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Methods for Comparative Analysis of Metagenomic Data
Sedlář, Karel ; Vinař,, Tomáš (oponent) ; Lexa, Matej (oponent) ; Provazník, Ivo (vedoucí práce)
Modern research in environmental microbiology utilizes genomic data, especially sequencing of DNA, to describe microbial communities. The field studying all genetic material present in an environmental sample is referred to as metagenomics. This doctoral thesis deals with metagenomics from the perspective of bioinformatics that is unreplaceable during the data processing. In the theoretical part of this thesis, two different approaches of metagenomics are described including their main principles and weaknesses. The first approach, based on targeted sequencing, is a well-established field with a wide range of bioinformatics techniques. Yet, methods for comparison of samples from several environments can be highly improved. The approach introduced in this thesis uses unique transformation of data into a bipartite graph, where one partition is formed by taxa, while the other by samples or environments. Such a graph fully reflects qualitative as well as quantitative aspect of analyzed microbial networks. It allows a massive data reduction to provide human comprehensible visualization without affecting the automatic community detection that can found clusters of similar samples and their typical microbes. The second approach utilizes whole metagenome shotgun sequencing. This strategy is newer and the corresponding bioinformatics techniques are less developed. The main challenge lies in fast clustering of sequences, in metagenomics referred to as binning. The method introduced in this thesis utilizes a genomic signal processing approach. By thorough analysis of redundancy of genetic information stored in genomic signals, a unique technique was proposed. The technique utilizes transformation of character sequences into several variants of phase signals. Moreover, it is able to directly process nanopore sequencing data in the form of a native current signal.
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Application Of Optimization Algorithms To
The Genome Assembly
Jugas, Robin
The paper results from development of new sequencing methods together with the need of suitable genome assembly algorithms. It combines the genomic signal processing, correlation techniques and optimization algorithms for solving assembly task. Genomic signals are made by conversion of letter-based DNA into the form of digital signal, thus the methods of digital signal processing can be applied. Possible overlaps between reads converted into signals are found by computing correlation coefficient similarly to cross-correlation. We acquire similarity matrix and the task is to find the path through it achieving minimum distance criterion. For the task, the two optimization techniques were employed: ant colony optimization (ACO) and simulated annealing (SA). The result implies the possibility of using the ACO at the task of creating path through similarly to graphtheory-based algorithms.
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Methods for Comparative Analysis of Metagenomic Data
Sedlář, Karel ; Vinař,, Tomáš (oponent) ; Lexa, Matej (oponent) ; Provazník, Ivo (vedoucí práce)
Modern research in environmental microbiology utilizes genomic data, especially sequencing of DNA, to describe microbial communities. The field studying all genetic material present in an environmental sample is referred to as metagenomics. This doctoral thesis deals with metagenomics from the perspective of bioinformatics that is unreplaceable during the data processing. In the theoretical part of this thesis, two different approaches of metagenomics are described including their main principles and weaknesses. The first approach, based on targeted sequencing, is a well-established field with a wide range of bioinformatics techniques. Yet, methods for comparison of samples from several environments can be highly improved. The approach introduced in this thesis uses unique transformation of data into a bipartite graph, where one partition is formed by taxa, while the other by samples or environments. Such a graph fully reflects qualitative as well as quantitative aspect of analyzed microbial networks. It allows a massive data reduction to provide human comprehensible visualization without affecting the automatic community detection that can found clusters of similar samples and their typical microbes. The second approach utilizes whole metagenome shotgun sequencing. This strategy is newer and the corresponding bioinformatics techniques are less developed. The main challenge lies in fast clustering of sequences, in metagenomics referred to as binning. The method introduced in this thesis utilizes a genomic signal processing approach. By thorough analysis of redundancy of genetic information stored in genomic signals, a unique technique was proposed. The technique utilizes transformation of character sequences into several variants of phase signals. Moreover, it is able to directly process nanopore sequencing data in the form of a native current signal.
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