ferro-hgvs 0.7.1

HGVS variant normalizer - Python bindings for the ferro bioinformatics toolkit

ferro-hgvs

A high-performance HGVS variant nomenclature parser and normalizer written in Rust.

WARNING: ALPHA SOFTWARE - USE AT YOUR OWN RISK

This software is currently in ALPHA. While we have extensively tested it across a wide variety of HGVS patterns, no guarantees are made regarding correctness or stability.

Features

• Full HGVS Parsing: All coordinate systems (g/c/n/r/p/m/o) and edit types
• Variant Normalization: 3'/5' shifting per HGVS specification
• High Performance: ~5M variants/sec single-threaded parsing (>12M/s parallel), zero-copy with nom
• Type-Safe: Leverages Rust's type system for correctness

Installation

Python

pip install ferro-hgvs

Pre-built wheels are available for Linux (x86_64, aarch64), macOS (x86_64, Apple Silicon), and Windows (x86_64) on Python 3.10+.

Rust

Add to your Cargo.toml:

[dependencies]
ferro-hgvs = "0.1"

Or install the CLI:

cargo install ferro-hgvs

Quick Start

CLI

# Parse a variant
ferro parse "NM_000088.3:c.459A>G"

# Parse from file
ferro parse -i variants.txt -f json

# Prepare reference data (downloads RefSeq, genome, cdot — RefSeq-only by default)
ferro prepare --output-dir ferro-reference

# Verify reference data is ready
ferro check --reference ferro-reference

# (Optional) pre-build the on-disk cdot cache as a setup step, so the one-time
# cache build doesn't slow the start of a real (or timed/benchmarked) run.
ferro check --reference ferro-reference --build-cache

# Normalize with reference
ferro normalize "NM_000088.3:c.459del" --reference ferro-reference/

Throughput tip: when normalizing many variants, feed them sorted by transcript accession (or by genomic position). ferro caches each resolved transcript, so consecutive variants on the same transcript skip the (dominant) cost of re-reading and re-building it from the reference. Sorted input keeps the relevant transcripts resident in the cache and is markedly faster on large batches — see Performance Comparison.

Optional reference data

A bare ferro prepare builds a RefSeq-only reference (accessions NM_/NR_/NP_/NG_). Two opt-in flags provision additional data — pass them at prepare time; they are what a fully-provisioned ("blessed") reference is built with:

# Add Ensembl support (accessions ENST/ENSG/ENSP). Downloads the Ensembl cdot
# metadata and cDNA FASTAs (~1 GB+); off by default. Without it, an ENST/ENSG/ENSP
# input reports "Reference not found" and the message points back at this flag.
ferro prepare --output-dir ferro-reference --ensembl

# Derive version-independent NG_ placements and the NG_→transcript-version map
# (ng_hosted_transcripts) for a curated list of RefSeqGene accessions. Required to
# resolve legacy gene-symbol selectors (NG_(GENE):c.…) and bare-NG_ hosted lookups.
ferro prepare --output-dir ferro-reference \
  --derive-ng-placements path/to/ng_accessions.txt

# A fully-provisioned reference combines both in one run:
ferro prepare --output-dir ferro-reference --ensembl \
  --derive-ng-placements path/to/ng_accessions.txt

Both flags are incremental: re-running ferro prepare over an existing reference adds the requested data and preserves already-provisioned artifacts.

Library

use ferro_hgvs::{parse_hgvs, HgvsVariant};

fn main() -> Result<(), ferro_hgvs::FerroError> {
    let variant = parse_hgvs("NM_000088.3:c.459A>G")?;

    match &variant {
        HgvsVariant::Cds(v) => println!("CDS variant: {}", v),
        HgvsVariant::Genome(v) => println!("Genomic variant: {}", v),
        _ => println!("Other: {}", variant),
    }

    Ok(())
}

Python

import ferro_hgvs

# Parse a variant
variant = ferro_hgvs.parse("NM_000088.3:c.459A>G")
print(variant.variant_type)  # "coding"
print(variant.reference)     # "NM_000088.3"
print(str(variant))          # "NM_000088.3:c.459A>G"

# Normalize with reference data
normalizer = ferro_hgvs.Normalizer(reference_json="ferro-reference/cdot.json")
normalized = normalizer.normalize("NM_000088.3:c.459del")

Supported HGVS Syntax

Type	Prefix	Example
Genomic	g.	NC_000001.11:g.12345A>G
Coding DNA	c.	NM_000088.3:c.459A>G
Non-coding	n.	NR_000001.1:n.100A>G
RNA	r.	NM_000088.3:r.459a>g
Protein	p.	NP_000079.2:p.Val600Glu
Mitochondrial	m.	NC_012920.1:m.3243A>G

Edit Types

• Substitution: A>G, Val600Glu
• Deletion: del, 100_200del
• Insertion: 100_101insATG
• Deletion-Insertion: 100_102delinsATG
• Duplication: 100_102dup
• Inversion: 100_200inv
• Repeat: 100CAG[20]

CLI Commands

The ferro CLI provides commands beyond parsing and normalization:

Command	Description
prepare	Download and prepare reference data for normalization
check	Verify reference data setup
parse	Parse and validate HGVS variants
normalize	Normalize HGVS variants (3'/5' shifting)
explain	Explain error/warning codes (e.g., ferro explain W1001)
annotate-vcf	Annotate VCF files with HGVS notation
vcf-to-hgvs	Convert VCF records to HGVS
hgvs-to-vcf	Convert HGVS to VCF format
liftover	Liftover coordinates between genome builds
describe	Generate HGVS from reference/observed sequences
effect	Predict protein effect from variant
backtranslate	Reverse translate protein to DNA variants
convert-gff	Convert GFF3/GTF to transcripts.json
generate	Generate HGVS descriptions from components
extract-hgvs	Extract HGVS from VEP-annotated VCFs

Error Handling

ferro-hgvs provides configurable error handling with three modes:

Mode	Behavior
strict	Reject non-conformant input (default)
lenient	Auto-correct with warnings
silent	Auto-correct silently

# Use lenient mode to auto-correct common issues
ferro parse --error-mode lenient "p.val600glu"  # Corrects to p.Val600Glu

# Ignore specific warnings
ferro parse --ignore W1001,W2001 "p.val600glu"

# Get help on any error/warning code
ferro explain W1001
ferro explain --list

Configuration File

Create .ferro.toml in your project directory:

[error-handling]
mode = "lenient"
ignore = ["W1001", "W2001"]  # Silently correct these
reject = ["W3003"]           # Always reject these

Why ferro-hgvs?

ferro-hgvs provides the most comprehensive HGVS variant normalization across all pattern types, with performance orders of magnitude faster than alternatives.

Normalization Capabilities Comparison

Pattern Type	ferro	mutalyzer	biocommons	hgvs-rs
Genomic (g.)	✓	✓	✓	✓
Coding (c.) exonic	✓	✓	✓	✓
Coding (c.) intronic	✓	✓**	✗	✗
Non-coding (n.)	✓	✓	✓	✓
RNA (r.)	✓	✓	✓	✓
Protein (p.)	✓	Net*	✗	✗

* mutalyzer protein normalization requires network access for NP_→NM_ lookups (cannot be cached locally). ** mutalyzer intronic support is enabled by default via genomic-context rewriting; disable with --no-rewrite-intronic.

Performance Comparison

All tools are benchmarked in ferro's offline configuration — best case for every tool. Reference data is preloaded locally (a local UTA database and SeqRepo) and the network is disabled, so the figures below measure parse/normalize compute, not I/O. Out of the box, hgvs-rs, biocommons/hgvs, and mutalyzer resolve each variant against a remote UTA/SeqRepo or the Mutalyzer web API — a network round-trip per variant (~100–1000 ms), i.e. roughly 1–10 variants/sec, hundreds to thousands of times slower than shown here (an order-of-magnitude estimate from per-call network latency, not separately benchmarked). That local, offline setup is exactly what ferro's prepare command builds; ferro needs no external service.

Median patterns/sec over 5 reps on an Apple M2 Max, local/offline. All tools draw from one stratified ClinVar population; per-tool sample sizes are calibrated so each tool is measured over a meaningful interval — fast cells (e.g. ferro/hgvs-rs parse) draw from millions of patterns, while slower cells (e.g. the per-tool normalize columns) draw from as few as tens to thousands. All tools exclude process/interpreter startup from the timed region — the mutalyzer/biocommons Python subprocesses are timed by their own internal startup-excluded timer, matching ferro/hgvs-rs. Only ferro parallelizes natively (rayon); the other tools are single-threaded libraries, so their normalize @8 workers figures come from the benchmark harness running 8 independent instances in parallel, while parsing is not sharded for them — hence the single-threaded label in their parse @8 workers column (mutalyzer normalize likewise shows no gain at 8 workers: per-call cache and IPC overhead dominate, so sharding does not help). Every tool runs fully offline against local reference data — a local UTA database and SeqRepo, with mutalyzer's network lookups disabled — the configuration ferro's prepare command enables; the figures therefore reflect compute throughput, not per-variant network latency. Reference-data load is excluded for all tools. ferro full-population peak: parse 20.0M/s, normalize 77.0k/s. See docs/BENCHMARK_RUNBOOK.md for the full method.

Parse

Tool	Throughput @ 1 worker	Throughput @ 8 workers	ferro speedup @ 8w
ferro	5.1M/s	12.2M/s	—
mutalyzer	352/s	single-threaded	35,000×
biocommons	3.9k/s	single-threaded	3,100×
hgvs-rs	3.6M/s	single-threaded	3×

Normalize

Tool	Throughput @ 1 worker	Throughput @ 8 workers	ferro speedup @ 8w
ferro	78.1k/s	260.2k/s	—
mutalyzer	4/s	4/s	73,000×
biocommons	368/s	818/s	320×
hgvs-rs	195/s	1.3k/s	200×

ferro thread scaling

Threads	1	2	4	8
ferro parse	5.1M/s	9.4M/s	16.0M/s	12.0M/s

Input ordering matters for batch throughput. Resolving a transcript (reading its full sequence from the reference and rebuilding its CDS/exon metadata) dominates per-variant cost. ferro memoizes resolved transcripts in a bounded in-memory cache, so repeated lookups of the same transcript are near-free. Providing variants sorted by transcript accession — or by genomic position, which clusters variants onto the same transcripts — maximizes the cache hit rate and can speed up large batches by an order of magnitude versus randomly-ordered input. Ordering matters most when the number of distinct transcripts in the run exceeds the cache capacity (very large or genome-wide inputs); below that, the working set stays resident regardless of order.

Reference Data: What ferro Prepares

The ferro prepare command downloads and organizes all reference data needed for comprehensive normalization. This data is then shared with other tools (mutalyzer, biocommons, hgvs-rs) to enable their local operation.

Data Type	Source	Size	Enables
RefSeq transcripts	NCBI	~1GB	NM_/NR_/XM_ normalization
cdot metadata	MANE	~200MB	Transcript-to-genome mappings
GRCh38 + GRCh37 genomes	NCBI	~4GB	NC_ genomic normalization
RefSeqGene (sequences + genome alignments)	NCBI	~600MB	NG_ gene-region normalization; projecting c./n. variants into an NG_ parent's own g. frame (via the RefSeqGene→genome alignment GFF3)
LRG sequences + XML	EBI	~50MB	LRG_ stable-reference normalization; projecting c./n. variants into an LRG_ parent's own g. frame (via the LRG XML genomic mapping)
Protein sequences	Derived from CDS	~200MB	NP_/XP_ protein normalization
Legacy transcript versions	NCBI	~50MB	Historical ClinVar variants

Key insight: Without ferro's reference preparation, other tools require network access for each variant lookup (adding 100-1000ms latency per variant). With ferro's cached reference data, all tools can operate fully offline with consistent, reproducible results.

Deriving version-independent NG_ placements (#728)

ferro prepare --derive-ng-placements <accessions.txt> derives genomic placements for the listed NG_ versions (one exact accession per line, e.g. NG_012337.3; blank lines and # comments ignored), writing derived_refseqgene_placements.json into the reference directory and wiring the manifest's derived_refseqgene_placements field. This fills version gaps the archived RefSeqGene→genome GFF3 snapshots do not cover. It needs cdot + the genome in the same prepare run and uses NCBI EFetch per accession; accessions that cannot be validated are skipped with a warning. The field is preserved across subsequent prepare runs.

Benchmark: Reference Data & Tool Comparison

The main ferro binary includes commands to prepare reference data (ferro prepare) and check its status (ferro check). The ferro-benchmark tool (build with --features benchmark) extends this for tool comparison benchmarks.

Command	Description
prepare <tool>	Prepare reference data for a tool
check <tool>	Verify tool configuration and dependencies
parse <tool>	Parse HGVS patterns with specified tool
normalize <tool>	Normalize HGVS patterns with specified tool
compare results	Compare parse/normalize results between tools
extract	Extract patterns from ClinVar, VCFs, or create samples
setup	Set up UTA database, SeqRepo, and other services
generate	Generate summary reports and configs
collate	Aggregate sharded results

Quick Start

# Prepare ferro reference (main binary - no special features needed)
ferro prepare --output-dir data/ferro

# Check reference data
ferro check --reference data/ferro

# Normalize with ferro
ferro normalize -i patterns.txt --reference data/ferro

# For tool comparison, build with benchmark support
cargo build --release --features benchmark

# Prepare other tools (uses ferro reference for transcript data)
ferro-benchmark prepare mutalyzer --ferro-reference data/ferro --output-dir data/mutalyzer
ferro-benchmark prepare biocommons --seqrepo-dir data/seqrepo --uta-dump uta_20210129b.pgd.gz --ferro-reference data/ferro

# Compare results between tools
ferro-benchmark normalize mutalyzer -i patterns.txt -o mutalyzer.json --mutalyzer-settings data/mutalyzer/mutalyzer_settings.conf
ferro-benchmark compare results normalize ferro.json mutalyzer.json -o comparison.json

Supported tools: ferro-hgvs, mutalyzer, biocommons/hgvs, hgvs-rs

Note: The pixi.toml and pixi.lock files in this repository define a pixi environment for the Python-based external tools (mutalyzer, biocommons/hgvs, seqrepo) used in benchmarking. Run pixi shell to activate it.

See docs/BENCHMARK_GUIDE.md for detailed usage.

Development

cargo build
cargo test
cargo clippy -- -D warnings

License

Licensed under the MIT License. See LICENSE for details.

Disclaimer

This software is under active development. While we make a best effort to test this software and to fix issues as they are reported, this software is provided as-is without any warranty (see the license for details). Please submit an issue, and better yet a pull request as well, if you discover a bug or identify a missing feature. Please contact Fulcrum Genomics if you are considering using this software or are interested in sponsoring its development.

Contributing

See CONTRIBUTING.md for guidelines.