https://docs.rs/biometal

biometal

ARM-native bioinformatics library with streaming architecture and evidence-based optimization

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What Makes biometal Different?

Stream data directly from networks and analyze terabyte-scale datasets on consumer hardware without downloading.

• Constant ~5 MB memory regardless of dataset size (99.5% reduction)
• 16-25× speedup using ARM NEON SIMD on Apple Silicon
• Network streaming from HTTP/HTTPS sources (no download needed)
• Evidence-based design (1,357 experiments, 40,710 measurements)

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🎉 NEW in v1.10.0: Extended Format Support

biometal now supports 12+ bioinformatics file formats with production-ready streaming parsers:

Sequences & Reads:

• FASTQ/FASTA: Read sequences with quality scores
• BAM/SAM: Binary alignment format with indexing (BAI)

Annotations & Features:

• BED/narrowPeak: Genomic intervals and ChIP-seq peaks (ENCODE)
• GFF3: Hierarchical gene features (genes, mRNAs, exons, CDS)
• GTF: Gene annotations for RNA-seq (GENCODE, Ensembl) [NEW]

Variants & Alignments:

• VCF: Genetic variants (SNPs, indels, structural variants)
• PAF: minimap2 pairwise alignments (long-read analysis) [NEW]

Graphs & Assembly:

• GFA: Assembly graphs (pangenomes, read overlap graphs)

Indices:

• FAI: FASTA index for O(1) sequence lookup
• TBI: Tabix index for O(log n) region queries

All formats support:

• ✅ Streaming architecture (constant ~5 MB memory)
• ✅ Automatic gzip decompression (.gz files)
• ✅ Python bindings with optimized memory usage
• ✅ Real-world validation (ENCODE, UCSC, Ensembl, 1000 Genomes)

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Quick Start

Installation

Rust:

[dependencies]
biometal = "1.10"

Python:

pip install biometal-rs  # Install
python -c "import biometal; print(biometal.__version__)"  # Test

Note: Package is biometal-rs on PyPI, but imports as biometal in Python.

Basic Usage

Rust:

use biometal::FastqStream;

// Stream FASTQ with constant memory (~5 MB)
let stream = FastqStream::from_path("dataset.fq.gz")?;

for record in stream {
    let record = record?;
    // Process one record at a time
}

Python:

import biometal

# Stream FASTQ with constant memory (~5 MB)
stream = biometal.FastqStream.from_path("dataset.fq.gz")

for record in stream:
    # ARM NEON accelerated (16-25× speedup)
    gc = biometal.gc_content(record.sequence)
    counts = biometal.count_bases(record.sequence)
    mean_q = biometal.mean_quality(record.quality)

---

📚 Documentation

Start Here

• 📘 User Guide - Comprehensive guide: installation, core concepts, common workflows, troubleshooting, and migration from pysam/samtools (NEW - v1.6.0)

In-Depth Resources

• 📖 DeepWiki AI Docs - AI-assisted documentation with Q&A
• 📓 Interactive Tutorials - Jupyter notebooks with real workflows
• 🦀 API Reference - Full Rust documentation
• 🐍 Python Guide - Python-specific documentation
• 🧬 BAM API Reference - Complete BAM/SAM parser API (v1.4.0)
• ⚡ BAM Performance Guide - Benchmarks and optimization (v1.4.0)
• 📐 Architecture - Technical design details
• ❓ FAQ - Frequently asked questions

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📓 Interactive Tutorials

Learn biometal through hands-on Jupyter notebooks (5 complete, ~2.5 hours):

Notebook	Duration	Topics
01. Getting Started	15-20 min	Streaming, GC content, quality analysis
02. Quality Control	30-40 min	Trimming, filtering, masking (v1.2.0)
03. K-mer Analysis	30-40 min	ML preprocessing, DNABert (v1.1.0)
04. Network Streaming	30-40 min	HTTP streaming, public data (v1.0.0)
05. BAM Alignment Analysis	30-40 min	BAM parsing, 4× speedup, filtering (v1.2.0+)
06. BAM Production Workflows	45-60 min	Tag parsing, QC statistics, production pipelines (v1.4.0)

👉 Browse all tutorials →

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🚀 Key Features

Streaming Architecture

• Constant ~5 MB memory regardless of dataset size
• Analyze 5TB datasets on laptops without downloading
• 99.5% memory reduction vs. traditional approaches

ARM-Native Performance

• 16-25× speedup using ARM NEON SIMD
• Optimized for Apple Silicon (M1/M2/M3/M4)
• Automatic scalar fallback on x86_64

Network Streaming

• Stream directly from HTTP/HTTPS (no download)
• Smart LRU caching + background prefetching
• Access public data (ENA, S3, GCS, Azure)

Operations Library

• Core operations: GC content, base counting, quality scores
• K-mer operations: Extraction, minimizers, spectrum (v1.1.0)
• QC operations: Trimming, filtering, masking (v1.2.0)
• BAM/SAM parser: Production-ready with 8.4× speedup via parallel BGZF + NEON + cloudflare_zlib
  • 5.82 million records/sec throughput
  • 92.0 MiB/s compressed file processing (+67% from cloudflare_zlib in v1.7.0)
  • Constant ~5 MB memory (streams terabyte-scale alignments)
  • Python bindings (v1.3.0): CIGAR operations, SAM writing, alignment metrics
  • Production polish (v1.4.0): Tag convenience methods, statistics functions
    • 6 tag accessors: edit_distance(), alignment_score(), read_group(), etc.
    • 4 statistics functions: insert_size_distribution(), edit_distance_stats(), strand_bias(), alignment_length_distribution()
  • NEON optimization (v1.5.0): ARM SIMD sequence decoding (4.62× faster)
  • BAI index (v1.6.0): Indexed region queries with 1.68-500× speedup
    • O(log n) random access to BAM files
    • Near-zero overhead (<1ms index loading)
    • Speedup scales with file size (10-500× for 1-10 GB files)
• Format Library (v1.8.0): Production-ready parsers for genomic annotation and assembly formats
  • BED (Browser Extensible Data): Genomic intervals with streaming architecture
    • BED3/6/12 format support
    • 0-based half-open coordinate system
    • Constant memory (~5 MB) for terabyte-scale peak files
  • GFA (Graphical Fragment Assembly): Assembly graph format
    • Segment, Link, Path record types
    • Graph connectivity validation
    • Streaming architecture for large assembly graphs
  • VCF (Variant Call Format): Genetic variant data
    • VCF 4.2 specification compliance
    • Header parsing with sample/contig/INFO extraction
    • SNP/indel classification
    • Multi-allelic variant support
  • GFF3 (General Feature Format): Hierarchical gene annotations
    • 1-based inclusive coordinate system
    • Parent-child relationship tracking (gene → mRNA → exon/CDS)
    • Attribute parsing with convenience methods
    • Coordinate conversion to BED (0-based)
  • Testing: 23 property-based tests + 6 real-world integration tests
  • Python bindings: Full streaming API for all formats
• 60+ Python functions for bioinformatics workflows

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Performance Highlights

Operation	Scalar	Optimized	Speedup
Base counting	315 Kseq/s	5,254 Kseq/s	16.7× (NEON)
GC content	294 Kseq/s	5,954 Kseq/s	20.3× (NEON)
Quality filter	245 Kseq/s	6,143 Kseq/s	25.1× (NEON)
BAM parsing	~11 MiB/s	92.0 MiB/s	8.4× (BGZF + NEON + cloudflare_zlib v1.7.0)

Dataset Size	Traditional	biometal	Reduction
100K sequences	134 MB	5 MB	96.3%
1M sequences	1,344 MB	5 MB	99.5%
5TB dataset	5,000 GB	5 MB	99.9999%

📊 Comprehensive Benchmark Comparison vs samtools/pysam →

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Platform Support

Platform	Performance	Tests	Status
Mac ARM (M1-M4)	16-25× speedup	✅ 551/551	Optimized
AWS Graviton	6-10× speedup	✅ 551/551	Portable
Linux x86_64	1× (scalar)	✅ 551/551	Portable

Test count: 551 library tests (including 65 new tests for GTF, PAF, narrowPeak) + 23 property-based tests

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Evidence-Based Design

biometal's design is grounded in comprehensive experimental validation:

• 1,357 experiments (40,710 measurements, N=30)
• Statistical rigor (95% CI, Cohen's d effect sizes)
• Full methodology: apple-silicon-bio-bench
• 6 optimization rules documented in OPTIMIZATION_RULES.md

---

Roadmap

v1.0.0 (Released Nov 5, 2025) ✅ - Core library + network streaming v1.1.0 (Released Nov 6, 2025) ✅ - K-mer operations v1.2.0 (Released Nov 6, 2025) ✅ - Python bindings for Phase 4 QC BAM/SAM (Integrated Nov 8, 2025) ✅ - Native streaming alignment parser with parallel BGZF (4× speedup) v1.3.0 (Released Nov 9, 2025) ✅ - Python BAM bindings with CIGAR operations and SAM writing v1.4.0 (Released Nov 9, 2025) ✅ - BAM tag convenience methods and statistics functions v1.5.0 (Released Nov 9, 2025) ✅ - ARM NEON sequence decoding (+27.5% BAM parsing speedup) v1.6.0 (Released Nov 10, 2025) ✅ - BAI index support (indexed region queries, 1.68-500× speedup) v1.7.0 (Released Nov 13, 2025) ✅ - cloudflare_zlib backend (1.67× decompression, 2.29× compression speedups) v1.8.0 (Released Nov 13, 2025) ✅ - Format library (BED, GFA, VCF, GFF3) with property-based testing

• 4 production-ready format parsers with streaming architecture
• 23 property-based tests + 6 real-world integration tests
• Tested against ENCODE, UCSC, Ensembl, 1000 Genomes data
• Full Python bindings for all formats

Next (Planned):

• CSI index support (for references >512 Mbp)
• Extended tag parsing (full type support)
• Additional alignment statistics
• Community feedback & benchmarking

Future (Community Driven):

• Extended operations (alignment, assembly)
• Additional formats (BCF, CRAM)
• Metal GPU acceleration (Mac-specific)

See CHANGELOG.md for detailed release notes.

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Mission: Democratizing Bioinformatics

biometal addresses barriers that lock researchers out of genomics:

1. Economic: Consumer ARM laptops ($1,400) deliver production performance
2. Environmental: ARM efficiency reduces carbon footprint
3. Portability: Works across ARM ecosystem (Mac, Graviton, Ampere, RPi)
4. Data Access: Analyze 5TB datasets on 24GB laptops without downloading

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Example Use Cases

Quality Control Pipeline

import biometal

stream = biometal.FastqStream.from_path("raw_reads.fq.gz")

for record in stream:
    # Trim low-quality ends
    trimmed = biometal.trim_quality_window(record, min_quality=20, window_size=4)

    # Length filter
    if biometal.meets_length_requirement(trimmed, min_len=50, max_len=150):
        # Mask remaining low-quality bases
        masked = biometal.mask_low_quality(trimmed, min_quality=20)

        # Check masking rate
        mask_rate = biometal.count_masked_bases(masked) / len(masked.sequence)
        if mask_rate < 0.1:
            # Pass QC - process further
            pass

K-mer Extraction for ML

import biometal

# Extract k-mers for DNABert preprocessing
stream = biometal.FastqStream.from_path("dataset.fq.gz")

for record in stream:
    # Extract overlapping k-mers (k=6 typical for DNABert)
    kmers = biometal.extract_kmers(record.sequence, k=6)

    # Format for transformer models
    kmer_string = " ".join(kmer.decode() for kmer in kmers)

    # Feed to DNABert - constant memory!
    model.process(kmer_string)

Network Streaming

import biometal

# Stream from HTTP without downloading
# Works with ENA, S3, GCS, Azure public data
url = "https://example.com/dataset.fq.gz"
stream = biometal.FastqStream.from_path(url)

for record in stream:
    # Analyze directly - no download needed!
    # Memory: constant ~5 MB
    gc = biometal.gc_content(record.sequence)

BAM Alignment Analysis (v1.4.0)

import biometal

# Stream BAM file with constant memory (~5 MB)
reader = biometal.BamReader.from_path("alignments.bam")

for record in reader:
    # Access alignment details
    print(f"{record.name}: MAPQ={record.mapq}, pos={record.position}")

    # NEW v1.4.0: Tag convenience methods
    edit_dist = record.edit_distance()  # NM tag
    align_score = record.alignment_score()  # AS tag
    read_group = record.read_group()  # RG tag
    print(f"  Edit distance: {edit_dist}, Score: {align_score}, RG: {read_group}")

    # CIGAR operations (v1.3.0)
    for op in record.cigar:
        if op.is_insertion() and op.length >= 5:
            print(f"  Found {op.length}bp insertion")

# NEW v1.4.0: Built-in statistics functions
# Insert size distribution (paired-end QC)
dist = biometal.insert_size_distribution("alignments.bam")
print(f"Mean insert size: {sum(s*c for s,c in dist.items())/sum(dist.values()):.1f}bp")

# Edit distance statistics (alignment quality)
stats = biometal.edit_distance_stats("alignments.bam")
print(f"Mean edit distance: {stats['mean']:.2f} mismatches/read")

# Strand bias (variant calling QC)
bias = biometal.strand_bias("alignments.bam", reference_id=0, position=1000)
print(f"Strand bias at chr1:1000: {bias['ratio']:.2f}:1")

# Alignment length distribution (RNA-seq QC)
lengths = biometal.alignment_length_distribution("alignments.bam")
print(f"Intron-spanning reads: {sum(c for l,c in lengths.items() if l > 1000)}")

BAI Indexed Region Queries (v1.6.0)

import biometal

# Load BAI index for fast random access
index = biometal.BaiIndex.from_path("alignments.bam.bai")

# Query specific genomic region (1.68× faster than full scan for small files)
# Speedup increases dramatically with file size (10-500× for 1-10 GB files)
for record in biometal.BamReader.query_region(
    "alignments.bam",
    index,
    "chr1",
    1000000,  # start position
    2000000   # end position
):
    # Only reads overlapping region are returned
    if record.is_mapped and record.mapq >= 30:
        print(f"{record.name}: {record.position}-{record.reference_end()}")

# Reuse index for multiple queries (index loading: <1ms overhead)
regions = [
    ("chr1", 1000000, 2000000),
    ("chr1", 5000000, 6000000),
    ("chr2", 100000, 200000),
]

for chrom, start, end in regions:
    count = sum(1 for _ in biometal.BamReader.query_region(
        "alignments.bam", index, chrom, start, end
    ))
    print(f"{chrom}:{start}-{end}: {count} reads")

# Full workflow: Coverage calculation for specific region
from collections import defaultdict

coverage = defaultdict(int)
for record in biometal.BamReader.query_region(
    "alignments.bam", index, "chr1", 1000, 2000
):
    if record.is_mapped and record.position is not None:
        # Calculate coverage from CIGAR
        pos = record.position
        for op in record.cigar:
            if op.consumes_reference():
                for i in range(op.length):
                    coverage[pos] += 1
                    pos += 1

print(f"Mean coverage: {sum(coverage.values())/len(coverage):.1f}×")

Performance Characteristics:

• Index loading: < 1ms (negligible overhead)
• Small region query (1 Kbp): ~11 ms vs 18 ms full scan (1.68× speedup)
• Speedup scales with file size:
  • 1 MB file: 1.7× speedup
  • 100 MB file: 10-20× speedup
  • 1 GB file: 50-100× speedup
  • 10 GB file: 200-500× speedup

Format Library: BED/GFA/VCF/GFF3 (v1.8.0)

import biometal

# BED: Parse genomic intervals (ChIP-seq peaks, gene annotations)
stream = biometal.Bed6Stream.from_path("peaks.bed.gz")
for record in stream:
    print(f"{record.chrom}:{record.start}-{record.end} score={record.score}")
    length = record.length()
    if length > 1000:
        print(f"  Long peak: {length}bp")

# GFA: Parse assembly graphs (genome assembly, pangenomes)
stream = biometal.GfaStream.from_path("assembly.gfa")
segments = []
for record in stream:
    if isinstance(record, biometal.GfaSegment):
        segments.append(record)
        print(f"Segment {record.name}: {len(record.sequence)}bp")

# VCF: Parse genetic variants (SNPs, indels)
stream = biometal.VcfStream.from_path("variants.vcf.gz")
header = stream.header()  # Note: header() not parse_header()
print(f"VCF version: {header.fileformat}, Samples: {len(header.samples)}")

for variant in stream:
    if variant.quality and variant.quality > 30:
        print(f"{variant.chrom}:{variant.pos} {variant.reference}→{variant.alternate[0]}")
        if variant.is_snp():
            print(f"  SNP with quality {variant.quality}")

# GFF3: Parse hierarchical gene annotations (genes, mRNAs, exons, CDS)
stream = biometal.Gff3Stream.from_path("annotations.gff3.gz")
for feature in stream:
    if feature.feature_type == "gene":
        gene_id = feature.get_id()
        length = feature.length()  # 1-based inclusive coordinates
        print(f"Gene {gene_id}: {length}bp on {feature.strand}")

    elif feature.feature_type == "exon":
        parent = feature.get_parent()
        # Note: interval() method not available in Python bindings
        # Use feature.start and feature.end directly (1-based inclusive)
        print(f"  Exon of {parent}: {feature.start}-{feature.end}")

Format Library Features:

• Streaming architecture: Constant ~5 MB memory for all formats
• Production-ready: Tested against real ENCODE, UCSC, Ensembl, 1000 Genomes data
• Property-based testing: 23 tests validating format invariants (round-trip parsing, coordinate systems, specification compliance)
• Real-world validation: 6 integration tests with production files (61,547 GFF3 features, 1,000 UCSC genes, 10 VCF variants)
• Python bindings: Full streaming API with Pythonic interfaces

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FAQ

Q: Why biometal-rs on PyPI but biometal everywhere else? A: The biometal name was taken on PyPI, so we use biometal-rs for installation. You still import as import biometal.

Q: What platforms are supported? A: Mac ARM (optimized), Linux ARM/x86_64 (portable). Pre-built wheels for common platforms. See docs/CROSS_PLATFORM_TESTING.md.

Q: Why ARM-native? A: To democratize bioinformatics by enabling world-class performance on consumer hardware ($1,400 MacBooks vs. $50,000 servers).

More questions? See FAQ.md

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Contributing

We welcome contributions! See CLAUDE.md for development guidelines.

biometal is built on evidence-based optimization - new features should:

1. Have clear use cases
2. Be validated experimentally (when adding optimizations)
3. Maintain platform portability
4. Follow OPTIMIZATION_RULES.md

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License

Licensed under either of:

• Apache License, Version 2.0 (LICENSE-APACHE)
• MIT license (LICENSE-MIT)

at your option.

---

Citation

If you use biometal in your research:

@software{biometal2025,
  author = {Handley, Scott},
  title = {biometal: ARM-native bioinformatics with streaming architecture},
  year = {2025},
  url = {https://github.com/shandley/biometal}
}

For the experimental methodology:

@misc{asbb2025,
  author = {Handley, Scott},
  title = {Apple Silicon Bio Bench: Systematic Hardware Characterization},
  year = {2025},
  url = {https://github.com/shandley/apple-silicon-bio-bench}
}

---

Status: v1.10.0 released 🚀
Latest: GTF + PAF + narrowPeak parsers with optimized Python bindings (Nov 14, 2025)
Tests: 551 library tests passing (including 65 new format tests) + 23 property-based
Performance: 5.82M records/sec, 92.0 MiB/s throughput, 50-60% Python memory reduction
Python Functions: 70+ (FASTQ/FASTA, BAM/BAI, BED/narrowPeak, GFA, VCF, GFF3, GTF, PAF)
Evidence Base: 1,357 experiments, 40,710 measurements