The Pathway Tools Omics Viewer uses the Metabolic Overview for an organism to illustrate the results of high-throughput experiments in a global metabolic pathway context. Genes (in the case of a gene expression experiment) and proteins (in the case of a proteomics experiment) that are involved in metabolism are mapped to reaction steps in the Metabolic Overview, and the range of data values levels in a given experimental dataset is mapped to a spectrum of colors. Reaction steps in the Metabolic Overview are colored according to the corresponding data value. Similarly, for metabolomics experiments, compound nodes are colored according to the data value for the corresponding compound. This facility enables the user to see instantly which pathways are active or inactive under some set of experimental conditions.
The Omics Viewer can be used for:
The Omics Viewer can show absolute data values (such as the concentration of a metabolite or protein, or the absolute expression level of a gene), or it can be used to compare two sets of experimental data by computing a ratio and mapping the ratios onto a color spectrum.
The superposition of multiple sets of experimental data on the metabolic overview can also be animated to show, for example, how gene expression levels of enzymes change with time over the course of an experiment.
|Single gene expression experiment:||Sample datafile and brief description||Sample display|
|Time series metabolomics animation:||Sample datafile and brief description||Sample display|
Note that if your browser permits popups from this site, the links to the sample displays will also pop up a new browser window or tab showing the genome overview. The sample displays are being generated upon request, so may take several minutes. In order to minimize generation time, the sample animation display shows only 6 (time points 10-15) of the 17 time points included in the sample data file.
<name-or-ID> <data-column1>...<data-columnN>Columns are separated by the tab character. Lines that start with
;are taken to be comment lines and are ignored by the program.
<name-or-ID> can be either a common name for an object (the BioCyc data typically includes extensive synonym lists, and every attempt is made to match a name to the appropriate target), or the BioCyc internal ID for the object. Gene IDs from sequencing projects (such as the E. coli B-numbers) are generally acceptable and unambiguous. For protein or reaction data, EC numbers may be used. You must specify whether the entities in the <name-or-ID> column are genes, proteins, reactions, compounds, or a mixture.
The numbers in the data columns can represent either absolute or relative values. If the data values represent absolute numbers, you may choose to visualize either a single column of absolute data values (select "Absolute" and one data column), or the ratio of two data columns as relative data values (select "Relative" and two data columns). If the data values themselves represent relative numbers, then you need supply only a single column number, and select "Relative". An entry (a row of data for a gene or other object) may contain any number of data columns (for example, if you wish to compile measurements from several experiments or time points into a single file), but only those data columns specified will be visualized at a time -- all other columns will be ignored.
A maximum cutoff value is chosen. By default, this is computed from the data. Alternatively, the user may supply a maximum cutoff value to use. Supplying the same maximum cutoff value for multiple experiments ensures that the same color scale is used for each one, so that the displays are directly comparable.
The minimum cutoff value is determined based on the maximum cutoff value and the other parameters. For absolute data values, we use a minimum cutoff value of zero. For relative data values that are not logs, we use the inverse of the maximum cutoff. For relative data values that are logs, we use the negative of the maximum cutoff. The color spectrum is then mapped evenly along a log scale between the maximum cutoff and the minimum cutoff.
In many cases, several genes or proteins, each with their own expression level or concentration, will map to a single reaction. This is because the reaction might be catalyzed by an enzyme complex made up of several gene products, or the reaction might be catalyzed by several isozymes, each with its own gene or genes. Since a reaction can only be colored a single color, we must choose which data value to use. For absolute data values, we choose the maximum. For relative data values, we choose the value whose log has the greatest deviation from zero, under the assumption that the user is primarily interested in identifying the entities whose behavior differ most between the two datasets.
Note that older browsers that do not support Dynamic HTML will not be able to run the animation.