DNA sequence compression using the normalized maximum likelihood model for discrete regression
I. Tabus, Gergely Korodi, J. Rissanen · 2003
The use of normalized maximum likelihood (NML) model for encoding sequences known to have regularities in the form of approximate repetitions was discussed. A particular version of the NML model was presented for discrete regression, which was shown to provide a very powerful yet simple model for encoding the approximate repeats in DNA sequences. Combining the model of repeats with a simple first order Markov model, a fast lossless compression method was obtained that compares favorably with the existing DNA compression programs. It is remarkable that a simple model, which recursively updates a small number of parameters, is able to reach the state of the art compression ratio for DNA sequences with much more complex models. Being a minimum description length (MDL) model, the NML model may later prove to be useful in studying global and local features of DNA or possibly of other biological sequences.