Factomics: A Cloud-enabled Web Portal Incorporating Gene Expression and GWAS Facilitating Disease Causation Analysis

Overview

Objectives/Goals Researchers and medical professionals are inundated with tremendous amounts of genomic "big data." However, there is a lack of tools that are capable of offering insightful interpretations of this raw data. To address this void, I developed Factomics, a cloud-enabled web portal, which provides a novel built-in workflow for performing disease correlation/causation analysis based on integrative genomics data. Methods/Materials The Factomics workflow is divided into 4 phases -- Launch, Discovery, Correlation and Causation. These phases take the user from an initial set of diseases to their candidate causal single nucleotide polymorphisms (SNPs), genes, and pathways based on industry standard algorithms, user-selected gene expression data which provide biological context, and genome-wide association studies (GWAS). Factomics is organized in a 3-tier architecture. The front-end is written in Google Apps Script (GAS; server-side JavaScript). The middle-tier is integrated with the analytical modules and with public data repositories including NCBI databases. Analytical findings are stored on Google Drive, the third tier. Results I demonstrated a use-case of Factomics with Alzheimer's disease, Type 2 Diabetes Mellitus, Ovarian Cancer, and Pancreatic Cancer. Linkage disequilibrium analysis identified non-synonymous (deleterious) and 13 regulatory candidate causal SNPs for Alzheimer's Disease and Ovarian Cancer. A multi-dimensional view of these diseases showed several overlapping upregulated genes and pathways. Some of the findings were corroborated by literature, and others were novel. This information can be used to optimize drug development, drug repositioning and diagnostic tools. Conclusions/Discussion My portal bridges the gap between current tools, and is capable of elucidating the biological mechanism for any disease. With relevant cached data, the workflow can distill millions of SNPs, thousands of genes, and thousands of pathways per disease to about 25 disease causation hypotheses, all in under 5 minutes. Moreover, it is scalable across a wide range of fields (bench biology, bioinformatics, pharmaceutics, and in clinical settings), while providing cloud-capabilities and a user-friendly interface. To the best of my knowledge, there is no existing tool offering similar functionality.

Competition history

• CSEF 2014 Biochemistry/ Molecular Biology · Entry S0521

Resources

• CSEF project summary ↗

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