NCI Data Jamboree (Project Abstract Submission): Submission #30

Presenter Information
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First Name: Nathaniel
Middle Initial: J
Last Name: Barton
Degree(s): B.S.
Position/Title/Career Status: PhD Student
Organization: Chiappinelli Lab, George Washington University
Organization Address:
Washington, D.C.

Email: nathaniel.barton@gwu.edu

Additional Authors
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List of Additional Authors:
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Abstract Information
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Abstract Category: Employing statistical, computational, and informatics tools, algorithms, and methods to integrate or analyze data
Abstract Keywords: transposable elements, epigenomics, spatial transcriptomics, multi-omic integration, single-cell ATAC-seq
Abstract Title: Bioinformatics Project Seeker — Multi-Omic Integration for Cancer Epigenomics
Abstract:
I am a second-year PhD student in the Genomics and Bioinformatics program at George Washington University, working in the Chiappinelli Lab at the GW Cancer Center, where my research focuses on transposable element (TE) reactivation and epigenomics in ovarian cancer. I regularly build and run RNA-seq and WGBS pipelines (Snakemake) on HPC, with a technical background in Python (pandas, scanpy, scikit-learn, DESeq2) and R (Seurat, edgeR, clusterProfiler) spanning bulk and single-cell genomics, plus classifier development and evaluation (SVM, ROC/AUC, batch correction, cross-validation). I've also worked with spatial transcriptomics data (Stereo-seq) and proteogenomic data from large public cohorts (CPTAC), giving me experience integrating and analyzing multi-omic datasets beyond my core focus.

I am seeking to join a project team to broaden my experience with multi-omic cancer datasets and collaborate with researchers across institutions. I'm especially interested in projects involving single-cell ATAC-seq (e.g., HTAN, IOTN), as an opportunity to learn chromatin accessibility at single-cell resolution; spatial transcriptomics (e.g., Visium), particularly methods for statistically testing spatial relationships between cell populations rather than qualitative/visual assessment alone; or mass spectrometry-based proteomics, given how functionally informative and comparatively underexplored this data type is relative to DNA/RNA. I'd also be excited to contribute to a project examining TE activation across multiple cancer types — comparing not just differences in TE expression, but mechanisms of activation by integrating DNA methylation and histone mark data — which relates to my thesis work but extends it beyond ovarian cancer.

I hope to gain hands-on experience with new data types and methods, build connections with the cancer genomics community, and contribute to a project whose approach or findings could inform my own thesis research.