title: Sequence complexity for biological sequence analysis.
abstract: 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.
authors.affiliation: School of Computer Science and Software Engineering, Monash University, Melbourne, Australia.
authors.forename: L
authors.initials: L
authors.lastname: Allison
authors.forename: L
authors.initials: L
authors.lastname: Stern
authors.forename: T
authors.initials: T
authors.lastname: Edgoose
authors.forename: T I
authors.initials: TI
authors.lastname: Dix
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D029721:Drosophila Proteins; D007257:Information Theory; D019476:Insect Proteins / Q000737:chemistry; D008390:Markov Chains; D009687:Nuclear Proteins / Q000737:chemistry; D012091:Repetitive Sequences, Nucleic Acid; D017422:Sequence Analysis, DNA
chemical_list: D029721:Drosophila Proteins; D019476:Insect Proteins; D009687:Nuclear Proteins; C085316:mam protein, Drosophila