DocumentCode
170588
Title
Similarity analysis of DNA sequences based on k-word
Author
Yingxin Hu ; Zhaohui Qi ; Lijuan Zheng ; Wenfeng Zhou
Author_Institution
Coll. of Inf. Sci. & Technol., Shijiazhuang Tiedao Univ., Shijiazhuang, China
fYear
2014
fDate
16-18 May 2014
Firstpage
621
Lastpage
625
Abstract
Based on the position information and numbers of k-words, a method is proposed to compare genetic sequences and infer evolutionary relationship. In this study a characteristic vector whose elements are the average distances from the beginning of the k-word is introduced to represent DNA sequences. The approach has one to one correspondence between DNA sequences and vectors. In the end, we choose 48 HEV (Hepatitis E virus) and some mammalian species as test datasets to reconstruct the phylogenetic trees based on Euclidean distance measure. With comparison to other methods, the results show that this method is efficient and suitable for similarity analysis.
Keywords
DNA; biology computing; genetics; microorganisms; DNA sequences; Euclidean distance measure; HEV; Hepatitis E virus; characteristic vector; evolutionary relationship; genetic sequences; k-words; mammalian species; phylogenetic trees; position information; similarity analysis; Bioinformatics; DNA; Hybrid electric vehicles; Phylogeny; Strain; Vectors; DNA sequences; Phylogenetic Analysis; k-word;
fLanguage
English
Publisher
ieee
Conference_Titel
Progress in Informatics and Computing (PIC), 2014 International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-4799-2033-4
Type
conf
DOI
10.1109/PIC.2014.6972409
Filename
6972409
Link To Document