The Omics Dashboard for interactive exploration of gene-expression data

Abstract

The Omics Dashboard is a software tool for interactive exploration and analysis of gene-expression datasets. The Omics Dashboard is organized as a hierarchy of cellular systems. At the highest level of the hierarchy the Dashboard contains graphical panels depicting systems such as biosynthesis, energy metabolism, regulation and central dogma. Each of those panels contains a series of X-Y plots depicting expression levels of subsystems of that panel, e.g. subsystems within the central dogma panel include transcription, translation and protein maturation and folding. The Dashboard presents a visual read-out of the expression status of cellular systems to facilitate a rapid top-down user survey of how all cellular systems are responding to a given stimulus, and to enable the user to quickly view the responses of genes within specific systems of interest. Although the Dashboard is complementary to traditional statistical methods for analysis of gene-expression data, we show how it can detect changes in gene expression that statistical techniques may overlook. We present the capabilities of the Dashboard using two case studies: the analysis of lipid production for the marine alga Thalassiosira pseudonana, and an investigation of a shift from anaerobic to aerobic growth for the bacterium Escherichia coli.

Figure 1.

Omics Dashboard showing changes in Escherichia coli gene expression in a 10 min time course following a shift to aerobic growth. Normalized average RNA-seq read counts of significantly differentially expressed genes are shown in a log scale on the Y-axis. T0 represents samples drawn before the shift to aerobic growth and T0.5, T1, T2, T5 and T10 are samples drawn at 0.5, 1, 2, 5 and 10 min after aeration started at 1 l/min.

Figure 2.

(A) The Amino Acid Biosynthesis panel summarizes the expression levels of genes involved in biosynthesis of each amino acid; because of space limitations we omit the portion of the diagram after glycine. (B) Clicking on the Arg plot in the window in (A) produces the window shown in (B), which depicts the expression levels of each individual gene involved in biosynthesis of L-arginine; this diagram is truncated to the right of argG.

Figure 3.

A zoom-in view into the glyoxylate bypass and TCA (tricarboxylic acid) cycle, a pathway that shows significant changes in gene expression. Multiple software windows are superimposed here to provide a compact illustration of the options available. The ‘Show/Filter Regulators’ option displays a panel (labeled ‘Regulators of TCA’) with a gene-expression profile of the regulators that control the pathway. Choosing a regulator (‘Regulators selection’): the box next to Cra is checked to show in the regulators panel the subset of genes in the pathway that are regulated by Cra. The ‘Show Pathway(s)’ option displays the pathway with gene-expression heat maps. The ‘Show Operons’ option displays all the operons in the pathway, and the positions of the regulators and their effects are either experimentally verified or computationally and human inferred. Only a subset of operons is shown here.

Figure 4.

The Omics Dashboard analytics tool provides a visual comparison of the gene-expression data from five experimental conditions. It is quickly apparent that the low-temperature condition (T, green) indicates a transition into a more optimal lipid accumulation environment. It is expressing genes to increase lipid synthesis, decrease lipid degradation and decrease carbohydrate synthesis. Note that the default display for these panels has been customized to remove subsystems not relevant to the analysis. The small dots represent the fold differences in expression between a treatment sample set and the nutrient-replete 20°C control samples using a base 2-logarithmic scale. The large dot represents the average of the small-dot values and the bar represents the sum of the small dot values.

Figure 5.

A drill down into the pathways that contribute to biosynthesis gives insight into what kinds of lipids are being synthesized. In the case of low temperature (green), palmitic acid and elongation have increased significantly, as well as very long chain fatty acid synthesis and unsaturated fatty acid synthesis. This is an indication that the metabolic system is in a state suitable for the production of omega 3 fatty acids, EPA and DHA. The small dots represent the fold differences in expression between a treatment sample set and the nutrient-replete 20°C control samples using a base 2-logarithmic scale. The large dot represents the average of the small-dot values and the bar represents the sum of the small dot values.

Figure 6.

The energy panel indicates that in the low-temperature (green) condition, glycolysis is upregulated to increase pyruvate production, photosynthesis has increased and carbon fixation is similar to the room temperature, nutrient-replete condition. These indicators are favorable for lipid accumulation. The small dots represent the fold differences in expression between a treatment sample set and the nutrient-replete 20°C control samples using a base 2-logarithmic scale. The large dot represents the average of the small-dot values and the bar represents the sum of the small dot values.

Figure 7.

Gene-expression profiles at the regulon level of 133 Escherichia coli genes involved in fermentation, respiration, glycolysis, TCA and in the glyoxylate bypass. Each vertical line depicts the expression levels at one time point of all genes controlled by the indicated transcriptional regulator. The bars represent the total counts for all genes on the vertical line within the bar. Regulons are sorted by the ‘Decreasing Maximum among series (Sum)’ option. Underneath a regulon name is the number of genes reported present in that regulon.

Figure 8.

Sigma factors panel from the Dashboard for Escherichia coli anaerobic to aerobic shift (linear scale).