A tutorial on Markov models based on Mendel's classical experiments.

Abstract

Hidden Markov Models (HMM) can be extremely useful tools for the analysis of data from biological sequences, and provide a probabilistic model of protein families. Most reviews and general introductions follow the excellent tutorial by Rabiner, where the focus is outside biology. Mendel's famous experiments in plant hybridisation were published in 1866 and are often considered the icebreaking work of modern genetics. He had no prior knowledge of the dual nature of genes, but through a series of experiments he was able to anticipate the hidden concept and name it "Elemente". In this paper we present the background, theory and algorithms of HMM based on examples from Mendel's experiments, and introduce the toolbox "mendelHMM". This approach is considered to have some intuitive advantages in a biological and bioinformatical setting. Applications to analysing bio-sequences like nucleic acids and proteins are also discussed.