Tutorial 2: Quality Control and Preprocessing

This tutorial demonstrates how to assess and improve the quality of genomic data before analysis. Quality control is essential for ensuring reliable downstream results.

Learning Objectives

By the end of this tutorial, you will understand:

• How to assess sequencing data quality using multiple metrics
• Common quality issues and their biological implications
• Preprocessing techniques for improving data quality
• Statistical approaches for quality assessment
• Best practices for quality control in different analysis types

Setup

import Pkg
if isinteractive()
    Pkg.activate("..")
end

import Test
import Mycelia
import FASTX
import Statistics
import Random

Random.seed!(42)

Part 1: Understanding Quality Metrics

Quality assessment involves multiple metrics that capture different aspects of data quality. Understanding these metrics helps identify problems and guide preprocessing decisions.

println("=== Quality Control Tutorial ===")

Phred Quality Scores

Phred scores represent the probability of base-calling errors:

• Q10 = 10% error rate (1 in 10 bases wrong)
• Q20 = 1% error rate (1 in 100 bases wrong)
• Q30 = 0.1% error rate (1 in 1000 bases wrong)
• Q40 = 0.01% error rate (1 in 10,000 bases wrong)

println("Quality Score Interpretation:")
println("Q10: 10% error rate (poor quality)")
println("Q20: 1% error rate (acceptable)")
println("Q30: 0.1% error rate (good quality)")
println("Q40: 0.01% error rate (excellent quality)")

Sequence Composition

Analyze nucleotide composition for bias detection

Generate test sequences with different quality characteristics

sequences = [
    ("High Quality", Mycelia.random_fasta_record(moltype=:DNA, seed=1, L=1000)),
    ("AT-Rich", Mycelia.random_fasta_record(moltype=:DNA, seed=2, L=1000)),  ## TODO: Add AT bias
    ("GC-Rich", Mycelia.random_fasta_record(moltype=:DNA, seed=3, L=1000)),  ## TODO: Add GC bias
]

for (name, seq) in sequences
    seq_str = String(FASTX.sequence(seq))

    # Calculate composition
    a_count = count(c -> c == 'A', seq_str)
    t_count = count(c -> c == 'T', seq_str)
    g_count = count(c -> c == 'G', seq_str)
    c_count = count(c -> c == 'C', seq_str)

    at_content = (a_count + t_count) / length(seq_str)
    gc_content = (g_count + c_count) / length(seq_str)

    println("\n$name Composition:")
    println("  AT content: $(round(at_content*100, digits=1))%")
    println("  GC content: $(round(gc_content*100, digits=1))%")
    println("  Length: $(length(seq_str)) bp")
end

Part 2: Simulating Quality Issues

Create datasets with common quality problems to understand their impact

println("\n=== Simulating Quality Issues ===")

TODO: Implement functions to simulate:

• Adapter contamination
• Quality degradation at read ends
• PCR duplicates
• Overrepresented sequences
• Coverage bias

Part 3: Quality Assessment Tools

Implement comprehensive quality assessment

println("\n=== Quality Assessment ===")

TODO: Implement quality assessment functions:

• Per-base quality scores
• Per-sequence quality scores
• GC content distribution
• Sequence length distribution
• Duplication levels
• Overrepresented sequences

Part 4: Preprocessing Techniques

Apply preprocessing to improve data quality

println("\n=== Preprocessing Techniques ===")

TODO: Implement preprocessing functions:

• Quality trimming
• Adapter removal
• Length filtering
• Complexity filtering
• Duplicate removal

Part 5: Quality Control for Different Analysis Types

Different analyses have different quality requirements

println("\n=== Analysis-Specific QC ===")

TODO: Implement analysis-specific QC:

• Genome assembly QC
• Variant calling QC
• RNA-seq QC
• Metagenomics QC

Part 6: Quality Control Visualization

Create plots to visualize quality metrics

println("\n=== Quality Visualization ===")

TODO: Implement quality visualization:

• Quality score distributions
• Per-base quality plots
• GC content plots
• Length distribution plots

Summary

println("\n=== Quality Control Summary ===")
println("✓ Understanding quality metrics and their biological implications")
println("✓ Identifying common quality issues in sequencing data")
println("✓ Applying appropriate preprocessing techniques")
println("✓ Tailoring quality control to specific analysis types")
println("✓ Visualizing quality metrics for assessment")

println("\nNext: Tutorial 3 - K-mer Analysis and Feature Extraction")

nothing