1st+idea+about+strategy+before+071012

Here's the basic strategy I attempt to use here.
 * Find motifs in the peptides.
 * To do this, I will use gemoda to generate all possible motifs for every possible combination of two peptides in the list
 * Maybe I'll call this: generateAllMotifs
 * Group similar motifs so that they just represent one motif group (or it could just be called a motif)
 * To do this, I will compute a similarity score between every pair of motifs using glam2. For example, peptide 2 will be selected and compared against all of the other peptides in the list after it. If the score is below some threshold, then the motifs will be grouped together. After this process there will be motif groups which are just subgroups of larger groups. The subgroups will be removed.
 * Find how many peptides belong to each motif group
 * glam2scan will be used to assign peptides to a group.
 * Find the B cell epitope score for each peptide belonging to a motif group, and then average these B cell epitope scores and assign this to the motif group
 * Plot the motif groups based on the number of peptides that belong to them and their B cell epitope score. Then see where the ELISA confirmed peptides fall within this plot. Perhaps the best peptides belong to motif groups with good B cell epitope scores and a lot of peptides with this same motif