Abstract 3159: The 5-base genome, a simultaneous detection of genomic and methylation signatures using a concise, integrated multiomic library prep
Abstract Multiomic (multiple omic) approaches provide an integrated tool set to power biological discovery due to their ability to generate comprehensive information about the molecular underpinnings of distinct phenotypes. Such integrated analyses have shown broad utility across a range of applications, such as cancer classification and rare disease prognosis. For example, a recent population-scale study on breast cancer by Staaf et al showed that the combination of WGS and DNA methylation can be useful to detect BRCA1/BRCA2-deficient tumors with greater sensitivity than DNA sequencing alone. However, multiomic studies that employ single-ome technologies are often time-consuming, costly, and require considerable sample input. A streamlined workflow which provides both genome and methylome annotations stands to greatly accelerate discovery and translational research. Here we present a novel methyl-conversion technology, capable of simultaneous genome variant detection and DNA methylation profiling. This methodology introduces a simplified workflow, requires less than seven hours turnaround time, as little as 50ng of genomic DNA input, and comes with comprehensive analysis pipelines, the combination of which deliver accurate dual-omic annotations for discovery. Here were present a comparative analysis of our 5-base genome technology against existing methodologies, including Whole Genome Sequencing, Enzymatic Sequencing (EM-Seq) and Illumina Methylation EPIC arrays. Citation Format: Evgenii Chekalin, William Jordan, Oscar Murillo, Danielle Goldberg, James Baye, Joshua Beytebiere, Eric Brustad, Kayla Busby, Bridget Dorsey, Sophie Fertel, Stephen Gross, Rebekah Karadeema, Huy Lam, Valerie Montel, June Pais, Suzanne Rohrback, Angelica Schalembier, David Tse, Kat Tzeng, Ali Crawford, Lisa Watson, Allison Yunghans, Fiona Kaper. The 5-base genome, a simultaneous detection of genomic and methylation signatures using a concise, integrated multiomic library prep [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 3159.
Authors
- David M. Goldberg (ORCID: https://orcid.org/0000-0003-2617-5143)
- Henry Lam (ORCID: https://orcid.org/0000-0001-7928-0364)
- Rebekah J. Karadeema (ORCID: https://orcid.org/0000-0001-6634-8411)
- Bryan W. Dorsey (ORCID: https://orcid.org/0000-0002-6783-3547)
- David Tse (ORCID: https://orcid.org/0000-0003-1460-5900)
- Ali Crawford
- Stephen Gross (ORCID: https://orcid.org/0000-0003-0711-787X)
- Fiona Kaper (ORCID: https://orcid.org/0000-0002-8142-0183)
- Suzanne Rohrback (ORCID: https://orcid.org/0000-0001-6435-1262)
- Evgenii Chekalin
- Kathy Tzeng
- Eric M. Brustad (ORCID: https://orcid.org/0000-0002-7031-5433)
- Valérie Montel (ORCID: https://orcid.org/0000-0002-7286-4209)
- June E. Pais
- William B. Jordan
- Kayla N. Busby (ORCID: https://orcid.org/0000-0002-3360-9707)
- Joshua R Beytebiere
- Oscar Murillo (ORCID: https://orcid.org/0000-0003-1938-3905)
- Lisa Watson
- Sophie Fertel
- Angelica Schalembier
- Allison Yunghans
- James Baye
Institutions
- Illumina (United States) (US)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-3159
- Citations
- 1
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.81