After the Human Genome Project completed the mapping of human DNA sequence in 2001, a new era began in biological and medical research. The genetic basis of various diseases, such as cancer, could be studied with higher precision than ever before. The map of human genome enabled next-generation sequencing (NGS) techniques and not only DNA sequencing got faster and cheaper to perform, also the amount of data started to increase exponentially. The field of bioinformatics, which combines both computer and life sciences, got a great challenge to handle all the data available and to dig out relevant information out of it. Various tools with heavily enhanced or completely new kinds of algorithms were developed for the demanding task of the analysis of NGS data, which are in the focus of this thesis.
For the search of cancer causing mutations, NGS methods enable genome scale studies with the precision of a single molecule. However, the spectacular scale and preciseness of the data offer another challenge – how to distinguish trivial data from the non-trivial, and furthermore, how to separate reliable data from erroneous. The raw data must be put through a pipeline of various processing tools, which organize and humanize the data with the help of the map of human genome. After data processing, the data is feasible for the actual cancer specific analysis, where causative mutations can be hunted down. For this purpose, I have developed an analysis and visualization software, Rikurator, which provides various features and tools to handle the NGS data. Rikurator is designed for comparative analysis of dozens of cancer samples, quality filtering, controlling and visualization to name a few. In addition to tools in data processing pipeline, this thesis will describe features and implementation of Rikurator.
