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Methods for fast sequence comparison and identification in metagenomic data
 Kupková, Kristýna ; Škutková, Helena (oponent) ; Sedlář, Karel (vedoucí práce)
The objective of this thesis is to create a method for identification of organisms in metagenomic data. Until this point methods based on sequence alignment with reference database have been sufficient for this purpose. However, the volume of data grows rapidly with evolvement of sequencing techniques and the alignment-based methods became inconvenient due to computationally demanding alignment. A new technique is introduced in this master’s thesis, which allows alignment-free metagenomic data classification. The method is based on transformation of sequences to genomic signals in form of phase representation, from which feature vectors are extracted. These features are three Hjorth descriptors, which are then subjected expectation maximization for Gaussian mixture model method allowing reliable binning of metagenomic data.
Úplný záznam
Methods for fast sequence comparison and identification in metagenomic data
 Kupková, Kristýna ; Škutková, Helena (oponent) ; Sedlář, Karel (vedoucí práce)
The objective of this thesis is to create a method for identification of organisms in metagenomic data. Until this point methods based on sequence alignment with reference database have been sufficient for this purpose. However, the volume of data grows rapidly with evolvement of sequencing techniques and the alignment-based methods became inconvenient due to computationally demanding alignment. A new technique is introduced in this master’s thesis, which allows alignment-free metagenomic data classification. The method is based on transformation of sequences to genomic signals in form of phase representation, from which feature vectors are extracted. These features are three Hjorth descriptors, which are then subjected expectation maximization for Gaussian mixture model method allowing reliable binning of metagenomic data.
Úplný záznam

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