that produces synthetic ribonucleotide sequences with configurable structural features. Generated sequences can include biologically relevant elements such as start and stop codons, untranslated regions, poly-A tails, and stem-loop structures, making them suitable as negative controls, test inputs for RNA analysis pipelines, and training data for machine learning models.
Random sequences are fundamental to computational RNA biology because they establish the statistical background against which real biological signals are measured. Tools for secondary structure prediction, motif discovery, and non-coding RNA classification all rely on random sequence baselines to calibrate significance thresholds and false-positive rates.
ProteinIQ runs RNAGenIQ directly in the browser with instant results. No account is required and no data leaves the local machine.
| Setting | Description | Default |
|---|---|---|
Number of sequences | How many sequences to generate | 1 |
Sequence length (nt) | Length of the RNA sequence in nucleotides | 100 |
GC content (%) | Target percentage of guanine and cytosine nucleotides | 50 |
Add start codon (AUG) | Prepend the AUG initiation codon | On |
Add stop codon | Append a stop codon (UAA, UAG, or UGA) | On |
Add 5' UTR | Include a 5' untranslated region containing a Kozak-like sequence | Off |
Add 3' UTR | Include a 3' untranslated region | Off |
Add poly-A tail | Append a polyadenylation tail | Off |
Poly-A tail length | Number of adenines in the poly-A tail (visible when Add poly-A tail is on) | 200 |
Add stem-loop structures | Insert complementary palindromic regions that form hairpin structures | Off |
Number of stem-loops | How many stem-loop structures to include (visible when Add stem-loop structures is on) | 1 |
Include pseudouridine | Replace a portion of uridines with pseudouridine, a modified nucleoside common in tRNA and rRNA | Off |
RNAGenIQ includes presets for common RNA types that automatically configure appropriate settings:
| Preset | Typical length | Key features enabled |
|---|---|---|
mRNA | 1000 nt | Start/stop codons, 5' and 3' UTRs, poly-A tail |
miRNA | 22 nt | Stem-loop structure |
tRNA | 75 nt | Three stem-loops, pseudouridine |
lncRNA | 2000 nt | Stem-loop structures |
Random RNA | 200 nt | No structural features |
Sequences are returned in FASTA format and can be copied to the clipboard or downloaded as a .fasta file.
The GC content parameter controls the ratio of guanine and cytosine to adenine and uracil in the generated sequence. GC base pairs form three hydrogen bonds compared to two for AU pairs, so GC-rich RNA tends to form more stable secondary structures and has a higher melting temperature.
Natural RNA GC content varies by organism and RNA type. Human mRNAs average roughly 50% GC, while thermophilic organisms produce RNAs with higher GC content to maintain structural stability at elevated temperatures. When generating sequences for use as controls, matching the GC content of the experimental dataset reduces compositional bias.
RNAGenIQ generates sequences by stochastic sampling and does not model the complex sequence constraints found in natural RNAs. The stem-loop structures inserted are generic palindromic motifs, not biologically accurate representations of specific structural elements like tRNA cloverleaf folds or riboswitch aptamer domains. For RNA secondary structure prediction of real or designed sequences, use RNAfold.