Prediction of auxin response elements based on data fusion in Arabidopsis thaliana.

The plant hormone "auxin" is a key regulator of plant development and environmental responses. Many genes in Arabidopsis thaliana are known to be up-regulated in response to auxin. Auxin response factors activate or repress the expression of genes by binding at their promoter regions within auxin response elements (AuxRE) that are key regulatory cis-acting motives. Therefore, the identification of auxin-response elements is among the most important issues to understand the auxin regulation mechanisms. Thus, searching the TGTCTC motif is an unreliable method to identify AuxRE because many AuxRE variants may also be functional. In the present study, we perform an In-silico prediction of AuxREs in promoters of primary auxin responsive genes. We exploit microarray data of auxin response in Arabidopsis thaliana seedlings, in order to provide biological annotation to AuxRE. We apply a data fusion method based on the combined use of evidence theory and fuzzy sets to scan upstream sequences of response genes.

Application of data fusion modeling for the prediction of auxin response elements in Zea mays for food security purposes.

Food security will be affected by climate change worldwide, particularly in the developingworld, where the most important food products originate from plants. Plants are often exposed to environmental stresses that may affect their growth, development, yield, and foodquality. Auxin is a hormone that plays a critical role in improving plants' tolerance of environmental conditions. Auxin controls the expression of many stress-responsive genes inplants by interacting with specific cis-regulatory elements called auxin-responsive elements (AuxREs). In this work, we performed an in silico prediction of AuxREs in promotersof five auxin-responsive genes in Zea mays. We applied a data fusion approach based onthe combined use of Dempster-Shafer evidence theory and fuzzy sets. Auxin has a directimpact on cell membrane proteins. The short-term auxin response may be represented bythe regulation of transmembrane gene expression. The detection of an AuxRE in the promoter of prolyl oligopeptidase (POP) in Z. mays and the 3-fold overexpression of this geneunder auxin treatment for 30 min indicated the role of POP in maize auxin response. POP isregulated by auxin to perform stress adaptation. In addition, the detection of two AuxRETGTCTC motifs in the upstream sequence of the bx1 gene suggests that bx1 can be regulated by auxin. Auxin may also be involved in the regulation of dehydration-responsive element-binding and some members of the protein kinase superfamily.

An Evidence Theory and Fuzzy Logic Combined Approach for the Prediction of Potential ARF-Regulated Genes in Quinoa.

Quinoa constitutes among the tolerant plants to the challenging and harmful abiotic environmental factors. Quinoa was selected as among the model crops destined for bio-saline agriculture that could contribute to the staple food security for an ever-growing worldwide population under various climate change scenarios. The auxin response factors (ARFs) constitute the main contributors in the plant adaptation to severe environmental conditions. Thus, the determination of the ARF-binding sites represents the major step that could provide promising insights helping in plant breeding programs and improving agronomic traits. Hence, determining the ARF-binding sites is a challenging task, particularly in species with large genome sizes. In this report, we present a data fusion approach based on Dempster-Shafer evidence theory and fuzzy set theory to predict the ARF-binding sites. We then performed an "In-silico" identification of the ARF-binding sites in Chenopodium quinoa. The characterization of some known pathways implicated in the auxin signaling in other higher plants confirms our prediction reliability. Furthermore, several pathways with no or little available information about their functions were identified to play important roles in the adaptation of quinoa to environmental conditions. The predictive auxin response genes associated with the detected ARF-binding sites may certainly help to explore the biological roles of some unknown genes newly identified in quinoa.

Dempster-Shafer Theory for the Prediction of Auxin-Response Elements (AuxREs) in Plant Genomes.

Auxin is a major regulator of plant growth and development; its action involves transcriptional activation. The identification of Auxin-response element (AuxRE) is one of the most important issues to understand the Auxin regulation of gene expression. Over the past few years, a large number of motif identification tools have been developed. Despite these considerable efforts provided by computational biologists, building reliable models to predict regulatory elements has still been a difficult challenge. In this context, we propose in this work a data fusion approach for the prediction of AuxRE. Our method is based on the combined use of Dempster-Shafer evidence theory and fuzzy theory. To evaluate our model, we have scanning the DORNRÖSCHEN promoter by our model. All proven AuxRE present in the promoter has been detected. At the 0.9 threshold we have no false positive. The comparison of the results of our model and some previous motifs finding tools shows that our model can predict AuxRE more successfully than the other tools and produce less false positive. The comparison of the results before and after combination shows the importance of Dempster-Shafer combination in the decrease of false positive and to improve the reliability of prediction. For an overall evaluation we have chosen to present the performance of our approach in comparison with other methods. In fact, the results indicated that the data fusion method has the highest degree of sensitivity (Sn) and Positive Predictive Value (PPV).