An Extended Self-Organizing Map (ESOM) For Hierarchical Clustering

Hashemi, Ray R.; Bahar, Mahmood; Agostino, Sergio De

doi:10.1109/icsmc.2005.1571583

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Contigs were sized selected (>3 kbp), processed using the scripts provided as part of the tetramerFreqs/Binning package ( https://github.com/tetramerFreqs/Binning ) ( 15 ), and binned with Databionic emergent self-organizing map tools (ESOM; http://databionic-esom.sourceforge.net/ ) ( 16 ). Tetranucleotide frequencies were determined for the contigs by the esomWrapper.pl script.…”

Section: Methodsmentioning

confidence: 99%

Potential Mechanisms for Microbial Energy Acquisition in Oxic Deep-Sea Sediments

Tully

Heidelberg

2016

Appl Environ Microbiol

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The South Pacific Gyre (SPG) possesses the lowest rates of sedimentation, surface chlorophyll concentration, and primary productivity in the global oceans. As a direct result, deep-sea sediments are thin and contain small amounts of labile organic carbon. It was recently shown that the entire SPG sediment column is oxygenated and may be representative of up to a third of the global marine environment. To understand the microbial processes that contribute to the removal of the labile organic matter at the water-sediment interface, a sediment sample was collected and subjected to metagenomic sequencing and analyses. Analysis of nine partially reconstructed environmental genomes, which represent approximately one-third of the microbial community, revealed that the members of the SPG surface sediment microbial community are phylogenetically distinct from surface/upper-ocean organisms. These genomes represent a wide distribution of novel organisms, including deep-branching Alphaproteobacteria, two novel organisms within the Proteobacteria, and new members of the Nitrospirae, Nitrospinae, and candidate phylum NC10. These genomes contain evidence for microbially mediated metal (iron/manganese) oxidation and carbon fixation linked to nitrification. Additionally, despite hypothesized energy limitation, members of the SPG microbial community had motility and chemotaxis genes and possessed mechanisms for the degradation of high-molecular-weight organic matter. This study contributes to our understanding of the metabolic potential of microorganisms in deep-sea oligotrophic sediments and their impact on local carbon geochemistry.IMPORTANCE This research provides insight into the microbial metabolic potential of organisms inhabiting oxygenated deep-sea marine sediments. Current estimates suggest that these environments account for up to a third of the global marine sediment habitat. Nine novel deep-sea microbial genomes were reconstructed from a metagenomic data set and expand the limited number of environmental genomes from deep-sea sediment environments. This research provides phylogeny-linked insight into critical metabolisms, including carbon fixation associated with nitrification, which is assignable to members of the marine group 1 Thaumarchaeota, Nitrospinae, and Nitrospirae and neutrophilic metal (iron/manganese) oxidation assignable to a novel proteobacterium.

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Section: Methodsmentioning

confidence: 99%

Potential Mechanisms for Microbial Energy Acquisition in Oxic Deep-Sea Sediments

Tully

Heidelberg

2016

Appl Environ Microbiol

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“…Fig.10 shows the basic structure of SOM and this architecture maps high dimensional data on to a two dimensional rectangular grid of output layer and the input layer is fully connected with the output layer and the output neurons have lateral connections to their neighbours. In comparison with other commonly used clustering techniques, SOM has been broadly applied in data analysis and tremendous clustering and classification performance have been reported (Hashemi et al, 2005). Moreover, SOM provides comprehensible graphical illustration of the input data patterns, which has been applied to identify new vulnerability patterns while new threats or attacks amplify.…”

Section: Self-organizing Map (Som)mentioning

confidence: 99%

“…The motivation behind the GSOM is that the exact topology and the size of the map often have a large impact on the training process of the SOM and the map is determined by the statistical regularity not by the programmer. A detailed description of GSOM is given in (Hashemi et al, 2005).…”

Section: Self-organizing Map (Som)mentioning

confidence: 99%