Genomic Analysis Example

This example demonstrates how to use Kwasa-Kwasa’s genomic extension to analyze DNA sequences. It shows various operations including finding open reading frames, calculating GC content, motif searching, and sequence manipulations.

Source Code

// Example of genomic sequence analysis using Turbulance

// Define a DNA sequence
item dna_sequence = "ATGCCCGGGTAATCGGTAACGGCTAGCATTGCATGCATCGA"

// Function to find open reading frames
funxn find_orfs(sequence):
    // Split the sequence into codons
    item codons = sequence / "codon"
    item orfs = []
    
    within sequence:
        // Find start codons (ATG)
        given contains("ATG"):
            item start_pos = index_of("ATG")
            item orf = extract_from(start_pos)
            
            // Ensure it has a valid length
            given len(orf) >= 6 and len(orf) % 3 == 0:
                // Check for stop codons
                given not contains(["TAA", "TAG", "TGA"]):
                    orfs.append(orf)
    
    return orfs

// Function to calculate GC content
funxn gc_content(sequence):
    item gc_count = 0
    
    within sequence as nucleotides:
        given nucleotide in ["G", "C"]:
            gc_count = gc_count + 1
    
    return gc_count / len(sequence)

// Function to find motifs
funxn find_motifs(sequence, motif_pattern):
    item locations = []
    item current_pos = 0
    
    while current_pos < len(sequence):
        item found_at = sequence.find(motif_pattern, current_pos)
        
        given found_at != -1:
            locations.append(found_at)
            current_pos = found_at + 1
        given otherwise:
            break
    
    return locations

Code Explanation

1. DNA Sequence Analysis Functions

Finding Open Reading Frames (ORFs)

funxn find_orfs(sequence):
    // Split the sequence into codons
    item codons = sequence / "codon"
    // ...

The find_orfs function:

• Splits DNA into codons (3-nucleotide units)
• Looks for start codons (ATG)
• Checks for valid ORF length
• Ensures no stop codons in the reading frame

GC Content Calculation

funxn gc_content(sequence):
    item gc_count = 0
    within sequence as nucleotides:
        given nucleotide in ["G", "C"]:
            gc_count = gc_count + 1

This function:

• Iterates through nucleotides
• Counts G and C bases
• Returns the GC percentage

Motif Finding

funxn find_motifs(sequence, motif_pattern):
    // ...

Searches for specific DNA patterns and returns their positions.

2. Sequence Operations

The example demonstrates various sequence operations:

// Addition: concatenation
item combined_exons = exon1 + exon2

// Division: split by pattern
item fragments = dna_sequence / "GGT"

// Multiplication: special joining (recombination)
item recombined = exon1 * exon2

// Subtraction: remove pattern
item filtered = dna_sequence - "GGT"

3. Gene Regulation Analysis

proposition GeneRegulation:
    motion Activation("Gene X activates Gene Y through binding site GGTA")
    motion Inhibition("Gene Z inhibits Gene X when bound to sequence ATGC")
    
    within dna_sequence:
        given contains("GGTA"):
            print("Found activation site for Gene Y")

This section demonstrates:

• Modeling gene regulatory networks
• Binding site analysis
• Regulatory motif detection

4. Advanced Pattern Analysis

funxn analyze_pattern_frequencies(sequence, pattern_size=3):
    item patterns = sequence / pattern_size
    // ...

Features:

• K-mer frequency analysis
• Shannon entropy calculation
• Pattern distribution analysis

Running the Example

1. Save the code in a file with .turb extension
2. Run using the Kwasa-Kwasa interpreter:

   kwasa run genomic_analysis.turb
   

Expected Output

Found 2 open reading frames
GC content: 52.50%
Found motif GGTA at positions: [8, 15]
Combined exons: ATGCCCGGGGCTAGCATT
Fragments after splitting by GGT: ["AT", "AACGGCTAGCATTGCATGCATCGA"]
Found activation site for Gene Y
Sequence entropy: 4.32 bits
Top 3 most common patterns:
  GCT occurs 2 times
  ATG occurs 2 times
  GGT occurs 2 times

Key Concepts Demonstrated

1. Sequence Analysis:
   • Open Reading Frame detection
   • GC content calculation
   • Motif searching
   • Pattern frequency analysis
2. Mathematical Operations:
   • Sequence concatenation (+)
   • Pattern-based splitting (/)
   • Recombination (*)
   • Pattern removal (-)
3. Biological Concepts:
   • Gene regulation
   • DNA motifs
   • Sequence patterns
   • Codon analysis
4. Advanced Features:
   • Pattern frequency analysis
   • Information entropy
   • Reverse complement
   • Regulatory network modeling