Issue No. 02 - March/April (2012 vol. 9)

ISSN: 1545-5963

pp: 330-344

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.96

Chung-Chin Kuo , National Tsing Hua University, Hsinchu

Biing-Feng Wang , National Tsing Hua University, Hsinchu

Chien-Hsin Lin , National Tsing Hua University, Hsinchu

Shang-Ju Liu , National Tsing Hua University, Hsinchu

ABSTRACT

Identifying conserved gene clusters is an important step toward understanding the evolution of genomes and predicting the functions of genes. A famous model to capture the essential biological features of a conserved gene cluster is called the gene-team model. The problem of finding the gene teams of two general sequences is the focus of this paper. For this problem, He and Goldwasser had an efficient algorithm that requires O(mn) time using O(m + n) working space, where m and n are, respectively, the numbers of genes in the two given sequences. In this paper, a new efficient algorithm is presented. Assume m \le n. Let C = \sum _{\alpha \in \Sigma } o_{1}(\alpha )o_{2}(\alpha ), where \Sigma is the set of distinct genes, and o_{1}(\alpha ) and o_{2}(\alpha ) are, respectively, the numbers of copies of α in the two given sequences. Our new algorithm requires O({\rm min}\{C {\rm lg} n, mn\}) time using O(m + n) working space. As compared with He and Goldwasser's algorithm, our new algorithm is more practical, as C is likely to be much smaller than mn in practice. In addition, our new algorithm is output sensitive. Its running time is O({\rm lg} n) times the size of the output. Moreover, our new algorithm can be efficiently extended to find the gene teams of k general sequences in O(k C lg (n_{1}n_{2} \ldots n_{k})) time, where n_i is the number of genes in the ith input sequence.

INDEX TERMS

Algorithms, data structures, gene teams, comparative genomics, conserved gene clusters.

CITATION

Chung-Chin Kuo, Biing-Feng Wang, Chien-Hsin Lin, Shang-Ju Liu, "A New Efficient Algorithm for the Gene-Team Problem on General Sequences",

*IEEE/ACM Transactions on Computational Biology and Bioinformatics*, vol. 9, no. , pp. 330-344, March/April 2012, doi:10.1109/TCBB.2011.96SEARCH