tailieunhanh - Báo cáo sinh học: "Prediction of plant promoters based on hexamers and random triplet pair analysis"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Prediction of plant promoters based on hexamers and random triplet pair analysis. | Azad et al. Algorithms for Molecular Biology 2011 6 19 http content 6 1 19 AMR ALGORITHMS FOR MOLECULAR BIOLOGY RESEARCH Open Access Prediction of plant promoters based on hexamers and random triplet pair analysis A K M Azad1 Saima Shahid2 Nasimul Noman3 and Hyunju Lee1 Abstract Background With an increasing number of plant genome sequences it has become important to develop a robust computational method for detecting plant promoters. Although a wide variety of programs are currently available prediction accuracy of these still requires further improvement. The limitations of these methods can be addressed by selecting appropriate features for distinguishing promoters and non-promoters. Methods In this study we proposed two feature selection approaches based on hexamer sequences the Frequency Distribution Analyzed Feature Selection Algorithm FDAFSA and the Random Triplet Pair Feature Selecting Genetic Algorithm RTPFSGA . In FDAFSA adjacent triplet-pairs hexamer sequences were selected based on the difference in the frequency of hexamers between promoters and non-promoters. In RTPFSGA random triplet-pairs RTPs were selected by exploiting a genetic algorithm that distinguishes frequencies of non-adjacent triplet pairs between promoters and non-promoters. Then a support vector machine SVM a nonlinear machinelearning algorithm was used to classify promoters and non-promoters by combining these two feature selection approaches. We referred to this novel algorithm as PromoBot. Results Promoter sequences were collected from the PlantProm database. Non-promoter sequences were collected from plant mRNA rRNA and tRNA of PlantGDB and plant miRNA of miRBase. Then in order to validate the proposed algorithm we applied a 5-fold cross validation test. Training data sets were used to select features based on FDAFSA and RTPFSGA and these features were used to train the SVM. We achieved 89 sensitivity and 86 specificity. Conclusions We compared our PromoBot algorithm .

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