DNAKit function tree¶
Core feature tree¶
DNAKit feature overview
├── Function Tree Overview
├── 1. Data preparation
│ ├── 1) Core data object
│ │ ├── Unified entrance for ordinary users: dnakit.DNA(...)
│ │ │ ├── A sequence + optional ID / topology / metadata / feature
│ │ │ ├── Multiple sequences or record mapping one by one
│ │ │ └── Subscripting and slicing still return DNA
│ │ ├── 1. DNA sequence object
│ │ ├── 2. DNA recording object
│ │ ├── 3. DNA Dataset Object
│ │ ├── 4. Characteristic Object
│ │ ├── 5. Gap object
│ │ ├── 6. sequence type declaration
│ │ └── 7. coordinate system
│ ├── 2) File reading and writing
│ │ ├── 1. sequence format
│ │ │ ├── read(..., mode="dna"): ordinary single/multiple reads
│ │ │ ├── read(..., mode="stream"): Streaming reading of large files
│ │ │ ├── write(...): Ordinary data or streaming data use the same write entry and exit
│ │ │ ├── Batch iteration: iter_chunks
│ │ │ └── FASTA/FASTQ index construction, loading, and random access: build_fasta_index / build_fastq_index / load_fasta_index / load_fastq_index / fetch
│ │ ├── 2. Annotation formats: BED / GFF3 / AGP reading/writing and GenBank features
│ │ ├── 3. table format
│ │ ├── 4. compressed file
│ │ ├── Metadata and result tables: filter, merge, select, validate, CSV/TSV/JSON/Parquet import/export
│ │ ├── MSA codecs: read_alignment / write_alignment (Biopython)
│ │ ├── Tree codecs: read_tree / write_tree (Newick / Nexus / phyloXML / NeXML; Biopython)
│ │ ├── FASTA / multiline FASTQ / gzip and Phred quality: Read / read_fastx / write_fastx
│ │ ├── JASPAR / MEME / TRANSFAC reading: read_motifs (Biopython)
│ │ ├── SnapGene / GenBank constructs: read_constructs (Biopython; optional permissive pydna parsing)
│ │ └── ABI file reading and Mott trimming: read_sanger
│ ├── 3) Download
│ │ ├── Common species names (download input assistance)
│ │ ├── 1. complete genome
│ │ ├── 2. Partial Genome Data Package
│ │ ├── 3. region sequence
│ │ ├── 4. genome annotation
│ │ ├── 5. gene sequence
│ │ ├── 6. protein sequence
│ │ ├── 7. assembly information
│ │ ├── 8. classification information
│ │ ├── 9. Gene Metadata
│ │ ├── 10. virus packet
│ │ ├── 11. raw sequencing data
│ │ ├── 12. comparison data
│ │ ├── 13. sequencing metadata
│ │ ├── 14. mutation data
│ │ ├── 15. ClinVar data
│ │ ├── 16. Expression Data
│ │ ├── 17. control data
│ │ ├── 18. Repeated Sequence Data
│ │ ├── 19. Conservative Data
│ │ ├── 20. Multi-species comparison
│ │ ├── 21. coordinate conversion file
│ │ ├── NCBI/ENA accession sequence downloads and ENCODE batch downloads
│ │ ├── General URL/file downloads, checksums, and manifests: download_url / download_file / dataset
│ │ └── External index construction: build_index (makeblastdb / BWA / Bowtie2 / minimap2 / samtools faidx, dict)
│ ├── 4) Data query
│ │ ├── 1. Discover and call database APIs
│ │ ├── 2. Sequence, assembly and sequencing queries
│ │ ├── 3. Disease, phenotype and ontology queries
│ │ ├── 4. Expression, regulation and cancer queries
│ │ ├── 5. Organism, taxonomy and microbiome queries
│ │ ├── 6. Pathway, interaction, protein and structure queries
│ │ ├── 7. Target, drug and association queries
│ │ ├── 8. Literature, preprint and clinical study queries
│ │ ├── 9. ENCODE experiment and file queries
│ │ └── 10. Genetic Variation and Population Associations
│ │ ├── 1) Variant Records and Population Frequencies
│ │ ├── 2) Structural Variant Queries
│ │ ├── 3) ClinVar Clinical Significance
│ │ ├── 4) Gene–Disease Evidence, Dosage and Actionability
│ │ ├── 5) Cancer Variants and Treatment Evidence
│ │ ├── 6) GWAS Studies and Associations
│ │ ├── 7) Cohort Phenotype Associations and Fine-mapping
│ │ └── 8) Populations, Samples and Linkage Disequilibrium
│ └── 5) Legality check
│ └── 1. Unified sequence, record and data set legality check
├── 2. Data processing
│ ├── 1) DNA sequence standardization
│ │ ├── 1. character standardization
│ │ ├── Uppercase N runs to known-length Gaps: normalize_gaps
│ │ └── AGP sequence assembly with Gaps and provenance: sequence_from_agp
│ ├── 2) Operation within sequence
│ │ ├── Unified suffix-free entry: insert/delete/substitute/mask/trim/reverse_complement/rotate/canonical_origin
│ │ ├── sequence completion
│ │ │ ├── 1) reverse order
│ │ │ ├── 2) Complementary
│ │ │ ├── 3) Reverse complementation
│ │ │ └── Explicit duplex reverse complementation: Duplex.reverse_complement
│ │ ├── Transcription and Translation
│ │ │ ├── 1) Transcription
│ │ │ └── 2) Translation
│ │ ├── subsequence extraction
│ │ │ ├── Intervals and BED/GTF parsing: Region / read_regions
│ │ │ ├── FASTA indexing and regional access: index_reference / IndexedReference.fetch (pysam)
│ │ │ └── Chromosome/strand-aware interval sets: query_regions (intersection / union / difference)
│ │ ├── Sequence Editing
│ │ │ ├── Explicit duplexes, offsets, topology, sticky/blunt ends and digestion: Duplex.from_sequence / ends / cut
│ │ │ ├── Trimming: trim
│ │ │ └── Masking: mask
│ │ ├── sequence generation
│ │ │ ├── 1) Mutation Generation
│ │ │ ├── 2) Insert generation
│ │ │ ├── 3) Delete generation
│ │ │ ├── 4) Evolutionary algorithm-like operation
│ │ │ ├── 5) Fragment rearrangement
│ │ │ ├── 6) k-mer randomly scrambled
│ │ │ ├── 7) Multiple sequence recombination/crossover
│ │ │ └── Mutation library generation
│ │ ├── Sequence Concatenation and Assembly
│ │ │ ├── 1) Ordinary sequence splicing
│ │ │ ├── 2) Remove overlapping splicing
│ │ │ ├── Short-read/hybrid de novo assembly: assemble_reads (SPAdes; optional Nanopore)
│ │ │ ├── Executable cloning steps and intermediates: CloningPlan.add / apply / steps
│ │ │ ├── Gibson / homologous overlap assembly: assemble_duplexes (pydna)
│ │ │ ├── Golden Gate part design and reaction assembly: design_golden_gate / assemble_golden_gate
│ │ │ ├── Sequence-level assembly simulation: simulate_assembly
│ │ │ └── Duplex end compatibility and ligation: Duplex.ligate
│ │ ├── Circular Sequence Operation
│ │ └── Sequence segmentation
│ │ ├── 1) Fixed length, no overlap
│ │ ├── 2) Fixed length, overlapping sliding windows
│ │ ├── 3) Random interval
│ │ ├── 4) Multi-scale
│ │ └── 5) Short first and then long
│ ├── 3) Sequence deduplication
│ │ ├── 1. Standard deduplication
│ │ ├── 2. Reverse complementary deduplication
│ │ ├── 3. Ring equivalent deduplication
│ │ ├── 4. IUPAC-aware deduplication
│ │ └── 5. Approximate deduplication
│ ├── 4) Sequence clustering
│ │ ├── 1. Identity clustering
│ │ ├── 2. k-mer clustering
│ │ ├── 3. Fingerprint clustering
│ │ ├── 4. Hierarchical clustering
│ │ ├── 5. Representative sequence selection
│ │ ├── 6. Neural network clustering
│ │ ├── Label-stratified diversity sampling: diversity_sample
│ │ └── Feature clustering: cluster_features
│ │ ├── kmeans / minibatch_kmeans / gmm / spectral / affinity_propagation
│ │ └── mean_shift / dbscan / agglomerative / birch / mcl
│ ├── 5) Data set organization and division
│ │ ├── ENCODE tracking, collection summaries, citation export and provenance
│ │ ├── 1. Random and stable hash partitioning
│ │ ├── 2. Stratified random division
│ │ ├── 3. Similarity division
│ │ ├── 4. Cluster split
│ │ ├── 5. Species classification
│ │ ├── 6. Chromosome division
│ │ ├── 7. Individual Division
│ │ ├── 8. Divide by custom label
│ │ ├── 9. Leak detection
│ │ ├── Temporal and joint constraint splitting: temporal_split / joint_split
│ │ ├── Exclude records by species, chromosome, or custom metadata
│ │ └── Split quality evaluation: evaluate_split_quality
│ ├── 6) Sequencing Data Processing
│ │ ├── Pairing and damaged four-line FASTQ recovery: pair_reads / repair_fastq (recovery/discard statistics)
│ │ ├── Sorting, shuffling, and common records: arrange_reads / common_reads
│ │ ├── Regex renaming and order-sensitive, ID-independent sequence digests: rename_reads / sequence_digest
│ │ └── BAM sorting, mate repair, optional duplicate marking, indexing, and statistics: prepare_bam (samtools)
│ └── 7) Sequence Optimization
│ ├── Rules and custom evaluators: specification / Specification / SpecEvaluation
│ ├── GC, pattern avoidance/counts, sequence/translation/length, locked positions: gc / avoid_pattern / pattern_count / sequence / translation / length / avoid_changes
│ ├── Unique k-mers, stop/forbidden/rare codons: unique_kmers / avoid_stop / avoid_codons / avoid_rare_codons
│ ├── Exact-complement structure/match rules: hairpin / heterodimer / avoid_matches / regions_compatibility
│ ├── Tm and codon objectives: tm / cai / codon_usage / harmonize
│ ├── External homology constraint: blast_match_specification (BLAST)
│ ├── Hard constraints first, weighted objectives, synonymous edits, and circular sequences: optimize_sequence
│ ├── GenBank qualifier rule import: specifications_from_record
│ └── Edits, failure locations, search budgets, and JSON reports: OptimizationResult.write_report
├── 3. Data analysis
│ ├── 1) DNA descriptors + representations + fingerprints
│ │ ├── DNA descriptor
│ │ │ ├── 1) Fuzzy base statistics
│ │ │ ├── 2) length characteristics
│ │ │ ├── 3) base composition
│ │ │ ├── 4) GC/AT features
│ │ │ ├── 5) base skew
│ │ │ ├── 6) CpG Characteristics
│ │ │ ├── 7) k-mer statistics
│ │ │ ├── 8) Sequence Entropy
│ │ │ ├── 9) sequence complexity
│ │ │ ├── 10) Homopolymer
│ │ │ ├── 11) Repeat Ratio
│ │ │ ├── 12) window descriptor
│ │ │ ├── 13) Codon Statistics
│ │ │ ├── 14) All descriptors: all_descriptors (240 output fields in a fixed schema)
│ │ │ └── Descriptor schema discovery and provenance-aware dinucleotide property table loading
│ │ ├── Sequence Representations and Feature Processing
│ │ │ ├── 1) integer encoding
│ │ │ ├── 2) One-hot encoding
│ │ │ ├── 3) k-mer features
│ │ │ ├── 4) k-mer Sketch (MinHash/FracMinHash)
│ │ │ ├── 5) Neural network representation
│ │ │ │ ├── 1. AlphaGenome
│ │ │ │ ├── 2. Caduceus
│ │ │ │ ├── 3. DNABERT-2
│ │ │ │ ├── 4. Enformer
│ │ │ │ ├── 5. Evo 2
│ │ │ │ ├── 6. GENERator
│ │ │ │ ├── 7. GROVER
│ │ │ │ ├── 8. HyenaDNA
│ │ │ │ ├── 9. JanusDNA
│ │ │ │ ├── 10. LucaOne (default)
│ │ │ │ └── 11. Nucleotide Transformer v2
│ │ │ ├── 44 DNA encodings: encode_dna / DNA_ENCODINGS
│ │ │ │ ├── Word frequencies and approximate/noncontiguous words: Kmer / RCKmer / Mismatch / Subsequence
│ │ │ │ ├── Bases and positions: NAC / ANF / ENAC / binary / PS2 / PS3 / PS4
│ │ │ │ ├── Dinucleotides: CKSNAP / ASDC / DBE / LPDF
│ │ │ │ ├── Chemical/EIIP/mutual information: NCP / EIIP / PseEIIP / MMI
│ │ │ │ ├── Z-curve: Z_curve_9bit / Z_curve_12bit / Z_curve_36bit / Z_curve_48bit / Z_curve_144bit
│ │ │ │ ├── Positional physicochemical properties: DPCP / TPCP
│ │ │ │ ├── Auto/cross covariance: DAC / DCC / DACC / TAC / TCC / TACC
│ │ │ │ ├── Autocorrelation: NMBroto / Moran / Geary
│ │ │ │ ├── Pseudo nucleotide composition: PseDNC / PseKNC / PCPseDNC / PCPseTNC / SCPseDNC / SCPseTNC
│ │ │ │ └── Supervised encodings: PSTNPss / PSTNPds / KNN (explicit training_ids; physicochemical encodings require explicit property tables)
│ │ │ ├── Fitting and held-out reuse: analyze_features / FeatureAnalysis.transform
│ │ │ ├── Scaling: standard / minmax / l1 / l2 / none
│ │ │ ├── Reduction: pca / tsne / lda / latent_dirichlet (t-SNE has no held-out transform)
│ │ │ ├── Selection: chi2 / f_classif / f_regression / mutual_info_classif / mutual_info_regression
│ │ │ └── Selection: information_gain / pearson / variance; none for preprocessing only
│ │ └── DNA fingerprint
│ │ ├── 1) Hashed k-mer bit fingerprint
│ │ ├── 2) Panel existence fingerprint
│ │ ├── Motifs / restriction sites: motif_fingerprint / restriction_fingerprint
│ │ ├── Spatial GC / repeats / coding: gc_spatial_fingerprint / repeat_fingerprint / coding_fingerprint
│ │ ├── Thermodynamic / multiscale / hybrid: thermodynamic_fingerprint / multiscale_fingerprint / hybrid_fingerprint
│ │ └── Feature preprocessing: fit_preprocessor / transform (missing values, scaling, normalization, low-variance filtering)
│ ├── 2) Sequence search
│ │ ├── Universal search
│ │ │ ├── 1) Exact search
│ │ │ ├── 2) Subsequence search
│ │ │ ├── 3) Approximate matching
│ │ │ ├── 4) Reverse complementary search
│ │ │ ├── 5) Nearest neighbor search
│ │ │ ├── 6) Database Index
│ │ │ ├── Sketch index construction and persistence: build_sketch_index / save_sketch_index / load_sketch_index
│ │ │ ├── Local nucleotide BLAST: build_blast_database / local_blast (external BLAST+)
│ │ │ ├── BLAST XML/tab and protein HMMER2/3 parsing: parse_search_results (Biopython)
│ │ │ └── Profile construction/search and nucleotide table parsing: build_profile / search_profile / parse_nhmmer_table (hmmbuild / nhmmer / hmmsearch)
│ │ └── Sequence function search
│ │ ├── 1) motif search
│ │ ├── 2) start and stop codons
│ │ ├── 3) promoter motif
│ │ ├── 4) TF motif
│ │ ├── 5) restriction enzyme site
│ │ ├── 6) CRISPR PAM
│ │ ├── 7) palindrome sequence
│ │ ├── 8) Repeat upside down
│ │ ├── 9) Tandem Repeat
│ │ ├── 10) Microsatellite
│ │ ├── Open reading frames: scan_orfs
│ │ ├── CpG islands and low-complexity regions: find_cpg_islands / find_low_complexity_regions
│ │ ├── Native counts / PWM / PSSM, backgrounds, and degenerate consensus: MotifModel / build_motif
│ │ ├── Conservative FPR thresholds and both-strand/circular scans: fpr_threshold / scan_motif_model
│ │ ├── Primer properties, primer matching, and PCR simulation
│ │ ├── Primer design requests and the Primer3 CLI design adapter
│ │ └── CRISPR candidate and off-target scanning: scan_crispr_candidates
│ ├── 3) Physical and chemical properties
│ │ ├── 1. molecular weight
│ │ ├── 2. 260 nm extinction coefficient
│ │ ├── 3. melting temperature Tm
│ │ ├── 4. salt concentration correction
│ │ ├── 5. local melting characteristics
│ │ ├── 6. Synthesis Risk
│ │ └── Thermodynamic properties
│ │ ├── 1) thermodynamic parameters
│ │ ├── 2) Nearest-neighbor
│ │ ├── 3) Duplex stability
│ │ ├── 4) base stacking
│ │ ├── 5) Hairpin
│ │ ├── 6) Self-dimer
│ │ └── 7) Heterodimer
│ ├── 4) Double-chain thermodynamic expansion
│ │ ├── 1. ΔH, ΔS, ΔG and Tm of perfectly complementary duplexes
│ │ ├── 2. Unify double-strand stability results
│ │ ├── 3. Contribution of adjacent base pairs to steps
│ │ ├── 4. Conditions and Na⁺/K⁺ monovalent salt
│ │ ├── 5. Ka, Kd and duplex ratio
│ │ ├── 6. Theoretical melting curve
│ │ ├── 7. 5′/3′ end stability
│ │ ├── 8. DMSO and formamide experience correction
│ │ └── 9. Mg²⁺, dNTP, mismatch and dangling end for Primer3 CLI
│ ├── 5) Secondary structure properties
│ │ ├── 1. Dot-bracket structure analysis
│ │ ├── 2. Pairing probability and window accessibility
│ │ ├── 3. Collection defects of target structure
│ │ ├── 4. Thermodynamic probability of target structure
│ │ ├── 5. NUPACK Passive Availability Check
│ │ ├── 6. NUPACK single-complex ensemble analysis
│ │ └── 7. NUPACK tube multi-complex equilibrium
│ ├── 6) Three-dimensional structure and mechanical properties
│ │ ├── 1. Read a single PDB model
│ │ ├── 2. Read PDB multi-model collection
│ │ ├── 3. Explicit coordinate geometry analysis
│ │ ├── 4. NMR/Multi-model RMSF Flexibility
│ │ ├── 5. 3DNA bp_step.par standard parameter analysis
│ │ └── 6. DSSR JSON summary parsing
│ ├── 7) Conversion
│ │ ├── 1. Single chain molar extinction coefficient at 260 nm
│ │ ├── 2. Single-chain/double-chain theoretical optical properties
│ │ ├── 3. 1 nmol and mass corresponding to OD260
│ │ ├── 4. A260 to molar concentration, mass concentration and total amount
│ │ ├── 5. Concentration, quantity and mass interchange of substances
│ │ └── 6. Explicit correction of dyes and modifying groups
│ ├── 8) Property Prediction: Deep Learning and Machine Learning
│ │ ├── 1. Custom Model Training and Prediction
│ │ │ ├── User-defined classification and regression
│ │ │ ├── Multi-format inspection, automatic/explicit field mapping, and DataReader extensions
│ │ │ ├── Binary, multiclass, multilabel, multi-output classification; single/multi-target regression
│ │ │ ├── Full fine-tuning (full) and frozen-representation linear probing (linear_probe)
│ │ │ ├── Machine learning (machine_learning): 37 algorithms and sequence/embedding/numeric features
│ │ │ │ ├── Classification and regression: ridge / sgd / svm / linear_svm / nu_svm / knn / decision_tree / extra_tree
│ │ │ │ ├── Classification and regression: random_forest / extra_trees / gradient_boosting / hist_gradient_boosting / adaboost / bagging
│ │ │ │ ├── Classification and regression: gaussian_process / mlp / dummy / xgboost / lightgbm / catboost
│ │ │ │ ├── Classification only: logistic_regression / perceptron / gaussian_nb / multinomial_nb / bernoulli_nb / complement_nb / lda / qda
│ │ │ │ └── Regression only: linear_regression / lasso / elastic_net / bayesian_ridge / ard / huber / ransac / theil_sen / kernel_ridge
│ │ │ ├── Data splits, training parameters, classification/regression metrics, and artifact export
│ │ │ ├── Training and reloaded inference: train_custom_property_model / predict_custom_properties
│ │ │ └── Optional LLM field analysis: provider presets, structure/bounded samples, declarative mapping
│ │ ├── 2. Expression, Regulatory Tracks and Contact Maps
│ │ │ ├── AlphaGenome genomic tracks: RNA-seq, CAGE, PRO-cap, ATAC-seq, and DNase-seq
│ │ │ ├── Histone modifications, transcription factor binding, and 3D DNA contact maps
│ │ │ └── Enformer human/mouse regulatory tracks
│ │ ├── 3. Splicing and Genomic Regions
│ │ │ ├── Splice sites, splice usage, and splice junction counts
│ │ │ ├── Fourteen-class single-nucleotide segmentation
│ │ │ │ ├── 1. Protein-coding gene
│ │ │ │ ├── 2. Long noncoding RNA (lncRNA)
│ │ │ │ ├── 3. Exon
│ │ │ │ ├── 4. Intron
│ │ │ │ ├── 5. Splice donor
│ │ │ │ ├── 6. Splice acceptor
│ │ │ │ ├── 7. 5′ UTR
│ │ │ │ ├── 8. 3′ UTR
│ │ │ │ ├── 9. CTCF-bound region
│ │ │ │ ├── 10. PolyA signal
│ │ │ │ ├── 11. Tissue-specific enhancer
│ │ │ │ ├── 12. Tissue-invariant enhancer
│ │ │ │ ├── 13. Tissue-specific promoter
│ │ │ │ └── 14. Tissue-invariant promoter
│ │ │ ├── Long-context exon probability
│ │ │ ├── Fully fine-tuned NT Revised tasks: splice acceptors, splice-site types, and splice donors
│ │ │ └── Fully fine-tuned Genomic Benchmarks task: coding/intergenic regions
│ │ ├── 4. Epigenetic Marks and Open Chromatin
│ │ │ ├── Fully fine-tuned NT Revised tasks: H2A.Z, H3K27ac, H3K27me3, H3K36me3, H3K4me1/2/3, H3K9ac/me3, and H4K20me1
│ │ │ └── Fully fine-tuned Genomic Benchmarks task: human open-chromatin regions
│ │ ├── 5. Promoters and Enhancers
│ │ │ ├── Fully fine-tuned NT Revised tasks: enhancer detection/types; promoters, non-TATA promoters, and TATA promoters
│ │ │ ├── Fully fine-tuned Genomic Benchmarks tasks: Drosophila enhancers and mouse enhancers
│ │ │ └── Human Cohn/Ensembl enhancers, human regulatory-element types, and human non-TATA promoters
│ │ ├── 6. Variant Effects and Protein Stability
│ │ │ ├── SNV differences in genomic modalities and human/mouse regulatory tracks
│ │ │ ├── Evo 2 long-context zero-shot variant score
│ │ │ ├── Allele-conditional-probability variant score
│ │ │ ├── Protein stability and ESM protein variant effects
│ │ │ └── Splicing variant effects: SpliceAI / Pangolin / maximum delta score
│ │ ├── 7. Taxonomy and Molecular Function Classification
│ │ │ ├── Trained nucleic-acid/protein downstream tasks: SupKTax, GenusTax, and species classification
│ │ │ ├── Prokaryotic protein localization and ncRNA families
│ │ │ └── Fully fine-tuned Genomic Benchmarks task: human/worm
│ │ ├── 8. Molecular Relationships and Interactions
│ │ │ ├── Trained nucleic-acid/protein downstream task: nucleic-acid/protein central-dogma relation
│ │ │ └── Influenza A antigenic relationships, protein/protein and ncRNA/protein interactions
│ │ └── 9. Protein Features and Structure
│ │ ├── ESM protein features and sequence scores
│ │ └── NVIDIA NIM: AlphaFold2 / ESMFold protein structure prediction
│ ├── 9) Gene names and intervals
│ │ ├── Gene identifiers, transcripts, homology and database annotations
│ │ ├── Genomic DNA + species → annotate_genes / GeneAnnotationConfig
│ │ ├── Default RefSeq assembly resolution, explicit assembly, or local FASTA + GFF3
│ │ ├── minimap2 reference alignment and existing annotation mapping (not de novo prediction)
│ │ ├── Gene names, IDs, strands, overlapping genes, and gene/intergenic/unknown segments
│ │ ├── Python: 0-based half-open intervals; JSON/CLI: 1-based closed intervals
│ │ └── Pinned assembly/annotation cache, checksums, offline reuse, and provenance
│ ├── 10) Phylogenetics and Molecular Evolution
│ │ ├── Native trees and construction: TreeNode / build_tree (NJ / cluster-size-weighted UPGMA)
│ │ ├── Native tree comparison, consensus, and pruning: robinson_foulds / consensus_tree / prune_tree
│ │ ├── Native community phylogenetic distances: unifrac (weighted / unweighted; rooted trees)
│ │ └── Nei–Gojobori dN/dS: selection-pressure APIs in comparative and database namespaces
│ ├── 11) Statistics, Differential Analysis and Enrichment
│ │ ├── 1. Statistical and Differential Analysis
│ │ │ ├── Distance validation and permutation tests: distance_matrix / distance_test (PERMANOVA / ANOSIM / centroid PERMDISP)
│ │ │ ├── Matrix correlations and environmental subset search: mantel / bioenv
│ │ │ ├── Principal coordinates and constrained ordination: pcoa / ordination (CA / CCA / RDA)
│ │ │ ├── Compositional transforms and inverses: composition_transform (CLR / ILR / ALR)
│ │ │ ├── Native compositional differential analysis: ancom
│ │ │ ├── Alpha diversity: alpha_diversity (observed / Shannon / Simpson / inverse Simpson)
│ │ │ ├── Beta distances: beta_diversity (Bray-Curtis / Jaccard / Euclidean / cityblock / cosine)
│ │ │ ├── Original ANCOM-BC: ancom_bc (OLS / sandwich variance / EM sampling-bias correction)
│ │ │ ├── Differential expression: differential_expression (unshrunk NB2 GLM / size factors / explicit contrast / Wald)
│ │ │ └── Multiple testing: adjust_pvalues (Benjamini–Hochberg)
│ │ └── 2. Gene Set and Pathway Enrichment
│ │ └── Enrichr / PANTHER / Reactome / STRING enrichment analysis
│ ├── 12) Sequencing Data Analysis
│ │ ├── BAM summaries, coverage, and reference-coordinate consensus: alignment_summary / coverage_consensus (pysam; no indels in consensus)
│ │ ├── DNA-seq: dna_seq_workflow (QC → mapping → BAM → variants)
│ │ ├── Single-sample RNA-seq: rna_seq_workflow (HISAT2 / StringTie / featureCounts)
│ │ ├── ChIP-seq from prepared BAMs: chip_seq_workflow (MACS3 peaks + samtools coverage)
│ │ └── Both-strand local alignment and reference-coordinate consensus: sanger_consensus
│ ├── 13) Variant Calling, Annotation and Scoring
│ │ ├── 1. Variant Calling and Statistics
│ │ │ ├── Variant calling, reference normalization, and VCF indexing: call_variants (bcftools)
│ │ │ ├── Count VCF variants with filters: vcf_count_variants
│ │ │ ├── Normalize VCF records and multiallelic sites: vcf_normalize
│ │ │ ├── Summarize variant types and sample genotypes: vcf_summary_stats
│ │ │ └── Coding-variant fraction: coding_variant_fraction
│ │ ├── 2. Variant Annotation and Identifier Conversion
│ │ │ └── Ensembl VEP / MyVariant / FAVOR / VariantValidator annotations and identifier conversion
│ │ └── 3. Variant Effect Scores and Experimental Data
│ │ └── AlphaMissense / CADD / EVE / MaveDB scores and experimental effect data
│ ├── 14) Inheritance Simulation
│ │ └── Simulate allele inheritance at independent loci: simulate_inheritance
│ └── 15) Genomics and DNA Engineering
│ ├── Genomic relationships and breeding prediction: genomic_relationship_matrix / genomic_prediction
│ ├── Prokaryotic CDS prediction: predict_prokaryotic_genes (Pyrodigal)
│ ├── Methylation summaries, DNA storage and nanostructure design
│ ├── Genotype PCA: genotype_pca (NumPy; dosage-matrix input)
│ └── Genome ANI: average_nucleotide_identity (PyFastANI; no FastANI executable)
├── 4. Data evaluation
│ ├── 1) Commonly used evaluation indicators
│ │ ├── 1. Validity
│ │ │ ├── 1.1 Syntax validity: evaluate_validity
│ │ │ └── 1.2 Reference-genome consistency: evaluate_reference_consistency
│ │ ├── 2. Uniqueness
│ │ ├── 3. Diversity (mean raw pairwise Levenshtein distance)
│ │ ├── 4. Novelty (similarity / mean nearest-reference Levenshtein)
│ │ ├── 5. Fréchet DNA distance
│ │ ├── 6. Frag
│ │ ├── 7. SNN
│ │ ├── 8. Ambiguity
│ │ ├── 9. Redundancy
│ │ ├── Complexity and quality evaluation: evaluate_complexity / evaluate_quality
│ │ ├── Reference libraries, nearest-reference lookup, and memorization evaluation
│ │ ├── Composite scorecard: evaluate_scorecard
│ │ ├── Raw read quality control: quality_control (FastQC)
│ │ ├── Custom scalar property matrices: sequence_properties
│ │ ├── Gaussian MMD² / polynomial KID: kernel_distance
│ │ ├── Precision / Recall / Density / Coverage: manifold_metrics
│ │ ├── Calibrated clipped density / coverage: clipped_manifold_metrics
│ │ ├── Authenticity relative to generator training data: authenticity
│ │ ├── Pareto layers and exact hypervolume: pareto_ranks / hypervolume
│ │ ├── Property interval hit rates and histogram KL: property_hit_rate / property_kl
│ │ └── KDE distribution conformity and standard error: conformity_score
│ ├── 2) Similarity calculation
│ │ ├── 1. k-mer similarity
│ │ ├── 2. Fingerprint similarity
│ │ ├── 3. Sketch similarity
│ │ ├── 4. Dashing sketch similarity
│ │ ├── 5. Reference similarity
│ │ ├── Descriptor distribution similarity: evaluate_distribution_similarity
│ │ └── Vectors and matrices: kmer_vector_similarity / similarity_matrix / pairwise_matrix
│ └── 3) Sequence distance and alignment
│ ├── 1. Sequence distance
│ │ ├── 1) Hamming distance
│ │ └── 2) Edit distance
│ ├── 2. Pairwise sequence alignment
│ │ ├── 2) 1 Global comparison
│ │ ├── 2) 2 Local comparison
│ │ └── 2) 3 Semi-global comparison
│ ├── Native MSA, width/ID validation, and p-distance: MultipleAlignment.from_sequences / distances
│ ├── Multiple alignment: align_sequences (external MAFFT / MUSCLE 5 / Clustal Omega)
│ └── Single/paired-end mapping and private indexes: map_reads (BWA / Bowtie2)
├── 5. Visualization
│ ├── 1) sequence text diagram
│ ├── 2) position highlighting
│ ├── 3) style control
│ ├── 4) Gap display
│ ├── 5) linear sequence diagram
│ ├── 6) circular DNA diagram
│ ├── 7) Custom character representation
│ ├── 8) Alignment diagram
│ ├── Similarity matrix heatmap: plot_similarity_matrix
│ ├── Self-contained, filterable, expandable HTML reports: build_html_report / save_html_report
│ ├── Image export: save_svg / save_image (PNG, SVG, JPG; TIFF/PDF also supported)
│ ├── Tree plots: plot_tree (Matplotlib)
│ ├── Forward/reverse exact k-mer dotplots: dotplot (Matplotlib)
│ ├── Sanger chromatograms: plot_chromatogram
│ └── Qualitative gel simulation: simulate_gel (Matplotlib)
├── 6. Large Language Model Tools
│ ├── Python API: general-purpose LLM calls (dnakit.llm)
│ │ ├── Configuration and results: LLMConfig / LLMResult
│ │ ├── Sync/async: call_llm / acall_llm; custom prompts and current-step data
│ │ ├── Text/JSON input, text/JSON-object output, and token usage
│ │ ├── Local text files: file_path; local preview with read_llm_file
│ │ ├── Encoding and file/request size limits; explicit file input sends the complete text
│ │ ├── Provider presets: DeepSeek / Gemini / Qwen / Kimi / OpenRouter / OpenAI / custom
│ │ ├── Preset discovery: available_llm_providers / get_llm_provider
│ │ └── Custom compatible endpoint, model, credentials, and extra_body parameters
│ ├── MCP: Agent and MCP
│ │ ├── Local stdio server: dnakit-mcp; optional dependency dnakit[agent]
│ │ ├── MCP client integration (Codex, Claude, Cursor, VS Code, etc.)
│ │ ├── Catalog statistics: dnakit_catalog_stats
│ │ ├── Categories: list_dnakit_categories
│ │ ├── Paginated catalog: list_dnakit_tools
│ │ ├── Capability search: search_dnakit_tools
│ │ ├── Parameter schema and execution attributes: describe_dnakit_tool
│ │ ├── JSON-argument execution: call_dnakit_tool
│ │ ├── Python registry and server construction: default_tool_registry / create_server
│ │ └── Bounded JSON output, provenance, Python-only markers, and allow_side_effects authorization
│ ├── Skills: install the DNAKit skill and find analysis workflows
│ └── Agent: built-in natural-language task execution (run_agent)
│ ├── Tool discovery, parameter inspection, execution, and feedback
│ ├── Optional Python execution, failure recovery, and run logs
│ └── Keyword/semantic/hybrid search, permissions, and step limits
└── FAQ
└── Engineering and Extensions
├── Bundled scripts, resource lookup, ENCODE credentials, notebooks and PDF reports
├── 1. unified backend interface
├── 2. Native and external tags
├── 3. Python API
├── 4. CLI
│ ├── Environment and backends: info / backends
│ ├── Data preparation: download-genome / convert / normalize / validate
│ ├── Analysis and comparison: describe / fingerprint / search / orfs / compare
│ ├── Datasets and reports: deduplicate / split / report / workflow
│ ├── Custom properties: property-models / inspect-property-data / train-property / predict-property
│ └── Gene annotation: annotate-genes
├── 5. Configuration workflows: load_workflow / run_workflow; artifact manifest I/O, verification, reuse
├── 6. Batch computation: iter_batch / run_batch; progress, cancellation, per-record errors
├── 7. Parallel computation: bounded threads and stable output order
├── 8. Chunking and Streaming
├── 9. cache
├── 10. Random Seed
├── 11. version tracking
├── 12. Errors and Warnings
├── 14. consistency verification
├── 16. Documentation and Tutorials
├── 17. Galaxy wrappers: convert / deduplicate / split / report (requires Galaxy)
├── Development validation: unit tests and consistency checks
└── DAG definitions, dependency/cycle checks, and failure propagation: Step / run_workflow
See the FAQ for catalog scope and counts.