Dataset Explorer

Data Explorer

This interactive figure presents the data assembled from the corresponding data sets1-4 after association of each protein with the appropriate macromolecular complex, functional assignment, and standardization (i.e. scaling to cell volume). This figure has two tabs – ‘Explore By Complex Function’ and ‘Explore By Individual Protein’ – which are clickable at the top of the interactive and are described below:

Explore By Complex Function

In this panel, you can explore each macromolecular complex detected across the data sets. These complexes are broken down by their Clusters of Orthologous Groups (COG) functional assignments. Selecting a COG category and a macromolecular complex triggers visualization of several features of the complex and its association with the data.

Plot of complex abundance vs growth rate. This plot shows the number of complexes present in each data set as a function of the growth rate. The number of complexes is computed by default as the mean number functional complexes that could be assembled given the observed abundances of each individual protein and its stoichiometry in the macromolecular complex. The method of aggregation can be selected by the aggregation button group to the right of the plot (minimum, maximum, median, and mean are provided aggregation functions). Note that for complexes consisting of a single protein, all aggregation functions produce the same result

EcoCyc complex record. The complex identification number is given on the right-hand side below the aggregation method buttons. This is presented as a clickable link to the EcoCyc web page associated with that complex

Explore by Individual Protein

Like the complex visualization, all individual proteins present in the data sets are assigned to a particular COG classification. Selecting a COG subclass populates another drop-down menu where individual proteins can be selected, populating the plot on the left-hand side. Below this dropdown is information about the annotated function of the protein of interest.

Bokeh Plot

This interactive figure was made using the Bokeh Python plotting library. The code and data used to generate this figure can be found on the GitHub Repository.

References

  1. Li, G.-W., Burkhardt, D., Gross, C., and Weissman, J. S. (2014). Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell, 157(3):624–635.
  2. Peebo, K., Valgepea, K., Maser, A., Nahku, R., Adamberg, K., and Vilu, R. (2015). Proteome reallocation in Escherichia coli with increasing specific growth rate. Molecular BioSystems, 11(4):1184–1193.
  3. Schmidt, A., Kochanowski, K., Vedelaar, S., Ahrné, E., Volkmer, B., Callipo, L., Knoops, K., Bauer, M., Aebersold, R., and Heinemann, M. (2016). The quantitative and condition-dependent Escherichia coli proteome. Nature Biotechnology, 34(1):104–110.
  4. Valgepea, K., Adamberg, K., Seiman, A., and Vilu, R. (2013). Escherichia coli achieves faster growth by increasing catalytic and translation rates of proteins. Molecular BioSystems, 9(9):2344.