|
|
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.
|
|
|
Dynamic metabolomic prediction from genetic variation
Nemčeková, Petra ; Weckwerth, Wolfram (referee) ; Schwarzerová, Jana (advisor)
Hordeum vulgare, tak ako mnoho ďalších plodín, trpí redukovaním genetickej rôznorodosti spôsobeným klimatickými zmenami. Preto je potrebné zlepšiť účinnosť jeho kríženia. Oblasť záujmu sa v poslednej dobe obracia na výskum nepriamych selekčných metód založených na výpočetných predikčných modeloch. Táto práca sa zaoberá dynamickou metabolomickou predikciou založenou na genomických dátach, ktoré pozostávajú z 33,005 jednonukleotidových polymorfizmov. Metabolomické dáta zahŕňajú 128 metabolitov 25 rodín Halle exotického jačmeňa. Hlavným cieľom tejto práce je vytvoriť metabolomické predikcie dynamických dát pomocou rôznych metód, ktoré boli vybrané na základe rôznych publikácií. Vytvorené modely napomôžu predikcii fenotypu alebo odhaleniu dôležitých vlastností rastliny Hordeum vulgare.
|
|
|
Metabolite Genome-Wide Association Studies Of Arabidopsis Thaliana
Schwarzerová, Jana
Current research based on the edge of bioinformatics and ecology engineering has hugepotential due to combination of laboratory analyses and advanced bioinformatics algorithms.The paper deals with a combination of GC-MS and LC-MS based metabolomic analysis for identificationand quantitation of metabolites in environmental perturbations with advanced bioinformaticsapproach of metabolite genome-wide association studies (mGWAS). This complex view is appliedto Arabidopsis thaliana. The main goal is to obtain genetic predictions focused on A. thalianaunder different environmental conditions. Currently, important ecological issues such as climatechange, pollution etc. have impact on the change of environment. It has a great effect on plantswhich serves as producers of oxygen or food. While simple observation reveals only a phenotypechange, changes in genotypes of organisms can be captured using mGWAS and further utilized inindustrial ecology and biotechnology.
|
|
|
Operon-Expresser: The Innovated Gene Expression-Based Algorithm For Operon Structures In-Ference
Schwarzerová, Jana
Current biotechnological research of bacterial genomes has huge potential due to the useof next-generation sequencing (NGS) platforms. NGS era opens paths for analysis of data for sufficientdescription of microorganisms with ecology and biotechnology potential in the future. Althoughsome tools for inference of specific structures in bacterial genomes exist, their pipelines andmethodologies are often based only on searching bacterial genome databases and comparison withmodel microorganisms. This paper deals with the design of a new algorithm for operon structuresinference in bacterial genomes. The algorithm combines searching in bacterial databases and geneexpression information processing. The algorithm was implemented in R language and tested onClostridium beijerinckii NRRL B-598. The bacterium is a typical performer in the field of biofuelsproduction due to its ability to produce butanol. Thanks to that this bacterial organism can be ofgreat potential from an ecological and biotechnological point of view. The paper also provides acomparison of operon structures derived by Operon-mapper and its extending by Operon-expresser.
|
guest ::
RSS feed.