Visualization Gallery

NOTE - API Design Documentation: This gallery represents the intended visualization API for Mycelia. Most plotting functions shown are planned but not yet implemented. This serves as:
A design specification for visualization features
A record of planned capabilities
Examples of the intended API when implemented
Currently Available: Limited plotting functions including plot_kmer_frequency_spectra, plot_per_base_quality, bandage_visualize, tree visualization, and taxonomic abundance plots. See source code in src/plotting-and-visualization.jl for implemented functions.

This gallery showcases the intended plotting and visualization capabilities of Mycelia across different bioinformatics analysis workflows.

Overview

Mycelia plans to provide comprehensive visualization capabilities for:

Data Quality Assessment - Evaluate sequencing data quality (partially implemented)
Sequence Analysis - K-mer analysis and composition (partially implemented)
Assembly Visualization - Assembly statistics and comparisons (planned)
Annotation Plots - Gene features and functional summaries (planned)
Comparative Analysis - Phylogenetics and pangenome visualization (partially implemented)
Performance Monitoring - Benchmark and resource usage plots (planned)

Data Quality & Preprocessing (Partially Implemented)

FASTQ Quality Assessment

Per-Base Quality Scores (Implemented: plotperbase_quality)

# Plot quality scores across read positions
Mycelia.plot_per_base_quality("reads.fastq")

Shows quality degradation patterns typical in sequencing data

Read Length Distribution (Planned)

# Visualize read length characteristics (planned)
length_dist = Mycelia.calculate_read_lengths("reads.fastq")
Mycelia.plot_read_length_distribution(length_dist)

Identifies sequencing platform characteristics and potential issues

GC Content Analysis

# Analyze sequence composition bias
gc_data = Mycelia.calculate_gc_content("reads.fastq")
Mycelia.plot_gc_content_distribution(gc_data)

Detects contamination and sequencing bias

Coverage Uniformity

# Assess coverage evenness across genome
coverage_data = Mycelia.calculate_coverage("reads.bam", "reference.fasta")
Mycelia.plot_coverage_uniformity(coverage_data)

Identifies coverage bias and potential assembly issues

Sequence Analysis & K-mers (Partially Implemented)

K-mer Frequency Spectra (Implemented: plotkmerfrequency_spectra)

# Generate k-mer frequency histograms
kmer_counts = Mycelia.count_kmers("reads.fastq", k=21)
Mycelia.plot_kmer_frequency_spectra(kmer_counts, title="21-mer Frequency Spectrum")

Enables genome size estimation and error detection

K-mer Composition Heatmaps

# Visualize k-mer composition patterns
kmer_matrix = Mycelia.build_kmer_composition_matrix(sequences, k=4)
Mycelia.plot_kmer_heatmap(kmer_matrix, title="Tetranucleotide Composition")

Reveals sequence composition patterns and contamination

Genome Size Estimation

# Plot k-mer based genome size estimation
estimation_data = Mycelia.estimate_genome_size_from_kmers(kmer_counts)
Mycelia.plot_genome_size_estimation(estimation_data)

Provides independent genome size validation

Assembly Visualization (Planned)

Assembly Statistics (Planned)

# Comprehensive assembly quality metrics
assembly_stats = Mycelia.evaluate_assembly("contigs.fasta")
Mycelia.plot_assembly_statistics(assembly_stats)

Multi-panel plot showing N50, contig sizes, and quality metrics

Contig Size Distributions

# Visualize assembly contiguity
contig_lengths = Mycelia.get_contig_lengths("contigs.fasta")
Mycelia.plot_contig_distribution(contig_lengths, log_scale=true)

Shows assembly fragmentation and contiguity

Assembly Comparison Dot Plots

# Compare assemblies or validate against reference
comparison = Mycelia.align_assemblies("assembly1.fasta", "assembly2.fasta")
Mycelia.plot_assembly_dotplot(comparison)

Identifies structural differences and misassemblies

Coverage vs Length Analysis

# Analyze relationship between contig size and coverage
coverage_length_data = Mycelia.calculate_coverage_per_contig("reads.bam", "contigs.fasta")
Mycelia.plot_coverage_vs_length(coverage_length_data)

Detects potential contamination and assembly artifacts

Annotation & Gene Features (Planned)

Genome Browser Tracks (Planned)

# Create genome browser-style visualization
annotations = Mycelia.load_gff3("annotations.gff3")
sequence = Mycelia.load_fasta("genome.fasta")
Mycelia.plot_genome_browser(sequence, annotations, region="chr1:1000-5000")

Interactive genome browser with multiple annotation tracks

Gene Density Plots

# Visualize gene distribution across genome
gene_positions = Mycelia.extract_gene_positions("annotations.gff3")
Mycelia.plot_gene_density(gene_positions, window_size=10000)

Identifies gene-rich and gene-poor regions

Functional Annotation Summary

# Summarize functional categories
functional_data = Mycelia.analyze_functional_annotations("annotations.gff3")
Mycelia.plot_functional_categories(functional_data, plot_type="pie")

Overview of gene functional classifications

GO Term Enrichment

# Visualize gene ontology enrichment
enrichment_results = Mycelia.perform_go_enrichment(gene_list, background)
Mycelia.plot_go_enrichment(enrichment_results, top_n=20)

Identifies overrepresented biological processes

Comparative Genomics (Partially Implemented)

Phylogenetic Trees (Partially Implemented)

# Multiple tree layouts and styles
# Tree visualization functions available: draw_dendrogram_tree, draw_radial_tree
tree = Mycelia.build_phylogenetic_tree(core_genes)
Mycelia.plot_phylogenetic_tree(tree, layout="circular", show_support=true)

Publication-ready phylogenetic trees with branch support

Pangenome Heatmaps

# Gene presence/absence visualization
pangenome_matrix = Mycelia.build_pangenome_matrix(genomes)
Mycelia.plot_pangenome_heatmap(pangenome_matrix, cluster_rows=true, cluster_cols=true)

Shows gene distribution across genomes with clustering

Synteny Analysis

# Visualize conserved gene order
synteny_data = Mycelia.calculate_synteny(genome1, genome2)
Mycelia.plot_synteny_dotplot(synteny_data, min_block_size=1000)

Identifies chromosomal rearrangements and conserved regions

Population Structure

# Principal component analysis of genomic variation
pca_data = Mycelia.perform_genomic_pca(snp_matrix)
Mycelia.plot_population_structure(pca_data, color_by="population")

Reveals population stratification and admixture

Pangenome Accumulation Curves

# Model pangenome size vs number of genomes
accumulation_data = Mycelia.calculate_pangenome_accumulation(genomes)
Mycelia.plot_pangenome_curves(accumulation_data, fit_model=true)

Determines if pangenome is open or closed

Performance & Benchmarking (Planned)

Performance Scaling (Planned)

# Benchmark performance across different scales
benchmark_data = Mycelia.run_scaling_benchmark(data_sizes, n_replicates=5)
Mycelia.plot_performance_scaling(benchmark_data, metric="time")

Shows computational scaling characteristics

Memory Usage Monitoring

# Track memory usage during analysis
memory_data = Mycelia.monitor_memory_usage(analysis_function, inputs)
Mycelia.plot_memory_usage(memory_data, show_peak=true)

Identifies memory bottlenecks and optimization opportunities

Resource Utilization Dashboard

# Multi-metric performance dashboard
resource_data = Mycelia.collect_resource_metrics(benchmark_results)
Mycelia.plot_resource_dashboard(resource_data)

Comprehensive view of CPU, memory, and I/O usage

Accuracy vs Performance Trade-offs

# Compare different algorithms or parameters
tradeoff_data = Mycelia.analyze_accuracy_performance_tradeoff(algorithms, datasets)
Mycelia.plot_accuracy_performance(tradeoff_data)

Customization and Export

Gallery Organization

The visualization gallery is organized to match typical bioinformatics workflows:

Start with data quality - Essential first step
Progress through analysis - Following logical workflow
End with interpretation - Publication-ready visualizations
Include performance monitoring - For optimization

Each visualization includes:

Code example - Complete, runnable code
Biological interpretation - What the plot tells you
Customization options - How to modify for your needs
Best practices - When and how to use effectively

Interactive Examples

For hands-on exploration, see the Tutorials which include executable visualization examples with real data.