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Eukaryotic and prokaryotic promoter prediction using hybrid approach

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Abstract

Promoters are modular DNA structures containing complex regulatory elements required for gene transcription initiation. Hence, the identification of promoters using machine learning approach is very important for improving genome annotation and understanding transcriptional regulation. In recent years, many methods have been proposed for the prediction of eukaryotic and prokaryotic promoters. However, the performances of these methods are still far from being satisfactory. In this article, we develop a hybrid approach (called IPMD) that combines position correlation score function and increment of diversity with modified Mahalanobis Discriminant to predict eukaryotic and prokaryotic promoters. By applying the proposed method to Drosophila melanogaster, Homo sapiens, Caenorhabditis elegans, Escherichia coli, and Bacillus subtilis promoter sequences, we achieve the sensitivities and specificities of 90.6 and 97.4% for D. melanogaster, 88.1 and 94.1% for H. sapiens, 83.3 and 95.2% for C. elegans, 84.9 and 91.4% for E. coli, as well as 80.4 and 91.3% for B. subtilis. The high accuracies indicate that the IPMD is an efficient method for the identification of eukaryotic and prokaryotic promoters. This approach can also be extended to predict other species promoters.

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Acknowledgments

The authors are grateful to the anonymous reviewers for their valuable suggestions and comments, which have led to the improvement of this article. This study was supported in part by the Fundamental Research Funds for the Central Universities (ZYGX2009J081) and the National Natural Science Foundation of China (61063016).

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  1. Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Hao Lin

  2. Laboratory of Theoretical Biophysics, School of Physical Sciences and Technology, Inner Mongolia University, Hohhot, 010021, China

    Qian-Zhong Li

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  1. Hao Lin
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Correspondence to Hao Lin.

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Lin, H., Li, QZ. Eukaryotic and prokaryotic promoter prediction using hybrid approach. Theory Biosci. 130, 91–100 (2011). https://doi.org/10.1007/s12064-010-0114-8

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