Transcriptomic tools

From Listeriomics Wiki
Jump to: navigation, search

General organization[edit]

A total of 492 files, 150 absolute value data, 342 relative expression data, were created and integrated in the Listeriomics website. For every experiment, relative expression data were always available whereas absolute expression data was found only in a quarter of the data.

The transcriptomics tools can be accessed by clicking on the button on the right of the banner of the Listeriomics website.

Transcriptomic datasets summary page

Filter panel

With this panel one can filter out the list of transcriptomics datasets by different key properties: Genome, Data type, intracellular growth. A search bar is available to do so, or by clicking on the check box. In parenthesis is displayed the number of datasets available for each property.

List of all Listeria transcriptomic datasets with biological condition information

All transcriptomic datasets available are displayed with all additional information: “TimePoint” for growth time; “Mutant” if a gene is removed or over-expressed; “Growth” indicates the growth phase from which the bacteria were extracted; “Temperature” for the growth media; “Media” gives the media properties for example if Glucose was added, or if bacteria were extracted from macrophages; “Strain used” and “Strain array” indicates the strain used for the experiment, and the strain used for designing the arrays or mapping RNASeq reads.

Link to Genome and Heatmap viewers

If transcriptomic datasets are selected in the summary table, there are two possibilities: Display them on the genome viewer for direct visualization, or look at each dataset in the heatmap viewer.

Transcriptomic database[edit]

We downloaded the 64 Listeria-related ArrayExpress experiments in MAGE-TAB standard. Every MAGE-TAB file included an IDF (Investigation Description Format) file giving general information about the experiment, SDRF (Sample and Data Relationship Format) file giving data relationship, and processed data. We manually curated every SDRF file and integrated the different information about the data.