Informatics Approaches to Linking Mutations to Biological Pathways, Networks and Clinical Data

• Main Author: Singh, Arti
• Other Authors: Mooney, Sean
• Language: en_US
• Published: 2011
• Subjects: Clinical Data; Networks; Linking Mutations; Biological Pathways; Human genetics > Variation > Research; Genetic disorders > Research
• Online Access: http://hdl.handle.net/1805/2608

Indiana University-Purdue University Indianapolis (IUPUI) === The information gained from sequencing of the human genome has begun to transform human biology and genetic medicine. The discovery of functionally important genetic variation lies at the heart of these endeavors, and there has been substantial progress in understanding the common patterns of single-nucleotide polymorphism (SNP) in humans- the most frequent type of variation in humans. Although more than 99% of human DNA sequences are the same across the population, variations in DNA sequence have a major impact on how we humans respond to disease; to environmental entities such as bacteria, viruses, toxins, and chemicals; and drugs and other therapies and thus studying differences between our genomes is vital. This makes SNPs as well other genetic variation data of great value for biomedical research and for developing pharmaceutical products or medical diagnostics. The goal of the project is to link genetic variation data to biological pathways and networks data, and also to clinical data for creating a framework for translational and systems biology studies. The study of the interactions between the components of biological systems and biological pathways has become increasingly important. It is known and accepted by scientists that it as important to study different biological entities as interacting systems, as in isolation. This project has ideas rooted in this thinking aiming at the integration of a genetic variation dataset with biological pathways dataset. Annotating genetic variation data with standardized disease notation is a very difficult yet important endeavor. One of the goals of this research is to identify whether informatics approaches can be applied to automatically annotate genetic variation data with a classification of diseases.