Identifying disease causing genetic variants is a complex process that requires experts from multiple fields, including bioinformatics, IT systems administration and disease pathology to work closely together. The size of sequencing data files also adds the requirement for large computational resources. As a result, performing genomic analyses is an expensive and lengthy process and is only fully adopted in large research institutions. This proposal aims to simplify this process, enabling medical professionals, including genetic counselors, physicians and diagnostic clinicians to perform powerful analyses, quickly and on their own laptop. The proposed product will be an intuitive web-based app, built on a cloud infrastructure, that will direct an analyst through a predefined, state of the art analysis pipeline. Intelligent quality control will be performed on all input data, to ensure that the conclusions reached are valid and comprehensive. The product will be built on the IOBIO platform that has been developed by the applicant team. Currently available apps built on this platform perform analysis in reatime, using visualizations to drive the analysis, and are already popular in the community; indeed they have been integrated into a number of large public projects to solve data visualization problems. These IOBIO apps will be expanded, providing new features necessary for clinical use, and consolidated into a single living report from which the entire analysis will be performed, shared, and managed. Core IOBIO infrastructure will be improved for commercial deployment, including support for massively parallel processing on the cloud, maintaining real time analysis across large data sets. The objective of this proposal is to develop a commercially viable product to significantly decrease the cost and expertise burden associated with clinical genomic analysis. This will ultimately result in an increase in the number of diagnosed patients and help minimize the diagnostic odyssey that they can often undergo.
Public Health Relevance Statement: Project narrative Diagnosing sick patients using DNA sequencing data is a complex and costly process requiring experts from many disparate fields, including computer science, bioinformatics and varied medical and biological disciplines. This project will develop a web-based, intuitive application to put analysis in the hands of medical experts, significantly reducing the cost of analysis and increasing the rate at which DNA information can be used to diagnose diseases.
Project Terms: Address; Adopted; base; Basic Science; Behavior; Bioinformatics; Biological; Clinic; Clinical; clinical Diagnosis; Clinical Research; Cloud Computing; Communication; Communities; Complex; computer science; computing resources; cost; Cost Analysis; Data; data access; Data Files; Data Quality; Data Set; Data Sources; data visualization; design; Detection; Development; Diagnosis; Diagnostic; Discipline; Disease; disease diagnosis; DNA; DNA sequencing; empowered; Engineering; Ensure; Environment; exome sequencing; Family Relationship; Gender; Genes; Genetic; genetic analysis; genetic counselor; genetic pedigree; genetic variant; genome sequencing; genomic data; Genomics; Imagery; improved; Information Systems; Institution; Internet; Intuition; Knowledge; laptop; Lead; Libraries; massive parallel processing; Medical; Modeling; neglect; Online Systems; Outcome; Pathology; Patients; Phase; Phenotype; Physicians; Positioning Attribute; Process; Quality Control; Reporting; Reproduction; Research; Research Infrastructure; Running; Sampling; Secure; Small Business Technology Transfer Research; Structure; Support System; task analysis; Technology; Testing; Time; time use; tool; Update; Variant; Visual; web app; web-based tool; Work
