Generalizations of Markov model to characterize biological sequences

Background: The currently used kth order Markov models estimate the probability of generating a single nucleotide conditional upon the immediately preceding (gap = 0) k units. However, this neither takes into account the joint dependency of multiple neighboring nucleotides, nor does it consider the long range dependency with gap>0. Result: We describe a configurable tool to explore generalizations of the standard Markov model. We evaluated whether the sequence classification accuracy can be improved by using an alternative set of model parameters. The evaluation was done on four classes of biological sequences

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Junwen Wang, Sridhar Hannenhalli
• Keywords: BMCBI 2005, Markov Model, Nucleosome Positioning Sequence, Sequence Classification Accuracy