The multiple sequence sets: problem and heuristic algorithms
- 76 Downloads
“Sequence set” is a mathematical model used in many applications such as biological sequences analysis and text processing. However, “single” sequence set model is not appropriate for the rapidly increasing problem size. For example, very large genome sequences should be separated and processed chunk by chunk. For these applications, the underlying mathematical model is “Multiple Sequence Sets” (MSS). To process multiple sequence sets, sequences are distributed to different sets and then sequences on each set are processed in parallel. Deriving effective algorithm for MSS processing is challenging.
In this paper, we have first defined the cost functions for the problem of Process of Multiple Sequence Sets (PMSS). The PMSS problem is then formulated as to minimize the total cost of process. Based on the analysis of the features of multiple sequence sets, we have proposed the Distribution and Deposition (DDA) algorithm and DDA* algorithm for PMSS problem. In DDA algorithm, the sequences are first distributed to multiple sets according to their alphabet contents; then sequences in each set are processed by deposition algorithm. The DDA* algorithm differs from the DDA algorithm in that the DDA* algorithm distributes sequences by clustering based on a set of sequence features. Experiments showed that the results of DDA and DDA* are always smaller than other algorithms, and DDA* outperformed DDA in most instances. The DDA and DDA* algorithms were also efficient both in time and space.
KeywordsMultiple sequence sets Distribution and deposition Shortest common supersequence Sequence features Performance ratio
Unable to display preview. Download preview PDF.
- Barone P, Bonizzoni P, Vedova GD, Mauri G (2001) An approximation algorithm for the shortest common supersequence problem: an experimental analysis. In: Symposium on applied computing, proceedings of the 2001 ACM symposium on applied computing, pp 56–60 Google Scholar
- Hannenhalli S, Hubell E, Lipshutz R, Pevzner PA (2002) Combinatorial algorithms for design of DNA arrays. Adv Biochem Eng Biotechnol 77:1–19 Google Scholar
- Rozen S, Skaletsky HJ (2000) Primer3 on the WWW for general users and for biologist programmers. Humana Press, Totowa Google Scholar
- Sankoff D, Kruskal J (1983) Time warps, string edits and macromolecules: the theory and practice of sequence comparisons. Addison-Wesley, Reading Google Scholar
- Storer JA (1988) Data compression: methods and theory. Computer Science Press, New York Google Scholar
- Timkovsky VG (1993) On the approximation of shortest common non-subsequences and supersequences. Technical report Google Scholar