Abstract
Bioinformatics is an area on which lots of researches have been done and it is used widely in drug design, cancer treatment, disease detection, functional analysis, phylogenetic research and food and cell mutation changes. The field benefits from the partnership of disciplines like computer science, biology, genetics, mathematics and statistics. The incredible rise in areas of computer science such as artificial intelligence, graph theory, software and hardware technology and kind of has provided also a direct contribution to Bioinformatics. The subject of this study is to examine the FANMOD tool which discovers and analyses network motifs and to carry out performance analysis which is depending on the R (number of random networks) parameter. Four different protein-protein interaction (PPI) networks were used in the experiments. The R parameter used in the experiments were taken as 100, 1000, 10000 and 100000 and the obtained results are presented both graphically and quantitatively. Furthermore the average experiment time of each network and characteristic features of the obtained motifs are given at the end of the paper.
Keywords
Bioinformatics Complex networks; FANMOD; Graph isomorphism; Network motif detection; Protein-protein interaction networks; System performance analysis
References
- Zhu, X., Gerstein, M. and Snyder, M., Getting connected: analysis and principles of biological networks, Genes & Dev, 21: 1010-1024, 2007.
- Wong, E., Baur, B., Quader, S. and Huang, C-H., Biological network motif detection: principles and practice, Briefings in bioinformatics, Vol 13, No 2, 202-215, 2011.
- Milo R., Shen-Orr S., Itzkovitz S., Kashtan N., Chklovskii D. and Alon U., Network motifs: Simple building blocks of complex networks, Science, 298:824–827, 2002.
- Grochow, J. A. and Kellis, M., "Network motif discovery using subgraph enumeration and symmetry-breaking." Research in Computational Molecular Biology. Springer Berlin Heidelberg, 2007.
- Hasan, M. M., Kavurucu, Y. and Kahveci, T., A scalable method for discovering significant subnetworks, BMC systems biology, 7(Suppl 4), S3, 2013.
- Wernicke, S. and Rasche, F., FANMOD: a tool for fast network motif detection. Bioinformatics, 22(9): 1152-1153, 2006.
- Wernicke, S., A faster algorithm for detecting network motifs, In Proceedings of the 5th Workshop on Algorithms in Bioinformatics (WABI’05), Lecture Notes in Bioinformatics, Vol. 3692, pp. 165–177, 2005.
- Ribeiro, P., Efficient and Scalable Algorithms for Network Motifs Discovery, Diss. PhD thesis, University of Porto, 2011.
- Zuba, M., A Comparative Study of Network Motif Detection Tools, UConn Bio-Grid, REU Summer, 2009.
- The COSIN Network data and Analysis, Available: http://pil.phys.uniroma1.it/~gcalda/cosinsite/extra/data/proteins/
- Kashtan, N., Itzkovitz, S., Milo, R. and Alon, U., Network Motif Detection Tool: mfinder Tool Guide. Weizmann Institute of Science, Depts of Mol Cell Bio and Comp Sci & Applied Math, Rehovot, Israel (2002-2005).
- Kavurucu, Y., Network Motifs and Indexing Techniques in Biological Networks, Journal of Naval Science and Engineering, 8.2 (2012): 87-102, 2012.
- Itzhack, R., Mogilevski, Y. and Louzoun, Y., An optimal algorithm for counting network motifs, Physica A: Statistical Mechanics and its Applications, 381, 482-490, 2007.
Abstract
References
- Zhu, X., Gerstein, M. and Snyder, M., Getting connected: analysis and principles of biological networks, Genes & Dev, 21: 1010-1024, 2007.
- Wong, E., Baur, B., Quader, S. and Huang, C-H., Biological network motif detection: principles and practice, Briefings in bioinformatics, Vol 13, No 2, 202-215, 2011.
- Milo R., Shen-Orr S., Itzkovitz S., Kashtan N., Chklovskii D. and Alon U., Network motifs: Simple building blocks of complex networks, Science, 298:824–827, 2002.
- Grochow, J. A. and Kellis, M., "Network motif discovery using subgraph enumeration and symmetry-breaking." Research in Computational Molecular Biology. Springer Berlin Heidelberg, 2007.
- Hasan, M. M., Kavurucu, Y. and Kahveci, T., A scalable method for discovering significant subnetworks, BMC systems biology, 7(Suppl 4), S3, 2013.
- Wernicke, S. and Rasche, F., FANMOD: a tool for fast network motif detection. Bioinformatics, 22(9): 1152-1153, 2006.
- Wernicke, S., A faster algorithm for detecting network motifs, In Proceedings of the 5th Workshop on Algorithms in Bioinformatics (WABI’05), Lecture Notes in Bioinformatics, Vol. 3692, pp. 165–177, 2005.
- Ribeiro, P., Efficient and Scalable Algorithms for Network Motifs Discovery, Diss. PhD thesis, University of Porto, 2011.
- Zuba, M., A Comparative Study of Network Motif Detection Tools, UConn Bio-Grid, REU Summer, 2009.
- The COSIN Network data and Analysis, Available: http://pil.phys.uniroma1.it/~gcalda/cosinsite/extra/data/proteins/
- Kashtan, N., Itzkovitz, S., Milo, R. and Alon, U., Network Motif Detection Tool: mfinder Tool Guide. Weizmann Institute of Science, Depts of Mol Cell Bio and Comp Sci & Applied Math, Rehovot, Israel (2002-2005).
- Kavurucu, Y., Network Motifs and Indexing Techniques in Biological Networks, Journal of Naval Science and Engineering, 8.2 (2012): 87-102, 2012.
- Itzhack, R., Mogilevski, Y. and Louzoun, Y., An optimal algorithm for counting network motifs, Physica A: Statistical Mechanics and its Applications, 381, 482-490, 2007.
Details
| Journal Section | Research Article |
|---|---|
| Authors | |
| Publication Date | August 28, 2016 |
| Published in Issue | Year 2016 |
