Winnow

What is Winnow?

Winnow is a Python-compatible known truth testing tool for genome wide association studies. Winnow is, quite simply, a tool which evaluates other tools. Winnow requires output from a GWAS tool (most GWAS tools have the same style of output, hence the reason it strictly measures these programs) after analyzing a data set. Given the "known truth" of the original data set, Winnow outputs a series of fit statistics—such as root mean-squared error and the false positive rate—to determine the validity of the GWAS tool and whether or not it was truly useful in analyzing the data. The two main statistics to look at are mean squared error (RMSE), and area under the receiver-operator curve (AUC).

How to Get Started

In your Validate Workflow v0.9 Atmosphere instance, the files for the program are located in the /usr/bin file, along with the documentation and the example files. To start, you can change your directory (though if you wish to just call Winnow from its location, this is not necessary). This can be done by opening the Terminal emulator, and typing:

...

Because of the program structure for Winnow, one may easily update or modify the source code with additional performance metrics, delimiter options, and the like. If you are interested in modifying the Winnow program or wish to add more fit statistics to the output, please consult this tutorial.

Further Information

Iplant profile for Dustin Landers, the original architect of Winnow: https://pods.iplantcollaborative.org/wiki/display/~landersda

Information on the AUC: https://www.kaggle.com/wiki/AreaUnderCurve

Example data for analysis can be found: http://mirrors.iplantcollaborative.org/browse/iplant/home/shared/iplantcollaborative/example_data/Validate/Validate_Test_Data