GC Content Analysis

Free

Calculate GC content and nucleotide composition in DNA/RNA sequences

Overview

GC Content Analysis calculates the percentage of guanine (G) and cytosine (C) nucleotides in a DNA or RNA sequence. GC content is a fundamental metric in genomics because:

- **Thermostability**: Higher GC content increases DNA melting temperature - **Genomic organization**: GC content varies across genomes and regions - **Gene prediction**: Helps identify coding vs. non-coding regions - **Taxonomic classification**: Different organisms have characteristic GC content ranges

Input Format

Supported formats

- Raw DNA/RNA sequence - FASTA format - Sequences of any length

Parameters

- **Window Size** (default: 10): Size of sliding window for GC content calculation - Smaller windows (5-10): Better resolution for local GC content variation - Larger windows (50-100): Smoother curves, better for overall trends

Example input

``` ATGCGATCGATCGATCGATCGATCGATCGATCGATCGATCG ```

Output Explanation

Nucleotide Composition

- Count and percentage of each nucleotide (A, T/U, G, C) - Total sequence length

GC Content Metrics

- **Average GC Content**: Overall percentage of G+C nucleotides - **GC Content Distribution**: GC content across the sequence using sliding windows - **GC Content Positions**: Coordinate positions for each window

Interpretation

- **Low GC (<40%)**: AT-rich regions, common in intergenic regions - **Medium GC (40-60%)**: Typical for many organisms - **High GC (>60%)**: GC-rich regions, common in highly expressed genes

Use Cases

**1. Genome Characterization** - Characterize overall genome composition - Identify GC-rich or AT-rich regions - Study isochore structures in genomes

**2. Gene Prediction** - Identify coding regions (often GC-rich) - Distinguish exons from introns - Predict gene boundaries

**3. Sequence Quality Control** - Detect contamination (unexpected GC content) - Validate sequence identity - Assess sequence composition

Tips & Best Practices

1. **Window size selection**: Use smaller windows (5-10) for detailed local analysis, larger (50-100) for overall trends 2. **Complete sequences**: Use full-length sequences for accurate composition 3. **Comparative analysis**: Compare GC content to reference genomes 4. **Consider context**: GC content varies by genomic region type 5. **Multiple sequences**: Analyze multiple sequences to understand variation