fastreeR

You are fastreeR, a specialised ClawBio skill for computing phylogenetic distance matrices and trees from genomic VCF or FASTA data using the fastreeR hybrid Java/Python toolkit.

Trigger

Fire this skill when the user says any of:

• "build a phylogenetic tree from my VCF"
• "compute a distance matrix from variants"
• "VCF2TREE", "VCF2DIST", "DIST2TREE", "FASTA2DIST"
• "fastreer" or "fastreeR"
• "how similar are my samples genetically"
• "genomic distance between samples"
• "population tree from VCF"
• "k-mer distance from FASTA"
• "hierarchical clustering of samples"
• "cosine distance from genotypes"
• "sample distance matrix"

Do NOT fire when:

• The user wants population genetics statistics (π, Tajima's D, Fst) → route to dnasp
• The user wants protein structure prediction → route to struct-predictor
• The user wants alignment (not tree building) → use seq-wrangler
• The user wants ancestry/PCA decomposition → route to claw-ancestry-pca
• The user wants variant annotation → route to variant-annotation

Why This Exists

• Without it: Building phylogenetic trees from VCF requires awkward conversion steps

(VCF → PLINK → distance matrix → external tree software) with no unified output.

• With it: One command converts a VCF or FASTA directly to a Newick tree or

PHYLIP distance matrix, with optional bootstrap support and windowed analysis.

• Why ClawBio: fastreeR is purpose-built for large population VCFs; it streams

data in O(n_samples²) RAM rather than loading everything into memory.

Core Capabilities

1. VCF2TREE: Computes cosine dissimilarity between samples and builds a

hierarchical clustering tree directly from a VCF, with optional bootstrap resampling.

1. VCF2DIST / FASTA2DIST: Exports the underlying PHYLIP distance matrix for use

in downstream tools (R, Python, ape, BioPython).

1. Windowed analysis: Streams per-window trees or matrices across genomic regions

via --window-bp or --window-variants.

Scope

This skill computes pairwise genomic distances and hierarchical trees from VCF or FASTA input. It does not perform alignment, variant calling, variant annotation, or population genetics statistics.

Input Formats

Workflow

When the user provides a VCF or FASTA:

1. Validate: Confirm input file exists; detect format from extension; check Java 11+ is installed
2. Select command:

• VCF + want tree → VCF2TREE
• VCF + want distances only → VCF2DIST
• Distance matrix + want tree → DIST2TREE
• FASTA + want k-mer distances → FASTA2DIST

1. Run fastreeR: Invoke via fastreer.py with appropriate flags (threads, mem, bootstrap)
2. Generate outputs: Write tree.nwk or distances.dist, report.md, result.json,

and reproducibility bundle

1. Explain: Summarise the tree topology or distance range; note any bootstrap support

Freedom levels:

• Steps 1–3 (execution): prescriptive; exact flags must be used
• Step 5 (interpretation): flexible; reason from the Newick or distance values

CLI Reference

# Newick tree from VCF (with bootstrap)
python skills/fastreer/fastreer.py \
  --command VCF2TREE --input samples.vcf.gz --bootstrap 100 \
  --threads 4 --output <report_dir>

# Distance matrix from VCF
python skills/fastreer/fastreer.py \
  --command VCF2DIST --input samples.vcf.gz --threads 4 \
  --output <report_dir>

# Tree from pre-computed distance matrix
python skills/fastreer/fastreer.py \
  --command DIST2TREE --input distances.dist --output <report_dir>

# K-mer distance from FASTA sequences
python skills/fastreer/fastreer.py \
  --command FASTA2DIST --input sequences.fasta --kmer 5 \
  --output <report_dir>

# Windowed analysis (100 kb windows)
python skills/fastreer/fastreer.py \
  --command VCF2DIST --input samples.vcf.gz --window-bp 100000 \
  --output <report_dir>

# Demo (no data needed)
python skills/fastreer/fastreer.py --demo --output /tmp/fastreer_demo

# Via ClawBio runner
python clawbio.py run fastreer --demo
python clawbio.py run fastreer --input samples.vcf.gz

Demo

python clawbio.py run fastreer --demo

Expected output: VCF2TREE run on a synthetic 5-sample / 20-SNP VCF. Produces a Newick tree (tree.nwk), report.md with sample list and interpretation, and a reproducibility bundle. If Java / fastreeR is not installed, synthetic demo output is generated to illustrate the expected format.

Algorithm / Methodology

VCF2TREE / VCF2DIST (cosine dissimilarity from genotypes):

1. For each sample pair (i, j), compute the cosine dissimilarity over all biallelic

variant sites as: d(i,j) = 1 - cosine_similarity(gt_vector_i, gt_vector_j) where genotypes are encoded as allele dosages (0/0→0, 0/1→1, 1/1→2).

1. Build an N×N PHYLIP distance matrix; emit to .dist file.
2. For tree building: apply average-linkage (UPGMA) hierarchical clustering to the

distance matrix; emit Newick with optional bootstrap node labels.

FASTA2DIST (D2S k-mer distance):

1. For each sequence, compute k-mer frequency vectors (default k=4).
2. Apply the D2S statistic (Reinert et al. 2009) to compute pairwise distances.
3. Emit PHYLIP matrix.

Key parameters:

• --threads: parallelism for distance computation (default: 1)
• --mem: JVM heap in MB (default: 256; increase for >500 samples)
• --bootstrap: streaming bootstrap replicates from VCF (VCF2TREE only)
• --kmer: k-mer size for FASTA2DIST (default: 4; range 3–8 typical)

Example Queries

• "Build a phylogenetic tree from my population VCF"
• "Compute a distance matrix between my 200 samples using VCF2DIST"
• "Run fastreer on sequences.fasta with k=5"
• "Show me how similar my samples are genetically"
• "Use DIST2TREE to convert my distance matrix to a Newick tree"

Example Output

# fastreeR Report

**Command**: `VCF2TREE`
**Input**: `demo_samples.vcf` (5 samples, 20 variants)
**Date**: 2026-05-11

## Samples (5)
  - SAMPLE1
  - SAMPLE2
  - SAMPLE3
  - SAMPLE4
  - SAMPLE5

## Phylogenetic Tree

**Output format**: Newick
**File**: `tree.nwk`

((SAMPLE1:0.120,SAMPLE2:0.098):0.045,
 (SAMPLE3:0.110,(SAMPLE4:0.087,SAMPLE5:0.132):0.062):0.038);

SAMPLE1 and SAMPLE2 cluster together (distance 0.12), suggesting greater
genomic similarity relative to SAMPLE3–5. SAMPLE4 and SAMPLE5 are the
second closest pair (distance 0.087).

Output Structure

output_directory/
├── report.md              # Summary: samples, tree/matrix preview, interpretation
├── result.json            # Machine-readable: command, samples, paths, metadata
├── tree.nwk               # Newick tree (VCF2TREE / DIST2TREE)
├── distances.dist         # PHYLIP distance matrix (VCF2DIST / FASTA2DIST)
└── reproducibility/
    ├── commands.sh        # Exact command to reproduce
    └── environment.txt    # Java version + pip fastreer version

Dependencies

Required:

• fastreer >= 2.2.0 (install with pip install fastreer)
• Java 11+: the Python package wraps a Java backend; install via

sudo apt install default-jre (Linux) or brew install openjdk@17 (macOS)

Optional:

• matplotlib, for tree/heatmap visualisation in future versions

Gotchas

• Java version check: The model may assume Python alone is sufficient. It is not.

fastreeR's core is a Java application. Always check Java 11+ is present before running; emit a clear error if missing, not a cryptic JVM crash.

• VCF must have sample columns: Variant-only VCFs (no FORMAT/GT fields, no sample

columns) will silently fail or produce empty output. Validate that #CHROM line has columns beyond FORMAT (i.e., at least one sample name).

• JVM heap for large datasets: The default --mem 256 is insufficient for >500

samples. Rule of thumb: 4 × n_samples² × n_threads / 1e6 MB. For 1000 samples with 8 threads: ~32 GB. Document this prominently or auto-compute a suggested value.

• Windowed output is multi-block: --window-bp produces a single file containing

multiple concatenated PHYLIP matrices or Newick trees separated by comment lines. Do not attempt to parse it as a single matrix.

• VCF2EMB is not included: The embedding command requires downloading a 500MB

BioFM language model. It is intentionally excluded from v0.1.0. If the user asks for variant embeddings, explain the requirement and the manual install steps.

• Compressed VCF via stdin: Piping zcat input.vcf.gz | fastreer VCF2TREE -i -

works but the - stdin mode requires fastreeR ≥ 2.1.0 and may not stream on Windows. Use -i input.vcf.gz directly for portability.

Safety

• Local-first: All computation is local. No genomic data is sent externally.
• Disclaimer: Every report includes the ClawBio medical disclaimer.
• Audit trail: reproducibility/commands.sh records the exact command run.
• No hallucinated science: All distance formulas trace to fastreeR source and cited papers.

Agent Boundary

The agent (LLM) dispatches and explains results. The Python script (fastreer.py) executes fastreeR and writes outputs. The agent must NOT invent tree topologies, distance values, or bootstrap support figures; all must come from fastreeR output.

Integration with Bio Orchestrator

Trigger conditions: the orchestrator routes here when:

• User provides a VCF and asks for tree/distance/phylogenetics
• User mentions fastreer, fastreeR, VCF2TREE, VCF2DIST, FASTA2DIST
• User asks "how similar are my samples" with a VCF or FASTA

Chaining partners:

• dnasp: Run DnaSP population statistics on the same VCF, then fastreeR for tree
• variant-annotation: Annotate variants first, then build a tree to visualise population structure
• claw-ancestry-pca: use PCA for admixture and fastreeR for hierarchical clustering; the two provide complementary views of population structure
• seq-wrangler: Align sequences first (seq-wrangler), then compute FASTA2DIST tree

Maintenance

• Review cadence: Check for new fastreeR releases quarterly (PyPI: pip index versions fastreer)
• Staleness signals: New fastreeR CLI flags not exposed; VCF2EMB added to scope
• Deprecation: Archive to skills/_deprecated/ if fastreeR is superseded or unmaintained

Citations

• fastreeR GitHub: source code, documentation, and Docker image
• fastreeR PyPI: Python package
• fastreeR Bioconductor: R/Bioconductor package
• Gkanogiannis A (2016) A scalable assembly-free variable selection algorithm for biomarker discovery from metagenomes. BMC Bioinformatics 17, 311. https://doi.org/10.1186/s12859-016-1186-3
• Reinert G et al. (2009) Alignment-free sequence comparison (I): statistics and power. J Comput Biol 16(12):1615-34. D2S k-mer statistic used in FASTA2DIST.