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Coronary artery disease (CAD) has the highest disease burden worldwide. To manage this burden, predictive models are required to screen patients for preventative treatment. A range of variables have been explored for their capacity to predict disease, including phenotypic (age, sex, BMI and smoking status), medical imaging (carotid artery thickness) and genotypic. We use a machine learning models and the UK Biobank cohort to measure the prediction capacity of these 3 variable categories, both in combination and isolation. We demonstrate that phenotypic variables from the Framingham risk score have the best prediction capacity, although a combination of phenotypic, medical imaging and genotypic variables deliver the most specific models. Furthermore, we demonstrate that Variant Spark, a random forest based GWAS platform, performs effective feature selection for SNP-based genotype variables, identifying 115 significantly associated SNPs to the CAD phenotype. [ABSTRACT FROM AUTHOR]

Healthcare data is a scarce resource and access is often cumbersome. While medical software development would benefit from real datasets, the privacy of the patients is held at a higher priority. Realistic synthetic healthcare data can fill this gap by providing a dataset for quality control while at the same time preserving the patient's anonymity and privacy. Existing methods focus on American or European patient healthcare data but none is exclusively focused on the Australian population. Australia is a highly diverse country that has a unique healthcare system. To overcome this problem, we used a popular publicly available tool, Synthea, to generate disease progressions based on the Australian population. With this approach, we were able to generate 100,000 patients following Queensland (Australia) demographics. [ABSTRACT FROM AUTHOR]

Genetic data is limited and generating new datasets is often an expensive, time-consuming process, involving countless moving parts to genotype and phenotype individuals. While sharing data is beneficial for quality control and software development, privacy and security are of utmost importance. Generating synthetic data is a practical solution to mitigate the cost, time and sensitivities that hamper developers and researchers in producing and validating novel biotechnological solutions to data intensive problems. Existing methods focus on mutation frequencies at specific loci while ignoring epistatic interactions. Alternatively, programs that do consider epistasis are limited to two-way interactions or apply genomic constraints that make synthetic data generation arduous or computationally intensive. To solve this, we developed Polygenic Epistatic Phenotype Simulator (PEPS). Our tool is a probabilistic model that can generate synthetic phenotypes with a controllable level of complexity. [ABSTRACT FROM AUTHOR]

With the advancement of genomic engineering and genetic modification techniques, the uptake of computational tools to design guide RNA increased drastically. Searching for genomic targets to design guides with maximum on-target activity (efficiency) and minimum off-target activity (specificity) is now an essential part of genome editing experiments. Today, a variety of tools exist that allow the search of genomic targets and let users customize their search parameters to better suit their experiments. Here we present an overview of different ways to visualize these searched CRISPR target sites along with specific downstream information like primer design, restriction enzyme activity and mutational outcome prediction after a double-stranded break. We discuss the importance of a good visualization summary to interpret information along with different ways to represent similar information effectively. [ABSTRACT FROM AUTHOR]