Identification of ribosome binding sites in Escherichia coli using neural network models

Artificial neural networks for prediction of mycobacterial promoter sequences

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.

ANN modeling of DNA sequences: new strategies using DNA shape code

Two new encoding strategies, namely, wedge and twist codes, which are based on the DNA helical parameters, are introduced to represent DNA sequences in artificial neural network (ANN)-based modeling of biological systems. The performance of the new coding strategies has been evaluated by conducting three case studies involving mapping (modeling) and classification applications of ANNs. The proposed coding schemes have been compared rigorously and shown to outperform the existing coding strategies especially in situations wherein limited data are available for building the ANN models.

Starts of bacterial genes: estimating the reliability of computer predictions

Exact mapping of gene starts is an important problem in the computer-assisted functional analysis of newly sequenced prokaryotic genomes. We describe an algorithm for finding ribosomal binding sites without a learning sample. This algorithm is particularly useful for analysis of genomes with little or no experimentally mapped genes. There is a clear correlation between the ribosomal binding site (RBS) properties of a given genome and the potential gene start prediction accuracy. This correlation is of considerable predictive power and may be useful for estimating the expected success of future genome analysis efforts. We also demonstrate that the RBS properties depend on the phylogenetic position of a genome.

Artificial neural networks for computer-based molecular design

The theory of artificial neural networks is briefly reviewed focusing on supervised and unsupervised techniques which have great impact on current chemical applications. An introduction to molecular descriptors and representation schemes is given. In addition, worked examples of recent advances in this field are highlighted and pioneering publications are discussed. Applications of several types of artificial neural networks to compound classification, modelling of structure-activity relationships, biological target identification, and feature extraction from biopolymers are presented and compared to other techniques. Advantages and limitations of neural networks for computer-aided molecular design and sequence analysis are discussed.

Evaluating the effectiveness of sequence analysis algorithms using measures of relevant information

Given vast quantities of molecular sequence data, and numerous different algorithms designed to discover, diagnose or model biologically interesting features in sequences, how is it possible to make objective evaluations of the diagnostic effectiveness of these algorithms and robust assessments of their relative strengths and limitations? An approach to this relatively neglected question is developed here, which is based on information measures of the diagnostic efficiency of different methods. From output lists of a procedure such as a database search, "relevance weights" are assigned that encode, for each sequence listed, the level of associated scientific evidence implicating that sequence as an example of a feature of interest. Relevance weights may be derived, following systematic protocols, from expert human judgement or, in principle, by automated information retrieval from electronic resources. Practical applications of this approach to algorithm assessment and development and parameter choice are demonstrated with examples of automated sequence motif modeling for the DNA-binding helix-turn-helix motif and the guanine exchange factor protein domain. The combined use of relevance weights and information measures appears to offer promising advantages over ROC analysis and may be generally applicable to diagnostic evaluation.

Artificial neural networks for molecular sequence analysis

Artificial neural networks provide a unique computing architecture whose potential has attracted interest from researchers across different disciplines. As a technique for computational analysis, neural network technology is very well suited for the analysis of molecular sequence data. It has been applied successfully to a variety of problems, ranging from gene identification, to protein structure prediction and sequence classification. This article provides an overview of major neural network paradigms, discusses design issues, and reviews current applications in DNA/RNA and protein sequence analysis.

Calliper randomization: an artificial neural network based analysis of E. coli ribosome binding sites

An artificial neural network based approach has been used in analyzing the translation initiation region of E. coli. The approach is based on using a trained network capable of recognizing a particular region and presenting the network with randomized calliper inputs of the true sequence. The network responds with an error when the regions which have been the main source of knowledge are randomized. Analysis of the E. coli ribosome binding sites using this approach reveal that the initiation codon and the Shine/Dalgarno sequence which are known to be important for translation initiation are also important in imparting knowledge to the network. Further, selectively changing the usually occurring initiation codon AUG, to GUG, UUG and AUU, which occur less frequently, decreases the network performance in accordance with the frequency of their occurrence. This approach can be used as a general method to derive consensus.