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GNOmics: Graphs 'N' Omics

Status: 
Active
Competition: 
2015 Disruptive Innovation in Genomics Competition
Sector: 
Health
Agriculture and Agri Food
Forestry
Fisheries and Aquaculture
Environment
Energy
Mining
Genome Centre(s):
Genome British Columbia
Project Leader(s):
Wyeth Wasserman (University of British Columbia)
GE3LS: 
No
Fiscal Year Project Launched: 
2016-2017
Project Description: 

Phase 1 Project

The reduced cost and increased quality of DNA sequencing technology, coupled with an ever-expanding collection of experimental results, allows for the regular sequencing of individual human genomes, which can lower healthcare costs and improve outcomes by permitting highly personalized treatment and preventive medicine. This goal can only be reached with deep understanding of normal genetic variation (e.g. the 1000 Genomes project). Organizing such large-scale data in a computationally convenient structure, a reference genome, is key. A reference genome serves the same purpose as a picture of a completed jigsaw puzzle – it accelerates the placement of pieces.

Dr. Wyeth Wasserman of the University of British Columbia will implement a novel graph model, the GNOmics Genome Model (GGM), for representing the human genome and other genetic data. GNOmics, an acronym for Graphs ‘N’ Omics, is the brand name used for the research, which is focused around the novel underlying graph model. This project will develop a robust new computational framework for the analysis of genetic variation that includes a unified reference database of publically available genetic data and will replace text-based references genomes. The framework will also include algorithms for the detection and analysis of DNA sequence variation either for assembly and annotation of individuals genomic sequence data or between multiple assembled genomes. The implementation of the GGM incorporates significantly more information when representing genomic data, potentially allowing for improved accuracy without compromising speed or dramatically increasing the strain on computational resources.