The easy-to-update and flexible data file schema of EASE comes at a substantial price in terms of speed. EASE must read the entire text file for every process from figuring out what genes your identifiers are referring to, to looking up gene categories for the entire population, to looking up how to make custom URLs and how to make the term-to-tag conversions sometimes required therein.

So the main trick to optimizing EASE is to trim the lines in all files to only those needed for the genes on your microarray. Say you typically refer to genes using Genbank accessions, but EASE is using LocusLink numbers as its standard gene identifiers. You could take all Genbank accessions for your microarray and annotate them with LocusLink numbers:

Paste all Genbank accessions into the main gene list.

[Select annotation fields]

[Add fields]

Browse to \Convert\LocusLink numbers.txt

Select it and click [open], then

[done]

[annotate genes]

Now you have a list of LocusLinks for your microarray, and you can use it with a database package to run queries of all the files in the \Data\, \Data\Class\, and \Data\Convert\ directories to trim the files to include only lines that begin with one of these LocusLinks.

Of course, this will practically destroy any efforts to "Enhance" your annotation. To also keep any "synonymous" LocusLink numbers, be sure to use the Enhance function when annotating your original list.

Another tip to speed up EASE is to eliminate any PMIDs from the optional third column of the categorical files. If you don't really care what articles "prove" a given categorical assignment, then it's best to not make EASE read though all of these PMIDs every time it loads that category.

One final note: EASE was designed to work most quickly with many lists run against the same population and same analysis options. When you want to change the population or your analysis options, you might see a substantial degradation in performance speed. To remedy this, close EASE and restart; then run the lists with the new options selected.