ArticleGigaScience2025
GFFx: A Rust-based suite of utilities for ultra-fast genomic feature extraction.
Article in GigaScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Genome assemblies and annotations are not static and need support for tracking their evolution.Briefings in bioinformatics · 2026Review
- xgt: a command-line interface for the Genome Taxonomy Database with cross-release taxonomic comparison.GigaScience · 2026Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundGenome annotations have become increasingly complex with the discovery of diverse regulatory elements and transcript variants, posing growing challenges for efficient data querying and storage. Existing tools often show performance bottlenecks when processing large-scale annotation files, especially for region-based searches and hierarchical feature extraction. Leveraging Rust's advantages in execution speed, memory safety, and multithreading offers a promising path toward scalable solutions for genome annotation access.
findingsWe present GFFx, a Rust-based toolkit for high-performance access to GFF annotation files. It employs a compact, model-aware indexing system and memory-mapped I/O to enable fast random access with minimal overhead. Benchmarks across multiple genomes show 10-80 times faster ID-based extraction, 20-60 times faster region retrieval, and 7-14 times faster coverage profiling than existing tools, while maintaining low memory use and small index size.
conclusionsGFFx offers a lightweight and scalable infrastructure for efficient genome annotation access and quantitative analysis. By combining Rust's performance and safety with an extensible design, it provides a robust foundation for large-scale and multi-omics workflows.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.