NCI Data Jamboree (Project Abstract Submission): Submission #30
Submission information
Submission Number: 30
Submission ID: 188905
Submission UUID: d3eaf302-6f54-454a-b87b-dafd8b554bd0
Submission URI: /nci/datajamboree/abstractsubmission
Submission Update: /nci/datajamboree/abstractsubmission?token=5sDrQTS0pNsJDVAg6zEnWkpLp4Y0o-FzwEu8cooxe4w
Created: Fri, 07/24/2026 - 12:10
Completed: Fri, 07/24/2026 - 12:10
Changed: Fri, 07/24/2026 - 12:10
Remote IP address: 10.208.28.116
Submitted by: Anonymous
Language: English
Is draft: No
Webform: NCI Data Jamboree (Abstracts)
Submitted to: NCI Data Jamboree (Project Abstract Submission)
Presenter Information
Nathaniel
J
Barton
B.S.
PhD Student
Chiappinelli Lab, George Washington University
Washington, D.C.
Additional Authors
Abstract Information
Employing statistical, computational, and informatics tools, algorithms, and methods to integrate or analyze data
transposable elements, epigenomics, spatial transcriptomics, multi-omic integration, single-cell ATAC-seq
Bioinformatics Project Seeker — Multi-Omic Integration for Cancer Epigenomics
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.
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.