Prediction of uridine modifications in tRNA sequences

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 struct...

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Autores principales: Panwar, Bharat, Raghava, Gajendra PS
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4287530/
https://www.ncbi.nlm.nih.gov/pubmed/25272949
http://dx.doi.org/10.1186/1471-2105-15-326
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author Panwar, Bharat
Raghava, Gajendra PS
author_facet Panwar, Bharat
Raghava, Gajendra PS
author_sort Panwar, Bharat
collection PubMed
description 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/) has been developed, which predicts UMs from both tRNA sequences and whole genome. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2105-15-326) contains supplementary material, which is available to authorized users.
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spelling pubmed-42875302015-01-09 Prediction of uridine modifications in tRNA sequences Panwar, Bharat Raghava, Gajendra PS BMC Bioinformatics Research Article 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/) has been developed, which predicts UMs from both tRNA sequences and whole genome. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2105-15-326) contains supplementary material, which is available to authorized users. BioMed Central 2014-10-02 /pmc/articles/PMC4287530/ /pubmed/25272949 http://dx.doi.org/10.1186/1471-2105-15-326 Text en © Panwar and Raghava; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Panwar, Bharat
Raghava, Gajendra PS
Prediction of uridine modifications in tRNA sequences
title Prediction of uridine modifications in tRNA sequences
title_full Prediction of uridine modifications in tRNA sequences
title_fullStr Prediction of uridine modifications in tRNA sequences
title_full_unstemmed Prediction of uridine modifications in tRNA sequences
title_short Prediction of uridine modifications in tRNA sequences
title_sort prediction of uridine modifications in trna sequences
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4287530/
https://www.ncbi.nlm.nih.gov/pubmed/25272949
http://dx.doi.org/10.1186/1471-2105-15-326
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