Methods for gene prediction in prokaryotic genomes

Tato bakalářská práce se zabývá metodami predikce genů v prokaryotických organismech. V první části je popsána prokaryotická buňka včetně genomu, exprese genetické informace a rozdělení metod pro predikci genů. Dále je zde uveden popis tří vybraných softwarů, které predikci provádějí. V praktické části je rozebráno testování softwarů a vyhodnocení jejich účinnosti na daném genomu. Nakonec je zde popsán vytvořený program pro hledání genů a jsou zde uvedeny výsledky jeho účinnosti.This bachelor thesis deals with methods of gene prediction in prokaryotic genomes. First part of the thesis introduces prokaryotic cell, its genome, expression of genetic information and methods for gene prediction. Following part describes three software products for gene prediction. Chosen software was tested against specific genome and next chapter presents obtained results. The last part describes program called Gene_finder and its results.

Word entropy-based approach to detect highly variable genetic markers for bacterial genotyping

Genotyping methods are used to distinguish bacterial strains from one species. Thus, distinguishing bacterial strains on a global scale, between countries or local districts in one country is possible. However, the highly selected bacterial populations (e.g. local populations in hospital) are typically closely related and low diversified. Therefore, currently used typing methods are not able to distinguish individual strains from each other. Here, we present a novel pipeline to detect highly variable genetic segments for genotyping a closely related bacterial population. The method is based on a degree of disorder in analyzed sequences that can be represented by sequence entropy. With the identified variable sequences, it is possible to find out transmission routes and sources of highly virulent and multiresistant strains. The proposed method can be used for any bacterial population, and due to its whole genome range, also noncoding regions are examined

Flavobacterium flabelliforme sp. nov. and Flavobacterium geliluteum sp. nov., two multidrug-resistant psychrotrophic species isolated from Antarctica

Despite unfavourable Antarctic conditions, such as cold temperatures, freeze-thaw cycles, high ultraviolet radiation, dryness and lack of nutrients, microorganisms were able to adapt and surprisingly thrive in this environment. In this study, eight cold-adapted Flavobacterium strains isolated from a remote Antarctic island, James Ross Island, were studied using polyphasic taxonomic approach to determine their taxonomic position. Phylogenomic analyses based on 16S rRNA gene and 92 core genes clearly showed that these strains formed two distinct phylogenetic clusters comprising three and five strains, with average nucleotide identities significantly below 90 % in between both proposed species as well as between their closest phylogenetic relatives. Phenotyping revealed a unique pattern of biochemical and physiological characteristics enabling differentiation from the closest phylogenetically related Flavobacterium spp. Chemotaxonomic analyses showed that type strains P4023T and P7388T were characterized by the major polyamine sym-homospermidine and a quinone system containing predominantly menaquinone MK-6. In the polar lipid profile phosphatidylethanolamine, an ornithine lipid and two unidentified lipids lacking a functional group were detected as major lipids. These characteristics along with fatty acid profiles confirmed, that these species belong to the genus Flavobacterium. Thorough genomic analysis revealed presence of numerous cold-inducible or cold-adaptation associated genes, such as cold-shock proteins, proteorhodopsin, carotenoid biosynthetic genes or oxidative-stress response genes. Genomes of type strains surprisingly harboured multiple prophages, with many of them predicted to be active. Genome-mining identified biosynthetic clusters in type strain genomes with majority not matching any known biosynthetic genes which indicates further research possibilities involving these psychrotrophic bacteria. Antibiotic susceptibility testing revealed multidrug-resistant phenotype that was correlated with in silico antibiotic resistance prediction. Interestingly, while typical resistance finder tools failed to detect genes responsible for antibiotic resistance, genomic prediction confirmed multidrug-resistant profile and suggested even broader resistance than tested. Results of this study confirmed and thoroughly characterized two novel psychrotrophic Flavobacterium species, for which names Flavobacterium flabelliforme sp. nov. and Flavobacterium geliluteum sp. nov. are proposed

The Professional Speech and Slang of Musicians

The analysis of musicians{\crq} professional speech is the subject of this thesis. The main stress is put to the profiling of various ways the professional speech vocabulary is being enriched: by taking over expressions from foreign languages, semantic forming of lexical units (metaphor, metonymy) and creation of new words. Therefore, the attention is also focused on the word-building structure of the lexical units. Also, the presence or absence of the expressive token is being observed. The goal of the work is to draw attention to the vocabulary of musicians{\crq} professional speech from the linguistical point of view. The explanatory directory of terms used by musicians during excercise of their profession is the integral part of the work. For easier orientation, the index is also included

Genotyping of Klebsiella pneumoniae isolates

Tato diplomová práce se zabývá typizací kmenů bakterie Klebsiella pneumoniae. V první části jsou představeny metody typizace včetně jejich výhod a nevýhod. Následně je charakterizován bakteriální genom a popsána bakterie Klebsiella pneumoniae. V praktické části je uveden postup složení jednotlivých genomů včetně otestování jejich kvality a je představen navržený algoritmus pro nalezení variabilních úseků genů, které vykazují vyšší míru variability. Poté jsou uvedeny získané výsledky, které jsou následně otestovány na dalších genomech Klebsiella pneumoniae.This master thesis deals with typing of Klebsiella pneumoniae isolates. The first part of the thesis introduces molecular typing methods. Then bacterial genomes and Klebsiella pneumoniae are characterized. Following part describes data validation, assembly of genomes and proposed algorithm for finding genes with high variability. In last part obtained results are presented and validated on other genomes of Klebsiella pneumoniae.

Advanced Computational Methods for Increasing the Discriminatory Power of Genotyping Methods

Tato disertační práce je zaměřena na vytvoření nových výpočetních metod, které zvýší diskriminačních schopnost genotypizačních metod. Hlavní důraz je kladen na odlišení blízce příbuzných bakterií, které pocházejí například z jedné nemocnice či jednoho oddělení. V první části práce jsou popsány současné typizační metody a jsou představeny nové postupy pro identifikaci genetických markerů s vysokou mírou sekvenční variability, pomocí kterých lze lépe rozlišit bakteriální populaci. Navržené metody jsou založeny na výpočtu signálů entropie a analýze nenamapovaných čtení. Druhá část práce se zabývá návrhem nových metod zpracování surových dat z nanopórového sekvenování, které lze použít pro rychlou vysoce citlivou typizaci bakterií bez nutnosti převádět proudové signály na nukleotidové sekvence. Předložená práce přispívá ke zlepšení a zpřesnění rutinně používaných typizačních metod pomocí navržených bioinformatických postupů a představuje unikátní přístup využití doposud experimentální techniky nanopórového sekvenování pro rychlou genotypizaci a analýzu bakterií

Genotyping Of Klebsiella Pneumoniae Isolates

Typing methods capable of distinguishing bacterial strain are essential for epidemiologist because they help to prevent and control infections. Unfortunately, many typing methods are timeconsuming, expensive and reproducibility of their results is questionable. Therefore, we propose a new genotyping method, which is capable of identifying genes with high rate of variability to distinguish different bacterial strains

Using deep learning for gene detection and classification in raw nanopore signals

Recently, nanopore sequencing has come to the fore as library preparation is rapid and simple, sequencing can be done almost anywhere, and longer reads are obtained than with next-generation sequencing. The main bottleneck still lies in data postprocessing which consists of basecalling, genome assembly, and localizing significant sequences, which is time consuming and computationally demanding, thus prolonging delivery of crucial results for clinical practice. Here, we present a neural network-based method capable of detecting and classifying specific genomic regions already in raw nanopore signals—squiggles. Therefore, the basecalling process can be omitted entirely as the raw signals of significant genes, or intergenic regions can be directly analyzed, or if the nucleotide sequences are required, the identified squiggles can be basecalled, preferably to others. The proposed neural network could be included directly in the sequencing run, allowing real-time squiggle processing