Thus it is important to be able to identify possible motifs from a set of related sequences, and be able to test whether a given string contains a given motif. There are two standard ways to summarize a consensus pattern across multiple sequences. A profile is a matrix of probabilities, where the rows represent possible bases, and the columns represent consecutive sequence positions. A perfect profile has one base with probability 1.0 in each column, while a perfectly useless profile has entries of equal probability. A motif is a concise representation of the highest probability characters of the profile, as a string or regular expression.
Motifs are suggestive of function preserved by evolution.
When a motif is represented by a single contiguous string, exact text search methods suffice to see if a given sequence contains it. When a profile does not contain indels, the probability that a given sequence containing the motif can be easily determined by multiplying the probabilities of each character in each position of each window. If one has a probabilistic model of how typical DNA sequences are generated, one can easily compute the probability that a given profile score is significant. When profiles or motifs have gaps, the optimal alignment can be computed using a dynamic programming-type algorithm. Finding Motifs Motifs stand out as highly conserved regions in a multiple sequence alignment Fragment assembly programs must have a multiple sequence alignment code to extract a consensus call to define each base in the final sequence, but base calling accuracy is high enough that this sequence alignment problem is much easier than for conserved regions under evolution.