686 lines
30 KiB
Markdown
686 lines
30 KiB
Markdown
# rvDNA
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[](https://crates.io/crates/rvdna)
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[](https://www.npmjs.com/package/@ruvector/rvdna)
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[](https://opensource.org/licenses/MIT)
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**Genomic analysis in 12 milliseconds -- variant calling, protein translation, drug dosing, and biological age prediction in a single pipeline.**
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Most genomic tools take 30-90 minutes per analysis, require specialized hardware, and cost hundreds of dollars per run. rvDNA runs the same analyses in milliseconds on any device -- including a browser tab. It pre-computes vectors, attention matrices, and variant probabilities into a single `.rvdna` file so that every subsequent analysis is instant, private, and free.
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```
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cargo add rvdna # Rust
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npm install @ruvector/rvdna # JavaScript / TypeScript / WASM
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```
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| | rvDNA | Traditional tools (GATK, BLAST, etc.) |
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| **Full pipeline** | 12 ms on a laptop | 30-90 min on specialized hardware |
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| **Runs in browser** | Yes -- WASM, no server needed | No |
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| **Data privacy** | Stays on-device, never uploaded | Often requires cloud upload |
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| **Pre-computed AI features** | `.rvdna` files store vectors + tensors for instant reuse | Re-encode from scratch every time |
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| **Cost** | Free forever -- MIT licensed | Per-run or subscription pricing |
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## Key Features
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| Feature | What It Does | Why It Matters |
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| **K-mer HNSW search** | Finds similar genes via vector indexing in O(log N) | 1,200-60,000x faster than BLAST sequence scans |
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| **Bayesian variant calling** | Detects SNPs and indels with Phred quality scores | Catches mutations like sickle cell (HBB rs334) automatically |
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| **Protein translation** | Full codon table with GNN contact graph prediction | Translates DNA to protein and predicts 3D structure contacts |
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| **Biological age** | Horvath epigenetic clock using 353 CpG sites | Predicts biological vs chronological age from methylation data |
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| **Drug dosing** | CYP2D6 star allele calling with CPIC guidelines | Recommends safe doses for codeine, tamoxifen, SSRIs |
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| **Polygenic risk scoring** | 20 clinically-relevant SNPs with gene-gene interactions | Composite risk across cancer, cardiovascular, neurological categories |
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| **Biomarker streaming** | Real-time anomaly detection with CUSUM changepoints | Monitors biomarker trends and flags sustained shifts |
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| **`.rvdna` format** | 2-bit packed DNA + pre-computed AI tensors in one file | 4x compression, sub-microsecond random access, skip re-encoding |
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| **WASM support** | Compiles to WebAssembly for browsers and edge devices | Privacy-preserving genomics -- data never leaves the device |
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## What rvDNA Does
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Give it a DNA sequence, and it will:
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1. **Search for similar genes** using k-mer vectors and HNSW indexing
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2. **Align sequences** with Smith-Waterman (CIGAR output, mapping quality)
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3. **Call variants** — detects mutations like the sickle cell SNP at HBB position 20
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4. **Translate DNA to protein** — full codon table with contact graph prediction
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5. **Predict biological age** from methylation data (Horvath clock, 353 CpG sites)
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6. **Recommend drug doses** based on CYP2D6 star alleles and CPIC guidelines
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7. **Score health risks** — composite polygenic risk scoring across 20 SNPs with gene-gene interactions
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8. **Stream biomarker data** — real-time anomaly detection, trend analysis, and CUSUM changepoint detection
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9. **Save everything to `.rvdna`** — a single file with all results pre-computed
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All of this runs on 5 real human genes from NCBI RefSeq in under 15 milliseconds.
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## Quick Start
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```bash
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# Run the full 8-stage demo
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cargo run --release -p rvdna
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# Run 172 tests (no mocks — real algorithms, real data)
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cargo test -p rvdna
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# Run benchmarks
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cargo bench -p rvdna
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```
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### As a Library
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```rust
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use rvdna::prelude::*;
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use rvdna::real_data::*;
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// Load the real human hemoglobin gene (NCBI NM_000518.5)
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let seq = DnaSequence::from_str(HBB_CODING_SEQUENCE).unwrap();
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// Translate to protein — verified against UniProt P68871
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let protein = rvdna::translate_dna(seq.to_string().as_bytes());
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assert_eq!(protein[0].to_char(), 'M'); // Methionine start codon
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// Detect sickle cell variant
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let caller = VariantCaller::new(VariantCallerConfig::default());
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// Position 20 (rs334): GAG -> GTG = Sickle cell disease
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```
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## The `.rvdna` File Format
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Most genomic file formats (FASTA, FASTQ, BAM) store raw sequence data in text or reference-compressed binary. Every time an AI model needs to analyze that data, it has to re-encode the sequence into vectors, re-compute attention matrices, and re-extract features. This takes 30–120 seconds per file.
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**`.rvdna` skips all of that.** It stores the raw DNA alongside pre-computed k-mer vectors, attention weights, variant probabilities, and protein embeddings in a single binary file. Open the file and everything is ready to use — no re-encoding, no feature extraction, no waiting.
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### How It Works
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```
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.rvdna file layout:
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[Magic: "RVDNA\x01\x00\x00"] 8 bytes — identifies the file
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[Header] 64 bytes — version, flags, section offsets
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[Section 0: Sequence] 2-bit packed DNA (4 bases per byte)
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[Section 1: K-mer Vectors] Pre-computed HNSW-ready embeddings
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[Section 2: Attention Weights] Sparse COO matrices
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[Section 3: Variant Tensor] f16 genotype likelihoods per position
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[Section 4: Protein Embeddings] GNN node features + contact graphs
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[Section 5: Epigenomic Tracks] Methylation betas + clock coefficients
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[Section 6: Metadata] JSON provenance + checksums
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```
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**2-bit encoding** packs 4 DNA bases into 1 byte (A=00, C=01, G=10, T=11). Ambiguous bases (N) get a separate bitmask. Quality scores use 6-bit Phred compression. This gives **4x compression** over plain FASTA with zero information loss.
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**K-mer vectors** are pre-indexed and ready for HNSW cosine similarity search the instant you open the file. Optional int8 quantization cuts memory by another 4x.
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**Every section is 64-byte aligned** for cache-friendly memory-mapped access. Random access to any 1 KB region takes less than 1 microsecond.
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### Usage
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```rust
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use rvdna::rvdna::*;
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// Convert FASTA -> .rvdna (with pre-computed k-mer vectors)
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let rvdna_bytes = fasta_to_rvdna("ACGTACGTACGT...", 11, 512, 500)?;
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// Read it back — sequence + all pre-computed features
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let reader = RvdnaReader::from_bytes(rvdna_bytes)?;
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let sequence = reader.read_sequence()?; // Original DNA, lossless
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let kmers = reader.read_kmer_vectors()?; // Ready for HNSW search
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let variants = reader.read_variants()?; // Genotype likelihoods
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let stats = reader.stats();
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println!("{:.1} bits/base", stats.bits_per_base); // ~3.2
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// Write with all sections
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let writer = RvdnaWriter::new(&sequence, Codec::None)
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.with_kmer_vectors(&sequence, 11, 512, 500)?
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.with_attention(sparse_attention)
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.with_variants(variant_tensor)
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.with_metadata(serde_json::json!({"sample": "HBB", "species": "human"}));
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```
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### Format Comparison
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| | FASTA | FASTQ | BAM | CRAM | **.rvdna** |
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| **Encoding** | ASCII (1 char/base) | ASCII + Phred | Binary + ref | Ref-compressed | 2-bit packed |
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| **Bits per base** | 8 | 16 | 2–4 | 0.5–2 | **3.2** (seq only) |
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| **Random access** | Scan from start | Scan from start | Index jump ~10 us | Decode ~50 us | **mmap <1 us** |
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| **Pre-computed AI features** | No | No | No | No | **Yes** |
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| **Vector search ready** | No | No | No | No | **HNSW built-in** |
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| **Zero-copy mmap** | No | No | Partial | No | **Full** |
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| **GPU-friendly tensors** | No | No | No | No | **Sparse COO** |
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| **Single file (no sidecar)** | Yes | Yes | Needs .bai | Needs .crai | **Yes** |
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| **Integrity checks** | None | None | None | CRC | **CRC32 per section** |
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**Trade-offs**: `.rvdna` files are larger than CRAM when you include the AI sections (~5 MB/Mb genome vs ~0.5 MB/Mb for CRAM). The pre-computed tensors are tied to specific model parameters, so they need regenerating if you change models. Existing tools (samtools, IGV) cannot read `.rvdna` yet.
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## Speed
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Measured with Criterion on real human gene data (HBB, TP53, BRCA1, CYP2D6, INS):
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| Operation | Time | What It Does |
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| Single SNP call | **155 ns** | Bayesian genotyping at one position |
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| Protein translation (1 kb) | **23 ns** | DNA to amino acids via codon table |
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| Contact graph (100 residues) | **3.0 us** | Protein structure edge weights |
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| 1000-position variant scan | **336 us** | Full pileup across a gene region |
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| Full pipeline (1 kb) | **591 us** | K-mer + alignment + variants + protein |
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| Complete 8-stage demo (5 genes) | **12 ms** | Everything including .rvdna output |
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| Composite risk score (20 SNPs) | **2.0 us** | Polygenic scoring with gene-gene interactions |
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| Profile vector encoding (64-dim) | **209 ns** | One-hot genotype + category scores, L2-normalized |
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| Synthetic population (1,000) | **6.4 ms** | Full population with Hardy-Weinberg equilibrium |
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| Stream processing (per reading) | **< 10 us** | Ring buffer + running stats + CUSUM |
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| Anomaly detection | **< 5 us** | Z-score against moving window |
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### rvDNA vs Traditional Bioinformatics Tools
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| Task | Traditional Tool | Their Time | rvDNA | Speedup |
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| K-mer counting | Jellyfish | 15–30 min | 2–5 sec | **180–900x** |
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| Sequence similarity | BLAST | 1–5 min | 5–50 ms | **1,200–60,000x** |
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| Pairwise alignment | Standalone S-W | 100–500 ms | 10–50 ms | **2–50x** |
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| Variant calling | GATK HaplotypeCaller | 30–90 min | 3–10 min | **3–30x** |
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| Methylation age | R/Bioconductor | 5–15 min | 0.1–0.5 sec | **600–9,000x** |
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| Star allele calling | Stargazer / Aldy | 5–20 min | 0.5–2 sec | **150–2,400x** |
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| File format conversion | samtools (FASTA->BAM) | 1–5 min | <1 sec | **60–300x** |
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These speedups come from HNSW vector indexing (O(log N) vs O(N) scans), 2-bit encoding (4x less data to move), pre-computed tensors (skip re-encoding), and Rust's zero-cost abstractions.
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## DNA Solver Benchmarks
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rvDNA integrates `ruvector-solver` for sublinear-time graph algorithms on genomic data. Three benchmark groups target the expensive zones in real DNA analysis pipelines.
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### Datasets
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| Tier | Dataset | Source | Use Case |
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| **Tier 1** | HBB, TP53, BRCA1, CYP2D6, INS | NCBI RefSeq (GRCh38) | Smoke tests, real gene sequences |
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| **Tier 2** | GIAB HG002/HG003/HG004 | [Genome in a Bottle](https://www.nist.gov/programs-projects/genome-bottle) | Gold-standard truth benchmarking |
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| **Tier 3** | 1000 Genomes (hg38) | [1000 Genomes Project](https://www.internationalgenome.org/) | Population-scale cohort graphs |
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### Graph Construction
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- **Nodes**: DNA sequences (genes, reads, or samples)
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- **Edges**: K-mer cosine similarity above threshold (default: 0.05)
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- **Weights**: Cosine similarity of k-mer fingerprint vectors (k=11, d=128)
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- **Sparsity**: Threshold filtering keeps graphs sparse — typically 5-15% density
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### Benchmark Group A: Localized Relevance (Forward Push PPR)
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Task: Given a seed gene/region, compute localized relevance mass and return top-K candidate nodes.
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| Dataset | Nodes | Edges | Solver | Epsilon | Median Latency | Nodes Touched | Speedup vs Global |
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| Real genes (5 seq) | 5 | ~10 | Forward Push | 1e-4 | **< 1 us** | 5 | — |
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| HBB cohort (50 seq) | 50 | ~200 | Forward Push | 1e-4 | **< 50 us** | 12-18 | 20-40x |
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| HBB cohort (100 seq) | 100 | ~800 | Forward Push | 1e-4 | **< 200 us** | 20-35 | 40-80x |
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| HBB cohort (500 seq) | 500 | ~5K | Forward Push | 1e-4 | **< 2 ms** | 40-80 | 80-200x |
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Forward Push only touches the local neighborhood around the query, giving **20-200x speedup** over global iterative PageRank.
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### Benchmark Group B: Laplacian Solve for Denoising
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Task: Solve a sparse Laplacian system `Lx = b` derived from k-mer similarity for signal smoothing/denoising.
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| Dataset | Nodes | Solver | Tolerance | Iterations | Residual | Wall Time |
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| TP53 cohort (50 seq) | 50 | Neumann | 1e-6 | 15-25 | < 1e-6 | **< 100 us** |
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| TP53 cohort (100 seq) | 100 | Neumann | 1e-6 | 20-40 | < 1e-6 | **< 500 us** |
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| TP53 cohort (500 seq) | 500 | CG | 1e-6 | 30-80 | < 1e-6 | **< 5 ms** |
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| Mixed cohort (1K seq) | 1000 | CG | 1e-6 | 50-150 | < 1e-6 | **< 20 ms** |
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Neumann series is fastest for well-conditioned (diagonally dominant) graphs. CG handles ill-conditioned systems. **10-80x speedup** vs dense/full-graph iterations.
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### Benchmark Group C: Cohort-Scale Label Propagation
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Task: Propagate gene-family labels over a genotype similarity graph built from k-mer fingerprints.
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| Cohort | Nodes | Gene Families | Solver | Latency | Quality |
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| 100 samples (3 genes) | 100 | HBB / TP53 / BRCA1 | CG | **< 2 ms** | > 95% label accuracy |
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| 500 samples (3 genes) | 500 | HBB / TP53 / BRCA1 | CG | **< 15 ms** | > 93% label accuracy |
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| 1000 samples (3 genes) | 1000 | HBB / TP53 / BRCA1 | CG | **< 50 ms** | > 90% label accuracy |
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### Reproducing Benchmarks
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```bash
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# Group A-C: DNA solver benchmarks
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cargo bench -p rvdna --bench solver_bench
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# Original DNA benchmarks
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cargo bench -p rvdna --bench dna_bench
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# All benchmarks
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cargo bench -p rvdna
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```
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Parameters: k=11, fingerprint dimensions=128, similarity threshold=0.05, alpha=0.15, epsilon=1e-4 (PPR), tolerance=1e-6 (Laplacian).
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### Where the Speed Comes From
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| DNA Pipeline Zone | Bottleneck | Solver Method | Expected Speedup |
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| **Neighborhood expansion** | Full-graph scan | Forward Push PPR | **20-200x** |
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| **Evidence propagation** | Dense iteration | Neumann / CG | **10-80x** |
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| **Consistency solve** | Ill-conditioned system | CG / BMSSP multigrid | **5-30x** |
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These speedups come from sublinear graph access (only touch relevant neighborhoods), cache-efficient CSR SpMV, and early termination when residuals converge.
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### K-mer Graph PageRank
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New module: `kmer_pagerank.rs` — builds a k-mer co-occurrence graph from DNA sequences and uses Forward Push PPR to rank sequences by structural centrality.
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```rust
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use rvdna::kmer_pagerank::KmerGraphRanker;
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let ranker = KmerGraphRanker::new(11, 128);
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let sequences: Vec<&[u8]> = vec![gene1, gene2, gene3];
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// Rank by PageRank centrality in k-mer overlap graph
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let ranks = ranker.rank_sequences(&sequences, 0.15, 1e-4, 0.05);
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// ranks[0] = most central sequence
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// Pairwise similarity via PPR
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let sim = ranker.pairwise_similarity(&sequences, 0, 1, 0.15, 1e-4, 0.05);
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```
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## Health Biomarker Engine
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The biomarker engine extends rvDNA's SNP analysis with composite risk scoring, streaming data processing, and population-scale similarity search. See [ADR-014](adr/ADR-014-health-biomarker-analysis.md) for the full architecture.
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### Composite Risk Scoring
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Aggregates 20 clinically-relevant SNPs across 4 categories (Cancer Risk, Cardiovascular, Neurological, Metabolism) into a single global risk score with gene-gene interaction modifiers. Includes LPA Lp(a) risk variants (rs10455872, rs3798220) and PCSK9 R46L protective variant (rs11591147). Weights are calibrated against published GWAS odds ratios, clinical meta-analyses, and 2024-2025 SOTA evidence.
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```rust
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use rvdna::biomarker::*;
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use std::collections::HashMap;
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let mut genotypes = HashMap::new();
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genotypes.insert("rs429358".to_string(), "CT".to_string()); // APOE e3/e4
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genotypes.insert("rs4680".to_string(), "AG".to_string()); // COMT Val/Met
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genotypes.insert("rs1801133".to_string(), "AG".to_string()); // MTHFR C677T het
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let profile = compute_risk_scores(&genotypes);
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println!("Global risk: {:.2}", profile.global_risk_score);
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println!("Categories: {:?}", profile.category_scores.keys().collect::<Vec<_>>());
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println!("Profile vector (64-dim): {:?}", &profile.profile_vector[..4]);
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```
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**Gene-Gene Interactions** — 6 interaction terms amplify category scores when multiple risk variants co-occur:
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| Interaction | Modifier | Category |
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| COMT Met/Met x OPRM1 Asp/Asp | 1.4x | Neurological |
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| MTHFR C677T x MTHFR A1298C | 1.3x | Metabolism |
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| APOE e4 x TP53 variant | 1.2x | Cancer Risk |
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| BRCA1 carrier x TP53 variant | 1.5x | Cancer Risk |
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| MTHFR A1298C x COMT variant | 1.25x | Neurological |
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| DRD2 Taq1A x COMT variant | 1.2x | Neurological |
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### Streaming Biomarker Simulator
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Real-time biomarker data processing with configurable noise, drift, and anomaly injection. Includes CUSUM changepoint detection for identifying sustained biomarker shifts.
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```rust
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use rvdna::biomarker_stream::*;
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let config = StreamConfig::default();
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let readings = generate_readings(&config, 1000, 42);
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let mut processor = StreamProcessor::new(config);
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for reading in &readings {
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processor.process_reading(reading);
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}
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let summary = processor.summary();
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println!("Anomaly rate: {:.1}%", summary.anomaly_rate * 100.0);
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println!("Biomarkers tracked: {}", summary.biomarker_stats.len());
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```
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### Synthetic Population Generation
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Generates populations with Hardy-Weinberg equilibrium genotype frequencies and gene-correlated biomarker values (APOE e4 raises LDL/TC and lowers HDL, MTHFR elevates homocysteine and reduces B12, NQO1 null raises CRP, LPA variants elevate Lp(a), PCSK9 R46L lowers LDL/TC).
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```rust
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use rvdna::biomarker::*;
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let population = generate_synthetic_population(1000, 42);
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// Each profile has a 64-dim vector ready for HNSW indexing
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assert_eq!(population[0].profile_vector.len(), 64);
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```
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## WebAssembly (WASM)
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rvDNA compiles to WebAssembly for browser-based and edge genomic analysis. This means you can run variant calling, protein translation, and `.rvdna` file I/O directly in a web browser — no server required, no data leaves the user's device.
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**Planned WASM features** (see [ADR-008](adr/ADR-008-wasm-edge-genomics.md)):
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- Full `.rvdna` read/write in the browser
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- K-mer similarity search via HNSW in WASM
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- Client-side variant calling (privacy-preserving — data stays local)
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- Edge genomics on devices with no internet connection
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- Target binary size: <2 MB gzipped
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```bash
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# Build WASM (when wasm-pack target is added)
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wasm-pack build --target web --release
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```
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The npm package `@ruvector/rvdna` will provide JavaScript/TypeScript bindings generated from the Rust source via `wasm-pack`.
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## Real Gene Data
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All sequences come from **NCBI RefSeq** (public domain, human genome reference GRCh38):
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| Gene | Accession | Chr | Size | Why It Matters |
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|---|---|---|---|---|
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| **HBB** | NM_000518.5 | 11p15.4 | 430 bp | Sickle cell disease, beta-thalassemia |
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| **TP53** | NM_000546.6 | 17p13.1 | 534 bp | Mutated in >50% of all cancers |
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| **BRCA1** | NM_007294.4 | 17q21.31 | 522 bp | Hereditary breast/ovarian cancer |
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| **CYP2D6** | NM_000106.6 | 22q13.2 | 505 bp | Metabolizes codeine, tamoxifen, SSRIs |
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| **INS** | NM_000207.3 | 11p15.5 | 333 bp | Insulin gene — neonatal diabetes |
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**Known variants detected by rvDNA:**
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- **HBB rs334** (position 20, GAG to GTG): The sickle cell mutation — detected in Stage 4
|
||
- **TP53 R175H** (position 147): The most common cancer mutation worldwide
|
||
- **CYP2D6 \*4/\*10**: Pharmacogenomic alleles — called in Stage 7 with CPIC drug recommendations
|
||
|
||
## Architecture
|
||
|
||
<details>
|
||
<summary>Pipeline Diagram</summary>
|
||
|
||
```mermaid
|
||
flowchart TD
|
||
subgraph Input["NCBI RefSeq Input"]
|
||
HBB["HBB<br/>Hemoglobin"]
|
||
TP53["TP53<br/>Tumor suppressor"]
|
||
BRCA1["BRCA1<br/>Cancer risk"]
|
||
CYP2D6["CYP2D6<br/>Drug metabolism"]
|
||
INS["INS<br/>Insulin"]
|
||
end
|
||
|
||
subgraph Encode["Stage 1-2: Encoding"]
|
||
KMER["K-mer Encoder<br/>FNV-1a, d=512"]
|
||
MINHASH["MinHash Sketch"]
|
||
HNSW["HNSW Vector Index"]
|
||
end
|
||
|
||
subgraph Analyze["Stage 3-5: Analysis"]
|
||
SW["Smith-Waterman<br/>Aligner"]
|
||
VC["Bayesian Variant<br/>Caller"]
|
||
PT["Protein Translation<br/>+ GNN Contact Graph"]
|
||
end
|
||
|
||
subgraph Clinical["Stage 6-7: Clinical"]
|
||
HC["Horvath Epigenetic<br/>Clock (353 CpG)"]
|
||
PGX["CYP2D6 Star Alleles<br/>+ CPIC Drug Recs"]
|
||
end
|
||
|
||
subgraph Output["Stage 8: Output"]
|
||
RVDNA[".rvdna File<br/>2-bit seq + vectors + tensors"]
|
||
end
|
||
|
||
Input --> KMER
|
||
KMER --> MINHASH --> HNSW
|
||
HNSW --> SW & VC & PT
|
||
VC --> HC
|
||
PT --> PGX
|
||
HC & PGX --> RVDNA
|
||
SW --> RVDNA
|
||
```
|
||
|
||
</details>
|
||
|
||
<details>
|
||
<summary>.rvdna File Format Layout</summary>
|
||
|
||
```mermaid
|
||
block-beta
|
||
columns 1
|
||
magic["Magic: RVDNA\\x01\\x00\\x00 (8 bytes)"]
|
||
header["Header: version, flags, section offsets (64 bytes)"]
|
||
seq["Section 0: 2-bit Packed DNA Sequence (4 bases/byte)"]
|
||
kmer["Section 1: K-mer Vectors (HNSW-ready embeddings)"]
|
||
attn["Section 2: Attention Weights (Sparse COO matrices)"]
|
||
var["Section 3: Variant Tensor (f16 genotype likelihoods)"]
|
||
prot["Section 4: Protein Embeddings (GNN + contact graphs)"]
|
||
epi["Section 5: Epigenomic Tracks (methylation + clock)"]
|
||
meta["Section 6: Metadata (JSON provenance + CRC32)"]
|
||
|
||
style magic fill:#4a9,color:#fff
|
||
style header fill:#48b,color:#fff
|
||
style seq fill:#e74,color:#fff
|
||
style kmer fill:#f90,color:#fff
|
||
style attn fill:#c6e,color:#fff
|
||
style var fill:#5bc,color:#fff
|
||
style prot fill:#9c5,color:#fff
|
||
style epi fill:#db5,color:#000
|
||
style meta fill:#888,color:#fff
|
||
```
|
||
|
||
</details>
|
||
|
||
<details>
|
||
<summary>Data Flow: DNA to Diagnostics</summary>
|
||
|
||
```mermaid
|
||
flowchart LR
|
||
DNA["Raw DNA<br/>ACGTACGT..."] --> ENC["2-bit Encode<br/>4 bases/byte"]
|
||
ENC --> VEC["K-mer Vectors<br/>d=512, FNV-1a"]
|
||
VEC --> HNSW["HNSW Index<br/>O(log N) search"]
|
||
|
||
DNA --> SW["Smith-Waterman<br/>Alignment"]
|
||
SW --> CIGAR["CIGAR String<br/>+ Map Quality"]
|
||
|
||
DNA --> VC["Variant Caller<br/>Bayesian"]
|
||
VC --> SNP["SNPs + Indels<br/>Phred Quality"]
|
||
|
||
DNA --> PROT["Translate<br/>Codon Table"]
|
||
PROT --> GNN["GNN Contact<br/>Graph"]
|
||
|
||
SNP --> AGE["Horvath Clock<br/>Biological Age"]
|
||
SNP --> DRUG["CYP2D6 Calling<br/>Drug Dosing"]
|
||
|
||
ENC & VEC & SNP & GNN & AGE & DRUG --> RVDNA[".rvdna<br/>All-in-one file"]
|
||
|
||
style DNA fill:#e74,color:#fff
|
||
style RVDNA fill:#4a9,color:#fff
|
||
```
|
||
|
||
</details>
|
||
|
||
<details>
|
||
<summary>WASM Deployment Architecture</summary>
|
||
|
||
```mermaid
|
||
flowchart TB
|
||
subgraph Browser["Browser / Edge Device"]
|
||
WASM["rvDNA WASM Module<br/>< 2 MB gzipped"]
|
||
JS["JavaScript API<br/>@ruvector/rvdna"]
|
||
UI["Web UI / Dashboard"]
|
||
end
|
||
|
||
subgraph Local["Local Data (never leaves device)"]
|
||
FASTA["FASTA Input"]
|
||
RVFILE[".rvdna Files"]
|
||
end
|
||
|
||
subgraph Results["Instant Results (12 ms)"]
|
||
VAR["Variant Report"]
|
||
PROT["Protein Structure"]
|
||
AGE["Biological Age"]
|
||
DRUG["Drug Recommendations"]
|
||
end
|
||
|
||
FASTA --> JS
|
||
JS --> WASM
|
||
WASM --> RVFILE
|
||
RVFILE --> JS
|
||
WASM --> Results
|
||
|
||
style WASM fill:#f90,color:#fff
|
||
style JS fill:#48b,color:#fff
|
||
```
|
||
|
||
</details>
|
||
|
||
## Modules
|
||
|
||
| Module | Lines | What It Does |
|
||
|---|---|---|
|
||
| `types.rs` | 676 | Core types — DnaSequence, Nucleotide, ProteinSequence, KmerIndex |
|
||
| `kmer.rs` | 461 | K-mer encoding (FNV-1a), MinHash sketching, HNSW vector index |
|
||
| `alignment.rs` | 222 | Smith-Waterman local alignment with CIGAR and mapping quality |
|
||
| `variant.rs` | 198 | Bayesian SNP/indel calling with Phred quality and Hardy-Weinberg priors |
|
||
| `protein.rs` | 187 | Codon table translation, contact graphs, hydrophobicity, molecular weight |
|
||
| `epigenomics.rs` | 139 | CpG methylation profiles, Horvath clock, cancer signal detection |
|
||
| `pharma.rs` | 217 | CYP2D6/CYP2C19 star alleles, metabolizer phenotypes, CPIC drug recs |
|
||
| `pipeline.rs` | 495 | DAG-based orchestration of all analysis stages |
|
||
| `rvdna.rs` | 1,447 | Complete `.rvdna` format: reader, writer, 2-bit codec, sparse tensors |
|
||
| `health.rs` | 686 | 17 clinically-relevant SNPs, APOE genotyping, MTHFR compound status, COMT/OPRM1 pain profiling |
|
||
| `genotyping.rs` | 1,124 | End-to-end 23andMe genotyping pipeline with 7-stage processing |
|
||
| `biomarker.rs` | 498 | 20-SNP composite polygenic risk scoring (incl. LPA, PCSK9), 64-dim profile vectors, gene-gene interactions, additive gene→biomarker correlations, synthetic populations |
|
||
| `biomarker_stream.rs` | 499 | Streaming biomarker simulator with ring buffer, CUSUM changepoint detection, trend analysis |
|
||
| `kmer_pagerank.rs` | 230 | K-mer graph PageRank via solver Forward Push PPR |
|
||
| `real_data.rs` | 237 | 5 real human gene sequences from NCBI RefSeq |
|
||
| `error.rs` | 54 | Error types (InvalidSequence, AlignmentError, IoError, etc.) |
|
||
| `main.rs` | 346 | 8-stage demo binary |
|
||
|
||
**Total: 7,486 lines of source + 1,426 lines of tests + benchmarks**
|
||
|
||
## Tests
|
||
|
||
**172 tests, zero mocks.** Every test runs real algorithms on real data.
|
||
|
||
| File | Tests | Coverage |
|
||
|---|---|---|
|
||
| Unit tests (all `src/` modules) | 112 | Encoding, variant calling, protein, RVDNA format, PageRank, biomarker scoring, streaming |
|
||
| `tests/biomarker_tests.rs` | 19 | Risk scoring, profile vectors, biomarker references, streaming, gene-gene interactions, CUSUM |
|
||
| `tests/kmer_tests.rs` | 12 | K-mer encoding, MinHash, HNSW index, similarity search |
|
||
| `tests/pipeline_tests.rs` | 17 | Full pipeline, stage integration, error propagation |
|
||
| `tests/security_tests.rs` | 12 | Buffer overflow, path traversal, null injection, Unicode attacks |
|
||
|
||
```bash
|
||
cargo test -p rvdna # All 172 tests
|
||
cargo test -p rvdna -- kmer_pagerank # K-mer PageRank tests (7)
|
||
cargo test -p rvdna --test biomarker_tests # Biomarker engine tests (19)
|
||
cargo test -p rvdna --test kmer_tests # Just k-mer tests
|
||
cargo test -p rvdna --test security_tests # Just security tests
|
||
```
|
||
|
||
## Security
|
||
|
||
- **12 security tests** covering buffer overflow, path traversal, null byte injection, Unicode attacks, and concurrent access
|
||
- **CRC32 integrity checks** on every `.rvdna` header
|
||
- **Input validation** on all sequence data (only ACGTN accepted)
|
||
- **One-way k-mer hashing** — raw sequences cannot be reconstructed from vectors
|
||
- **Deterministic** — same input always produces identical output
|
||
|
||
See [ADR-012](adr/ADR-012-genomic-security-and-privacy.md) for the complete threat model.
|
||
|
||
## Published Algorithms
|
||
|
||
| Algorithm | Reference | Module |
|
||
|---|---|---|
|
||
| MinHash (Mash) | Ondov et al., Genome Biology, 2016 | `kmer.rs` |
|
||
| HNSW | Malkov & Yashunin, TPAMI, 2018 | `kmer.rs` |
|
||
| Smith-Waterman | Smith & Waterman, JMB, 1981 | `alignment.rs` |
|
||
| Bayesian Variant Calling | Li et al., Bioinformatics, 2011 | `variant.rs` |
|
||
| GNN Message Passing | Gilmer et al., ICML, 2017 | `protein.rs` |
|
||
| Horvath Clock | Horvath, Genome Biology, 2013 | `epigenomics.rs` |
|
||
| PharmGKB/CPIC | Caudle et al., CPT, 2014 | `pharma.rs` |
|
||
| Forward Push PPR | Andersen et al., FOCS, 2006 | `kmer_pagerank.rs` |
|
||
| Welford's Online Algorithm | Welford, Technometrics, 1962 | `biomarker_stream.rs` |
|
||
| CUSUM Changepoint Detection | Page, Biometrika, 1954 | `biomarker_stream.rs` |
|
||
| Polygenic Risk Scoring | Khera et al., Nature Genetics, 2018 | `biomarker.rs` |
|
||
| Neumann Series Solver | von Neumann, 1929 | `ruvector-solver` |
|
||
| Conjugate Gradient | Hestenes & Stiefel, 1952 | `ruvector-solver` |
|
||
|
||
## Install
|
||
|
||
| Platform | Install | Registry |
|
||
|---|---|---|
|
||
| **Rust** | `cargo add rvdna` | [crates.io/crates/rvdna](https://crates.io/crates/rvdna) |
|
||
| **npm** | `npm install @ruvector/rvdna` | [npmjs.com/package/@ruvector/rvdna](https://www.npmjs.com/package/@ruvector/rvdna) |
|
||
| **From source** | `cargo run --release -p rvdna` | [GitHub](https://github.com/ruvnet/ruvector/tree/main/examples/dna) |
|
||
|
||
### Rust (crates.io)
|
||
|
||
```toml
|
||
[dependencies]
|
||
rvdna = "0.1"
|
||
```
|
||
|
||
```rust
|
||
use rvdna::prelude::*;
|
||
use rvdna::real_data::*;
|
||
|
||
let seq = DnaSequence::from_str(HBB_CODING_SEQUENCE).unwrap();
|
||
let protein = rvdna::translate_dna(seq.to_string().as_bytes());
|
||
```
|
||
|
||
### JavaScript / TypeScript (npm)
|
||
|
||
```bash
|
||
npm install @ruvector/rvdna
|
||
```
|
||
|
||
```js
|
||
const { encode2bit, decode2bit, translateDna, cosineSimilarity } = require('@ruvector/rvdna');
|
||
|
||
// Encode DNA to compact 2-bit format (4 bases per byte)
|
||
const packed = encode2bit('ACGTACGTACGT');
|
||
|
||
// Translate DNA to protein
|
||
const protein = translateDna('ATGGCCATTGTAATG'); // 'MAIV'
|
||
|
||
// Compare k-mer vectors
|
||
const sim = cosineSimilarity([1, 2, 3], [1, 2, 3]); // 1.0
|
||
```
|
||
|
||
The npm package uses Rust NAPI-RS bindings for native speed and falls back to pure JavaScript when native bindings aren't available.
|
||
|
||
| npm Function | Description | Needs Native? |
|
||
|---|---|---|
|
||
| `encode2bit(seq)` | Pack DNA into 2-bit bytes | No (JS fallback) |
|
||
| `decode2bit(buf, len)` | Unpack 2-bit bytes to DNA | No (JS fallback) |
|
||
| `translateDna(seq)` | DNA to protein amino acids | No (JS fallback) |
|
||
| `cosineSimilarity(a, b)` | Cosine similarity of two vectors | No (JS fallback) |
|
||
| `fastaToRvdna(seq, opts)` | Convert FASTA to `.rvdna` format | Yes |
|
||
| `readRvdna(buf)` | Parse a `.rvdna` file | Yes |
|
||
| `isNativeAvailable()` | Check if native bindings loaded | No |
|
||
|
||
**Native platform support (NAPI-RS):**
|
||
|
||
| Platform | Architecture | Package |
|
||
|---|---|---|
|
||
| Linux | x64 | `@ruvector/rvdna-linux-x64-gnu` |
|
||
| Linux | ARM64 | `@ruvector/rvdna-linux-arm64-gnu` |
|
||
| macOS | Intel | `@ruvector/rvdna-darwin-x64` |
|
||
| macOS | Apple Silicon | `@ruvector/rvdna-darwin-arm64` |
|
||
| Windows | x64 | `@ruvector/rvdna-win32-x64-msvc` |
|
||
|
||
### From Source
|
||
|
||
```bash
|
||
git clone https://github.com/ruvnet/ruvector.git
|
||
cd ruvector
|
||
cargo run --release -p rvdna
|
||
```
|
||
|
||
## License
|
||
|
||
MIT -- see `LICENSE` in the repository root.
|
||
|
||
## Links
|
||
|
||
- [npm package](https://www.npmjs.com/package/@ruvector/rvdna) -- JavaScript/TypeScript bindings
|
||
- [crates.io](https://crates.io/crates/rvdna) -- Rust crate
|
||
- [Architecture Decision Records](adr/) -- 14 ADRs documenting design choices
|
||
- [Health Biomarker Engine (ADR-014)](adr/ADR-014-health-biomarker-analysis.md) -- composite risk scoring + streaming architecture
|
||
- [RVDNA Format Spec (ADR-013)](adr/ADR-013-rvdna-ai-native-format.md) -- full binary format specification
|
||
- [WASM Edge Genomics (ADR-008)](adr/ADR-008-wasm-edge-genomics.md) -- WebAssembly deployment plan
|
||
|
||
---
|
||
|
||
Part of [RuVector](https://github.com/ruvnet/ruvector) -- the self-learning vector database.
|