Abstract
This paper presents a novel self-configuration single particle optimizer (SCSPO) for DNA sequence compression. Particularly, SCSPO searches an optimal compression codebook of all unique repeat patterns and then DNA sequences are compressed by replacing the duplicate fragments with the indexes of the corresponding matched code vectors in the codebook. Featured with a crucial self-configuration process, SCSPO optimizes the codebook with no predefined parameter settings required. Experimental results on benchmark numerical functions and real-world DNA sequences demonstrate that SCSPO is capable of attaining better fitness value than many other PSO variants and the proposed DNA sequence compression algorithm based on SCSPO attains encouraging compression performance.







Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Reference
Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Rapp BA, Wheeler DL (2008) GenBank. Nucl Acids Res 36:D25–D30
Chen X, Kwong S, Li M (1999) A compression algorithm for DNA sequences and its applications in genome comparison. In: Proceedings of the 10th workshop on genome informatics, pp 51–61
Chen X, Li M, Ma B, Tromp J (2002) DNACompress: fast and effective DNA sequence compression. Bioinformatics 18(12):1696–1698
Galperin MY, Cochrane GR (2009) Petabyte-scale innovations at the European nucleotide archive. Nucl Acids Res 37:D1–D4
Gao Y, Xie SL (2004) Chaos particle swarm optimization algorithm. Comput Sci 31(8):13–15
Grumbach S, Tahi F (1994) A new challenge for compression algorithms: genetic Sequences. Inf Process Manag 30(6):875–886
Gupta R, Mittal A, Gupta S (2006) An efficient algorithm to detect palindromes in DNA sequences using periodicity transform. Signal Process 86(8):2067–2073
Ji Z, Zhou JR, Liao HL, Wu QH (2010) A novel intelligent single particle optimizer. Chin J Comput 33(3):556–561
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, pp 1942–1948
Korodi G, Tabus I (2007) DNA sequence compression-based on the normalized maximum likelihood model. IEEE Signal Process Mag 24(1):47–53
Liang JJ, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization. In: Proceedings of IEEE swarm intelligence symposium, pp 68–75
Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295
Ma B, Tromp J, Li M (2002) PatternHunter—faster and more sensitive homology search. Bioinformatics 18(3):440–445
Matsumoto T, Sadakane K, Imai H (2000) Biological sequence compression algorithms. In: Proceedings of genome informatics workshop, pp 43–52
Osborne M (2000) Predicting DNA sequences using a backoff language model. Available: http://www.cogsci.ed.ac.uk/osborne/dna-backoff.ps.gz
Salomon D, Motta G, Bryant D (2006) Data compression: the complete reference, 4th edn. Springer, America
Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proceedings of IEEE international congress on evolutionary computation, pp 69–73
Srinivasa KG, Jagadish M, Venugopal KR and Patnaik LM (2006) Efficient compression of non-repetitive DNA sequences using dynamic programming. In: Proceedings of the international conference on advanced computing and communications, pp 569–574
Tran TT, Emanuele VA, Zhou GT (2004) Techniques for detecting approximate tandem repeats in DNA. In: Proceedings of international conference on acoustics, speech and signal processing, pp 449–452
Wu S, Manber U (1992) Fast text searching: allowing errors. Commun ACM 35(10):83–91
Zhan ZH, Zhang J, Li Y, Shi YH (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15(6):832–847
Zhou JR, Ji Z, Huang WG, Tian T (2010) Face recognition using gabor wavelet and self-adaptive intelligent single particle optimizer. In: Proceedings of Chinese conference on pattern recognition, pp 1–5
Acknowledgments
This work was supported partially by the National Natural Science Foundation of China, under Grants 61171125 and 61001185, the NSFC-RS joint project under grant 61211130120, the Fok Ying-Tung Education Foundation, Guangdong Natural Science Foundation, under Grants 10151806001000002, the Foundation for Distinguished Young Talents in Higher Education of Guangdong, under Grant LYM10113, Scientific Research Foundation for the Returned Overseas Chinese Scholars, Ministry of Education of China, the Science Foundation of Shenzhen City, under grant JC201105170650A, and the Shenzhen City Foundation for Distinguished Young Scientists.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ji, Z., Zhou, J., Zhu, Z. et al. Self-configuration single particle optimizer for DNA sequence compression. Soft Comput 17, 675–682 (2013). https://doi.org/10.1007/s00500-012-0939-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-012-0939-9