Completely agree with the Completeness issue pointed by Michael. Still you could do some analysis with available data. I have done something similar to analyze hits from a genome scale RNAi screening.
Get all genes for a KO:Pathway in your list by parsing the ko.list
For example for the KO:Pathway path:ko0001
you can get all the components by parsing ko.list.
Now check how many ko components from your list are present.
You could assign a percentage to quantify this :
Completeness of pathway X = (No. of KO components in KO pathway X / No. of KO components in your list that belong to pathway X ) 100
This depends a bit on your definition of "see." ;-) For interactive exploration, I would recommend iPath which is based on KEGG data. There you can upload a list of genes and check the pathways by eye.
Computationally, you should be able to check if you have all the proteins for a pathway (e.g. if you have at least one hit for all the KO). But some pathways are more of a collection of independent modules, while in another pathway you might miss a crucial enzyme. So "completeness" is another difficult term.
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It's a pity that I can't accept two answers as correct! Both of you guys helped a lot!
:) It is alright Panos. Glad to know that the answers are helpful.
:) It is alright Panos. Glad to know that the answers were helpful.