Fidan Gelişim Algoritması Yardımı ile DNA Motiflerinin Keşfi

Yıl 2011, Cilt: 8 Sayı: 1, - , 01.05.2011

Murat Demir Ali Karcı Mehmet Özdemir

Öz

Motif, a DNA particle, has an important role in the formation of DNA sequences
or in the placement of the regular DNA particles. The discovery of motif is the
operation of finding out the potential DNA particles that are able to transform into motifs
in a given DNA sequence. In this study, with the help of Sapling Growing up Algorithm,
Motif discovery has been realized on the DNA sequences.
Sapling Growing up Algorithm is an algorithm developed as a result of the study concerning
sapling growth. In this method, the data that may be of help in solving the problem are
put into strings of solution that are called “sapling”. Sowing of the saplings, mating, branching, and vaccinating are taken as operators. Sowing of the saplings provides the
formation of new saplings (solutions) in the search space. Branching provides the local
searching, and mating provides the global searching. Vaccinating, however, provides the
exchange of information between similar saplings.
In literature, some of the methods on motif discovery studies are as follows: AlignACE,
MEME, MEME3, MotifSampler, Consensus, Weeder, etc. The results attained in this
study have been compared with AlignACE, MEME, MEME3, MotifSampler, Consensus,
Weeder methods’ results in the conclusion part of the paper. The data in this paper have
been obtained form TRANSFAC database.

Anahtar Kelimeler

DNA, motifs of DNA, saplings growing up algorithm

Kaynakça

• [1] DNA. http://www.biltek.tubitak.gov.tr/bdergi/poster/icerik/dna.pdf, 1999. Son erisim: 23-Mayıs-2011.
• [2] S. Mahony, P. V. Benos, T. J. Smith and A. Golden, Self-organizing neural networks to support the discovery of DNA-binding motifs, Neural Networks 19 (2006), 950–962.
• [3] T. H. Cormen, C. E. Leiserson and R. L. Rivest, Introduction to Algorithms, The MIT Press, Massachusetts 1990.
• [4] R. Riviere, D. Barth, J. Cohen and A. Denise, Shuffling biological sequences with motif constraints, Journal of Discrete Algorithms 6 (2008), 192–204.
• [5] P. D’haeseleer, How does DNA sequence motif discovery work? Nature Biotechnology 24 (2006), 959–961.
• [6] M. Kaya, MOGAMOD: Multi-objective genetic algorithm for motif discovery, Expert Systems with Applications 36 (2009), 1039–1047.
• [7] J. Hu, B. Li and D. Kihara, Limitations and potentials of current motif discovery algorithms, Nucleic Acids Research 33 (2005), 4899–4913.
• [8] X. Liu, D. L. Brutlag and J. S. Liu, BioProspector: discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes, Pacific Symposium on Biocomputing 6 (2001), 127–138.
• [9] W. Thompson, E. C. Rouchka and C. E. Lawrence, Gibbs Recursive Sampler: finding transcription factor binding sites, Nucleic Acids Research 31 (2003), 3580–3585.
• 10] G. Thijs, K. Marchal, M. Lescot, S. Rombauts, B. De Moor, P. Rouze and Y. Moreau, A Gibbs sampling method to detect overrepresented motifs in the upstream regions of coexpressed genes, Journal of Computational Biology 9 (2002), 447–464.
• [11] A. Karci, M. Yigiter and M. Demir, Natural inspired computational intelligence method: Saplings growing up algorithm, International Kazakh-Kyrgyz Electronics and Computer Conference (IKECCO’2007), Almaty 2007.
• [12] M. Demir, M. Yigiter and A. Karci, Application of saplings growing up algorithm to clustering medical data, International Kazakh-Kyrgyz Electronics and Computer Conference (IKECCO’2007), Almaty 2007.
• [13] M. Demir ve A. Karcı, Veri kumelemede fidan gelisim algoritmasının kullanılması, Elektrik Elektronik Bilgisayar Biyomedikal Muhendisligi 12. Ulusal Kongresi ve Sergisi, Eskisehir 2007.
• [14] S. Arslan Tuncer, Fidan Gelisim Algoritması ile Protein Ikincil Yapı Tahmini, Yuksek Lisans Tezi, Fırat Universitesi, Elazıg 2008.
• [15] E. Wingender, P. Dietze, H. Karas and R. Knuppel, TRANSFAC: A database on transcription factors and their DNA binding sites, Nucleic Acids Research 24 (1996), 238–241.