Pattern Discovery from Biosequences

In this thesis we have developed novel methods for analyzing biological data, the primary sequences of the DNA and proteins, the microarray based gene expression data, and other functional genomics data. The main contribution is the development of the pattern discovery algorithm SPEXS, accompanied by several practical applications for analyzing real biological problems. For performing these biological studies that integrate different types of biological data we have developed a comprehensive web-based biological data analysis environment Expression Profiler (http://ep.ebi.ac.uk/). Biosequences, i.e., the primary sequences of DNA, RNA, and protein molecules, represent the most basic type of biological information. Features of these sequences that are reused by nature help us to understand better the basic mechanisms of gene structure, function, and regulation. The SPEXS algorithm has been developed for the discovery of the biologically relevant features that can be represented in the form of sequence patterns. SPEXS is a fast exhaustive search algorithm for the class of generalized regular patterns. This class is essentially the same as used in the PROSITE pattern database, i.e. it allows patterns to consist of fixed character positions, group character positions (ambiguities), and wildcards of variable lengths. The biological relevance of the patterns can be estimated according to several different mathematical criteria, which have to be chosen according to the application. We have used SPEXS for the analysis of real biological problems, where we have been able to find biologically meaningful patterns in a variety of different applications. For example, we have studied gene regulation mechanisms by a systematic