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Numerical methods for classification of metagenomic data
Vaněčková, Tereza ; Sedlář, Karel (referee) ; Škutková, Helena (advisor)
This thesis deals with metagenomics and numerical methods for classification of metagenomic data. Review of alignment-free methods based on nucleotide word frequency is provided as they appear to be effective for processing of metagenomic sequence reads produced by next-generation sequencing technologies. To evaluate these methods, selected features based on k-mer analysis were tested on simulated dataset of metagenomic sequence reads. Then the data in original data space were enrolled for hierarchical clustering and PCA processed data were clustered by K-means algorithm. Analysis was performed for different lengths of nucleotide words and evaluated in terms of classification accuracy.
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Web Application for Primer Selection for 16S rRNA Amplicon Sequencing
Jurča, Jan ; Martínek, Tomáš (referee) ; Smatana, Stanislav (advisor)
Main goal of this thesis was an implementation of web application, that can evalutate and recommend primer pairs for 16S rRNA amplicon sequencing, according to users specific needs and by this simplify the procces of primer pair selection. Primer pair evaluation is based on database of primer pairs, which contains data about senzitivity and specifity of each primer pair. Part of the work was also the performance optimization of the primer pair analysis algorithm, that computes senzitivity and specifity data. This optimization helped in an integration of algorithm into the application, which means that users can submit their own primer pair sequences, run analysis and get informations about position of amplified region, sensitivity and specifity.
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The use of parallel sequencing methods in microbiology.
Pavlíková, Magdaléna ; Najmanová, Lucie (advisor) ; Vopálenský, Václav (referee)
The thesis describes the history of development of sequencing methods with special focus on the modern effective parallel sequencing methods and their application in microbiology. The development and improvements of sequencing systems lead to the acceleration of the process and considerable decrease of price, which consequently allow wider spectrum of applications. Each of the sequencing systems has its characteristic features including drawbacks stemming from the principle of the respective method. Not every method suitable for all the applications. In the thesis the sequencing methods are compared and examined with respect to their appropriateness for certain application fields in microbiology. The currently available sequencing methods are usually categorized into three "generations", distinguished by sets of typical features. First generation methods include the systems of Sanger and Maxam-Gilbert; "next generation" is represented by methods 454, Illumina, SOLiD and Helicos; and finally SMRT, Ion Torrent and the commercially not yet available nanopore sequencing are usually called "next-next generation". Now the sequencing becomes a standard technology of molecular biology, not only in the basic microbiological research, but it is also widely applied in medicine (quick identification of patogenes,...
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Tool for Visualization of Microbiome Data
Mišáková, Silvia ; Martínek, Tomáš (referee) ; Smatana, Stanislav (advisor)
Táto práca sa zameriava na vytvorenie nového nástroja pre vizualizáciu mikrobiomových dát. Vytvorený nástroj používa pre redukciu dimenzií analýzu hlavných komponent (PCA) a analýzu hlavných súradníc (PCoA). V prípade výpočtu dištančnej matice sú použité metriky Bray-Curtis odlišnosť a UniFrac. Spracované dáta sú následne ofarbené na základe užívateľom zvolených metadát. Výsledky sú prezentované pomocou dvoch typov grafov. Prvý z nich je stĺpcový a zobrazuje podiel každej hlavnej zložky. Druhý, bodový graf, vizualizuje konečný výsledok požadovanej analýzy. V rámci práce bola pridaná možnosť stiahnuť si vypočítanú maticu a taktiež tabuľku prvých N hlavných zložiek vypočítaných danou analýzou.
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Detection of chimeras in amplicon sequencing
Heřmánková, Kristýna ; Jurečková, Kateřina (referee) ; Sedlář, Karel (advisor)
Chimeric sequences are the most common artifacts that can occur in sequencing data after the sample amplification using the polymerase chain reaction. The presence of these artifacts can negatively affect results of the analysis. Therefore, the detection and subsequent filtration of chimeric sequences is an important step in the computational processing of sequencing data. This work deals with the principle of chimera formation and the possibility of reducing their occurrence. The aim of this work is to implement an algorithm for chimeras detection in R language and testing its accuracy on data provided by the Veterinary Research Institute in Brno.
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Bacteria Classification Based on Marker Genes
Pelantová, Lucie ; Hon, Jiří (referee) ; Smatana, Stanislav (advisor)
The aim of this work is proposal of new method for bacteria classification based on sequences of marker genes. For this purpose was chosen 10 marker genes. Resulting MultiGene classifier processes data set by dividing it in several groups and choosing gene for each group which can distinguish this group with best results. This work describes implementation of MultiGene classifier and its results in comparison with other bacteria classifiers and with classification based entirely on gene 16S rRNA.
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Bioinformatic Tool for Estimation of Abundances of Bacterial Functional Molecules in Biological Samples Based on 16S rRNA Metagenomic Data
Bieliková, Michaela ; Hon, Jiří (referee) ; Smatana, Stanislav (advisor)
Ľudské telo je prostredím pre život neuveriteľného množstva mikróbov. Niektoré z nich môžu spôsobovať rôzne choroby, ale ďalšie, napríklad črevný mikrobióm, sú pre život a zdravie človeka nepostrádateľné. Nanešťastie, črevný mikrobióm nie je detailne preštudovaný, pretože obsahuje tisíce rôznych druhov baktérií, z ktorých väčšina sa nedá kultivovať v laboratórnych podmienkach. Riešením tohto problému sú nové rýchle metódy sekvenovania v kombináciou s bioinformatickými nástrojmi na výpočet funkčného profilu baktérií vo vzorke. V tejto práci si predstavíme existujúce nástroje predpovedajúce funkčný profil, a následne navrhneme nový nástroj, ktorý môže implementovať konsenzus nad výsledkami existujúcich nástrojov, alebo sa môže jednať o úplne nový nástroj.
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Bioinformatic Tool for Classification of Bacteria into Taxonomic Categories Based on the Sequence of 16S rRNA Gene
Valešová, Nikola ; Hon, Jiří (referee) ; Smatana, Stanislav (advisor)
Tato práce se zabývá problematikou automatizované klasifikace a rozpoznávání bakterií po získání jejich DNA procesem sekvenování. V rámci této práce je navržena a popsána nová metoda klasifikace založená na základě segmentu 16S rRNA. Představený princip je vytvořen podle stromové struktury taxonomických kategorií a používá známé algoritmy strojového učení pro klasifikaci bakterií do jedné ze tříd na nižší taxonomické úrovni. Součástí práce je dále implementace popsaného algoritmu a vyhodnocení jeho přesnosti predikce. Přesnost klasifikace různých typů klasifikátorů a jejich nastavení je prozkoumána a je určeno nastavení, které dosahuje nejlepších výsledků. Přesnost implementovaného algoritmu je také porovnána s několika existujícími metodami. Během validace dosáhla implementovaná aplikace KTC více než 45% přesnosti při predikci rodu na datových sadách BLAST 16S i BLAST V4. Na závěr je zmíněno i několik možností vylepšení a rozšíření stávající implementace algoritmu.
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Tool for Classification of Lifestyle Traits Based on Metagenomic Data from the Large Intestine
Kubica, Jan ; Hon, Jiří (referee) ; Smatana, Stanislav (advisor)
This thesis deals with analysis of human microbiome using metagenomic data from large intestine. The main focus is placed on bacteria composition in a sample on different taxonomic levels regarding the lifestyle traits of an individual. For this purpose, a tool for classification of several attributes was created. It considers attributes like diet type and eating habits (vegetarian, vegan, omnivore), gluten and lactose intolerance, body mass index, age or sex. From range of machine learning perspectives considering K Nearest Neighbours (kNN), Random Forest (RF) and Support Vector Machines (SVM) were used. Datasets for training and final evaluation of the classifier were taken from American Gut project. The thesis also focuses on particular problems with metagenomic datasets like its multidimensionality, sparsity, compositional character and class imbalance.
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