MEME represents motifs as position-dependent letter-probability matrices which describe the probability of each possible letter at each position in the pattern.
MEME takes as input a group of DNA or protein sequences (the training set) and outputs as many motifs as requested.
It uses statistical modeling techniques to automatically choose the best width, number of occurrences, and description for each motif.
From a biological point of view, MEME identifies and characterizes shared motifs in a set of unaligned sequences.
[citation needed] MEME can be used to find similar biological functions and structures in different sequences.