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. 2011;6(7):e21750.
doi: 10.1371/journal.pone.0021750. Epub 2011 Jul 15.

Prediction of drought-resistant genes in Arabidopsis thaliana using SVM-RFE

Yanchun Liang  1 , Fan Zhang, Juexin Wang, Trupti Joshi, Yan Wang, Dong Xu
Affiliations

Prediction of drought-resistant genes in Arabidopsis thaliana using SVM-RFE

Yanchun Liang et al. PLoS One. 2011.

Abstract

Background: Identifying genes with essential roles in resisting environmental stress rates high in agronomic importance. Although massive DNA microarray gene expression data have been generated for plants, current computational approaches underutilize these data for studying genotype-trait relationships. Some advanced gene identification methods have been explored for human diseases, but typically these methods have not been converted into publicly available software tools and cannot be applied to plants for identifying genes with agronomic traits.

Methodology: In this study, we used 22 sets of Arabidopsis thaliana gene expression data from GEO to predict the key genes involved in water tolerance. We applied an SVM-RFE (Support Vector Machine-Recursive Feature Elimination) feature selection method for the prediction. To address small sample sizes, we developed a modified approach for SVM-RFE by using bootstrapping and leave-one-out cross-validation. We also expanded our study to predict genes involved in water susceptibility.

Conclusions: We analyzed the top 10 genes predicted to be involved in water tolerance. Seven of them are connected to known biological processes in drought resistance. We also analyzed the top 100 genes in terms of their biological functions. Our study shows that the SVM-RFE method is a highly promising method in analyzing plant microarray data for studying genotype-phenotype relationships. The software is freely available with source code at http://ccst.jlu.edu.cn/JCSB/RFET/.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. LOOCV for twelve samples.
Figure 2
Figure 2. Algorithm flowchart for identifying drought-resistant genes.
Figure 3
Figure 3. The occurrence of selected 10 genes in the top-10 and top-30 lists when conducting 100 times of 6-CV.
The gene order is the same as that in Table 3.
See this image and copyright information in PMC

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