Electronic Thesis and Dissertation Repository


Master of Science


Computer Science


Lucian Ilie

2nd Supervisor

Roberto Solis-oba

Joint Supervisor


Structural variations (SVs) are changes in the human genome that are reported in several studies to be associated with some diseases. Therefore, designing methods to find these types of variations would help us for early detection of those diseases and utilizing new treatment methods such as personalized medicine. Currently computational methods are applied to find structural variations from short reads obtained by Next Generation Sequencing (NGS) platforms. Usually each method has more power in finding particular types or sizes of SVs and limitations in finding others. Thus, still new approaches and methods are on demand for SV discovery.

In this thesis, we introduce two new methods based on a de novo assembly framework called SAGE for detecting SVs. We compare our proposed methods with existing ones which are based on the same approaches. This comparison shows that our methods are able to detect more SVs from the validation sets (true SVs) than the compared methods.