Function Index

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

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

`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`

Identify and characterize peaks in k-mer frequency spectra.

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

`annotate_functions`

Assign functional annotations to predicted genes.

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

`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`

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`

Construct k-mer overlap graphs from sequences.

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

`build_pangenome`

Construct pangenome from multiple genome assemblies.

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

`build_phylogenetic_tree`

Construct phylogenetic trees from sequence alignments.

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

C

`calculate_assembly_stats`

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

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

`calculate_codon_usage`

Analyze codon usage patterns in coding sequences.

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

`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`

Assess genome complexity using k-mer diversity metrics.

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

`calculate_synteny`

Identify syntenic regions between genomes.

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

`compare_genomes`

Comprehensive pairwise genome comparison.

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

`construct_phylogeny`

High-level phylogenetic tree construction interface.

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

`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`

Generate interactive quality control dashboard.

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

D

`detect_contamination_kmers`

Identify contamination using k-mer profile analysis.

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

`detect_host_contamination`

Screen for host organism contamination in sequencing data.

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

`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

`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`

Comprehensive assembly quality evaluation.

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

F

`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

`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`

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`

Generate comprehensive quality control reports.

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

H

`hifiasm_assembly`

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`

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`

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

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

N

`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`

Create visualizations of assembly quality metrics.

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

`plot_kmer_spectrum`

Visualize k-mer frequency spectra.

Module: Visualization
Usage: plot_kmer_spectrum(spectrum, log_scale=true)
See: Visualization

`plot_phylogenetic_tree`

Create phylogenetic tree visualizations.

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

`predict_genes`

Predict genes in genome assemblies.

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

R

`read_fasta`

Read sequences from FASTA files.

Module: File I/O
Usage: read_fasta("sequences.fasta")
See: FASTA/FASTQ Data Types

`remove_adapters`

Remove adapter sequences from sequencing reads.

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

S

`simulate_hifi_reads`

Simulate PacBio HiFi sequencing reads.

Module: Data Simulation
Usage: simulate_hifi_reads(genome, coverage=30)
See: Data Acquisition

`simulate_random_genome`

Generate random genome sequences for testing.

Module: Data Simulation
Usage: simulate_random_genome(length=100000, gc_content=0.45)
See: Data Acquisition

V

`validate_assembly`

Validate genome assembly using multiple approaches.

Module: Assembly Validation
Usage: validate_assembly("contigs.fasta", "reads.fastq")
See: Assembly Validation

W

`write_fastq`

Write sequences to FASTQ files.

Module: File I/O
Usage: write_fastq("output.fastq", sequences)
See: FASTA/FASTQ Data Types

Function Categories

Data Acquisition (15 functions)

Functions for downloading and simulating genomic data.

Quality Control (23 functions)

Functions for assessing and improving data quality.

Sequence Analysis (31 functions)

Functions for k-mer analysis and sequence composition.

Genome Assembly (18 functions)

Functions for assembling genomes from sequencing reads.

Assembly Validation (20 functions)

Functions for validating and assessing assembly quality.

Gene Annotation (16 functions)

Functions for predicting and annotating genes.

Comparative Genomics (22 functions)

Functions for comparing genomes and phylogenetic analysis.

Visualization (28 functions)

Functions for creating plots and visualizations.

File I/O (12 functions)

Functions for reading and writing various file formats.

Utilities (25 functions)

Helper functions and utilities.

Total: 210 documented functions

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