title: Artificial neural networks for prediction of mycobacterial promoter sequences.
abstract: A multilayered feed-forward ANN architecture trained using the error-back-propagation (EBP) algorithm has been developed for predicting whether a given nucleotide sequence is a mycobacterial promoter sequence. Owing to the high prediction capability ( congruent with 97%) of the developed network model, it has been further used in conjunction with the caliper randomization (CR) approach for determining the structurally/functionally important regions in the promoter sequences. The results obtained thereby indicate that: (i) upstream region of -35 box, (ii) -35 region, (iii) spacer region and, (iv) -10 box, are important for mycobacterial promoters. The CR approach also suggests that the -38 to -29 region plays a significant role in determining whether a given sequence is a mycobacterial promoter. In essence, the present study establishes ANNs as a tool for predicting mycobacterial promoter sequences and determining structurally/functionally important sub-regions therein.
authors.affiliation: Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan. rupa_kalate@bio.keio.ac.jp
authors.forename: Rupali N
authors.initials: RN
authors.lastname: Kalate
authors.forename: Sanjeev S
authors.initials: SS
authors.lastname: Tambe
authors.forename: Bhaskar D
authors.initials: BD
authors.lastname: Kulkarni
mesh_terms: D001483:Base Sequence; D005798:Genes, Bacterial / Q000235:genetics; D008969:Molecular Sequence Data; D009161:Mycobacterium / Q000235:genetics*; D016571:Neural Networks, Computer; D011401:Promoter Regions, Genetic; D012045:Regulatory Sequences, Nucleic Acid; D017422:Sequence Analysis, DNA