Abstract
The plant transcriptome—from integrating observations to models
Highlights
Given the relative simplicity of doing so, much information has been gleaned from microarray datasets by assuming guiltby-association
Tohge and Fernie extend the use of the co-expression approach for the annotation of assumed gene function and discuss bringing in further experimental “evidence” as provided by metabolomics, proteomics, or physiological measurements (Tohge et al, 2005; De Boldt et al, 2012)
Junker et al (2012a) follow a similar direction extending on ideas put forward in their recent Trends in Biotechnology review (Junker et al, 2012b) here focusing their attention on visual analysis of the transcriptome. They provide an overview of plant transcriptomics repositories and detail how these can serve as useful resources for visualization programs such as HIVE as well as detailing how the color-coded output from such programs can be integrated with known biological networks using analysis of floral homeotic gene expression patterns and seed expression profiles as exemplary case studies
Summary
Given the relative simplicity of doing so, much information has been gleaned from microarray datasets by assuming guiltby-association. The success of this approach is summarized by articles of Provart (2012) and Tohge and Fernie (2012), as are recent studies that go beyond transcription and link in physiological and metabolic aspects.
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