Two algorithms based on Markov chains and their application to recognition of protein coding genes in prokaryotic genomes

• Autorzy: Małgorzata Grabińska , Paweł Błażej , Paweł Mackiewicz
• Języki publikacji: EN

Abstrakty

EN

Methods based on the theory of Markov chains are most commonly used in the recognition of protein coding sequences. However, they require big learning sets to fill up all elements in transition probability matrices describing dependence between nucleotides in the analyzed sequences. Moreover, gene prediction is strongly influenced by the nucleotide bias measured by e.g. G+C content. In this paper we compare two methods: (i) the classical GeneMark algorithm, which uses a three-periodic non-homogeneous Markov chain, and (ii) an algorithm called PMC that considers six independent homogeneous Markov chains to describe transition between nucleotides separately for each of three codon positions in two DNA strands. We have tested the efficiency (in terms of true positive rate) of these two Markov chain methods for the model bacterial genome of Escherichia coli depending on the size of the learning set, uncertainty of ORFs' function annotation, and model order of these algorithms. We have also applied the methods with different model orders for 163 prokaryotic genomes that covered a wide range of G+C content. The PMC algorithm of different chain orders turns out to be more stable in comparison to the GeneMark algorithm. The PMC also outperforms the GM algorithm giving a higher fraction of coding sequences in the tested set of annotated genes. Moreover, it requires much smaller learning sets than GM to work properly.

Kategorie tematyczne

• 92D20: Protein sequences, DNA sequences
• 60J20: Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.)
• 62P10: Applications to biology and medical sciences
• 60J10: Markov chains (discrete-time Markov processes on discrete state spaces)

• Wydawca: Institute of Mathematics Polish Academy of Sciences
• Czasopismo: Applicationes Mathematicae
• Rocznik: 2013
• Tom: 40
• Numer: 4
• Strony: 447-457

Daty

wydano

2013

Twórcy

autor

Małgorzata Grabińska

• Department of Genomics, Faculty of Biotechnology, University of Wrocław, Przybyszewskiego 63/77, 51-148 Wrocław, Poland

autor

Paweł Błażej

• Department of Genomics, Faculty of Biotechnology, University of Wrocław, Przybyszewskiego 63/77, 51-148 Wrocław, Poland

autor

Paweł Mackiewicz

• Department of Genomics, Faculty of Biotechnology, University of Wrocław, Przybyszewskiego 63/77, 51-148 Wrocław, Poland

Identyfikatory

DOI

10.4064/am40-4-5