![edmund severn polish dance pdf edmund severn polish dance pdf](https://idoc.pub/img/crop/300x300/d47eq8jq8dn2.jpg)
One important note: Since HOMER uses an Oligo Table for much of the internal calculations of motif enrichment, where it does not explicitly know how many of the original sequences contain the motif, it approximates this number using the total number of observed motif occurrences in background and target sequences. exects smaller number for promoter analysis and uses the hypergeometric by default. However, if you use your own background that has a limited number of sequences, it might be a good idea to switch to the hypergeometric (use ' -h' to force use of the hypergeometric). As a result it is the default statistic for where the number of sequences is typically higher. In these cases, the binomial is preferred since it is faster to calculate. The difference between them is usually minor if there are a large number of sequences and the background sequences > target sequences. The motif enrichment problem is more accurately described by the hypergeometric, however, the binomial has advantages. The hypergeometric and binomial distributions are similar, except that the hypergeometric assumes sampling without replacement, while the binomial assumes sampling with replacement. From these numbers we can calculate the probability of observing the given number (or more) of target sequences with the motif by chance if we assume there is no relationship between the target sequences and the motif. The statistics consider the total number of target sequences, background sequences and how many of each type contains the motif that is being checked for enrichment. background) is independent of the occurence of motifs within them. These two statistics assume that the classification of input sequences (i.e. Motif enrichment is calculated using either the cumulative hypergeometric or cumulative binomial distributions.