| Popis: |
Next Generation Sequencing techniques like whole genome, exome etc., generate rich variant annotation outputs from the high-throughput sequencing data. These outputs may contain millions of gene variants depending on the sequencing techniques used and need to be filtered to a set of potential gene variant(s) for a disease of interest. Many systems and models have addressed the problem of prioritising gene variants from annotated variant outputs. There is a need to provide a flexible computational model that integrates the contextual knowledge from diverse sources and provides control of the filtering and prioritisation process to the bio-informaticians. Furthermore, to the best of our knowledge, there has been limited efforts to build an inter-operable, reusable and machine-readable knowledge framework to link identified potential variants for multiple known genetic conditions for the Indian population that can be searched, explored, analysed and applied for future cases with increased accuracy. In this paper, we present Sandhi Gene Variant Analysis (Sandhi GVA) that combines individual gene variant analysis summaries of patients, their clinical data and patient information into rich linked data fostering the application of semantic intelligence in effective filtering and prioritisation of potential gene variants in new cases. |
