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Abstract
Genomic sequences contain instructions for protein and cell production. Therefore
understanding and identification of biologically and functionally meaningful
patterns in DNA sequences is of paramount importance. Modeling of DNA sequences
in its turn can help to better understand and identify such patterns and
dependencies between them. It is well-known that genomic data contains various
regions with distinct functionality and thus also statistical properties. In this
work we focus on modeling of such individual regions of distinct functionalities.
We apply the concept of context trees to model these DNA regions. Based on
the Minimum Description Length principle, we use the estimated compression
rate of a genomic region, given such models, as a similarity measure. We show
that the constructed model can be used to distinguish specific genes within DNA
sequences.
Original language | English |
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Title of host publication | Proceedings of the 36th WIC Symposium on Information Theory in the Benelux and the 5th Joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, May 6-7, 2015, Brussels, Belgium |
Editors | J. Roland, F. Horlin |
Place of Publication | Brussel |
Publisher | ULB |
Pages | 96-103 |
ISBN (Print) | 978-2-8052-0277-3 |
Publication status | Published - 2015 |
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Dive into the research topics of 'DNA sequence modeling based on context trees'. Together they form a unique fingerprint.Projects
- 1 Finished
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Authentication and authorisation for Entrusted Unions
Kusters, C. J. & Ignatenko, T.
1/12/13 → 1/12/15
Project: Research direct