StropE: Streptomyces operon visualisation server

  restart analysis




This site is a customised version of Operate, a tool that allows users to visualise the Pearson correlation and polarity (if any) of expression (or other -omic derived data) across a predicted operon or set of adjacent genes in a particular genome.

How to use

To visualise the correlation and/or polarity of expression across the operon of interest you will need to perform three steps before submitting the information:

  1. Enter a single gene name of interest to retrieve the predicted operon for that gene or two gene names denoting the start and end of a set of adjacent genes in the form of start_gene:end_gene . Please note that the gene names or synonyms accepted are only those documented in the NCBI RefSeq file for the genome of choice. Please be aware that non-unique synonyms due to paralogous copies of the same gene annotated in the genome exist can cause errors in the software, it is therefore reccommended that the unique gene identifer (e.g. SCO00001 or AM1_0003) is used to select a particular gene(s).
  2. Select the genome of interest. This should be the genome in which you know that the unique gene identifier entered in Step 1 exists.
  3. Select the data source to be used for correlation and polarity calculations. Depending on the available information for your chosen genome you may be able to choose from pre-loaded microarray experiments or experiments stored in a maxd database and/or upload your own data file. The data file should be a tab-delimited text (.txt) file with genes as the rows and experiments as the columns, a header row is optional. The data file must have one column containing unique names that can be used as a gene identifier column (this must contain the gene identifiers denoted by your genome e.g. AM_0001 or SCO0001 NOT common names e.g. thrL) and at least two columns containing numerical values to be correlated. It is possible to ignore columns for selecting particular conditions (columns of data) to use. Missing values can be represented by NA, na, NaN or nan. Of course the gene(s) of interest must be represented within the data file, if not an error will be produced and no output will be given; to avoid this scenario missing genes in your data set should be represented by columns of NA. An example data file can be found here.

Source of operon predictions

Operon predictions are taken from the work of Laing and co-workers (in preparation).



Please contact with any queries or problems.

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