Function Index

Alphabetical listing of all Mycelia functions with brief descriptions and links to detailed documentation.

Note: Functions marked with @ref links have complete API documentation. Functions without links are planned or have incomplete documentation. Functions marked (planned) are not yet implemented.

A

`add_sequencing_errors` (planned)

Add realistic sequencing errors to simulated reads.

Module: Data Simulation
Usage: add_sequencing_errors(reads, error_rate=0.01)
See: Data Acquisition

`Mycelia.analyze_fastq_quality`

Comprehensive quality analysis of FASTQ files.

Module: Quality Control
Usage: analyze_fastq_quality("reads.fastq")
See: Quality Control

`analyze_functional_annotations` (planned)

Analyze functional annotation categories and distributions.

Module: Gene Annotation
Usage: analyze_functional_annotations("annotations.gff3")
See: Gene Annotation

`analyze_kmer_connectivity` (planned)

Analyze connectivity patterns in k-mer graphs.

Module: Sequence Analysis
Usage: analyze_kmer_connectivity(kmer_graph)
See: Sequence Analysis

`analyze_spectrum_peaks` (planned)

Identify and characterize peaks in k-mer frequency spectra.

Module: Sequence Analysis
Usage: analyze_spectrum_peaks(spectrum)
See: Sequence Analysis

`annotate_functions` (planned)

Assign functional annotations to predicted genes.

Module: Gene Annotation
Usage: annotate_functions(proteins, database="uniprot")
See: Gene Annotation

`Mycelia.assemble_genome`

Main genome assembly function supporting multiple assemblers.

Module: Genome Assembly
Usage: Mycelia.assemble_genome("reads.fastq", assembler="hifiasm")
See: Genome Assembly

`assess_assembly_readiness` (planned)

Evaluate if sequencing data is suitable for genome assembly.

Module: Quality Control
Usage: assess_assembly_readiness("reads.fastq")
See: Quality Control

B

`build_kmer_graph` (planned)

Construct k-mer overlap graphs from sequences.

Module: Sequence Analysis
Usage: build_kmer_graph(sequences, k=31)
See: Sequence Analysis

`build_pangenome` (planned)

Construct pangenome from multiple genome assemblies.

Module: Comparative Genomics
Usage: build_pangenome(genome_list, threshold=0.95)
See: Comparative Genomics

`build_phylogenetic_tree` (planned)

Construct phylogenetic trees from sequence alignments.

Module: Comparative Genomics
Usage: build_phylogenetic_tree(alignment, method="ml")
See: Comparative Genomics

C

`calculate_assembly_stats` (planned)

Calculate standard assembly quality metrics (N50, L50, etc.).

Module: Assembly Validation
Usage: calculate_assembly_stats("contigs.fasta")
See: Assembly Validation

`calculate_codon_usage` (planned)

Analyze codon usage patterns in coding sequences.

Module: Sequence Analysis
Usage: calculate_codon_usage("cds.fasta", genetic_code="standard")
See: Sequence Analysis

`Mycelia.calculate_gc_content`

Calculate GC content for sequences or sequence collections.

Module: Sequence Analysis
Usage: calculate_gc_content("sequences.fasta")
See: Sequence Analysis

`calculate_genome_complexity` (planned)

Assess genome complexity using k-mer diversity metrics.

Module: Sequence Analysis
Usage: calculate_genome_complexity(kmer_counts)
See: Sequence Analysis

`calculate_synteny` (planned)

Identify syntenic regions between genomes.

Module: Comparative Genomics
Usage: calculate_synteny(genome1, genome2)
See: Comparative Genomics

`compare_genomes` (planned)

Comprehensive pairwise genome comparison.

Module: Comparative Genomics
Usage: compare_genomes(genome1, genome2, method="synteny")
See: Comparative Genomics

`construct_phylogeny` (planned)

High-level phylogenetic tree construction interface.

Module: Comparative Genomics
Usage: construct_phylogeny(core_genes, method="ml")
See: Comparative Genomics

`Mycelia.count_kmers`

Count k-mers in sequences with various options and optimizations.

Module: Sequence Analysis
Usage: count_kmers("reads.fastq", k=21)
See: Sequence Analysis

`create_quality_dashboard` (planned)

Generate interactive quality control dashboard.

Module: Quality Control
Usage: create_quality_dashboard(quality_data)
See: Quality Control

D

`detect_contamination_kmers` (planned)

Identify contamination using k-mer profile analysis.

Module: Sequence Analysis
Usage: detect_contamination_kmers("sample.fastq", expected_profile)
See: Sequence Analysis

`detect_host_contamination` (planned)

Screen for host organism contamination in sequencing data.

Module: Quality Control
Usage: detect_host_contamination("reads.fastq", "host_genome.fasta")
See: Quality Control

`Mycelia.download_genome_by_accession`

Download genome sequences from NCBI by accession number.

Module: Data Acquisition
Usage: Mycelia.download_genome_by_accession("NC_001422.1")
See: Data Acquisition

E

`Mycelia.estimate_genome_size_from_kmers`

Estimate genome size using k-mer frequency spectrum analysis.

Module: Sequence Analysis
Usage: estimate_genome_size_from_kmers(kmer_counts)
See: Sequence Analysis

`evaluate_assembly` (planned)

Comprehensive assembly quality evaluation.

Module: Assembly Validation
Usage: evaluate_assembly("contigs.fasta")
See: Assembly Validation

F

`Mycelia.fasta_list_to_dense_kmer_counts`

Generate dense k-mer count matrices from multiple FASTA files.

Module: Sequence Analysis
Usage: fasta_list_to_dense_kmer_counts(file_list, k=21)
See: Sequence Analysis

`Mycelia.fasta_list_to_sparse_kmer_counts`

Generate sparse k-mer count matrices from multiple FASTA files.

Module: Sequence Analysis
Usage: fasta_list_to_sparse_kmer_counts(file_list, k=21)
See: Sequence Analysis

`filter_by_quality` (planned)

Filter sequencing reads based on quality score thresholds.

Module: Quality Control
Usage: filter_by_quality("reads.fastq", min_quality=20)
See: Quality Control

G

`generate_quality_report` (planned)

Generate comprehensive quality control reports.

Module: Quality Control
Usage: generate_quality_report("reads.fastq", format="html")
See: Quality Control

H

`hifiasm_assembly` (planned)

Run hifiasm assembler with optimized parameters.

Module: Genome Assembly
Usage: hifiasm_assembly("hifi_reads.fastq", output_dir="assembly")
See: Genome Assembly

I

`identify_error_kmers` (planned)

Identify k-mers likely to contain sequencing errors.

Module: Sequence Analysis
Usage: identify_error_kmers(kmer_counts, min_coverage=3)
See: Sequence Analysis

K

`kmer_frequency_spectrum` (planned)

Generate k-mer frequency spectrum from k-mer counts.

Module: Sequence Analysis
Usage: kmer_frequency_spectrum(kmer_counts)
See: Sequence Analysis

N

`Mycelia.ncbi_genome_download_accession`

Download complete genome assembly packages from NCBI.

Module: Data Acquisition
Usage: ncbi_genome_download_accession("GCF_000819615.1")
See: Data Acquisition

P

`plot_assembly_stats` (planned)

Create visualizations of assembly quality metrics.

Module: Visualization
Usage: plot_assembly_stats(assembly_data)
See: Visualization

`Mycelia.plot_kmer_frequency_spectra`

Visualize k-mer frequency spectra.

Module: Visualization
Usage: Mycelia.plot_kmer_frequency_spectra(counts, log_scale=log2)
See: Visualization

`plot_phylogenetic_tree` (planned)

Create phylogenetic tree visualizations.

Module: Visualization
Usage: plot_phylogenetic_tree(tree, layout="circular")
See: Visualization

`predict_genes` (planned)

Predict genes in genome assemblies.

Module: Gene Annotation
Usage: predict_genes("genome.fasta", method="prodigal")
See: Gene Annotation

R

`Mycelia.open_fastx`

Open and read sequences from FASTA or FASTQ files.

Module: File I/O
Usage: for record in Mycelia.open_fastx("sequences.fasta"); ...; end
See: Data Acquisition

`remove_adapters` (planned)

Remove adapter sequences from sequencing reads.

Module: Quality Control
Usage: remove_adapters("reads.fastq", adapter_sequences)
See: Quality Control

S

`Mycelia.simulate_pacbio_reads`

Simulate PacBio HiFi sequencing reads.

Module: Data Simulation
Usage: Mycelia.simulate_pacbio_reads(fasta="genome.fasta", quantity="30x")
See: Data Acquisition

`Mycelia.generate_test_sequences` (enhanced configurability planned)

Generate random genome sequences for testing. Currently generates random DNA sequences; additional configurability options (GC content control, gene features, realistic structure) are planned.

Module: Data Simulation
Usage: Mycelia.generate_test_sequences(genome_size=100000, n_sequences=1)
See: Data Acquisition