The sheer volume of genomic data tends to defeat machine-learning techniques, and Denis Bauer's team at Australia's Commonwealth Scientific and Industrial Research Organization has overcome this barrier with VariantSpark, a machine-learning library that analyzes genomic data in real time using the Apache Spark engine for big data processing.
Once disease-inducing genes have been identified and analyzed, Bauer says, the CRISPR genome engineering system is tested to edit those genes in humans.
She notes improving success rates require accelerating the identification of where gene editing can be performed, and cites the Amazon Web Services Lambda serverless computing service for making it possible to "trigger many functions in parallel easily and cheaply enough."
Even so, Bauer says her team has had to devise workarounds to enable workload parallelization.
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