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bioRxiv · 10.1101/2024.12.03.626631

Tokenvizz: GraphRAG-Inspired Tokenization Tool for Genomic Data Discovery and Visualization

Oguztuzun, C.·Gao, Z.·Xu, R.
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Abstract

SummaryOne of the primary challenges in biomedical research is the interpretation of complex genomic relationships and the prediction of functional interactions across the genome. Tokenvizz is a novel tool for genomic analysis that enhances data discovery and visualization by combining GraphRAG-inspired tokenization with graph-based modeling. In Tokenvizz, genomic sequences are represented as graphs, where sequence k-mers (tokens) serve as nodes and attention scores as edge weights, enabling researchers to visually interpret complex, non-linear relationships within DNA sequences. Through a web-based visualization interface, researchers can interactively explore these genomic relationships and extract biologically meaningful insights about regulatory patterns and functional elements. Applied to promoter-enhancer interaction prediction tasks, Tokenvizz outperformed traditional sequential models while providing interpretable insights into genomic features, demonstrating the advantage of graph-based representations for biological discovery. Availability and ImplementationTokenvizz, along with its user guide, is freely accessible on GitHub at: https://github.com/ceragoguztuzun/tokenvizz. ACM Reference FormatCera[g] O[g]uztuzun, Zhenxiang Gao, and Rong Xu. 2024. Tokenvizz: GraphRAG Inspired Tokenization Tool for Genomic Data Discovery and Visualization. In Proceedings of (Bioinformatics). ACM, New York, NY, USA, 7 pages. https://doi.org/XXXXXXX.XXXXXXX

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Oguztuzun, C., Gao, Z., Xu, R.. 2024-12-06. Tokenvizz: GraphRAG-Inspired Tokenization Tool for Genomic Data Discovery and Visualization. https://doi.org/10.1101/2024.12.03.626631

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