DREME Tutorial


DREME is a tool for discovering short regular expression motifs that are enriched in the provided dataset. It is limited to working with DNA or RNA as the large combination space for amino acids makes DREME's approach unfeasible. DREME also has the capability of using two datasets to find motifs that are enriched in one when compared to the other.

How DREME works

Note the following refers to the sequence set in which you are finding motifs as the positive sequences and to the control sequence set as the negative sequences.

  1. If there are no negative sequences provided then di-nucleotide shuffle the positive sequences to create one.
  2. Count the number of positive sequences and the number of negative sequences.
  3. Find all unique subsequences with no ambiguity characters that have a length in the range given (3 to 8 nucleotides by default) in the positive sequences.
  4. For each of the subsequences
    1. Count the number of sequences it occurs in for the positive and negative sequences.
    2. Use Fisher's exact test to determine the significance.
    3. Add the subsequence to a sorted (by p-value) set of regular expression motifs.
  5. Repeatedly pick the top motifs (default 100) to generalize by replacing one position with each possible ambiguity code and estimating the resultant p-value. This is done enough times to allow each position to have an ambiguity code.
  6. For each of the top (default 100) generalized RE motifs
    1. Count the number of sequences matched in the positive and negative sequences.
    2. Use Fisher's exact test to determine the significance.
  7. Pick the best RE motif and (assuming it meets the E-value threshold) scan for all matching sites to build up a frequency matrix and report it.
  8. Mask the matched sites with the wildcard character 'N'.
  9. If the limits have not been met then loop back to step 3 to find more motifs.

Sequence set

DREME works best with lots of short (~100bp) sequences. If you have a couple of long sequences then it might be beneficial to split them into many smaller (~100bp) sequences. With ChIP-seq data we recommend using 100bp regions around the peaks.

Comparative sequence set

DREME always uses a control sequence set but you don't have to supply it as DREME can create it by using di-nucleotide shuffling. If you wish to use your own sequence set then there are a few guidelines you should follow.

The sequence lengths of the control sequences should be roughly the same as the sequences to search for motifs. This is because the null model assumes that the probability of finding a match in a sequence in either sequence set will be roughly the same for an uninteresting motif. If the control sequences are longer this provides more locations that the motif could match making it more likely it will match and hence skewing the p-value calculations, possibly excluding a motif you would be interested in.