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Detection of Transcription Factor Binding Sites
Hlávka, Ondřej ; Vogel, Ivan (referee) ; Martínek, Tomáš (advisor)
Nowadays, it is very important to study gene expression mechanism in molecular biology. Gene expression is also regulated by sequence specific transcription factors which binds to regulatory regions of the genes. Searching for this specific sequences can be very problematic because transcription factor binding sites can be very degenerative. There are several possible methods that can be aplied to this problem. First part of this paper describes few algorithms for transcription binding sites search. Second part contains design and implementation of algorithm for searching binding sites of transcription factor p53.
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Predicting the Effect of Amino Acid Substitutions on Protein Function Using MAPP Method
Pelikán, Ondřej ; Vogel, Ivan (referee) ; Bendl, Jaroslav (advisor)
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.
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Optimization of the Next-Generation Sequencing Data Alignment
Šalanda, Vojtěch ; Bendl, Jaroslav (referee) ; Vogel, Ivan (advisor)
This thesis presents short DNA alignment tools optimization. These short DNA reads are products of next\nobreakdash-generation sequencing technologies. The results produced by existing align\-ment tools can be influenced by various parameters. For this purpose, an optimization framework to find the optimal values of selected parameters was developed. This framework is based on differencial evolution algorithm and its main goal is to maximize the alignment accuracy. The functionality of the framework was tested on both real and generated data sets of short DNA reads. An accurate alignment is crucial for correct prediction of various genetic characteristics.
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Prediction of Transposons in DNA
Černohub, Jan ; Vogel, Ivan (referee) ; Martínek, Tomáš (advisor)
Cílem práce je seznámení se s problematikou uchovávání informace v DNA, provést rešerši na téma transpozony, bioinformatické nástroje a algoritmy, které jsou používány k jejich detekci v nasekvenovaných genomech a vytvořit tak stručný úvod do obsáhle problematiky, včetně jejího zasazení do kontextu současně probíhajícího výzkumu v dané oblasti. Na základě přehledu stávajících algoritmů a nástrojů pro detekci transpozonů je navržen a implementován nástroj pro hledání tzv. LTR transpozonů.
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Prediction of the Effect of Amino Acid Substitutions on the Secondary Structure of Proteins
Kadlec, Miroslav ; Vogel, Ivan (referee) ; Bendl, Jaroslav (advisor)
This thesis is focused on amino acid substitutions and their impact on protein secondary structure. The main aim is to prove, that although the protein sequence is frequently mutated during the evolution, protein secondary structure is more robust against changes. In this case, the elements of protein secondary structure stay almost unchanged although a significant number of substitutions is observed. The proof of this hypothesis was obtained by developed simulator of evolution which employs two well-estabilished predicting tools: PSIPRED for prediction of protein secondary structure and PhD-SNP for prediction of the effect of amino acid substitution on protein function. The results of the experiments are provided as graphs and their meainings is discussed.
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Application for the Data Processing in the Area of Evolutionary Biology
Vogel, Ivan ; Burgetová, Ivana (referee) ; Očenášek, Pavel (advisor)
Phylogenetic tree inference is a very common method for visualising evolutionary relationships among species. This work focuses on explanation of mathematical theory behind molecular phylogenetics as well as design of a modified algorithm for phylogenetic tree inference based on intra-group analysis of nucleotide and amino acid sequences. Furthermore, it describes the object design and implementation of the proposed methods in Python language, as well as its integration into powerful bioinformatic portal. The proposed modified algorithmic solutions give better results comparing to standard methods, especially on the field of clustering of predefined groups. Finally, future work as well as an application of proposed methods to other fields of information technology are discussed.
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Localization of Methylation Sites in Transposons
Kmeť, Miroslav ; Martínek, Tomáš (referee) ; Vogel, Ivan (advisor)
This master's thesis deals with the creation of a tool for the extraction of methylation level from transposon sequences. Transposons are DNA elements with ability to move or copy themselves and their activity is regulated by DNA methylation. Sequence methylation information is stored in the bisulfite data and their processing is done with parts of two existing tools in a combination with implemented modules. Created tool takes into consideration unique challenges brought in the methylation calling process by transposable elements and it's functionality is presented on a set of experiments with simulated and real data.
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Query Language for Biological Databases
Bahurek, Tomáš ; Vogel, Ivan (referee) ; Martínek, Tomáš (advisor)
With rising amount of biological data, biological databases are becoming more important each day. Knowledge discovery (identification of connections that were unknown at the time of data entry) is an essential aspect of these databases. To gain knowledge from these databases one has to construct complicated SQL queries, which requires advanced knowledge of SQL language and used database schema. Biologists usually don't have this knowledge, which creates need for tool, that would offer more intuitive interface for querying biological databases. This work proposes ChQL, an intuitive query language for biological database Chado. ChQL allows biologists to assemble query using terms they are familiar without knowledge of SQL language or Chado database schema. This work implements application for querying Chado database using ChQL. Web interface guides user through process of assembling sentence in ChQL. Application translates this sentence to SQL query, sends it to Chado database and displays returned data in table. Results are evaluated by testing queries on real data.
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Prediction of p53 Protein Binding Sites
Radakovič, Jozef ; Vogel, Ivan (referee) ; Martínek, Tomáš (advisor)
Protein p53 which is encoded by gene TP53 plays crucial role in cell cycle as a regulator of transcription of genes in cases when cell is under stress. Therefore p53 acts like tumor suppressor. Understanding the pathway of p53 regulation as well as predicting its binding sites on p53 regulated genes is one of the major concerns of modern research in genetics and bioinformatics. In first part of this project we aim to introduce basics from molecular biology to better understand the p53 protein pathway in gene transcription and introduction to analysis of prediction of p53 binding sites. Second part is about implementation and testing of tool which would be able to predict transcription factor binding sites for protein p53.
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Classification of Small Noncoding RNAs
Žigárdi, Tomáš ; Martínek, Tomáš (referee) ; Vogel, Ivan (advisor)
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).
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