Open Access Open Badges Research article

Exploration of multivariate analysis in microbial coding sequence modeling

Tahir Mehmood1*, Jon Bohlin2, Anja Bråthen Kristoffersen34, Solve Sæbø1, Jonas Warringer56 and Lars Snipen1

Author Affiliations

1 Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Aas, Norway

2 EpiCenter, Department of Food Safety and Infection Biology, , Oslo, Norway

3 Section for Epidemiology, Norwegian Veterinary Institute, Oslo, Norway

4 Department of Informatics, University of Oslo, Oslo, Norway

5 Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden

6 Center of Integrative Genetics (CIGENE) and Department of animal and aquaculture, Norwegian University of Life Sciences, Aas, Norway

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BMC Bioinformatics 2012, 13:97  doi:10.1186/1471-2105-13-97

Published: 14 May 2012



Gene finding is a complicated procedure that encapsulates algorithms for coding sequence modeling, identification of promoter regions, issues concerning overlapping genes and more. In the present study we focus on coding sequence modeling algorithms; that is, algorithms for identification and prediction of the actual coding sequences from genomic DNA. In this respect, we promote a novel multivariate method known as Canonical Powered Partial Least Squares (CPPLS) as an alternative to the commonly used Interpolated Markov model (IMM). Comparisons between the methods were performed on DNA, codon and protein sequences with highly conserved genes taken from several species with different genomic properties.


The multivariate CPPLS approach classified coding sequence substantially better than the commonly used IMM on the same set of sequences. We also found that the use of CPPLS with codon representation gave significantly better classification results than both IMM with protein (p < 0.001) and with DNA (p < 0.001). Further, although the mean performance was similar, the variation of CPPLS performance on codon representation was significantly smaller than for IMM (p < 0.001).


The performance of coding sequence modeling can be substantially improved by using an algorithm based on the multivariate CPPLS method applied to codon or DNA frequencies.