|
|
Comprehensive analysis of putrescine metabolism in A thaliana using GWAS, genetic risk score, metabolic modelling and data mining
Schwarzerová, Jana ; Bartoň, Vojtěch ; Walther, Dirk ; Weckwerth, Wolfram
Polyamines are known to be functionally involvedin plant responses to stress conditions. Despite a large amount ofphysiological and genetic data, the mode of action of polyamines,especially putrescine, at the molecular level still remains unclear.An increasing number of studies point to a role of putrescine instress defense of plants due to influencing the formation of reactiveoxygen species (ROS) in cellular stress conditions. Moreover,putrescine has been found to modulate abscisic acid (ABA)biosynthesis at the transcriptional level in response to lowtemperature, revealing a novel mode of action of polyamines asregulators of hormone biosynthesis. This study presents a newholistic approach towards the analysis of putrescine metabolismusing genome-wide association studies and the calculation ofgenetic risk scores extended by Boolean analysis in metabolitesnetwork as well as data mining from available databases andliterature. It can lead to a better understanding of biologicalprocesses involved in adaptation of plants to environmentalchanges.
|
|
|
Correction of the concept of drift in prediction models
Michálková, Eva ; Provazník, Valentine (referee) ; Schwarzerová, Jana (advisor)
The main goal of this bachelor thesis is the analysis of concept drift in metabolomics. Concept drift is an undesirable phenomenon and can be caused by nonstationary data. It can have a negative impact on the performance and reliability of predictive modelling. This challenge can be solved by concept drift detection and subsequent correction. One of the fields where this issue has recently emerged is metabolomic diagnostics. Metabolomic data analysis can lead to early detection of several serious diseases, which can help with the recovery process. When diagnosing an illnes predictive models present a way to make the process more efficient, faster and give the option of personalization. The first part of this thesis specifies concept drift, it’s detection and correction methods and the importance of metabolomics and prediction models. The second part deals with the implementation of some available algorithms for concept drift detection and correction and the implementation of automatic concept drift correction. Finally, in the second part results and their discussion are described.
|
|
|
Analysis of horizontal transfer of genetic components using static network analysis
Labanava, Anastasiya ; Jurečková, Kateřina (referee) ; Schwarzerová, Jana (advisor)
The bachelor's thesis focuses on the issue of horizontal genetic elements transfer between bacteria of different strains and the software analysis implementation that enables horizontally transferred genes identification. The packages and tools used were tested on a dataset of bacterial genomes from several strains. The thesis’ theoretical part provides a detailed description of the genetic components transfer between bacteria and describes modern laboratory techniques that enable genome sequencing in various ways. In the practical part, the thesis deals with the preprocessing of genomic files to obtain suitable data for annotation. To detect the horizontal transfer of genetic elements between bacteria, a script is introduced, which organizes annotated bacteria to tables and searches for the same genes in their genomes that, under theoretical assumptions, were horizontally transferred. Furthermore, the gene transfer is visualized using tools that graphically represent phylogenetic relations between bacteria. In the final step, bacterial genomes are connected into networks, and based on their static analysis, a discussion is conducted on the results accuracy and the success of the proposed analysis.
|
|
|
RNA secondary structure prediction
Polzerová, Nikola ; Schwarzerová, Jana (referee) ; Jurečková, Kateřina (advisor)
Sekundární struktura RNA se stala v posledních letech fenoménem. Hraje klíčovou roli v pochopení principů genové exprese a stability RNA, a také pokládá důležitý základ pro správnou predikci terciálních struktur. Proto dochází k velice rychlému rozvoji predikce pomocí výpočetních metod a také pomocí metod strojového učení. V rámci práce byly popsány nejčastěji citované metody strojového učení pro predikci sekundárních struktur RNA. Na základě získaných znalostí byla implementována reziduální neuronová síť. Implementovaná reziduální síť byla netrénována, validována a otestována na sekvencích z datasetu bpRNA-1m, jejichž sekundární struktury neobsahují pseudouzly.
|
|
|
Identification of horizontal genes transfer elements across strains inhabiting the same niche using pan-genome analysis
Schwarzerová, J. ; Čejková, D.
Tracing horizontal gene flux across strains in farm animals is one of the important steps for research focused on detection and genomic enzymology of genes conferring antibiotic resistance. In this study, we have built the comprehensive computational methodology for the detection of horizontal genes transfer elements via pan-genome analysis. In total, 133 anaerobes isolated from chicken gastrointestinal tract were examined for the presence of traits of horizontal transfer. The shared genes from all isolates, so called core genome genes were identified and characterised in order to assign the function to the gene within individual bacterial cell and within community of cells. This study provides an evidence that horizontal transmission frequently occurs not only between closely related bacteria, but also between distant taxonomical groups. Hence chickens are known primary reservoirs of antibiotic resistance genes, and the dissemination of these genes to other bacterial pathogens often leads to life-threatening infections, even within human population. Thus, the research on this subject, and the associated results are of a great importance for public health.
|
|
|
Dynamic metabolomic prediction based on genetic variation for Hordeum vulgare
Nemčeková, P. ; Schwarzerová, J.
Hordeum vulgare, like many other crops, suffers from the reduction of genetic diversity caused by climate changes. Therefore, it is necessary to improve the performance of its breeding. Nowadays, the area of interest in current research focuses on indirect selection methods based on computational prediction modeling. This study deals with dynamic metabolomic prediction based on genomic data consisting of 33,005 single nucleotide polymorphisms. Metabolomic data include 128 metabolites belonging to 25 Halle exotic barley families. The main goal of this study is creating dynamic metabolomic predictions using different approaches chosen upon various publications. Our created models will be helpful for the prediction of phenotype or for revealing important traits of Hordeum vulgare.
|
|
|
Concept Drift Detection in Prediction Classifiers for Determining Gender in Metabolomics Analysis
Kostova, A. ; Schwarzerova, J.
Currently, one of the most challenges in data analysis is connected to prediction modeling including dynamic information. Metabolomics analysis focuses on data presented dynamic information in real-time such as time-series data. Unfortunately, prediction models based on time series data are often affected by a phenomenon called concept drift. This phenomenon can reduce the accuracy of prediction models which is an unwanted effect. On the other hand, concept drift analysis can be useful in finding confounding factors. This study is divided into two parts. The first part presents the modeling of prediction classifiers based on metabolite data. The second part of this study brings concept drift detection in the created classified models. This study presented approaches to identify one of the confounding factors in human biology.
|
|
|
Operon identifier: Identification of operon structures in the whole genome
Nejezchlebová, Julie ; Schwarzerová, Jana
Currently, operon prediction is based on the distance of neighboring genes on the functional relationships of their products that encode proteins in a given nucleotide sequence, or on ORF distances. This study deals with the design of a new function that detects operon structures based on information from gene expression or alternatively in combination with previous information from current online available tools. The function was implemented in Python language and tested on Clostridium beijerinckii NRRL B-598. This bacterium has huge potential in biotechnology and research due to its fermentation product, butanol.
|
|
|
Operon structures inference in genome-wide analysis
Nejezchlebová, Julie ; Jurečková, Kateřina (referee) ; Schwarzerová, Jana (advisor)
The bachelor thesis is devoted to the problem of derivation of operon structures and creation of a software tool that allows prediction of operon structures. The tool both predicts operons based on gene expression information, but also refines already predicted operons with gene expression information. The tool is tested on the bacteria Escherichia coli BW25113 and Clostridium beijerinckii NRRL B-598. The theoretical part is devoted to description of operon structure and function, genome sequencing, transcriptome analysis, Clostridium beijerinckii NRRL B-598, Escherichia coli BW25113 and already available online tools for inferring operon structures. In the practical part of the thesis, the pre-processing of raw transcriptomic data to obtain a suitable format for the prediction of operon structures, testing of online tools and the actual implementation of the tool itself are discussed.
|
|
|
Concept drift in metabolomic analysis
Koštoval, Aleš ; Provazník, Ivo (referee) ; Schwarzerová, Jana (advisor)
This bachelor thesis deals with machine learning, specifically the analysis of the concept drift. This is an unwanted phenomenon that can be detected in predictive models. Through detection followed by correction of the concept drift, predictive models become more reliable and can respond adequately to input data representing dynamic information. Metabolomic data can be considered a suitable representative of such data. Metabolomic data and their analysis can help to detect diseases such as diabetes mellitus or cancer early. In the first part of this bachelor thesis, the theoretical background of concept drift analysis and metabolomics analysis are described. The second part discusses the process of modeling predictive classifiers and implementing algorithms for concept drift detection. The practical part of the work was implemented in the Python programming language. Finally, the second part describes the results obtained and their discussion.
|
guest ::
RSS feed.