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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., Results: We apply such a model to find features across chromosomes of Cyanidioschyzon merolae. We present a tool that provides useful linear transformations to investigate and save new sequences. Various examples illustrate the methodology, finding features for sequences alone and in different contexts. We also show how to highlight all sets of self-repetition features, in this case within Plasmodium falciparum chromosome 2., Conclusion: The methodology finds features that are significant and that biologists confirm. The exploration of long information sequences in linear time and space is fast and the saved results are self documenting.

We describe a model for strings of characters that is loosely based on the Lempel Ziv model with the addition that a repeated substring can be an approximate match to the original substring; this is close to the situation of DNA, for example. Typically there are many explanations for a given string under the model, some optimal and many suboptimal. Rather than commit to one optimal explanation, we sum the probabilities over all explanations under the model because this gives the probability of the data under the model. The model has a small number of parameters and these can be estimated from the given string by an expectation-maximization (EM) algorithm. Each iteration of the EM algorithm takes O(n2) time and a few iterations are typically sufficient. O(n2) complexity is impractical for strings of more than a few tens of thousands of characters and a faster approximation algorithm is also given. The model is further extended to include approximate reverse complementary repeats when analyzing DNA strings. Tests include the recovery of parameter estimates from known sources and applications to real DNA strings.

An information-theoretic DNA compression scheme devised by Milosavljevic and Jurka (1993) has been used in many places in the literature for both the discovery of new genes and the compression of DNA. Their compression method applies an encoding of previously occurring runs. They use 5 different code-words: four being the DNA bases, A, C, G and T, and the other being a pointer to a previously occurring run. They advocate a code-word of length log2 5 for each of these and then encoding a run by a code-word of length 2 x log2 n, where n is the length of the sequence. This scheme encodes the start of the sequence with a code-word of length log2 n and likewise encodes the end of the sequence with a code-word of length log2 n. In this paper, we show the above coding scheme to be inefficient in various ways and improve upon it so that it can compress DNA. We discuss our implementation of various schemes some of which run in linear time.

Restriction mapping generally requires the application of information from various digestions by restriction enzymes to find solution sets. We use both the predicate calculus and constraint solving capabilities of CLP(R) to develop an engine for restriction mapping. Many of the techniques employed by biologists to manually find solutions are supported by the engine in a consistent manner. We provide generalized pipeline and cross-multiply operators for combining sub-maps. Our approach encourages the building of maps iteratively. We show how other techniques can be readily incorporated.