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Abstract:
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The analysis of biological sequence similarity between different species is significant in
identifying functional, structural or evolutionary relationships among the species. Biological
sequence similarity and analysis of newly discovered nucleotide and amino acid sequences are
demanding tasks in bioinformatics.
As biological data is growing exponentially, new and innovative algorithms are needed to be
constantly developed to get faster and more effective data processing. The challenge in sequence
similarity analysis algorithms is that sequence does not always have obvious features and the
dimension of sequence features may be very high for applying regular feature selection methods on
sequences. It is important to have a simple and effective algorithm for determining biological
sequence relationships.
This thesis proposes two new methods for sequence transformation in feature vectors that
takes into consideration statistically significant repetitive parts of analyzed sequences, as well as
includes different approaches for determination of nucleotide sequence similarity and sequence
classification for predicting taxonomy groups of biological sequence data. The first method is based
on information theory and fact that both position and frequency of repeated sequences are not
expected to occur with the identical presence in a random sequence of the same length. The second
method includes building signatures of biological sequences and profiles of taxonomic classes
based on repetitive parts of sequences and distances between these repeats.
Proposed methods have been validated on multiple data sets and compared with results
obtained using different well known and accepted methods in this field like BLAST, Clustal Omega
and methods based on k-mers. Resulted precision for proposed methods is close to values provided
for existing methods for the majority of tested data-sets, and time performance depends strictly to
used infrastructure and sequence type. Methods provide results that are comparable with other
commonly used methods focused on resolving the same problem, taking into consideration
statistically significant repetitive parts of sequences with different characteristics. |