Machine Learning

, Volume 21, Issue 1, pp 51-80

First online:

Unsupervised learning of multiple motifs in biopolymers using expectation maximization

  • Timothy L. BaileyAffiliated withDepartment of Computer Science and Engineering, University of California, San Diego
  • , Charles ElkanAffiliated withDepartment of Computer Science and Engineering, University of California, San Diego


The MEME algorithm extends the expectation maximization (EM) algorithm for identifying motifs in unaligned biopolymer sequences. The aim of MEME is to discover new motifs in a set of biopolymer sequences where little or nothing is known in advance about any motifs that may be present. MEME innovations expand the range of problems which can be solved using EM and increase the chance of finding good solutions. First, subsequences which actually occur in the biopolymer sequences are used as starting points for the EM algorithm to increase the probability of finding globally optimal motifs. Second, the assumption that each sequence contains exactly one occurrence of the shared motif is removed. This allows multiple appearances of a motif to occur in any sequence and permits the algorithm to ignore sequences with no appearance of the shared motif, increasing its resistance to noisy data. Third, a method for probabilistically erasing shared motifs after they are found is incorporated so that several distinct motifs can be found in the same set of sequences, both when different motifs appear in different sequences and when a single sequence may contain multiple motifs. Experiments show that MEME can discover both the CRP and LexA binding sites from a set of sequences which contain one or both sites, and that MEME can discover both the −10 and −35 promoter regions in a set ofE. coli sequences.


Unsupervised learning expectation maximization consensus sequence motif biopolymer promoter binding site DNA protein sequence analysis