Dozens of public data sets are available for almost any human disease, which makes it possible to study the effects of gene expression, genetic variation, metabolites, and many other features without performing new experiments. However, finding, retrieving, integrating, and analyzing public data sets is a time-consuming and difficult process. Common challenges include:
• Choosing a reliable database
• Finding relevant data sets
• Comparing data sets from different sources and technologies
• Re-analyzing millions of data points from various experiments
• Integrating new data with public data
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Fios Genomics has over a decade of experience navigating and managing these common issues. These videos explain how we can help you make the most of the vast amount of public data available today.
In this first video, John Davey examines how Fios Genomics can integrate public Cancer Cell Line Encyclopedia data sets with sensitivity data for particular genes, linking gene expression, mutation or copy number to effects in particular cell types, and proposing the functional pathways involved. He also discusses how Fios can help you find relevant datasets for a disease of interest.
In this second video, Grant Neilson provides a brief overview on one of the most widely used publicly available databases for cancer research, The Cancer Genome Atlas (TCGA). He then discusses how this resource can be effectively mined to gain valuable insight into a wide range of indications by utilizing a multitude of omics data available on this portal, along with some ideas of how it can enhance your sensitivity analysis. Lastly, he presents an in-depth case study on work which Fios Genomics has carried out for a client, using TCGA to help them solve a specific bioinformatics challenge.
John Davey, PhD
John Davey has worked in bioinformatics for over 15 years. After training in Computer Science at the University of Durham, he completed a PhD in Neuroscience at the University of Edinburgh. He has worked on several bioinformatics projects at the Universities of Edinburgh, Cambridge, and York, contributing to 35 journal articles with over 4,500 citations.
Grant Neilson is a bioinformatician at Fios Genomics. Graduating in 2019 with a master’s degree in Bioinformatics from the University of Southampton, his main area of research was investigating polygenic risk scores in patients with Alzheimer's disease. He then worked within the University of Exeter Medical School, investigating the epigenetic drivers of neurodegenerative diseases, with a particular focus in Alzheimer's disease. He then joined Fios Genomics where he specializes in using machine learning to build predictive models and generate disease signatures.