Analysis and prediction of translation rate based on sequence and functional features of the mRNA.

Protein concentrations depend not only on the mRNA level, but also on the translation rate and the degradation rate. Prediction of mRNA's translation rate would provide valuable information for in-depth understanding of the translation mechanism and dynamic proteome. In this study, we developed...

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Autores principales:	Tao Huang, Sibao Wan, Zhongping Xu, Yufang Zheng, Kai-Yan Feng, Hai-Peng Li, Xiangyin Kong, Yu-Dong Cai
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3bcabe1c452f4cb092a278cb9c6e9734
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spelling	oai:doaj.org-article:3bcabe1c452f4cb092a278cb9c6e97342021-11-18T07:00:43ZAnalysis and prediction of translation rate based on sequence and functional features of the mRNA.1932-620310.1371/journal.pone.0016036https://doaj.org/article/3bcabe1c452f4cb092a278cb9c6e97342011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21253596/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Protein concentrations depend not only on the mRNA level, but also on the translation rate and the degradation rate. Prediction of mRNA's translation rate would provide valuable information for in-depth understanding of the translation mechanism and dynamic proteome. In this study, we developed a new computational model to predict the translation rate, featured by (1) integrating various sequence-derived and functional features, (2) applying the maximum relevance & minimum redundancy method and incremental feature selection to select features to optimize the prediction model, and (3) being able to predict the translation rate of RNA into high or low translation rate category. The prediction accuracies under rich and starvation condition were 68.8% and 70.0%, respectively, evaluated by jackknife cross-validation. It was found that the following features were correlated with translation rate: codon usage frequency, some gene ontology enrichment scores, number of RNA binding proteins known to bind its mRNA product, coding sequence length, protein abundance and 5'UTR free energy. These findings might provide useful information for understanding the mechanisms of translation and dynamic proteome. Our translation rate prediction model might become a high throughput tool for annotating the translation rate of mRNAs in large-scale.Tao HuangSibao WanZhongping XuYufang ZhengKai-Yan FengHai-Peng LiXiangyin KongYu-Dong CaiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 1, p e16036 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tao Huang Sibao Wan Zhongping Xu Yufang Zheng Kai-Yan Feng Hai-Peng Li Xiangyin Kong Yu-Dong Cai Analysis and prediction of translation rate based on sequence and functional features of the mRNA.
description	Protein concentrations depend not only on the mRNA level, but also on the translation rate and the degradation rate. Prediction of mRNA's translation rate would provide valuable information for in-depth understanding of the translation mechanism and dynamic proteome. In this study, we developed a new computational model to predict the translation rate, featured by (1) integrating various sequence-derived and functional features, (2) applying the maximum relevance & minimum redundancy method and incremental feature selection to select features to optimize the prediction model, and (3) being able to predict the translation rate of RNA into high or low translation rate category. The prediction accuracies under rich and starvation condition were 68.8% and 70.0%, respectively, evaluated by jackknife cross-validation. It was found that the following features were correlated with translation rate: codon usage frequency, some gene ontology enrichment scores, number of RNA binding proteins known to bind its mRNA product, coding sequence length, protein abundance and 5'UTR free energy. These findings might provide useful information for understanding the mechanisms of translation and dynamic proteome. Our translation rate prediction model might become a high throughput tool for annotating the translation rate of mRNAs in large-scale.
format	article
author	Tao Huang Sibao Wan Zhongping Xu Yufang Zheng Kai-Yan Feng Hai-Peng Li Xiangyin Kong Yu-Dong Cai
author_facet	Tao Huang Sibao Wan Zhongping Xu Yufang Zheng Kai-Yan Feng Hai-Peng Li Xiangyin Kong Yu-Dong Cai
author_sort	Tao Huang
title	Analysis and prediction of translation rate based on sequence and functional features of the mRNA.
title_short	Analysis and prediction of translation rate based on sequence and functional features of the mRNA.
title_full	Analysis and prediction of translation rate based on sequence and functional features of the mRNA.
title_fullStr	Analysis and prediction of translation rate based on sequence and functional features of the mRNA.
title_full_unstemmed	Analysis and prediction of translation rate based on sequence and functional features of the mRNA.
title_sort	analysis and prediction of translation rate based on sequence and functional features of the mrna.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/3bcabe1c452f4cb092a278cb9c6e9734
work_keys_str_mv	AT taohuang analysisandpredictionoftranslationratebasedonsequenceandfunctionalfeaturesofthemrna AT sibaowan analysisandpredictionoftranslationratebasedonsequenceandfunctionalfeaturesofthemrna AT zhongpingxu analysisandpredictionoftranslationratebasedonsequenceandfunctionalfeaturesofthemrna AT yufangzheng analysisandpredictionoftranslationratebasedonsequenceandfunctionalfeaturesofthemrna AT kaiyanfeng analysisandpredictionoftranslationratebasedonsequenceandfunctionalfeaturesofthemrna AT haipengli analysisandpredictionoftranslationratebasedonsequenceandfunctionalfeaturesofthemrna AT xiangyinkong analysisandpredictionoftranslationratebasedonsequenceandfunctionalfeaturesofthemrna AT yudongcai analysisandpredictionoftranslationratebasedonsequenceandfunctionalfeaturesofthemrna
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Analysis and prediction of translation rate based on sequence and functional features of the mRNA.

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