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Title: Homologous synteny block detection based on suffix tree algorithms
Authors: Chen, Y.-L.
Chen, C.-M.
Pai, T.-W.
Leong, H.-W. 
Chong, K.-F.
Keywords: Collinear
orthologous gene
suffix tree
synteny block
Issue Date: Dec-2013
Citation: Chen, Y.-L., Chen, C.-M., Pai, T.-W., Leong, H.-W., Chong, K.-F. (2013-12). Homologous synteny block detection based on suffix tree algorithms. Journal of Bioinformatics and Computational Biology 11 (6) : -. ScholarBank@NUS Repository.
Abstract: A synteny block represents a set of contiguous genes located within the same chromosome and well conserved among various species. Through long evolutionary processes and genome rearrangement events, large numbers of synteny blocks remain highly conserved across multiple species. Understanding distribution of conserved gene blocks facilitates evolutionary biologists to trace the diversity of life, and it also plays an important role for orthologous gene detection and gene annotation in the genomic era. In this work, we focus on collinear synteny detection in which the order of genes is required and well conserved among multiple species. To achieve this goal, the suffix tree based algorithms for efficiently identifying homologous synteny blocks was proposed. The traditional suffix tree algorithm was modified by considering a chromosome as a string and each gene in a chromosome is encoded as a symbol character. Hence, a suffix tree can be built for different query chromosomes from various species. We can then efficiently search for conserved synteny blocks that are modeled as overlapped contiguous edges in our suffix tree. In addition, we defined a novel Synteny Block Conserved Index (SBCI) to evaluate the relationship of synteny block distribution between two species, and which could be applied as an evolutionary indicator for constructing a phylogenetic tree from multiple species instead of performing large computational requirements through whole genome sequence alignment. © 2013 Imperial College Press.
Source Title: Journal of Bioinformatics and Computational Biology
ISSN: 02197200
DOI: 10.1142/S021972001343004X
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