Genome Compression based on Hilbert Space Filling Curve

DOI

10.2991/meici-15.2015.294How to use a DOI?

Keywords

Genome sequence compression; Hilbert space filling; Context weighting; Description length.

Abstract

The genome compression algorithm based on the Hilbert space-filling curve is proposed in this paper. In order to utilize the correlations among the bases in the genome sequence, our algorithm firstly use the Hilbert space filling curve to map the genome sequence into a new 2-D image. Then the image obtained is encoded by the context weighting modeling technology. For context weighting, the values of weights are corresponding to the description length of the context model which is corresponding to their weights respectively. When the receiver obtain the mapping image, the reverse Hilbert space filling matrix is used to help the decoding procedure. The experiments results indicate that the final compression results by our algorithm are better than the results by other similar algorithms, although the useless area which will lead to the reduce of the compression efficiency is resulted in the mapping image by our algorithm.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hongyi Guo
AU  - Min Chen
AU  - Xi Liu
AU  - Mengmeng Xie
PY  - 2015/06
DA  - 2015/06
TI  - Genome Compression based on Hilbert Space Filling Curve
BT  - Proceedings of the 2015 International Conference on Management, Education, Information and Control
PB  - Atlantis Press
SP  - 1685
EP  - 1689
SN  - 1951-6851
UR  - https://doi.org/10.2991/meici-15.2015.294
DO  - 10.2991/meici-15.2015.294
ID  - Guo2015/06
ER  -