100,000 Genome Project Aided by GenomOncology
News Sep 26, 2016
Genomics England is partnering with GenomOncology LLC (GO) to utilise the GO Knowledge Management System (GO KMS) as a tool for clinical reporting enablement.
Genomics England will integrate the GO KMS as a key content driver to augment clinical reporting in the 100,000 Genomes Project’s cancer programme, coupling Genomics England curated database with the GO KMS’s data for a comprehensive clinical report comprised of the most relevant drugs, prognoses, and clinical trials.
The GO KMS enables Precision Medicine by allowing users the ability to aggregate and analyse biomarker-based data. The GO KMS leverages a large number of existing data sources including FDA, NCCN, and ASCO guidelines as well as providing exclusive API access to the expertly curated data of My Cancer Genome. The GO KMS allows users to analyse genomic variants within a ‘genomics-aware’ framework that includes a diverse set of annotations including genes, pathways, drugs, alterations, transcripts, and a disease ontology. In addition, the GO KMS is designed to empower researchers and clinicians alike to build and maintain their own curated knowledge repositories.
GO and Genomics England will work through an initial implementation phase that will focus on extending the GO KMS to include NICE Guidelines and UK-specific clinical trials, as well as a variety of other enhancements to support clinical reporting, leading to more personalised care for NHS patients.
Augusto Rendon, Director of Bioinformatics at Genomics England said: “GenomOncology brings to the table a widely used knowledge base, as the people behind My Cancer Genome. Their data was easy to integrate into variant annotation and interpretation pipelines due to their consistent variant nomenclature. Through exhaustive curation GenomOncology has made great progress in solving the difficult problem of representing cancer variants consistently in order to support genomic workflows”.
Analytical Tool Predicts Disease-Causing GenesNews
Predicting genes that can cause disease due to the production of truncated or altered proteins that take on a new or different function, rather than those that lose their function, is now possible thanks to an international team of researchers that has developed a new analytical tool to effectively and efficiently predict such candidate genes.
‘Good Cholesterol’ May Not Always be Good for Postmenopausal WomenNews
Postmenopausal factors may have an impact on the heart-protective qualities of high-density lipoproteins (HDL) – also known as ‘good cholesterol’ – according to a study led by researchers in the University of Pittsburgh Graduate School of Public Health.READ MORE
Researchers Move Closer to Completely Optical Artificial Neural NetworkNews
Researchers have shown that it is possible to train artificial neural networks directly on an optical chip. The significant breakthrough demonstrates that an optical circuit can perform a critical function of an electronics-based artificial neural network and could lead to less expensive, faster and more energy efficient ways to perform complex tasks such as speech or image recognition.