Recent progress in molecular and biomedical technologies provides us ever-larger sets of biological data. It is easy to see that simple analyses are not capable to reveal all valuableinformation hidden in the data. For example current growth in genomic data has made it impossible to perform complete genome-wide analyses without full computational expertise. I present some applications of computational methods in mutation discovery and uncovering their impact in diseases. I introduce our novel algorithm that uses somatic mutations in multiple, related tissue samples as lineage markers for phylogenetic tree reconstruction. The method then leverages the inferred phylogeny to improve the accuracy of mutation discovery. I also present our new method to measure the impact of mutations on RNA structure as one possible causal factor in disease association.
Raheleh Salari obtained her PhD in computational biology from Simon Fraser University, Canada - with a background on data structure and algorithm design. After her PhD, she joined the National Center for Biotechnology and Information (NCBI) at the National Institutes of Health (NIH) as a Research Fellow. Currently, she is a Postdoctoral Fellow at Stanford University. Although the focus of her research has been on RNA structure and interaction prediction problem, she worked on various additional biological problems including protein alignment, protein-protein interaction network, and genetic variation discovery.