AMIVA-F Tutorial:

Hello, thanks for using the AMIVA-F package in order to predict the pathogenicity for your mutation of interest.
AMIVA-F works fully automated and is easy to use, even in the absence of knowledge about the underlying parameters which are used as input for the neural network.

Step 1)

AMIVA-F works at the protein annotation level, therefore if you have mutations of interest in the c notation (DNA), look up the corresponding p.notation.

Once you have your mutation of interest in protein notation, enter it in the entry field location directly above the green button ("Calculate everything for me!").
The required input should look like this:

	M82K             

This input would correspond to the single point mutation at position 82 in FLNc, where the wildtype amino acid (M, Methionine) is substituted
by the mutated amino acid (K, Lysine).
If you by any chance submit a wrong amino acid (the amino acid you specified for the wildtype position is in fact not what you submitted, e.g FLNc position 82
corresponds to methionine, but you wrote S82K, which would correspond to serine), then AMIVA-F automatically corrects you and offers you to proceed calculations with the correct amino acid in in place.

Step 2)

Click on the button "AMIVA-F Analysis". This will take a couple of seconds and give you afterwards your prediction. 

After you received your prediction, you can save the prediction and its associated parameters used as input for AMIVA-F in simple text format by clicking "Save Mutation prediction".
This will prompt you in order to choose a directory where the outputfile will be saved.
In this outputfile you will not only find the prediction together with its input parameters, but furthermore also details about the associated training set accuracy and many more statistically interesting things.

Step 3) (Optional)

If you want to make another mutation, simply delete (or click "clear") and enter your new mutation into the entry field at the beginning.
It is sufficient to simply delete the old mutation and enter the new mutation and then to click "AMIVA-F Analysis". The old parameters will be deleted and everything will proceed as if you just started AMIVA-F.
When you are done, simply leave AMIVA-F by clicking " Exit AMIVA-F" which will turn off the Java Virtual Machine in the background.




Additional Information:

For additional information about the posttranslational sites/ binding partners which are used to feed AMIVA-F, there are 2 buttons where you can take a look into them.
Regarding performance prediction accuracy:
Currently this seems to be a bit buggy (Given that it only gives you only the summary of 1 out of the 10 crossfold validated runs (Instead the average of 10) and can take values a bit lower or higher than reported in the paper.
If you take the trainingset to WEKA (a desktop installed version) and do the cross validation there, you will find the exact numbers specified in the publication of 78.6% accuracy.
For your prediction it can happen that you will find prediction values of 76 - 80%, but these are not true values but instead single models from the 10x cross validation procedure.
We are working to fix this issue in order to provide the average accuracy of 78.6 %, which results after averaging all 10 models generated in cross validation.










