Abstract
The complexity and temporal as well as spatial resolution of transcriptome datasets is constantly increasing due to extensive technological developments. Here we present methods for advanced visualization and intuitive exploration of transcriptomics data as necessary prerequisites in order to facilitate the gain of biological knowledge. Color-coding of structural images based on the expression level enables a fast visual data analysis in the background of the examined biological system. The network-based exploration of these visualizations allows for comparative analysis of genes with specific transcript patterns and supports the extraction of functional relationships even from large datasets. In order to illustrate the presented methods, the tool HIVE was applied for visualization and exploration of database-retrieved expression data for master regulators of Arabidopsis thaliana flower and seed development in the context of corresponding tissue-specific regulatory networks.
Highlights
The development of complex multicellular plant structures and the coordination of plant growth in response to external and internal stimuli are the result of elaborate gene activity patterns in space and time
As a result of increasing complexity and resolution transcriptomics datasets are able to cover the whole development of plant organs in its multitude of spatial and temporal dimensions such as the transcriptomic landscape of Arabidopsis seed development (Le et al, 2010) and the atlas of Arabidopsis root gene expression (Birnbaum et al, 2003)
We demonstrate its applicability by visualizing database-retrieved expression values of transcription factor genes implicated with Arabidopsis thaliana flower and seed development in the context of the corresponding anatomical structures and by integration of these images into organ-specific gene-regulatory networks
Summary
The development of complex multicellular plant structures and the coordination of plant growth in response to external and internal stimuli are the result of elaborate gene activity patterns in space and time. Bioanalytical advances and the accompanying decrease of costs for high-throughput technologies led to the generation of comprehensive transcriptomics datasets spanning multiple developmental stages, as well as different tissues and organs Sampling techniques such as laser microdissection (Nelson et al, 2006; Day, 2010) allow the precise isolation of very small amounts of tissue thereby substantially increasing the resolution of transcriptomics analyses from the level of small tissues (Cai and Lashbrook, 2008; Brooks et al, 2009; Matas et al, 2011; Endo et al, 2012) to the level of single cells (Nelson et al, 2008; Schmidt et al, 2011; Thiel et al, 2012; Yang et al, 2012). As a result of increasing complexity and resolution transcriptomics datasets are able to cover the whole development of plant organs in its multitude of spatial and temporal dimensions such as the transcriptomic landscape of Arabidopsis seed development (Le et al, 2010) and the atlas of Arabidopsis root gene expression (Birnbaum et al, 2003)
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