A novel method for high accuracy sumoylation site prediction from protein sequences.

A novel method for high accuracy sumoylation site prediction from protein sequences. Research Abstract Details 

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A novel method for high accuracy sumoylation site prediction from protein sequences. Abstract Text:

Jialin Xu,Yun He,Boqin Qiang,Jiangang Yuan,Xiaozhong Peng,Xian-Ming Pan,

BACKGROUND: Protein sumoylation is an essential dynamic, reversible post translational modification that plays a role in dozens of cellular activities, especially the regulation of gene expression and the maintenance of genomic stability. Currently, the complexities of sumoylation mechanism can not be perfectly solved by experimental approaches. In this regard, computational approaches might represent a promising method to direct experimental identification of sumoylation sites and shed light on the understanding of the reaction mechanism. RESULTS: Here we presented a statistical method for sumoylation site prediction. A 5-fold cross validation test over the experimentally identified sumoylation sites yielded excellent prediction performance with correlation coefficient, specificity, sensitivity and accuracy equal to 0.6364, 97.67%, 73.96% and 96.71% respectively. Additionally, the predictor performance is maintained when high level homologs are removed. CONCLUSION: By using a statistical method, we have developed a new SUMO site prediction method - SUMOpre, which has shown its great accuracy with correlation coefficient, specificity, sensitivity and accuracy.

A novel method for high accuracy sumoylation site prediction from protein sequences. Publishing Authors By Initials

J Xu,Y He,B Qiang,J Yuan,X Peng,XM Pan,

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A novel method for high accuracy sumoylation site prediction from protein sequences. Journal Published:

PUBLICATION TYPE: Research Support, Non-U.S. Gov

Journal: BMC bioinformatics

VOLUME: 9

Page Numbers: 8

Journal Abbreviation: BMC Bioinformatics

ISSN: 1471-2105

DAY: 8

MONTH: 01

YEAR: 2008

A novel method for high accuracy sumoylation site prediction from protein sequences. Information

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LANGUAGE: eng

NlmUniqueID: 100965194

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Grant and Affiliation Information for A novel method for high accuracy sumoylation site prediction from protein sequences.

AFFILIATION: The Key Laboratory of Bioinformatics, Ministry of Education, China, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing, 100084, China. jialinx@gmail.com

Country: England

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MEDLINETA: BMC Bioinformatics

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