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	Bipartite graphs for microbiome analysis Šafárová, Marcela ; Provazník, Ivo (referee) ; Sedlář, Karel (advisor) Microorganisms are all around us. Some of them even live in our body and are essential for our healthy being. Study of microbial communities based on their genetic content has become very popular with the development of new technologies, which enable easy reading of DNA or RNA. The key role of these studies is usually to characterize significant microbial patterns of an environment. However, currently used visualization tools have many drawbacks for such analyses. The subject of this thesis is to design a R/Bioconductor package for simple creation of bipartite graphs from microbial data. This type of visualization brings many advantages for microbiome analysis. Benefits of bipartite graphs are further demonstrated by analysis of main parameters affecting computer processing of microbial data. Detailed record
	Analysis Of Main Otu Picking Methods By Bipartite Graphs Šafárová, Marcela Studies of ecosystems containing millions of microbial organisms, so called microbiomes, using DNA sequencing became very popular. The main step involves fast clustering of sequences belonging to the same operational taxonomic units (OTU) that represent the same or taxonomically related organisms. This step, known as OTU picking, can be performed by several techniques and significantly affects the final interpretation of the data. In this paper, a novel R/Biconductor package for microbiome data presentation using bipartite graphs is introduced while its benefits are demonstrated during comparison of 3 OTU picking techniques. Detailed record
	Bipartite graphs for microbiome analysis Šafárová, Marcela ; Provazník, Ivo (referee) ; Sedlář, Karel (advisor) Microorganisms are all around us. Some of them even live in our body and are essential for our healthy being. Study of microbial communities based on their genetic content has become very popular with the development of new technologies, which enable easy reading of DNA or RNA. The key role of these studies is usually to characterize significant microbial patterns of an environment. However, currently used visualization tools have many drawbacks for such analyses. The subject of this thesis is to design a R/Bioconductor package for simple creation of bipartite graphs from microbial data. This type of visualization brings many advantages for microbiome analysis. Benefits of bipartite graphs are further demonstrated by analysis of main parameters affecting computer processing of microbial data. Detailed record

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