Meiosis is functionally conserved across eukaryotes, thus not expected to vary considerably among different species, and even less so among lineages within a species. However, recent studies showed that this is not necessarily the case in Arabidopsis arenosa. Genome scanning identified an excess differentiation in meiosis genes between A. arenosa diploids and tetraploids, interpreted as meiosis adaptation to the whole genome duplication in tetraploids and differentiation was also found between two diploid lineages. Thus, I present a population-based analysis of positive selection acting on meiosis proteins across multiple lineages of A. arenosa. I showed that meiosis proteins were under positive selection in all diploid lineages, mainly in the Pannonian and South-eastern Carpathian lineage. The evidence for positive selection in diploid lineages suggested differential pathways of meiosis adaptations in the species, probably reflecting the necessity to adapt to local environments, among all to temperature. The highest enrichment of amino acid substitutions (AASs) under positive selection was identified in tetraploids, in consistence with previous genome-scan results. As several interacting meiosis proteins were under positive selection in the same A. arenosa lineage, I hypothesize that the close...

This masters's thesis contains description of designed and implemented tool for classification of plant microRNA without genome. Properties of mature and star sequences in microRNA duplexes are used. Implemented method is based on clustering of RNA sequences (with CD-HIT) to mainly reduce their count. Selected representants from each clusters are classified using support vector machine. Performance of classification is more than 96% (based on cross-validation method using the training data).

This thesis discusses the issue of predicting the effect of amino acid substitutions on protein function using MAPP method. This method requires the multiple sequence alignment and phylogenetic tree constructed by third-party tools. Main goal of this thesis is to find the combination of suitable tools and their parameters to generate the inputs of MAPP method on the basis of analysis on one massively mutated protein. Then, the MAPP method is tested with chosen combination of parameters and tools on two large independent datasets and consequently is compared with the other tools focused on prediction of the effect of mutations. Apart from this the web interface for the MAPP method was created. This interface simplifies the use of the method since the user need not to install any tools or set any parameters.

This dissertation deals with alternative techniques for analysis of genetic information of organisms. The theoretical part presents two different approaches for evaluation of relationship between organisms based on mutual similarity of genetic information contained in their DNA sequences. The first approach is currently standardized phylogenetics analysis of character based records of DNA sequences. Although this approach is computationally expensive due to the need of multiple sequence alignment, it allows evaluation of global and local similarity of DNA sequences. The second approach is represented by techniques for classification of DNA sequences in a form of numerical vectors representing characteristic features of their genetic information. These methods known as „alignment free“ allow fast evaluation of global similarity but cannot evaluate local changes. The new method presented in this dissertation combines the advantages of both approaches. It utilizes numerical representation similar to 1D digital signal, i.e. representation that contains specific trend along x-axis. The experimental part of dissertation deals with design of a set of appropriate tools for genomic signal processing to allow evaluation mutual similarity of taxonomically specific trends. On the basis of the mutual similarity of genomic signals, the classification in the form of dendrogram is applied. It corresponds to phylogenetic trees used in standard phylogenetics.

This master thesis deals with the implementation of various types of recurrent neural networks via programming language lua using torch library. It focuses on finding optimal strategy for training recurrent neural networks and also tries to minimize the duration of the training. Furthermore various types of regularization techniques are investigated and implemented into the recurrent neural network architecture. Implemented recurrent neural networks are compared on the speech recognition task using AMI dataset, where they model the acustic information. Their performance is also compared to standard feedforward neural network. Best results are achieved using BLSTM architecture. The recurrent neural network are also trained via CTC objective function on the TIMIT dataset. Best result is again achieved using BLSTM architecture.