Self-organizing maps: a tool to ascertain taxonomic relatedness based on features derived from 16S rDNA sequence.

Exploitation of microbial wealth, of which almost 95% or more is still unexplored, is a growing need. The taxonomic placements of a new isolate based on phenotypic characteristics are now being supported by information preserved in the 16S rRNA gene. However, the analysis of 16S rDNA sequences retrieved from metagenome, by the available bioinformatics tools, is subject to limitations. In this study, the occurrences of nucleotide features in 16S rDNA sequences have been used to ascertain the taxonomic placement of organisms. The tetra- and penta-nucleotide features were extracted from the training data set of the 16S rDNA sequence, and was subjected to an artificial neural network (ANN) based tool known as self-organizing map (SOM), which helped in visualization of unsupervised classification. For selection of significant features, principal component analysis (PCA) or curvilinear component analysis (CCA) was applied. The SOM along with these techniques could discriminate the sample sequences with more than 90% accuracy, highlighting the relevance of features. To ascertain the confidence level in the developed classification approach, the test data set was specifically evaluated for Thiobacillus, with Acidiphilium, Paracocus and Starkeya, which are taxonomically reassigned. The evaluation proved the excellent generalization capability of the developed tool. The topology of genera in SOM supported the conventional chemo-biochemical classification reported in the Bergey manual.

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.

Optimum DNA curvature using a hybrid approach involving an artificial neural network and genetic algorithm.

In the present paper, a hybrid technique involving artificial neural network (ANN) and genetic algorithm (GA) has been proposed for performing modeling and optimization of complex biological systems. In this approach, first an ANN approximates (models) the nonlinear relationship(s) existing between its input and output example data sets. Next, the GA, which is a stochastic optimization technique, searches the input space of the ANN with a view to optimize the ANN output. The efficacy of this formalism has been tested by conducting a case study involving optimization of DNA curvature characterized in terms of the RL value. Using the ANN-GA methodology, a number of sequences possessing high RL values have been obtained and analyzed to verify the existence of features known to be responsible for the occurrence of curvature. A couple of sequences have also been tested experimentally. The experimental results validate qualitatively and also near-quantitatively, the solutions obtained using the hybrid formalism. The ANN-GA technique is a useful tool to obtain, ahead of experimentation, sequences that yield high RL values. The methodology is a general one and can be suitably employed for optimizing any other biological feature.

Analysis of DNA curvature using artificial neural networks.

MOTIVATION: Our aim is to utilize an artificial neural network (ANN) for the prediction of DNA curvature in terms of retardation anomaly. RESULTS: An ANN capturing the role of phasing, increased helix flexibility, run of poly(A) tracts and flanking base pair effects in determining the extent of DNA curvature has been developed. The network predictions validate the known experimental results and also explain how the base pairs other than ApA affect the curvature. The results suggest that ANN can be used as a model-free tool for studying DNA curvature. AVAILABILITY: The optimal weights and the procedure to compute the retardation anomaly value are available on request from the authors. CONTACT: bdk@ems. ncl.res.in

Analysis of transcription control signals using artificial neural networks.

The role of the upstream region in controlling the transcription efficiency of a gene is well established. However, the question of predicting the extent of gene expressed given the upstream region has so far remained unresolved. Using an artificial neural network (ANN) to capture the internal representation associated with the transcription control signal, the present work predicts the rate of mRNA synthesis based on the pattern contained in the upstream region. Further, the model has been used to predict the transcription efficiency for all possible single base mutations associated with the beta-globin promoter. The simulation results reveal that apart from the experimental observation that alpha-79G-A and -78G-A mutation increases the efficiency of transcription, mutation in these regions by C or T also causes an increase in transcription. Furthermore the simulation results verify that mutations in the conserved region, in general, decrease the transcriptional efficiency. However, the results also show that certain sequence elements, when mutated, either cause a marginal increase in the level of transcription or have no effect on transcription levels. The simulation results can be used as a guide in designing mutation experiments since an a priori estimate of the possible outcome of a mutation can be obtained.

Application of artificial neural networks for prokaryotic transcription terminator prediction.

Artificial neural networks (ANN) to predict terminator sequences, based on a feed-forward architecture and trained using the error back propagation technique, have been developed. The network uses two different methods for coding nucleotide sequences. In one the nucleotide bases are coded in binary while the other uses the electron-ion interaction potential values (EIIP) of the nucleotide bases. The latter strategy is new, property based and substantially reduces the network size. The prediction capacity of the artificial neural network using both coding strategies is more than 95%.

Steroidogenesis in sheep ovarian antral follicles in culture: time course study and supplementation with a precursor.

The present study was designed to explore the steroidogenic responsiveness of ovine antral follicles of different sizes when cultured for varying time-periods with different doses of pregnenolone. Antral follicles of different sizes were isolated from sheep ovaries and cultured in medium 199 with or without pregnenolone in the presence or absence of FSH for 1, 6, 10, and 24 hours at 37 C. The levels of progesterone and estradiol in the spent medium were estimated. In the absence of pregnenolone, steroid production by the follicles did not increase significantly beyond 1 hour of culture. However, in the presence of pregnenolone there was a significant increase in progesterone production at 6, 10, and 24 hours of culture compared to controls. Estradiol levels were unaffected. Addition of FSH in combination with pregnenolone failed to increase progesterone or estradiol levels beyond that seen with pregnenolone alone. These data demonstrate that short-term incubation of follicles is sufficient for the secretion of steroids into the culture medium and supplementation of the culture medium with an immediate precursor is essential for optimal steroidogenesis in vitro.