KM-Finder: Uma Ferramenta para Detecção de Motivos
Abstract
The identification of patterns within specific regions of the HPV genome may contribute to the understanding of the viral pathogenesis. In this work, a tool for searching nucleotide patterns (motifs) in one or a group of DNA sequences was developed, which continues to be a computational challenge.
References
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and experimentation. pages 140–156. Proc. Workshop on Algorithms for Bioinformatics (WABI).
Bailey, T. L., Boden, M., Buske, F., Frith, M., vGrant, C., and et al. (2009). Meme suite: tools for motif discovery and searching. pages 202–208. Nucleic Acids Research.
Bailey, T. L. and Elkan, C. (1994). Fitting a mixture model by expectation maximization to discover motifs in biopolymers. pages 28–36. Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology.
Bernard, H.-U. (2013). Regulatory elements in the viral genome. Virology, 445(1):197–204.
Buhler, J. and Tompa, M. (2001). Finding motifs using random projections. pages 269–278. Proc. Fifth Annual International Conference on Computational Molecular Biology (RECOMB).
D’haeseleer, P. (2006). What are dna sequence motifs? pages 423–425. Nature Biotechnology.
Dinh, H., Rajasekaran, S., and Davila, J. (2012). qpms7: A fast algorithm for finding (l,d)-motifs in dna and protein sequences. In PLoS ONE.
Hamming, R. W. (1950). Error detecting and error correcting codes. The Bell System Technical Journal.
Jones, N. C. and Pevzner, P. A. (2004). An Introduction to Bioinformatics Algorithms. The MIT Press. Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics-Doklady.
Marsan, L. and Sagot, M.-F. (2000). Algorithms for extracting structured motifs using a sufix tree with an application to promoter and regulatory site consensus identification. pages 345–362. Journal of Computational Biology.
Pevzner, P. and Sze, S. H. (2000). Combinatorial approaches to finding subtle signals in dna sequences. pages 269–278. Proc. Eighth International Conference on Intelligent Systems for Molecular Biology.
Rajasekaran, S. (2005). Algorithms for Motif Search in Handbook of Computational Molecular Biology. Chapman and Hall/CRC. chapter 37.
and experimentation. pages 140–156. Proc. Workshop on Algorithms for Bioinformatics (WABI).
Bailey, T. L., Boden, M., Buske, F., Frith, M., vGrant, C., and et al. (2009). Meme suite: tools for motif discovery and searching. pages 202–208. Nucleic Acids Research.
Bailey, T. L. and Elkan, C. (1994). Fitting a mixture model by expectation maximization to discover motifs in biopolymers. pages 28–36. Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology.
Bernard, H.-U. (2013). Regulatory elements in the viral genome. Virology, 445(1):197–204.
Buhler, J. and Tompa, M. (2001). Finding motifs using random projections. pages 269–278. Proc. Fifth Annual International Conference on Computational Molecular Biology (RECOMB).
D’haeseleer, P. (2006). What are dna sequence motifs? pages 423–425. Nature Biotechnology.
Dinh, H., Rajasekaran, S., and Davila, J. (2012). qpms7: A fast algorithm for finding (l,d)-motifs in dna and protein sequences. In PLoS ONE.
Hamming, R. W. (1950). Error detecting and error correcting codes. The Bell System Technical Journal.
Jones, N. C. and Pevzner, P. A. (2004). An Introduction to Bioinformatics Algorithms. The MIT Press. Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics-Doklady.
Marsan, L. and Sagot, M.-F. (2000). Algorithms for extracting structured motifs using a sufix tree with an application to promoter and regulatory site consensus identification. pages 345–362. Journal of Computational Biology.
Pevzner, P. and Sze, S. H. (2000). Combinatorial approaches to finding subtle signals in dna sequences. pages 269–278. Proc. Eighth International Conference on Intelligent Systems for Molecular Biology.
Rajasekaran, S. (2005). Algorithms for Motif Search in Handbook of Computational Molecular Biology. Chapman and Hall/CRC. chapter 37.
Published
2016-07-04
How to Cite
MONTERA, Luciana; VILHAGRA, Lucas Akayama; RAIOL, Tainá.
KM-Finder: Uma Ferramenta para Detecção de Motivos. In: PROCEEDINGS OF THE THEORY OF COMPUTATION MEETING (ETC), 1. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 895-898.
ISSN 2595-6116.
DOI: https://doi.org/10.5753/etc.2016.9854.
