A Novel method for similarity analysis and protein sub-cellular localization prediction

Motivation: Biological sequence was regarded as an important study by many biologists, because the sequence contains a large number of biological information, what is helpful for scientists' studies on biological cells, DNA and proteins. Currently, many researchers used the method based on prot...

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Bibliographic Details
Published inBioinformatics Vol. 26; no. 21; pp. 2678 - 2683
Main Authors Liao, Bo, Liao, Benyou, Sun, Xingming, Zeng, Qingguang
Format Journal Article
LanguageEnglish
Published Oxford Oxford University Press 01.11.2010
Subjects
Algorithms
Biological and medical sciences
Databases, Protein
Fundamental and applied biological sciences. Psychology
General aspects
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Proteins - analysis
Proteins - chemistry
Sequence Alignment - methods
Sequence Analysis, Protein - methods
Analysis method
Similarity
Localization
Analysis
Protein
Prediction
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Summary:Motivation: Biological sequence was regarded as an important study by many biologists, because the sequence contains a large number of biological information, what is helpful for scientists' studies on biological cells, DNA and proteins. Currently, many researchers used the method based on protein sequences in function classification, sub-cellular location, structure and functional site prediction, including some machine-learning methods. The purpose of this article, is to find a new way of sequence analysis, but more simple and effective. Results: According to the nature of 64 genetic codes, we propose a simple and intuitive 2D graphical expression of protein sequences. And based on this expression we give a new Euclidean-distance method to compute the distance of different sequences for the analysis of sequence similarity. This approach contains more sequence information. A typical phylogenetic tree constructed based on this method proved the effectiveness of our approach. Finally, we use this sequence-similarity-analysis method to predict protein sub-cellular localization, in the two datasets commonly used. The results show that the method is reasonable. Contact: dragonbw@163.com
Bibliography:To whom correspondence should be addressed.
istex:940FFCBF772EF865A4C662DA978AED66A2BD5890
ArticleID:btq521
ark:/67375/HXZ-RQPQV8NN-D
Associate Editor: John Quackenbush
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btq521