pyfgs 0.0.2

PyO3 bindings and Python interface to FragGeneScanRs, a gene prediction model for short and error-prone reads.

🔗🐍⏭️ pyfgs

PyO3 bindings and Python interface to FragGeneScanRs, a gene prediction model for short and error-prone reads.

Why pyfgs?

Built for noisy data

Standard ab initio predictors (like Prodigal or Pyrodigal) are fantastic for pristine, fully assembled contigs. However, they struggle with raw metagenomic reads or error-prone assemblies because they immediately break the open reading frame at the first sign of an indel. pyfgs uses an error-tolerant Hidden Markov Model trained on specific sequencing profiles (Illumina, 454, Sanger) to power through these sequencing errors, dynamically correct the reading frame, and salvage the translated protein.

Native frameshift tracking

Instead of just silently stitching broken genes together, pyfgs exposes the exact coordinates of every hallucinated or skipped base directly to Python. This allows you to rigorously track structural variants, correctly annotate INSDC-compliant pseudogenes, or export exact frameshift coordinates for downstream quality control.

No subprocess I/O tax

Running standard CLI bioinformatics tools from Python usually requires a heavy I/O penalty: dumping sequences to a temporary FASTA file, firing a subprocess, and parsing the text outputs back into memory. pyfgs binds directly to the underlying Rust engine. The HMM runs entirely in memory and yields native Python objects ready for immediate analysis.

True multithreading and zero-copy memory

pyfgs is designed to process massive datasets efficiently:

• GIL-Free Inference: The Rust backend completely releases the Python Global Interpreter Lock (GIL) during the heavy HMM math. You can drop the predictor into a standard ThreadPoolExecutor and achieve true parallel processing across all your CPU cores.

• Zero-Copy Bytes: The engine borrows raw byte slices (&[u8]) directly from Python's memory, bypassing the overhead of copying strings between languages.

• Lazy Translation: Translating DNA to amino acids is computationally expensive. pyfgs evaluates sequence strings lazily, meaning you only pay the CPU and memory cost of string allocation if you explicitly request the sequence data.

A Pythonic API

Bioinformatics coordinates are notoriously messy. pyfgs outputs standard 0-based, half-open intervals ([start, end)), allowing you to slice sequence arrays immediately without wrestling with 1-based GFF3 coordinate math. When you do need standardized files, it includes heavily optimized, native-Rust context managers to stream perfectly compliant VCF, BED, GFF3, and FASTA files directly to disk without bloating your RAM.

🔧 Installing

This project is supported on Python 3.10 and later.

pyfgs can be installed directly from PyPI:

pip install pyfgs

⚡️ Power users ⚡️ can force your local machine to compile the Rust engine specifically for your own CPU by running:

RUSTFLAGS="-C target-cpu=native" pip install --no-binary pyfgs pyfgs

💻 Usage

API Usage

For full API usage, please refer to the documentation.

import concurrent.futures
import pyfgs
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.SeqFeature import SeqFeature, FeatureLocation
from Bio import SeqIO

def main():
    # 1. Initialize the GeneFinder
    # Set whole_genome=False to force the HMM to hunt for frameshifts.
    finder = pyfgs.GeneFinder(pyfgs.Model.Complete, whole_genome=False)

    # 2. Parse the genome into memory 
    # (Safe for assemblies! For massive raw read FASTQs, use an itertools chunker instead)
    contigs = list(pyfgs.FastaReader("bacterial_assembly.fasta"))
    seqs = [seq for _, seq in contigs]
    
    # 3. Process concurrently
    # The GIL is released, and map perfectly preserves our sequence order!
    with concurrent.futures.ThreadPoolExecutor() as executor:
        all_genes = list(executor.map(finder.find_genes, seqs))

    # 4. Format into INSDC-compliant GenBank records
    records = []
    for (header_bytes, seq_bytes), genes in zip(contigs, all_genes):
        header_str = header_bytes.decode('utf-8')
        record = SeqRecord(
            Seq(seq_bytes.decode('utf-8')), 
            id=header_str, 
            name=header_str, 
            description="Annotated by pyfgs"
        )
        
        for i, gene in enumerate(genes):
            # Query the Rust backend for structural variants
            mutations = gene.mutations(seq_bytes)
            
            # INSDC Standard: Frameshifted ORFs cannot be 'CDS', must be 'pseudogene'
            feature_type = "pseudogene" if mutations else "CDS"
            
            qualifiers = {
                "source": "pyfgs",
                "inference": "ab initio prediction:pyfgs",
                "ID": f"{header_str}_FGS_{i+1}"
            }
            
            if mutations:
                qualifiers["pseudogene"] = ["unknown"]
                qualifiers["note"] = [
                    f"Frameshift {'insertion' if mut.mut_type == 'ins' else 'deletion'} "
                    f"at pos {mut.pos} (codon {mut.codon_idx}). {mut.annotation}"
                    for mut in mutations
                ]
            else:
                # Only strictly intact CDS features receive a translation qualifier
                qualifiers["translation"] = [gene.translation().decode('utf-8')]
            
            # Biopython's FeatureLocation is natively 0-based and half-open, 
            # mapping perfectly to our Gene.start and Gene.end!
            location = FeatureLocation(gene.start, gene.end, strand=gene.strand)
            feature = SeqFeature(location=location, type=feature_type, qualifiers=qualifiers)
            record.features.append(feature)
        
        records.append(record)

    # 5. Export to GenBank
    SeqIO.write(records, "annotated_genome.gbk", "genbank")
    print(f"Successfully annotated {len(records)} contigs!")

if __name__ == "__main__":
    main()

CLI Usage

For CLI usage, type pyfgs --help

usage: pyfgs <seq> [options]

🔗🐍⏭️	PyO3 bindings and Python interface to FragGeneScanRs,
	a gene prediction model for short and error-prone reads.

Input options 💽:

  seq                 Sequence file (or '-' for stdin)
  -m, --model         Sequence error model (default: complete)
                       - short1: Illumina sequencing reads with about 0.1% error rate
                       - short5: Illumina sequencing reads with about 0.5% error rate
                       - short10: Illumina sequencing reads with about 1% error rate
                       - sanger5: Sanger sequencing reads with about 0.5% error rate
                       - sanger10: Sanger sequencing reads with about 1% error rate
                       - pyro5: 454 pyrosequencing reads with about 0.5% error rate
                       - pyro10: 454 pyrosequencing reads with about 1% error rate
                       - pyro30: 454 pyrosequencing reads with about 3% error rate
                       - complete: Complete genomic sequences or short sequence reads without sequencing error
  -r, --reads         Force FASTQ parsing (Overrides auto-detection)
  -w, --whole-genome  Strict contiguous ORFs. Disables error-tolerant frameshift detection.

Output options ⚙️:
  Provide a PATH to save to a file, or use the flag alone to print to stdout.

  --faa [PATH]        Output protein FASTA
  --fna [PATH]        Output nucleotide FASTA
  --bed [PATH]        Output BED6+1 format
  --gff [PATH]        Output GFF3 format
  --vcf [PATH]        Output VCF v4.2 format

Other options 🚧:

  -t, --threads       Number of threads (default: optimal)
  -v, --version       Print version and exit
  -h, --help          Print help and exit

Performance

pyfgs is continuously benchmarked against NCBI RefSeq ground-truth datasets on every commit to main to ensure we never introduce performance regressions.

pyfgs was benchmarked against pyrodigal (the excellent standard for Python-based gene prediction) to compare both raw inference speed and accuracy against NCBI RefSeq ground-truth annotations.

Because pyfgs is powered by pre-trained Hidden Markov Models in Rust, it does not need to perform an initial training scan over the sequence to calculate transition probabilities. This allows it to scale incredibly well on larger genomes and massive metagenomic datasets.

⏱️ Speed: The "No-Training" Advantage

Test Conditions: Pure inference time (excluding I/O) on an M-series Mac, using complete reference genomes. pyrodigal was run in single-genome mode (meta=False, requiring a training step), and pyfgs was run with whole_genome=True.

Organism	Genome Size	pyrodigal Time	pyfgs Time	Speedup
S. aureus (Low GC)	2.8 Mb	0.85s	0.85s	1.0x (Tie)
E. coli (Standard)	4.6 Mb	2.26s	1.42s	1.6x
P. aeruginosa (High GC)	6.3 Mb	3.30s	1.37s	2.4x

Note: For complete genomes, pyrodigal's dynamic programming engine must first scan the entire sequence to build a statistical model. pyfgs completely bypasses this upfront compute tax, resulting in massive time savings on larger genomes.

🎯 Accuracy & The whole_genome Trade-off

pyfgs offers two distinct modes of operation, allowing you to choose between strict RefSeq-style conservative calling and highly sensitive frameshift-aware predictions.

1. Complete Genomes (whole_genome=True) When working with pristine, high-quality assemblies, setting whole_genome=True forces the Viterbi algorithm to only traverse standard codon states.

• Result: Blistering speed and highly conservative gene calls that closely mirror strict NCBI RefSeq annotations (~93-97% exact 3' stop codon matches).

2. Noisy Reads & Metagenomes (whole_genome=False) When working with raw Oxford Nanopore reads, error-prone contigs, or complex metagenomes, setting whole_genome=False unlocks the true power of the pyfgs HMM.

• The Compute Tax: The Rust backend activates "Indel" states, mathematically evaluating the probability of a frameshift insertion or deletion at every single nucleotide. This increases the compute time by ~30-50%.
• The Sensitivity Boost: The algorithm becomes incredibly forgiving. It successfully rescues broken genes, pseudogenes, and fragmented ORFs that standard dynamic programming tools completely discard. This results in a higher number of overall predicted ORFs, ensuring you don't miss crucial biological signals hidden behind sequencing errors.

🔖 Citation

For now, please cite the original FragGeneScanRs paper:

Van der Jeugt, F., Dawyndt, P. & Mesuere, B. FragGeneScanRs: faster gene prediction for short reads. BMC Bioinformatics 23, 198 (2022). https://doi.org/10.1186/s12859-022-04736-5

💭 Feedback

⚠️ Issue Tracker

Found a bug ? Have an enhancement request ? Head over to the GitHub issue tracker if you need to report or ask something. If you are filing in on a bug, please include as much information as you can about the issue, and try to recreate the same bug in a simple, easily reproducible situation.

🏗️ Contributing

Contributions are more than welcome! See CONTRIBUTING.md for more details.

📋 Changelog

This project adheres to Semantic Versioning and provides a changelog in the Keep a Changelog format.

⚖️ License

This library is provided under the GNU General Public License v3.0. The FragGeneScanRs code was written by Peter Dawyndt, Bart Mesuere and Felix Van der Jeugt and is distributed under the terms of the GPLv3 as well. See https://github.com/FragGeneScanRs/LICENSE for more information.

This project is in no way affiliated, sponsored, or otherwise endorsed by the original FragGeneScanRs authors Peter Dawyndt, Bart Mesuere and Felix Van der Jeugt. It was developed by Tom Stanton during his Post-doc project at Monash University in the Wryes Lab.