Deterministic Pattern Mining On Genetic Sequences

Introduction

The Human Genome Project (NIH, 2007; Cooper, 1994) has led to the development of a set of new biological techniques. These new technological breakthroughs have in turn resulted on an exponential growth of biological data. In particular, the development of sequencing techniques culminated in a massive accumulation of genetic (DNA and protein) sequence data, which have then become freely available through specialized internet databases. For instance, the GenBank database contains approximately 85 759 586 764 nucleotides in 82 853 685 sequences (February 2008). The UniProtKB/Swiss-Prot contains 392 667 sequence entries, comprising 141 217 034 amino acids (July 2008). Figure 1 depicts the growth of UniProtKB/Swiss-Prot in the last years. Such amount of data raises big challenges both at the organizational and analysis level. In fact, the way that molecular biology research is done has changed significantly, being now a much more data-oriented and computationally based discipline. This fact, combined with the lack of robust theories to interpret these data opens a new and vast research area. The focus is now not on how to obtain the data, but on how to understand it. In the particular case of sequence data, this poses the question of how to retrieve biologically relevant patterns.

Figure 1.

Growth in the number of protein sequences entered in the UniProtKB/Swiss-Prot database.

In the next two sections we briefly motivate the use of sequence mining to better understand DNA and protein mechanisms. More information about these topics can be easily found in any biology text book, for instance (Alberts, 2002; John, 1995). We also refer the reader to the following introductory articles (Hunter, 1993; Koonin, 2003; Brazma, 2001; Lesk, 2002).