GFFx: A Rust-based suite of utilities for ultra-fast genomic feature extraction

  1. Baohua Chen
  2. Dongya Wu
  3. Guojie Zhang

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Abstract

Genome annotations are becoming increasingly comprehensive due to the discovery of diverse regulatory elements and transcript variants. However, this improvement in annotation resolution poses major challenges for efficient querying, especially across large genomes and pangenomes. Existing tools often exhibit performance bottlenecks when handling large-scale genome annotation files, particularly for region-based queries and hierarchical model extraction. Here, we present GFFx , a Rust-based toolkit for ultra-fast and scalable genome annotation access. GFFx introduces a compact, model-aware indexing system inspired by binning strategies and leverages Rust’s strengths in execution speed, memory safety, and multithreading. It supports both feature- and region-based extraction with significant improvements in runtime and scalability over existing tools. Distributed via Cargo, GFFx provides a cross-platform command-line interface and a reusable library with a clean API, enabling seamless integration into custom pipelines. Benchmark results demonstrate that GFFx offers substantial speedups and makes a practical, extensible solution for genome annotation workflows.

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  1. GigaScience

    AbstractGenome annotations are becoming increasingly comprehensive due to the discovery of diverse regulatory elements and transcript variants. However, this improvement in annotation resolution poses major challenges for efficient querying, especially across large genomes and pangenomes. Existing tools often exhibit performance bottlenecks when handling large-scale genome annotation files, particularly for region-based queries and hierarchical model extraction. Here, we present GFFx, a Rust-based toolkit for ultra-fast and scalable genome annotation access. GFFx introduces a compact, model-aware indexing system inspired by binning strategies and leverages Rust’s strengths in execution speed, memory safety, and multithreading. It supports both feature- and region-based extraction with significant improvements in runtime and scalability over existing tools. Distributed via Cargo, GFFx provides a cross-platform command-line interface and a reusable library with a clean API, enabling seamless integration into custom pipelines. Benchmark results demonstrate that GFFx offers substantial speedups and makes a practical, extensible solution for genome annotation workflows.

    This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf124), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

    Reviewer 2: Andrew Su

    This paper describes GFFx, a new fast and efficient toolkit for working with GFF files. The tool describes a notable advance over curent state of the art, and the manuscript overall is well-written. I have only the following minor suggestions for consideration:

    • In figure S1 and the corresponding discussion, the authors test GFFx on 4 different GFF annotation databases of differing sizes, and differences between the performance is attributed solely to the different dataset sizes. The authors should consider subsetting the largest annotation database (hg38) to more smoothly track how performance and memory use vary with annotation database size, and to confirm there are no organism-specific effects that could underlie the observed differences.

    • The authors should consider changing the line charts in figures 2 and 3 to bar charts — I think the line implies a linear relationship between the tools along the x-axis that is not intended.

    • For the purposes of benchmarking, the authors used random sampling to extract subsets of the benchmark datasets (e.g., lines 85 and 107). The authors should confirm that the exact same subsets were used when running each tool.

    • In addition to depositing the code and benchmarks on Github, the authors should also deposit snapshots in an archival data repository (like Zenodo).

  2. GigaScience

    AbstractGenome annotations are becoming increasingly comprehensive due to the discovery of diverse regulatory elements and transcript variants. However, this improvement in annotation resolution poses major challenges for efficient querying, especially across large genomes and pangenomes. Existing tools often exhibit performance bottlenecks when handling large-scale genome annotation files, particularly for region-based queries and hierarchical model extraction. Here, we present GFFx, a Rust-based toolkit for ultra-fast and scalable genome annotation access. GFFx introduces a compact, model-aware indexing system inspired by binning strategies and leverages Rust’s strengths in execution speed, memory safety, and multithreading. It supports both feature- and region-based extraction with significant improvements in runtime and scalability over existing tools. Distributed via Cargo, GFFx provides a cross-platform command-line interface and a reusable library with a clean API, enabling seamless integration into custom pipelines. Benchmark results demonstrate that GFFx offers substantial speedups and makes a practical, extensible solution for genome annotation workflows.

    This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf124), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

    Reviewer 1: Xingtan Zhang

    The overall research appears comprehensive; however, further attention to the tool's capabilities and methodological rigor would strengthen its validity and broader applicability.

    1. In the "Performance benchmark in annotation indexing" section, the authors utilized genome annotations from four species (Homo sapiens hg38, Pungitius sinensis ceob_ps_1.0, Drosophila melanogaster dm6, and Arabidopsis thaliana tair10.1) as representatives for benchmarking and subsequent analyses. Nevertheless, a robust GFF processing suite should ideally demonstrate reliability across a broader spectrum of genome types, irrespective of their frequency of use. To enhance the generalizability of GFFx and cater to a wider user base, it is recommended that additional genomes—such as those of Triticum aestivum, Mus musculus, and Sus scrofa—be included in the benchmarks. This would better validate the tool's robustness across species with varying genome complexities.

    2. While the 20-kb interval length used in the region-based retrieval benchmarks is biologically relevant, corresponding to typical gene sizes, it does not fully capture the diversity of genomic query scenarios. To comprehensively assess GFFx's performance across diverse genomic contexts, it is suggested that supplementary benchmarks be conducted using interval lengths of 10 kb and 100 kb. This would help validate the tool's robustness across varying interval scales, which is critical for its practical utility in diverse research workflows.

    3. To further broaden the software's applicability, it is recommended to incorporate an additional functionality that enables the extraction of the number of reads covering specific intervals from BAM files based on positional information derived from GFF3 files, thereby facilitating the calculation of sequencing depth. This feature would be analogous to the functionality provided by bedtools coverage, enhancing GFFx's utility in integrating genome annotation data with sequencing read coverage analyses.

  3. Version published to 10.1101/2025.08.08.669426 on bioRxiv