Dr. Gholson Lyon explores the use of the VAAST software system and explains the advantages of the VAAST technique versus other methods and talks about additional research that his team has planned to make VAAST even more effective for personal genomics applications.
Researchers at the University of Utah and informatics company Omicia, Inc., are using a software program called the Variant Annotation, Analysis & Search Tool (VAAST) to find the root cause of a disease caused by a rare genetic disorder. After sequencing two families, the scientists used the program to identify
Using software developed by Omicia and the University of Utah, geneticists have identified the mutation that causes a mysterious, previously unnamed affliction that has killed several baby boys born to an Ogden, Utah family.
Scientists from the University of Utah and Omicia, Inc., a privately held company developing tools to interpret personal genome sequences, today announced the publication in Genome Research of a new software tool called VAAST, the Variant Annotation, Analysis and Selection Tool, a probabilistic disease-causing mutation finder for individual human genomes.
Harnessing the new generation of rapid, highly accurate gene-sequencing techniques, a research team has identified the disease-causing mutation in a newly characterized rare genetic disease, by analyzing DNA from just a few individuals.
The VAAST genome analysis tools speedily tracks down previously unknown mutation.
Scientists at the University of Utah and a Bay Area software company, Omicia, have released details of a powerful computational tool for identifying disease-causing mutations by individual genome sequencing. Researchers have used the software to rapidly screen genomic data to identify a deleterious mutation in a fatal X-linked disorder.
At the Cold Spring Harbor Laboratory Biology of Genomes meeting, researchers presented a new computer program called VAAST that can automate finding disease genes.
Omicia and collaborators at the University of Utah are mining personal genome sequences, identifying all known alleles associated with ill health, and then relating that information to known genetic variants in an easy manner for rapid clinical reports.
Omicia is taking genome data and extracting clinical meaning, focusing on DNA variation, rather than gene expression or pathways. Omicia is also developing algorithms to predict the effect of protein-coding variants to better understand which mutations are medically relevant.
