Metagenomics promises to shed light on the functioning of microbial communities and their surrounding ecosystem. In metagenomic studies the genomic sequences of a collection of microorganisms are directly extracted from a specific environment. Up to 99% of microbes cannot be cultivated in the lab; thus, traditional analysis techniques have very limited applicability in this challenging setting. By directly extracting the sequences from the environment, metagenomic studies circumvents this dilemma. Thus, metagenomics has become a powerful tool in the analysis of the diversity and metabolic capability of environmental microbes. However, metagenomic studies have challenges of their own.
In this thesis we investigate several aspects of metagenomic data set analysis, focusing on means of (1) verifying adequacy of taxonomic unit and enzyme representation and annotation in the sample, (2) highlighting similarities between samples by principal component analysis, (3) visualizing metabolic pathways with manually drawn metabolic maps from the Kyoto Encyclopedia of Genes and Genomes, and (4) estimating taxonomic distributions of pathways with a novel strategy.
A case study of deep bedrock groundwater metagenomic samples will illustrate these methods. Water samples from boreholes, up to 2500 meter deep, of two different sites of Finland display the applicability and limitations of aforementioned methods. In addition publicly available metagenomic and genomic samples serve as baseline references.
Our analysis resulted in a taxonomic and metabolic characterization of the samples. We were able to adequately retrieve and annotate the metabolic content based on the deep bedrock samples. The visualization provided a tool for further investigation. The microbial community distribution could be characterized on higher levels of abstraction. Previously suspected similarities to fungi or archaea were not verified. First promising results were observed with the novel strategy in estimating taxonomic distributions of pathways.
Further results can be found at: http://www.cs.helsinki.fi/group/urenzyme/deepfun/
