Information-Theory Analysis of Skewed Coding for Suppression of Pattern-Dependent Errors in Digital Communications

2007 ◽  
Vol 55 (2) ◽  
pp. 237-241 ◽  
Author(s):  
Alexander Shafarenko ◽  
Konstantin S. Turitsyn ◽  
Sergei K. Turitsyn
Keyword(s):  
Information Theory ◽  
Digital Communications ◽  
Theory Analysis

scholarly journals Illuminating a plant’s tissue-specific metabolic diversity using computational metabolomics and information theory

2016 ◽  
Vol 113 (47) ◽  
pp. E7610-E7618 ◽  
Author(s):  
Dapeng Li ◽  
Sven Heiling ◽  
Ian T. Baldwin ◽  
Emmanuel Gaquerel
Keyword(s):  
Information Theory ◽  
Secondary Metabolism ◽  
Gene Function ◽  
Tissue Level ◽  
Two Dimensions ◽  
Molecular Networks ◽  
Specific Gene ◽  
Theory Analysis ◽  
Tissue Specific ◽  
Metabolite Diversity

Secondary metabolite diversity is considered an important fitness determinant for plants’ biotic and abiotic interactions in nature. This diversity can be examined in two dimensions. The first one considers metabolite diversity across plant species. A second way of looking at this diversity is by considering the tissue-specific localization of pathways underlying secondary metabolism within a plant. Although these cross-tissue metabolite variations are increasingly regarded as important readouts of tissue-level gene function and regulatory processes, they have rarely been comprehensively explored by nontargeted metabolomics. As such, important questions have remained superficially addressed. For instance, which tissues exhibit prevalent signatures of metabolic specialization? Reciprocally, which metabolites contribute most to this tissue specialization in contrast to those metabolites exhibiting housekeeping characteristics? Here, we explore tissue-level metabolic specialization in Nicotiana attenuata, an ecological model with rich secondary metabolism, by combining tissue-wide nontargeted mass spectral data acquisition, information theory analysis, and tandem MS (MS/MS) molecular networks. This analysis was conducted for two different methanolic extracts of 14 tissues and deconvoluted 895 nonredundant MS/MS spectra. Using information theory analysis, anthers were found to harbor the most specialized metabolome, and most unique metabolites of anthers and other tissues were annotated through MS/MS molecular networks. Tissue–metabolite association maps were used to predict tissue-specific gene functions. Predictions for the function of two UDP-glycosyltransferases in flavonoid metabolism were confirmed by virus-induced gene silencing. The present workflow allows biologists to amortize the vast amount of data produced by modern MS instrumentation in their quest to understand gene function.

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