A new open-source software package promises to make one of genomics’ most powerful yet notoriously awkward data structures far easier to work with. GraTools, described in BMC Bioinformatics by a team of French researchers led by Sebastien Ravel of the Plant Health Institute of Montpellier and colleagues at the University of Montpellier, CIRAD, IRD and Syngenta Seeds, is a command-line tool designed to explore and manipulate pangenome variation graphs without the format conversions and fragmented workflows that have long frustrated researchers in the field. Released under the GNU GPLv3 licence and accompanied by full documentation, the tool is part of a broader software collection called GraSuite and is aimed squarely at biologists and bioinformaticians who need to interrogate genomic diversity across populations quickly and reproducibly.
To understand why GraTools matters, it helps to understand what a pangenome variation graph actually is. Traditional genomics has relied on a single reference genome, against which the genomes of individuals or varieties are compared. That approach works well enough when the population is closely related to the reference, but it systematically distorts our view of species that carry large amounts of variation. Sequences absent from the reference simply vanish from the analysis, and alignments involving insertions, inversions and other structural changes become cumbersome. A pangenome variation graph, or PVG, sidesteps this problem by representing the genomes of many individuals simultaneously as a graph, in which shared sequences form common paths and variable regions branch into alternative paths. The result is a single data structure that captures the full spectrum of genomic diversity within a species, from the core genome shared by everyone to the dispensable genome present in only some individuals.
The catch has always been usability. Building such graphs has become increasingly tractable thanks to graph construction pipelines, but once the graph exists, working with it is another matter. Existing tools for querying and manipulating PVGs tend to be scattered across different software packages, each with its own input formats, and researchers frequently find themselves converting files back and forth between formats before any analysis can proceed. These conversions are not merely annoying; they consume time, introduce opportunities for error, and often prove computationally expensive when graphs grow to represent dozens or hundreds of genomes. The authors of GraTools identified this lack of integration and efficiency as the central bottleneck hindering the wider adoption of graph-based pangenomics, and they designed their tool specifically to eliminate it.
GraTools takes a deliberately pragmatic approach. Its input is the GFA file, the standard Graphical Fragment Assembly format in which pangenome variation graphs are typically produced, and it works directly on that original file rather than requiring researchers to transform it into intermediate representations. The workflow begins with a one-time graph import, during which GraTools converts the graph data into standard formats, namely BAM and BED, that are already supported by a mature ecosystem of highly optimised bioinformatics tools. This design decision is central to the tool’s philosophy: rather than reinventing indexing and querying infrastructure, GraTools leverages what already works best, storing the graph-derived data in formats that allow efficient storage and rapid querying of even large and complex graph structures.
Once the import is complete, the tool opens up a range of downstream operations that can be executed rapidly and repeatedly. Users can extract subgraphs corresponding to regions of interest, retrieve the underlying DNA sequences in FASTA format, and run comprehensive analyses such as calculating the ratio between the core and dispensable genome, a fundamental statistic in pangenomics that describes how much of the species’ genetic repertoire is universal versus optional. GraTools can also identify group-specific segments, meaning stretches of sequence found exclusively in particular subsets of individuals, which is exactly the kind of signal breeders and population geneticists look for when hunting for markers associated with subspecies identity, adaptation or agronomic traits. Crucially, all of this is done through an intuitive command-line interface that provides real-time execution feedback and a detailed logging system, so users always know what the tool is doing and can audit their analyses afterwards.
Scalability was a clear engineering priority. Pangenome graphs are among the largest data structures in modern genomics, and a tool that grinds to a halt on realistic datasets would be of little use. GraTools therefore employs a modular architecture that supports parallel processing and asynchronous input-output operations, allowing it to take full advantage of modern multi-core computing environments. The authors acknowledge the ISO 9001 certified IRD i-Trop HPC facility in Montpellier and the Core Cluster of the Institut Français de Bioinformatique as the computing resources on which the work was performed, a hint that the tool was developed and tested in a genuine high-performance setting rather than only on toy examples.
Another technically significant feature is GraTools’ handling of coordinate systems. In a pangenome graph, positions can be defined relative to the primary reference genome or relative to alternative genomes embedded within the graph, and switching between these perspectives has traditionally required additional processing. GraTools supports coordinates defined on both the primary reference and the alternative genomes within the graph without requiring the user to re-import the data, which means researchers can move fluidly between reference-based and graph-based views of their regions of interest. The tool’s outputs, meanwhile, are deliberately designed to be compatible with external software, so that results can be visualised or further manipulated using the visualisation and analysis tools researchers already have in their pipelines.
The demonstration case study will resonate with anyone working in crop genomics. The team applied GraTools to an Asian rice pangenome graph comprising 13 accessions, a dataset representative of the scale at which many research groups currently operate. Using this graph, they showed that GraTools could easily extract subgraphs, compute depth statistics across the graph structure, and identify segments specific to rice subspecies. Rice is one of the world’s most important staple crops and a model species for pangenome research, with indica and japonica subspecies carrying substantial differentiated sequence content, so the ability to pull out subspecies-specific segments directly from a graph, without elaborate format juggling, illustrates the practical value of the tool for real biological questions in breeding and diversity studies.
The potential applications extend well beyond rice. Because pangenome variation graphs are now being built for humans, livestock, pathogens and countless plant species, a general-purpose manipulation tool has relevance across population genetics, breeding and even genomic medicine, where graph-based representations are increasingly used to capture human diversity more faithfully than a single reference can. The authors position GraTools as suitable for both biologists and bioinformaticians, and its combination of an accessible command-line interface, transparent logging and standard-format outputs lowers the barrier for experimental groups that lack dedicated graph-genomics specialists. The fact that the software is open source under GNU GPLv3, with documentation hosted at gratools.readthedocs.io, should further encourage adoption and community contributions.
GraTools arrives at a moment when pangenomics is shifting from a specialist pursuit to a mainstream expectation. Major pangenome projects across multiple species have demonstrated that single references underrepresent true diversity, and funders and journals increasingly expect analyses that account for it. What has been missing is the connective tissue between graph construction and graph interpretation, and this is precisely the gap GraTools fills. By unifying subgraph extraction, sequence retrieval, core and dispensable genome statistics and group-specific segment detection behind a single, fast interface built on standard file formats, the tool offers what the authors describe as an interesting alternative to existing approaches for manipulating PVGs, facilitating rapid, efficient and flexible downstream analyses. For a field whose growth has been throttled less by ideas than by plumbing, that plumbing fix may prove to be the most consequential contribution of all.
Subject of Research: A software tool for exploring and manipulating pangenome variation graphs
Article Title: GraTools, a user-friendly tool for exploring and manipulating pangenome variation graphs
Article References: Ravel, S., Marthe, N., Carrette, C., Mohamed, M., Sabot, F., & Tranchant-Dubreuil, C. (2026). GraTools, a user-friendly tool for exploring and manipulating pangenome variation graphs. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06657-z
Image Credits: AI Generated
DOI: 10.1186/s12859-026-06657-z
Keywords: pangenome, variation graph, bioinformatics, GFA format, core genome, dispensable genome, subgraph extraction, rice pangenome, genomic diversity, open-source software, population genetics, plant breeding
