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Theoretical and Applied Genetics

, Volume 119, Issue 1, pp 151–164 | Cite as

Single feature polymorphisms between two rice cultivars detected using a median polish method

  • Weibo Xie
  • Ying Chen
  • Gang Zhou
  • Lei Wang
  • Chengjun Zhang
  • Jianwei Zhang
  • Jinghua Xiao
  • Tong Zhu
  • Qifa Zhang
Original Paper

Abstract

Expression levels measured in microarrays of oligonucleotide probes have now been adapted as a high throughput approach for identifying DNA sequence variation between genotypes, referred to as single feature polymorphisms (SFPs). Although there have been increasing interests in this approach, there is still need for improving the algorithm in order to achieve high sensitivity and specificity especially with complex genome and large datasets, while maintaining optimal computational performance. We obtained microarray datasets for expression profiles of two rice cultivars and adapted a median polish method to detect SFPs. The analysis identified 6,655 SFPs between two the rice varieties representing 3,131 rice unique genes. We showed that the median polish method has the advantage of avoiding fitting complex linear models thus can be used to analyze complex transcriptome datasets like the ones in this study. The method is also superior in sensitivity, accuracy and computing time requirement compared with two previously used methods. A comparison with data from a resequencing project indicated that 75.6% of the SFPs had SNP supports in the probe regions. Further comparison revealed that SNPs in sequences immediately flanking the probes also had contributions to the detection of SFPs in cases where the probes and the targets had perfectly matched sequences. It was shown that differences in minimum free energies caused by flanking SNPs, which may change the stability of RNA secondary structure, may partly explain the SFPs as detected. These SFPs may facilitate gene discovery in future studies.

Keywords

Minimum Free Energy Median Polish Single Feature Polymorphism Flank SNPs Hybridization Affinity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank Dr. James Ronald and Dr. Rachel B. Brem for help and suggestions in yeast data. This work was supported by grants from the National Special Key Project of China on Functional Genomics of Major Plants and Animals, and the National Natural Science Foundation of China.

Supplementary material

122_2009_1025_MOESM1_ESM.pdf (4 kb)
Supplementary file 1 is a figure showing the effect of SNP position for SFP detection. The description of the supplementary file 1: The x axis shows the distance of SNP position from the edge of the 25 mer probe and the y axis is the false negative rate of SNPs. (PDF 4 kb)
122_2009_1025_MOESM2_ESM.pdf (22 kb)
Supplementary file 2 is a figure showing the distribution of ΔG RM – BY , indicated the influence of minimum free energy of RNA towards the binding affinity. The description of the supplementary file 2: The x axis shows the difference of minimum RNA free energy of RM minus BY (ΔG RM - BY ). Grey lines indicate the 50% quantile (0.601 kJ) of all 3,439 ΔG RM - BY . The distribution of all ΔG is demonstrated in grey. The distribution of subset sequence pairs of which the corresponding probes have higher residuals of median polish to RM targets than to BY (\( \bar{E}_{RM} > \bar{E}_{BY} \), BH adjusted P value <0.5) is denoted in red while the distribution of probes with lower residuals to RM targets \( \left( {\bar{E}_{RM} > \bar{E}_{BY} } \right) \) is showed in green. The enrichment of positive ΔG RM - BY in the \( \bar{E}_{RM} > \bar{E}_{BY} \) group and negative ΔG RM - BY in the \( \bar{E}_{RM} > \bar{E}_{BY} \) group provide an instinct consideration that the minimum free energy of RNA is correlated positively to the residuals thus is correlated positively to binding affinity. (PDF 22 kb)

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Weibo Xie
    • 1
  • Ying Chen
    • 1
  • Gang Zhou
    • 1
  • Lei Wang
    • 1
  • Chengjun Zhang
    • 1
  • Jianwei Zhang
    • 1
  • Jinghua Xiao
    • 1
  • Tong Zhu
    • 1
  • Qifa Zhang
    • 1
  1. 1.National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhanChina

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