Genomic Data Analysis Network (GDAN)

GDAN’s overall goal was to help the cancer research community leverage the genomic data and resources produced by NCI for the benefit of cancer patients through the following activities: 

• Developing and implementing new bioinformatic and computational tools to capture key biological insights about cancer.
• Developing data processing and quality control methods for working with large-scale genomic characterization data. 
• Processing and integrating a variety of analytical data types to generate disease-level findings and perform cross-disease analyses. 

• DNA Mutations – Identifying mutations in coding and non-coding regions of the genome, classifying mutations as driver or passenger mutations, identifying chromosomal rearrangement events leading to fusion proteins, and determining potential enhancer or suppressor functionality of mutations.
• Gene Expression – Identifying mRNA expression patterns and correlating with relevant clinical parameters, identifying translocation or rearrangement events.
• Copy number and tumor purity - Clustering cases according to copy number alteration or loss-of-heterozygosity events, identifying candidate drivers of copy number alterations, estimating tumor purity of the samples.
• miRNA analysis - Analyzing miRNA expression to correlate with patterns of mRNA expression and identify expression regulation networks, correlating miRNA data with relevant clinical parameters.
• Long non coding RNA (lnRNA) – Analyzing lnRNA expression patterns and correlating with patterns of mRNA expression or expression regulation networks.
• Batch effects and data integration – Identifying batch effects that might have been accrued during processing of samples, devising bioinformatics methods to correct such effects, determining biologically relevant groups that can subsequently be analyzed in the context of clinical data.
• Methylation analysis – Identifying DNA methylation patterns of interest and correlating patterns with relevant clinical parameters, correlating patterns with mRNA expression data to propose gene regulation mechanisms.
• Pathway analysis – Identifying biological pathways that have been altered, performing multi-omic data analyses to identify altered pathways and potential clinical relevance.
• Single cell RNA sequencing – Identifying cell clusters according to gene expression patterns, extracting expression levels and correlating with relevant clinical parameters, identifying translocation/rearrangement events, and identifying cell clusters or subclones of interest.
• Circulating cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA) – Analyzing “liquid biopsies,” or blood samples for cfDNA or ctDNA to establish correlations between mutations in tumor tissue and ctDNA, developing methods to utilize the technology as a diagnostic and prognostic tool, and creating models of disease burden and progression in cancer development.
• Long-read sequencing – Assembling genomes, identifying structural variants, sequencing through repetitive regions, phasing critical variants.
• Spatial genomics – Analyzing gene expression data with spatial information, produced from different emerging spatial genomics platforms.  
• Digital Imaging – Mining histopathology images for elements that aid in diagnostic or prognostic efforts, applying machine learning to learn relevant features. 

For additional information about GDAN, please contact Dr. Liming Yang.

Phase 2: Genome Data Analysis Centers (GDACs) (U24s)

Phase 1: Specialized Genomic Data Centers (U24s)

Phase 1: Processing Genomic Data Center (U24)

Phase 1: Visualization Genomic Data Center (U24)