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Biological sequence classification utilizing lossless data compression algorithms
 Kruml, Ondřej ; Provazník, Ivo (oponent) ; Škutková, Helena (vedoucí práce)
This master thesis is developing the idea of using lossless compression algorithms as a mean of classification of biological sequences. At first an overview of lossless data compression algorithms is presented, based on which the dictionary algorithm created by A. Lempel and J. Ziv in 1976 (LZ77) has been selected. This algorithm, that commonly serves for data compression, has been modified in order to enable the classification of biological sequences. Further modifications have been introduced to enhance the classification capabilities of the algorithm. Several datasets of biological sequences have been collected enabling a correct assessment of the LZ algorithm capability. The algorithm was compared to the classical alignment based methods: Jukes-Cantor, Tamura and Kimura. It has been proven that the algorithm has comparable results in the field of classification of biological sequences and even surpasses the alignment methods in 20% of the datasets. Best results are especially achieved with distant sequences.
Úplný záznam
Biological sequence classification utilizing lossless data compression algorithms
 Kruml, Ondřej ; Provazník, Ivo (oponent) ; Škutková, Helena (vedoucí práce)
This master thesis is developing the idea of using lossless compression algorithms as a mean of classification of biological sequences. At first an overview of lossless data compression algorithms is presented, based on which the dictionary algorithm created by A. Lempel and J. Ziv in 1976 (LZ77) has been selected. This algorithm, that commonly serves for data compression, has been modified in order to enable the classification of biological sequences. Further modifications have been introduced to enhance the classification capabilities of the algorithm. Several datasets of biological sequences have been collected enabling a correct assessment of the LZ algorithm capability. The algorithm was compared to the classical alignment based methods: Jukes-Cantor, Tamura and Kimura. It has been proven that the algorithm has comparable results in the field of classification of biological sequences and even surpasses the alignment methods in 20% of the datasets. Best results are especially achieved with distant sequences.
Úplný záznam

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