Abstract 6296: Unlocking scalable and efficient multiomic analysis of 5- and 6-base genomes

We present a computational toolkit to analyze 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) Cytosine modifications at scale and describe its performance on a liquid biopsy dataset. Methylation data has diverse applications in cancer, including early-stage diagnosis through liquid biopsy, classification to guide treatment pathways, and prognosis. However, analyzing methylation data poses significant challenges. Many existing tools are difficult to use and struggle to scale as sample sizes grow. This lack of scalability makes standard tasks, such as identifying differentially methylated regions (DMRs) or summarizing methylation fractions across genomic regions, time- and memory-intensive, often requiring large-scale compute resources. A recently introduced technology, duet multiomics solution evoC, enables the reading of 6-base information from DNA, further increasing the scale and complexity of data that can be extracted from a single sequencing experiment. With this expanded multiomic dataset, the lack of scalability is compounded, hindering interactive data analysis that provides rapid and detailed insight. To address this, we present a fast and scalable array-based python package (modality) for the analysis of 6-base genomes (A, T, G, C, 5-mC and 5-hmC), using multi-core out-of-memory processing to enable extremely efficient computation, even for datasets that are too large to fit into memory. Our approach enables local analysis of large datasets, scaling to thousands of samples. Unlike existing tools that exceed typical laptop memory with ∼10 samples, the modality package efficiently generates hundreds of thousands of features for classification, processing a colorectal cancer liquid biopsy dataset of over 100 samples in minutes on a standard laptop. Furthermore, it runs a complex DMR model (logistic regression with covariates) genome-wide in under an hour, a task infeasible with current tools. modality combines efficient computation with tools for exploratory (e.g., plotting, methylation summaries) and downstream analyses (e.g., DMR identification, PCA). Designed for efficiency and ease of use, it enables users to rapidly transition from raw data to actionable insights and publication-ready results. As multiomic data become the standard in cancer research, our data structure supports the integration of additional data types, allowing us to handle combined genomic and epigenomic data from solutions like duet evoC. This will enable streamlined and efficient multiomic analysis to uncover deeper biological insights. Citation Format: Sabri Jamal, Michael J. Wilson, Jean Teyssandier, Gary Frewin, Mark S. Hill, William Stark, Nicola Wong, Robert Blanshard, Andrada Tomoni, Tom Charlesworth, Nicholas J. Harding, Páidí Creed. Unlocking scalable and efficient multiomic analysis of 5- and 6-base genomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6296.