This article is part of the supplement: European Molecular Biology Network (EMBnet) Conference 2008: 20th Anniversary Celebration. Leading applications and technologies in bioinformatics

Open Access Proceedings

Statistical assessment of discriminative features for protein-coding and non coding cross-species conserved sequence elements

Teresa M Creanza12, David S Horner2, Annarita D'Addabbo1, Rosalia Maglietta1, Flavio Mignone3, Nicola Ancona1* and Graziano Pesole45

Author Affiliations

1 Istituto di Studi sui Sistemi Intelligenti per l'Automazione, CNR, Via Amendola 122/D-I, Bari, Italy

2 Dipartimento di Scienze Biomolecolari e Biotecnologie, Università di Milano, Via Celoria 26, Milano, Italy

3 Dipartimento di Chimica Strutturale e Stereochimica Inorganica, Università di Milano, Via Celoria 26, Milano, Italy

4 Dipartimento di Biochimica e Biologia Molecolare, Università di Bari, Via E. Orabona 4, Bari, Italy

5 Istituto Tecnologie Biomediche, CNR, via Amendola 122/D, Bari, Italy

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BMC Bioinformatics 2009, 10(Suppl 6):S2  doi:10.1186/1471-2105-10-S6-S2

Published: 16 June 2009



The identification of protein coding elements in sets of mammalian conserved elements is one of the major challenges in the current molecular biology research. Many features have been proposed for automatically distinguishing coding and non coding conserved sequences, making so necessary a systematic statistical assessment of their differences. A comprehensive study should be composed of an association study, i.e. a comparison of the distributions of the features in the two classes, and a prediction study in which the prediction accuracies of classifiers trained on single and groups of features are analyzed, conditionally to the compared species and to the sequence lengths.


In this paper we compared distributions of a set of comparative and non comparative features and evaluated the prediction accuracy of classifiers trained for discriminating sequence elements conserved among human, mouse and rat species. The association study showed that the analyzed features are statistically different in the two classes. In order to study the influence of the sequence lengths on the feature performances, a predictive study was performed on different data sets composed of coding and non coding alignments in equal number and equally long with an ascending average length. We found that the most discriminant feature was a comparative measure indicating the proportion of synonymous nucleotide substitutions per synonymous sites. Moreover, linear discriminant classifiers trained by using comparative features in general outperformed classifiers based on intrinsic ones. Finally, the prediction accuracy of classifiers trained on comparative features increased significantly by adding intrinsic features to the set of input variables, independently on sequence length (Kolmogorov-Smirnov P-value ≤ 0.05).


We observed distinct and consistent patterns for individual and combined use of comparative and intrinsic classifiers, both with respect to different lengths of sequences/alignments and with respect to error rates in the classification of coding and non-coding elements. In particular, we noted that comparative features tend to be more accurate in the classification of coding sequences – this is likely related to the fact that such features capture deviations from strictly neutral evolution expected as a consequence of the characteristics of the genetic code.