N4: A precise and highly sensitive promoter predictor using neural network fed by nearest neighbors

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  • Askary Amjad
    Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics and COE in Biomathematics, University of Tehran Department of Biotechnology, College of Science, University of Tehran
  • Masoudi-Nejad Ali
    Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics and COE in Biomathematics, University of Tehran
  • Sharafi Roozbeh
    Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics and COE in Biomathematics, University of Tehran
  • Mizbani Amir
    Department of Biotechnology, College of Science, University of Tehran
  • Parizi Sobhan Naderi
    Department of Biotechnology, College of Science, University of Tehran
  • Purmasjedi Malihe
    Department of Biotechnology, College of Science, University of Tehran

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Promoters, the genomic regions proximal to the transcriptional start sites (TSSs) play pivotal roles in determining the rate of transcription initiation by serving as direct docking platforms for the RNA polymerase II complex. In the post-genomic era, correct gene prediction has become one of the biggest challenges in genome annotation. Species-independent promoter prediction tools could also be useful in meta-genomics, since transcription data will not be available for micro-organisms which are not cultivated. Promoter prediction in prokaryotic genomes presents unique challenges owing to their organizational properties. Several methods have been developed to predict the promoter regions of genomes in prokaryotes, including algorithms for recognition of sequence motifs, artificial neural networks, and algorithms based on genome’s structure. However, none of them satisfies both criteria of sensitivity and precision. In this work, we present a modified artificial neural network fed by nearest neighbors based on DNA duplex stability, named N4, which can predict the transcription start sites of Escherichia coli with sensitivity and precision both above 94%, better than most of the existed algorithms.<br>

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