Calculate GC content, complement, reverse complement, RNA transcription, and protein translation
This analyzer processes your DNA sequence to calculate GC content (percentage of G and C bases), generate the complementary strand (A↔T, G↔C), produce the mRNA transcript (T→U), and identify open reading frames. Each analysis applies fundamental molecular biology rules to transform or characterize your input sequence in seconds.
DNA sequence analysis is foundational to modern biology and medicine. It enables gene identification, mutation detection, evolutionary studies, forensic identification, and personalized medicine. Researchers analyze sequences to understand disease mechanisms, develop targeted therapies, trace ancestry, and engineer organisms for biotechnology applications ranging from agriculture to pharmaceuticals.
GC content affects DNA stability because G-C base pairs form three hydrogen bonds versus two for A-T pairs. Higher GC content means higher melting temperature and greater structural stability. GC content varies across organisms—human DNA averages ~41%, while some thermophilic bacteria exceed 70%. It also influences PCR primer design and gene expression levels.
Enter only valid nucleotide characters (A, T, G, C). Remove header lines and spaces from FASTA-formatted sequences before pasting. For RNA sequences, replace U with T before analysis. Verify results against known reference sequences in databases like NCBI GenBank. When analyzing coding regions, ensure your sequence starts with ATG (start codon) for accurate ORF detection.
DNA (deoxyribonucleic acid) is the molecule that carries genetic instructions in all living organisms. Analyzing DNA sequences is fundamental to molecular biology, genetics research, forensic science, and biotechnology.
GC content refers to the percentage of nitrogenous bases in a DNA molecule that are either guanine (G) or cytosine (C). This metric is important because:
DNA consists of two strands that are complementary to each other. The base pairing rules are:
The reverse complement is essential for PCR primer design, sequencing analysis, and understanding gene structure.
Transcription is the process of creating RNA from DNA. During transcription:
Translation is the process of synthesizing proteins from mRNA. The genetic code is read in triplets called codons:
| Codon | Amino Acid | Codon | Amino Acid | Codon | Amino Acid |
|---|---|---|---|---|---|
| AUG | Met (Start) | UUU/UUC | Phe | UAU/UAC | Tyr |
| GUU/GUC/GUA/GUG | Val | UUA/UUG/CUU/CUC/CUA/CUG | Leu | UAA/UAG/UGA | Stop |
| GCU/GCC/GCA/GCG | Ala | CCU/CCC/CCA/CCG | Pro | CAU/CAC | His |
| GGU/GGC/GGA/GGG | Gly | ACU/ACC/ACA/ACG | Thr | CAA/CAG | Gln |
| UCU/UCC/UCA/UCG/AGU/AGC | Ser | AAU/AAC | Asn | AAA/AAG | Lys |
| CGU/CGC/CGA/CGG/AGA/AGG | Arg | GAU/GAC | Asp | GAA/GAG | Glu |
| AUU/AUC/AUA | Ile | UGU/UGC | Cys | UGG | Trp |
GC content is the percentage of bases in a DNA sequence that are guanine (G) or cytosine (C). It is calculated as (G + C) / (A + T + G + C) multiplied by 100. GC content is an important characteristic because it affects the physical properties of DNA, including its melting temperature and structural stability. Different organisms have characteristic GC content ranges, from around 25% in some parasites to over 70% in certain thermophilic bacteria.
GC content matters because G-C base pairs form three hydrogen bonds compared to two for A-T pairs, making GC-rich regions more thermally stable with higher melting temperatures. This affects PCR primer design (primers need 40-60% GC for reliable annealing), DNA denaturation conditions, probe hybridization stringency, and gene expression. GC content also varies across genomes, helping identify coding regions, and is used in species identification and evolutionary studies.
An open reading frame (ORF) is a stretch of DNA that begins with a start codon (ATG) and ends with a stop codon (TAA, TAG, or TGA) with no intervening stop codons. To find ORFs, scan the sequence in all three reading frames on both strands (six frames total). Look for ATG start codons and read in triplets until a stop codon is reached. Longer ORFs are more likely to encode real proteins. Bioinformatics tools automate this process and can also account for codon usage bias to improve predictions.