CSIRO is trailblazing cloud-based micro-services for computationally guided genome engineering

Recent developments in molecular engineering technology enables the accurate editing of genomes. The new technology, called CRISPR-Cas9, can be programmed to recognize and edit specific locations in the genome by pattern-matching unique sequences of DNA. This powerful new tool created unprecedented demand for large-scale computation to find the ideal target site for each gene in the entire genome. Earlier this year, the US National Institutes of Health (NIH) has approved the use of these technologies for human health. This has the potential to revolutionize cancer treatments and also adds a new time-critical dimension to the compute requirements.

The new NIH approved trial will use CRISPR-Cas9 to develop a different treatment approach. In this, the patient’s own immune system is boosted through specific modifications of the cells that natively fight cancer. This has the potential of being effective for a wide range of different tumors, with the current trial including patients with specific blood and solid cancers, as well as melanoma. 

Cloud services for computationally guided genome engineering

This new application in human health requires an increase in robustness and efficiency of CRISPR-Cas9 design in order to meet the time constraints of clinical care. Built on AWS cloud-services, researchers in the eHealth program of the Commonwealth Scientific and Industrial Research Organization (CSIRO) in Australia, developed GT-Scan2, a novel software tool to address this issue.

“Compared to other available methods, GT-Scan2 identifies genomic location with higher sensitivity and specificity,” says Dr. Denis Bauer who is leading the transformational bioinformatics team. 

GT-Scan2 improves the effectiveness of the system by finding sites that are unique in the genome. This avoids diluting the effect due to “off-targets”, which are other sites in the genome with high sequence similarity. It also optimizes robustness by finding sites that are easier to modify.

“While it was known that the three-dimensional genome organization plays a role in CRISPR binding, GT-Scan2 is the first tool to also leverage other components that are crucial for Cas9 activity,” says Dr. Laurence Wilson whose research focuses on computational genome engineering. 

Specifically the off-target search is a compute intensive task traditionally reserved for researchers at large institutes with high-performance-compute infrastructure as every location in the 3 billion letter long genomic sequence needs to be investigated. GT-Scan2 democratizes the ability to find optimal sites by offering this complex computation as a cloud-service using AWS Lambda functions. 

Scaling instantaneously for personalized treatments

GT-Scan2 leverages the instantaneous scalability that the event-driven AWS Lambda service offers. This is crucial for personalized treatment, as complexity of the targeted gene can vary dramatically.

“The off-target search as well as the robustness analysis can be subdivided into independent, modular tasks that can run in parallel” says Aidan O’Brien who designed and implemented the system after Lambda’s official Asia-Pacific launch in April at the AWS Summit 2016. A typical job takes less than a minute and the variation between jobs range from 1 second to 5 minutes. This fast fluctuation in load over minutes rather than hours perfectly leverages the instantaneous scalability of a Lambda-based solution. GT-Scan2 also leverages API calls to trigger Lambda functions directly out of the DynamoDB database. This allows the solution to be freely scalable without creating a bottleneck.  

Adapting to leverage the power of Lambda-based microservies

The complexity of the off-target search required the team to overcome the resource limitations Lambda-functions impose on the computations. “GT-Scan2 divides the genome into smaller blocks to fit the Lambda specifications” explains Adrian White (Research & Technical Computing, APAC) who supported the CSIRO team during development. For an average run, GT-Scan2 hence triggers 500-1000 individual Lambda functions, which simultaneously update the scores for the different putative targets in DynamoDB. During this process, the frontend is polling this table via API Gateway and updating the webpage as results come in, eliminating the need for server-side compute. 

“One of the best things about Lambda is that users will be able to easily swap-in different machine learning algorithms that are better suited for specific CRISPR applications” says Dr. Wilson.

“AWS’s Lambda has given us a great framework to develop a future-ready software package able to support medical genome engineering applications,” says Dr. Bauer.

GT-Scan2 was featured on the prestigious Jeff Barr blog as it brings together no scientific insights and unprecedented cloud-compute capacity thereby democratizing high quality CRISPR target site search to power new research applications in the future.