No abstract

A method has been developed for discovering patterns in DNA sequences. Loosely based on the well-known Lempel Ziv model for text compression, the model detects repeated sequences in DNA. The repeats can be forward or inverted, and they need not be exact. The method is particularly useful for detecting distantly related sequences, and for finding patterns in sequences of biased nucleotide composition, where spurious patterns are often observed because the bias leads to coincidental nucleotide matches. We show here the utility of the method by applying it to genomic sequences of Plasmodium falciparum. A single scan of chromosomes 2 and 3 of P. falciparum, using our method and no other a priori information about the sequences, reveals regions of low complexity in both telomeric and central regions, long repeats in the subtelomeric regions, and shorter repeat areas in dense coding regions. Application of the method to a recently sequenced contig of chromosome 10 that has a particularly biased base composition detects a long internal repeat more readily than does the conventional dot matrix plot. Space requirements are linear, so the method can be used on large sequences. The observed repeat patterns may be related to large-scale chromosomal organization and control of gene expression. The method has general application in detecting patterns of potential interest in newly sequenced genomic material.

Background: Features of a DNA sequence can be found by compressing the sequence under a suitable model; good compression implies low information content. Good DNA compression models consider repetition, differences between repeats, and base distributions. From a linear DNA sequence, a compression model can produce a linear information sequence. Linear space complexity is important when exploring long DNA sequences of the order of millions of bases. Compressing a sequence in isolation will include information on self-repetition. Whereas compressing a sequence Y in the context of another X can find what new information X gives about Y. This paper presents a methodology for performing comparative analysis to find features exposed by such models.

A new statistical model for DNA considers a sequence to be a mixture of regions with little structure and regions that are approximate repeats of other subsequences, i.e. instances of repeats do not need to match each other exactly. Both forward-and reverse-complementary repeats are allowed. The model has a small number of parameters which are fitted to the data. In general there are many explanations for a given sequence and how to compute the total probability of the data given the model is shown. Computer algorithms are described for these tasks. The model can be used to compute the information content of a sequence, either in total or base by base. This amounts to looking at sequences from a data-compression point of view and it is argued that this is a good way to tackle intelligent sequence analysis in general.