Elucidation of the human genome sequence was a significant milestone in the life sciences (Lander et al., 2001; Venter et al., 2001; International Human Genome Sequencing Consortium, 2004). However, with access to this information an obvious but entirely non-trivial problem was encountered. What does all the genetic information in the form of some three billion bases represent in biological terms? One important category of information is the sequences that specify genes, i.e. regions that give rise to mRNAs that in turn encode specific protein molecules. Not only proteins are specified by the genome; also a large number of RNAs are transcribed from DNA that do not give rise to mRNA, but have other functions. (These are non-coding RNAs and will be discussed in Chapter 17.) In the next three chapters we will deal with the computational problems of finding proteins and non-coding RNA genes, starting out with a genomic sequence.

When it comes to the protein-coding genes of a mammalian genome, only a very small fraction, about 1.5–2%, of the genome codes for protein. For these genomes we are faced with the problem of identifying relatively small and scattered coding regions in a vast sea of non-coding material. There is a striking difference in this respect between mammals and a bacterium like Escherichia coli, whose genome contains as much as 83% of coding sequence. In the next chapter the focus will be on prediction of exon regions of protein-coding genes. Here we will address another sub-problem of finding protein-coding genes. We will see how a simple prediction of regions known as CpG islands will help us to locate sites in the genome that are close to the transcription start sites of genes.