AnnoRefine
High-performance genome annotation refinement toolkit using RNA-seq data
AnnoRefine is a Rust-based toolkit for refining genome annotations and generating gene prediction hints from RNA-seq evidence. It provides both command-line tools and Python bindings for seamless integration into bioinformatics pipelines.
Features
- 🔧 UTR Refinement - Extend and trim UTRs based on RNA-seq coverage
- 🔀 Splice Site Refinement - Adjust intron boundaries using junction evidence
- 🆕 Novel Gene Detection - Discover new genes from RNA-seq data
- 🎯 Hint Generation - Convert BAM alignments to Augustus/GeneMark hints
- 📊 Hint Processing - Join and filter hints from multiple sources
- ⚡ High Performance - Multi-threaded Rust implementation
- 🐍 Python Bindings - Easy integration into Python workflows
- 🧭 Strand-Aware - Supports all RNA-seq library types (FR, RF, UU)
Installation
Python Package (Recommended):
pip install annorefine
Standalone Binary: Download from GitHub Releases
Build from Source:
git clone https://github.com/nextgenusfs/annorefine.git
cd annorefine
cargo build --release
See the Installation Guide for detailed instructions.
Quick Start
Python API:
import annorefine
# Refine annotations
result = annorefine.refine(
fasta_file="genome.fa",
gff3_file="annotations.gff3",
bam_file="alignments.bam",
output_file="refined.gff3"
)
# Generate hints for gene prediction
result = annorefine.bam2hints(
bam_file="alignments.bam",
output_file="hints.gff",
library_type="RF",
contig_map={'NC_000001.11': 'chr1'} # Optional: rename contigs
)
# Join hints from multiple sources
result = annorefine.join_hints(
input_files=["bam_hints.gff", "protein_hints.gff"],
output_file="joined_hints.gff"
)
Command Line:
# Refine annotations
annorefine utrs \
--fasta genome.fa \
--gff3 annotations.gff3 \
--bam alignments.bam \
--output refined.gff3
# Generate hints
annorefine bam2hints \
--in alignments.bam \
--out hints.gff \
--stranded RF
# Join hints
annorefine join-hints \
--input bam_hints.gff protein_hints.gff \
--output joined_hints.gff
See the User Guide for more examples.
Use Cases
- Annotation Refinement - Improve existing gene models with RNA-seq evidence
- Augustus Gene Prediction - Generate hints for ab initio gene prediction
- GeneMark-ETP - Create intron-only hints for GeneMark
- funannotate2 Integration - Seamless integration with gene prediction pipelines
Documentation
Performance
- Multi-threaded - Parallel processing with Rust backend
- Memory efficient - Streaming BAM processing
- Scalable - Handles mammalian-sized genomes efficiently
Typical performance:
- Human genome (~20K genes): 10-30 minutes on 8 cores
- Memory usage: 2-8 GB depending on genome size
Support
Citation
Palmer, J. (2025). AnnoRefine: High-performance genome annotation refinement using RNA-seq data.
GitHub: https://github.com/nextgenusfs/annorefine
License
MIT License - see LICENSE file for details.
Built with ❤️ in Rust | Documentation | PyPI
1 maintainer
Metadata
1 maintainer
Release files for annorefine 2026.2.22
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