Data mining and influential analysis of gene expression data for plant resistance gene identification in tomato (Solanum lycopersicum)

Abstract

Background: Molecular mechanisms of plant–pathogen interactions have been studied thoroughly but much about them is still unknown. A better understanding of these mechanisms and the detection of new resistance genes can improve crop production and food supply. Extracting this knowledge from available genomic data is a challenging task. Results: Here, we evaluate the usefulness of clustering, data-mining and regression to identify potential new resistance genes. Three types of analyses were conducted separately over two conditions, tomatoes inoculated with Phytophthora infestans and not inoculated tomatoes. Predictions for 10 new resistance genes obtained by all applied methods were selected as being the most reliable and are therefore reported as potential resistance genes. Conclusion: Application of different statistical analyses to detect potential resistance genes reliably has shown to conduct interesting results that improve knowledge on molecular mechanisms of plant resistance to pathogens.

Keywords

• Classification
• Data mining
• Functional gene prediction
• GEE models
• Gene expression data
• Plant immunity genes