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

Creanza TM, Horner DS, D'Addabbo A, Maglietta R, Mignone F, Ancona N, Pesole G - BMC Bioinformatics (2009)

Bottom Line: We found that the most discriminant feature was a comparative measure indicating the proportion of synonymous nucleotide substitutions per synonymous sites.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 <or= 0.05).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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Istituto di Studi sui Sistemi Intelligenti per l'Automazione, CNR, Via Amendola 122/D-I, Bari, Italy. creanza@ba.issia.cnr.it

ABSTRACT

Background: 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.

Results: 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

Conclusion: 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.

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The reading frame conservation. This figure shows the reading frame conservation test by M. Kellis et al. (2004).
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Figure 1: The reading frame conservation. This figure shows the reading frame conservation test by M. Kellis et al. (2004).

Mentions: (see Figure 1). In detail, we labeled the nucleotides of the first sequence (skipping the gaps) by their codon position beginning with the first one, and labeled the nucleotides of the second sequence beginning once with the first, once with the second and once with third codon position. Then we counted the percentage of nucleotides equally labeled in each pairwise comparison RFCi and selected the maximum value RFC [6]. We expected that this value was greater in the coding sequences data set than in the non coding one.


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

Creanza TM, Horner DS, D'Addabbo A, Maglietta R, Mignone F, Ancona N, Pesole G - BMC Bioinformatics (2009)

The reading frame conservation. This figure shows the reading frame conservation test by M. Kellis et al. (2004).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2697643&req=5

Figure 1: The reading frame conservation. This figure shows the reading frame conservation test by M. Kellis et al. (2004).
Mentions: (see Figure 1). In detail, we labeled the nucleotides of the first sequence (skipping the gaps) by their codon position beginning with the first one, and labeled the nucleotides of the second sequence beginning once with the first, once with the second and once with third codon position. Then we counted the percentage of nucleotides equally labeled in each pairwise comparison RFCi and selected the maximum value RFC [6]. We expected that this value was greater in the coding sequences data set than in the non coding one.

Bottom Line: We found that the most discriminant feature was a comparative measure indicating the proportion of synonymous nucleotide substitutions per synonymous sites.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 <or= 0.05).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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Istituto di Studi sui Sistemi Intelligenti per l'Automazione, CNR, Via Amendola 122/D-I, Bari, Italy. creanza@ba.issia.cnr.it

ABSTRACT

Background: 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.

Results: 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

Conclusion: 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.

Show MeSH