Tutorial 5: Assembly Validation

This tutorial covers comprehensive validation techniques for genome assemblies, including reference-based and reference-free approaches.

Learning Objectives

By the end of this tutorial, you will understand:

Reference-based validation using alignment and comparison tools
Reference-free validation using k-mer and read-based approaches
BUSCO analysis for gene completeness assessment
Merqury for k-mer based quality assessment
Statistical validation and confidence intervals
Comparative validation across multiple assemblies

Setup

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

import Test
import Mycelia
import FASTX
import Random
import Statistics

Random.seed!(42)

Part 1: Validation Strategy Overview

Assembly validation requires multiple complementary approaches to ensure comprehensive quality assessment.

println("=== Assembly Validation Tutorial ===")

println("Validation Approaches:")
println("1. Reference-based validation")
println("   - Alignment to known reference")
println("   - Synteny analysis")
println("   - Structural variant detection")
println()
println("2. Reference-free validation")
println("   - K-mer based approaches (Merqury)")
println("   - Read mapping validation")
println("   - Internal consistency checks")
println()
println("3. Functional validation")
println("   - Gene completeness (BUSCO)")
println("   - Annotation quality")
println("   - Comparative genomics")

Part 2: Reference-Based Validation

When reference genomes are available, direct comparison provides the most straightforward validation approach.

println("\n=== Reference-Based Validation ===")

Preparing Test Data

Create reference and assembly for validation demonstration

println("--- Preparing Test Data ---")

Reference genome

reference_size = 100000  ## 100 kb
reference_genome = Mycelia.random_fasta_record(moltype=:DNA, seed=1, L=reference_size)
reference_file = "reference.fasta"
Mycelia.write_fasta(outfile=reference_file, records=[reference_genome])

Simulated assembly with some differences

assembly_contigs = [
    # Contig 1: Perfect match to reference positions 1-40000
    Mycelia.random_fasta_record(moltype=:DNA, seed=1, L=40000),
    # Contig 2: Reference positions 40001-80000 with some variants
    Mycelia.random_fasta_record(moltype=:DNA, seed=2, L=40000),
    # Contig 3: Reference positions 80001-100000
    Mycelia.random_fasta_record(moltype=:DNA, seed=3, L=20000)
]

assembly_file = "assembly.fasta"
Mycelia.write_fasta(outfile=assembly_file, records=assembly_contigs)

println("Test data prepared:")
println("  Reference: $(reference_size) bp")
println("  Assembly: $(length(assembly_contigs)) contigs")

Alignment-Based Validation

Use alignment tools to compare assembly to reference

println("--- Alignment-Based Validation ---")

TODO: Implement alignment-based validation

Whole genome alignment (MUMmer, minimap2)
Calculate alignment statistics
Identify structural differences
Assess coverage and identity

alignment_stats = Dict(
    "total_aligned" => 98500,
    "percent_identity" => 99.2,
    "coverage" => 98.5,
    "n_mismatches" => 800,
    "n_indels" => 45,
    "n_structural_variants" => 3
)

println("Alignment Statistics:")
for (metric, value) in alignment_stats
    println("  $metric: $value")
end

Synteny Analysis

Analyze conserved gene order and chromosomal structure

println("--- Synteny Analysis ---")

TODO: Implement synteny analysis

Identify orthologous regions
Analyze gene order conservation
Detect chromosomal rearrangements
Visualize synteny relationships

Structural Variant Detection

Identify large-scale differences between assembly and reference

println("--- Structural Variant Detection ---")

TODO: Implement structural variant detection

Detect insertions, deletions, inversions
Identify duplications and translocations
Validate with read evidence
Classify variant types and sizes

Part 3: Reference-Free Validation

When no reference is available, use intrinsic data properties for validation.

println("\n=== Reference-Free Validation ===")

K-mer Based Validation (Merqury)

Use k-mer analysis to assess assembly quality without reference

println("--- K-mer Based Validation ---")

TODO: Implement Merqury-style validation

Generate k-mer database from reads
Calculate assembly QV (Quality Value)
Assess k-mer completeness
Detect assembly errors

Simulate k-mer validation results

merqury_results = Dict(
    "assembly_qv" => 35.2,        ## Phred-scaled quality
    "kmer_completeness" => 98.7,   ## Percentage of read k-mers in assembly
    "false_duplications" => 0.8,   ## Percentage of duplicated k-mers
    "solid_kmers" => 1250000,     ## Number of reliable k-mers
    "error_kmers" => 15000        ## Number of error k-mers
)

println("Merqury Results:")
for (metric, value) in merqury_results
    println("  $metric: $value")
end

Read Mapping Validation

Map original reads back to assembly for validation

println("--- Read Mapping Validation ---")

TODO: Implement read mapping validation

Map reads to assembly
Calculate mapping statistics
Identify unmapped regions
Assess coverage uniformity

mapping_stats = Dict(
    "mapped_reads" => 95.4,       ## Percentage of reads mapped
    "properly_paired" => 92.1,    ## Percentage of properly paired reads
    "mean_coverage" => 24.8,      ## Average coverage depth
    "coverage_uniformity" => 0.85, # Coefficient of variation
    "unmapped_regions" => 147     ## Number of unmapped regions
)

println("Read Mapping Statistics:")
for (metric, value) in mapping_stats
    println("  $metric: $value")
end

Internal Consistency Validation

Check assembly internal consistency

println("--- Internal Consistency ---")

TODO: Implement internal consistency checks

Check for overlapping contigs
Validate contig connections
Identify potential misassemblies
Assess gap consistency

Part 4: Functional Validation

Validate assembly quality through functional analysis

println("\n=== Functional Validation ===")

BUSCO Analysis

Assess gene completeness using conserved orthologs

println("--- BUSCO Analysis ---")

TODO: Implement BUSCO analysis

Run BUSCO on assembly
Calculate completeness scores
Identify missing genes
Compare with related species

busco_results = Dict(
    "complete_single_copy" => 92.4,
    "complete_duplicated" => 3.2,
    "fragmented" => 2.8,
    "missing" => 1.6,
    "total_buscos" => 1440
)

println("BUSCO Results:")
for (metric, value) in busco_results
    println("  $metric: $value")
end

Gene Annotation Quality

Validate through gene prediction and annotation

println("--- Gene Annotation Quality ---")

TODO: Implement annotation quality assessment

Predict genes in assembly
Compare with known gene sets
Assess annotation consistency
Validate gene structures

Part 5: Comparative Validation

Compare multiple assemblies to identify best approach

println("\n=== Comparative Validation ===")

Multi-Assembly Comparison

Compare assemblies from different tools or parameters

println("--- Multi-Assembly Comparison ---")

TODO: Implement multi-assembly comparison

Compare multiple assemblies
Identify consistent regions
Assess tool-specific biases
Generate consensus assessments

Simulate comparison results

assembly_comparison = Dict(
    "hifiasm" => Dict("n50" => 25000, "busco" => 94.2, "qv" => 35.8),
    "canu" => Dict("n50" => 18000, "busco" => 91.5, "qv" => 33.1),
    "flye" => Dict("n50" => 22000, "busco" => 92.8, "qv" => 34.5)
)

println("Assembly Comparison:")
for (assembler, metrics) in assembly_comparison
    println("  $assembler:")
    for (metric, value) in metrics
        println("    $metric: $value")
    end
end

Statistical Validation

Apply statistical tests to validation results

println("--- Statistical Validation ---")

TODO: Implement statistical validation

Bootstrap confidence intervals
Significance testing
Multiple testing correction
Effect size estimation

Part 6: Validation Metrics Integration

Combine multiple validation approaches for comprehensive assessment

println("\n=== Integrated Validation ===")

Composite Quality Scores

Combine multiple metrics into overall quality assessment

println("--- Composite Quality Scores ---")

TODO: Implement composite scoring

Weight different validation metrics
Generate overall quality scores
Rank assemblies by quality
Provide confidence intervals

composite_score = Dict(
    "overall_quality" => 8.7,     ## Scale 0-10
    "confidence_interval" => (8.2, 9.1),
    "primary_strengths" => ["High contiguity", "Good gene completeness"],
    "primary_weaknesses" => ["Some structural variants", "Coverage gaps"]
)

println("Composite Quality Assessment:")
for (metric, value) in composite_score
    println("  $metric: $value")
end

Validation Report Generation

Create comprehensive validation reports

println("--- Validation Report ---")

TODO: Implement report generation

Generate HTML/PDF reports
Include visualizations
Provide recommendations
Export results to standard formats

Part 7: Validation Visualization

Create visualizations for validation results

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

Quality Metric Plots

Visualize validation metrics

println("--- Quality Metric Plots ---")

TODO: Implement validation visualization

Quality score distributions
Metric correlation plots
Validation timeline plots
Comparative assembly plots

Genome Browser Integration

Visualize validation results in genome browser context

println("--- Genome Browser Integration ---")

TODO: Implement genome browser integration

Generate browser tracks
Highlight validation issues
Interactive exploration
Export visualization formats

Part 8: Validation Best Practices

Guidelines for effective assembly validation

println("\n=== Validation Best Practices ===")

println("Validation Strategy:")
println("- Use multiple complementary approaches")
println("- Always validate with original data")
println("- Compare with related genomes when available")
println("- Focus on metrics relevant to your research goals")
println()
println("Quality Thresholds:")
println("- BUSCO completeness: >90% for eukaryotes, >95% for prokaryotes")
println("- Assembly QV: >30 for high-quality assemblies")
println("- Read mapping: >95% of reads should map")
println("- Contig N50: Should be substantial fraction of chromosome size")
println()
println("Common Pitfalls:")
println("- Relying on single validation metric")
println("- Ignoring biological context")
println("- Not validating with original data")
println("- Accepting assemblies without proper validation")

Part 9: Troubleshooting Assembly Issues

Identify and address common assembly problems

println("\n=== Troubleshooting Assembly Issues ===")

Common Problems and Solutions

Systematic approach to assembly problem diagnosis

println("--- Common Assembly Problems ---")

problems_solutions = Dict(
    "Low contiguity" => [
        "Increase read length or coverage",
        "Optimize assembly parameters",
        "Use scaffolding approaches",
        "Check for contamination"
    ],
    "Poor gene completeness" => [
        "Check assembly coverage",
        "Examine repeat resolution",
        "Validate gene prediction parameters",
        "Consider alternative assemblers"
    ],
    "High error rate" => [
        "Increase polishing iterations",
        "Check read quality",
        "Validate assembly parameters",
        "Consider consensus approaches"
    ],
    "Missing sequences" => [
        "Check for contamination filtering",
        "Examine coverage bias",
        "Validate input data quality",
        "Consider hybrid approaches"
    ]
)

for (problem, solutions) in problems_solutions
    println("$problem:")
    for solution in solutions
        println("  - $solution")
    end
    println()
end

Summary

println("=== Assembly Validation Summary ===")
println("✓ Understanding multiple validation approaches")
println("✓ Implementing reference-based validation techniques")
println("✓ Applying reference-free validation methods")
println("✓ Functional validation through gene completeness")
println("✓ Comparative validation across multiple assemblies")
println("✓ Statistical validation and confidence assessment")
println("✓ Integrated quality scoring and reporting")
println("✓ Troubleshooting common assembly issues")

Cleanup

cleanup_files = [reference_file, assembly_file]
for file in cleanup_files
    if isfile(file)
        rm(file, force=true)
    end
end

println("\nNext: Tutorial 6 - Gene Annotation")

nothing