RESUMO
Motivation: For a number of neurological diseases, such as Alzheimer's disease, amyotrophic lateral sclerosis, and many others, certain genes are known to be involved in the disease mechanism. A common question is whether a structural variant in any such gene may be related to drug response in clinical trials and how this relationship can contribute to the lifecycle of drug development. Results: To this end, we introduce VariantSurvival, a tool that identifies changes in survival relative to structural variants within target genes. VariantSurvival matches annotated structural variants with genes that are clinically relevant to neurological diseases. A Cox regression model determines the change in survival between the placebo and clinical trial groups with respect to the number of structural variants in the drug target genes. We demonstrate the functionality of our approach with the exemplary case of the SETX gene. VariantSurvival has a user-friendly and lightweight graphical user interface built on the shiny web application package.
RESUMO
Background: The identification of differentially expressed genes and their associated biological processes, molecular function, and cellular components are important for genetic diseases studies because they present potential biomarkers and therapeutic targets. Methods: In this study, we developed an o²S²PARC template representing an interactive pipeline for the gene expression data visualization and ontologies data analysis and visualization. To demonstrate the usefulness of the tool, we performed a case study on a publicly available dataset. Results: The tool enables users to identify the differentially expressed genes (DEGs) and visualize them in a volcano plot format. The ontologies associated with the DEGs are determined and visualized in barplots. Conclusions: The "Expression data visualization" template is publicly available on the o²S²PARC platform.