BMC Bioinformatics | |
Prediction of uridine modifications in tRNA sequences | |
Bharat Panwar2  Gajendra PS Raghava1  | |
[1] Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India | |
[2] Present address: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA | |
关键词: tRNAmod; 5-methyl-uridine; Dihydrouridine; Pseudouridine; Uridine modifications; | |
Others : 1085677 DOI : 10.1186/1471-2105-15-326 |
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received in 2014-04-24, accepted in 2014-09-24, 发布年份 2014 | |
【 摘 要 】
Background
In past number of methods have been developed for predicting post-translational modifications in proteins. In contrast, limited attempt has been made to understand post-transcriptional modifications. Recently it has been shown that tRNA modifications play direct role in the genome structure and codon usage. This study is an attempt to understand kingdom-wise tRNA modifications particularly uridine modifications (UMs), as majority of modifications are uridine-derived.
Results
A three-steps strategy has been applied to develop an efficient method for the prediction of UMs. In the first step, we developed a common prediction model for all the kingdoms using a dataset from MODOMICS-2008. Support Vector Machine (SVM) based prediction models were developed and evaluated by five-fold cross-validation technique. Different approaches were applied and found that a hybrid approach of binary and structural information achieved highest Area under the curve (AUC) of 0.936. In the second step, we used newly added tRNA sequences (as independent dataset) of MODOMICS-2012 for the kingdom-wise prediction performance evaluation of previously developed (in the first step) common model and achieved performances between the AUC of 0.910 to 0.949. In the third and last step, we used different datasets from MODOMICS-2012 for the kingdom-wise individual prediction models development and achieved performances between the AUC of 0.915 to 0.987.
Conclusions
The hybrid approach is efficient not only to predict kingdom-wise modifications but also to classify them into two most prominent UMs: Pseudouridine (Y) and Dihydrouridine (D). A webserver called tRNAmod (http://crdd.osdd.net/raghava/trnamod/ webcite) has been developed, which predicts UMs from both tRNA sequences and whole genome.
【 授权许可】
2014 Panwar and Raghava; licensee BioMed Central Ltd.
【 预 览 】
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