Digital Genome Engineering

Laurence Wilson

Laurence Wilson
Team Leader
Digital Genome Engineering

Analytics and web-services to improve Genome Engineering applications in the health and biosecurity spaces.

Read more at https://bioinformatics.csiro.au/

Our Science

New genome engineering technologies are currently revolutionising science, with impact in fields such as precision health, diagnostics and biosecurity. However there are many challenges that need to be overcome before its full potential can be used. Specifically, the natural genetic variations among individuals can give rise to significantly altered off-target risk profiles, the distinct chromatin environments of different tissues can dramatically influence the efficiency of targeted efficiency, and the complexity of genetic pathways make deciding on an intervention point quite difficult.

Digital genome engineering, the application of machine learning and modelling approaches to these systems, can help address these challenges. We are able to model and predict the effectiveness of editing approaches based on target site sequence as well as the expanded chromatin sequence, allowing for tissue-specific predictions. Additionally, we have developed pipelines that incorporate an individual or a population’s genetic variance to identify their specific risk profile. Finally, we are developing methods for the detection of artificial genome manipulation, providing a method for the monitoring of genomes in the biosecurity space.

We are applying our technologies in a number of specific application cases, including developing complex disease model systems, immuno-engineering (the tailoring of an individual’s immune system to fight specific applications) and gene-therapy treatments to correct disease causing mutations.

Impact on the Health System

By applying digital genome engineering techniques, we will help to enable precision health applications. These include tailoring immune systems to fight infections diseases (through immuno-engineering) or correcting disease causing mutations through gene-therapy applications.

Our Solutions

  • GT-Scan: flexible and high-throughput evaluation of off-targets
  • GT-Scan2: chromatin aware prediction of CRISPR-Cas9 on-target efficiency
  • TUSCAN: high-throughput, sequence based prediction of CRISPR-Cas9 on-target efficiency
  • CUNE: Sequence based prediction of HDR mediated knock-in efficiency
  • VARSCOT: Variant aware off-target prediction and evaluation
  • GOANA: high-throughput, generalizable measurement of on-target efficiency for genome editing applications

Case Studies

 

Team Members:

Laurence Wilson

Aidan O’Brien

Daniel Reti

Aidan Tay

Brendan Hosking

Suzanne Scott

 

 

 

 

 

 

 

 

 

 

 

Laurence Wilson
Team leader and Research Scientist.
CSIRO Profile

Aidan O’Brien
PhD Student researching HDR mediated genome engineering.
CSIRO Profile

Daniel Reti
Research Officer developing tailored pipelines for genome engineering applications.

Aidan Tay
Postdoctoral Fellow developing genome engineering methods in the biosecurity space.

Brendan Hosking
Solutions Engineer implementing our tools within the AWS cloud.

Suzanne Scott
Postdoctoral Fellow collaborating with Children’s Medical Research Institute on gene therapy applications.

Jake Bradford
Joint PhD Student with QUT (Dimitri Perrin).

Publications

  1. O’Brien, A., Wilson, L., Gurgio, G. and Bauer, D. “Unlocking HDR-mediated nucleotide editing by identifying high-efficiency target sites using machine learning” Scientific Reports 2019; 9:2788-2797 https://doi.org/10.1038/s41598-019-39142-0
  2. Wilson, L., Reti, D., O’Brien, A., Dunne, R. and Bauer, D. “High Activity Target-Site Identification Using Phenotypic Independent CRISPR-Cas9 Core Functionality” The CRISPR Journal 2018; 1(2):182-190 http://doi.org/10.1089/crispr.2017.0021
  3. Wilson, L., O’Brien, A. and Bauer, D. “The Current State and Future of CRISPR-Cas9 gRNA Design Tools” Frontiers in Pharmacology 2018; 9:749 https://doi.org/10.3389/fphar.2018.00749
  4. O’Brien A. and Bailey, T. “GT-Scan: identifying unique genomic targets” Bioinformatics 2014; 30(18):2673-2675 https://doi.org/10.1093/bioinformatics/btu354