Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data
OBJECTIVES A number of published predictors are based on various algorithms and disordered protein sequence properties. Although many predictors have been published, the study of protein disordered region prediction is ongoing because different prediction methods can find different disordered regions in a protein sequence. METHODS Therefore we have used a new approach to find the more varying disordered regions for more efficient and accurate prediction of protein structures. In this study, we propose a novel approach called "emerging subsequence (ES) mining" without using the characteristics of the disordered protein. We first adapted the approach to generate emerging protein subsequences on public protein sequence data. Second, the disordered and ordered regions in a protein sequence were predicted by searching the generated emerging protein subsequence with a sliding window, which tends to overlap. Third, the scores of the overlapping regions were calculated based on support and growthrate values in both classes. Finally, the score of predicted regions in the target class were compared with the score of the source class, and the class having a higher score was selected. RESULTS In this experiment, disordered sequence data and ordered sequence data was extracted from DisProt 6.02 and PDB respectively and used as training data. The test data come from CASP 9 and CASP 10 where disordered and ordered regions are known. CONCLUSION Comparing with several published predictors, the results of the experiment show higher accuracy rates than with other existing methods.