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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.
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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.
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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.
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Experimentální srovnání triangulačních heuristik na transformovaných sítích BN2O
Vomlel, Jiří ; Savický, Petr
In this paper we present results of experimental comparisons of several triangulation heuristics on bipartite graphs. Our motivation for testing heuristics on the family of bipartite graphs is the rank-one decomposition of BN2O networks. A BN2O network is a Bayesian network having the structure of a bipartite graph with all edges directed from the top level toward the bottom level and where all conditional probability tables are noisy-or gates. After applying the rank-one decomposition, which adds an extra level of auxiliary nodes in between the top and bottom levels, and after removing simplicial nodes of the bottom level we get so called BROD graph. This is an undirected bipartite graph. It is desirable for efficiency of the inference to find a triangulation of the BROD graph having the sum of table sizes for all cliques of the triangulated graph as small as possible. From this point of view, the minfill heuristics perform in average better than other tested heuristics (minwidth, h1, and mcs).
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