C. Redoing everything for Cheek Swab

(To get back to index, click here)

Now let's convert the entire history into another workflow, which we will apply to Cheek Swab samlpes

C.1. Convert history to a new workflow

Click the options button and select "Extract Workflow":

Name the workflow "PCR replicate preparation":

and click the "Create Workflow" button

C.2. Rename your current history

So far our history had no name:

This is not good. Let's click on "Unnamed history", type "Blood" and hit Enter:

C.3. Create a new history

Now its time to start processing cheek swab samples. To make things simple let's create a new history called "cheek". To do this click "Options" and select "Create New":

This will give you a new unnamed history, which we will call "cheek" just like we did in C.2. above:

C.4. Import Cheek datasets.

Click Library link at the top of Galaxy interface:

Select library "mtProjectDemo" by clicking on its name:

Click checkboxes next to the two Cheek datasets and click "Go" button:

Click "Analyze Data" at the top of Galaxy interface :

and you will see this:

C.5. Analyze Cheek datasets

In section C.1. we created a workflow that will now use to analyze cheek datasets in one click. To do so click on "Workflow" link at the top of Galaxy interface:

select "PCR replicate preparation" workflow we have created in C.1. and click "Run":

Provide the two datasets in your history as inputs:

scroll down and click "Run workflow":

That's it! In just one click you replicated the entire analysis we discussed in sections A and B! Now go get some coffee again, and will start tallying up polymorphisms Also, if you look at the result of the last step in this workflow you will an estimation of the methodological error in the cheek sample is 0.0101 or 1.0%:

#sum	mean	stdev	0%	25%	50%	75%	100%6.38336	0.000572089	0.000847046	0	0	4.4831e-05	0.000879089	0.0101868

Finally, rename this history as "cheek":

Now, let's continue to the final part D.