# DREME Tutorial

## Overview

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

## How DREME works

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

1. If there are no negative sequences provided then di-nucleotide shuffle the postive 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 which 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 generalise 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 benificial 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 comparative 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 guidlines you should follow.

The sequence lengths of the comparative 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 uninteresing motif. If the comparative 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.