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Abstract:
Motivation: As genome graphs are powerful data structures for representing the genetic diversity within populations, they can help identify genomic variations that traditional linear references miss, but their complexity and size makes the analysis of genome graphs challenging. We sought to develop a genome graph analysis tool that helps these analyses to become more accessible by addressing the limitations of existing tools. Specifically, we improve scalability and user-friendliness, and we provide many new statistics for graph evaluation.
Results: We developed an efficient, comprehensive, and integrated tool, gretl, to analyse genome graphs and gain insights into their structure and composition by providing a wide range of statistics. gretl can be utilised to evaluate different graphs, compare the output of graph construction pipelines with different parameters, as well as perform an in-depth analysis of individual graphs, including sample-specific analysis. With the assistance of gretl, novel patterns of genetic variation and potential regions of interest can be identified, for later, more detailed inspection. We demonstrate that gretl outperforms other tools in terms of speed, particularly for larger genome graphs.
Availability and implementation: gretl is implemented in Rust. Commented source code is available under MIT licence at https://github.com/MoinSebi/gretl. Examples of how to run gretl are provided in the documentation. Several Jupyter notebooks are part of the repository and can help visualise gretl results.
