In-silico knockout and over expression
Metabolizer allows you to simulate effect of knockout (KO) and/or over-expression (OE) of one or several genes or the effect of drug(s) in the activity of metabolic modules.
2 different usage of Knockout tool exist:
The tool can be accessed from the main menu bar, by clicking on the Knockout button, see Workflow for further information.
The main page of the tool is its filling in form. This form includes all the information and parameters that the tool needs to process a job. The form is divided in different panels:
Once the form has been filled in, press the Launch job button to launch a job. Your job will be listed in the My jobs panel, see Workflow for further information.
1) Using sample
- If you upload expression data for 1 sample, you do not need to upload design data and can not use auto knockout option.
2) Using a dataset (Multiple Samples)
OUTPUT
The results page of the Knockout tool includes different output results. You can download any table or image showed in the results page by clicking on the name right above it.
When the job is done you will get results and and interactive KO/OE page. Below you can find explanation of each section you will see on the results page.
1-2) This section shows the input data and parameters that you used for current job. Missing genes are the genes which are not contained by the expression data you provided. Therefore user may not use them for knockout or over expression.
3) An informative plot for the normalized (between 0 and 1) gene expression values.
4) Class probability table shows results of the auto knockout. Table contains class probability of selected sample before knockout and after knockout. On this table you can find results for manipulation of all metabolic module genes. e.g. If the dataset contains only tumor and normal tissue samples and if the selected sample is a tumor sample, in this case decrease in class probability after KO will be what you may observe for finding efficient anti-tumor drug or target. The table is sorted by ProbabilityDiff. column. User can combine genes which have highest probability difference and test their effect applying a new manual KO/OE.
5) The interactive pathway viewer lets user to see the results in network structure. Please check the color and shape codings of pathway viewer. You can right click on nodes to get detailed information about them. Left click will show you the expression value of the gene(s)which involved with the selected reaction. You can modify the expression value from these window (7).
6) If you have list of genes which you want to knockout. You can search them and update their expression values on add genes panel If selected genes targeted by any drug or chemical, they will shown on “selected gene related drugs panel” (8). User can choose any of these drugs to knockout/overexpress the genes.
9) If you have a list of drugs which you want to know their effects on activity of metabolic modules. You can search drugs on “add drugs” panel. Genes which are targeted by drug you selected on add drugs panel will be shown on genes affected by drugs panel (10).
11) You can search and navigate between metabolic networks using Pathways section. tide the selected metabolic pathway. In this section you can select module and highlight it to see enire module structure.
13) Once you set all knockout/overexpresion rules, you can click on update button to calculate the effect of the gene expresion manuplations.
14) If you want to return initial results and do new knockout/overexpression combinations. You can click on clear button.sdcasd
When update process finishes you will see your results on the same page.
15) This table shows which genes you knockout/overexpressed, type of the knockout and weigth of the manuplation.
16) Module impact table shows which modules and how they are affected from gene expression manipulations. Under this table you can find class probabilty of sample for before and after gene expreesion mauolatin