Overview
Introduction
Meta-MEME is a software toolkit for building and using motif-based hidden Markov models of biological sequences. The input to Meta-MEME is a set of similar DNA or protein sequences, as well as a set of motif models discovered by MEME. Meta-MEME combines these models into a single, motif-based hidden Markov model and uses this model to search a sequence database for homologs.Program inputs
Meta-MEME takes as input two files:- The sequence file contains a set of similar DNA or protein sequences that you are interested in modeling. Typically, these will be sequences that are homologous to one another or that share homologous domains. The sequences can be in various formats.
- The MEME motif models are based upon the sequence file. A MEME motif model is a position-specific scoring matrix; i.e., a matrix in which position (i,j) contains the probability that residue i will appear in position j in the motif. MEME motifs are gapless, so the model contains no gap opening or extension penalties. MEME motif models can be generated by the MEME web server at the San Diego Supercomputer Center. Meta-MEME requires the MEME model in HTML format.
Step one: Building a model
During the first, model-building step, Meta-MEME takes the single-motif models from the given MEME file and combines them into a motif-based hidden Markov model (HMM). An HMM is a generalization of a finite state machine in which each state in the model corresponds to a single residue. Probability distributions at each state represent the probabilities of point mutations; transition probabilities among states allow for insertions, deletions and, for some model topologies, repeated or shuffled domains.Meta-MEME's hidden Markov models differ from standard HMMs such as the ones produced by SAM and HMMER in that Meta-MEME's models are motif-based. Thus, the non-motif spacer regions are modelled imprecisely, thereby significantly reducing the number of parameters that need to be learned. This reduction in parameter space allows Meta-MEME models to be accurately trained from smaller sets of sequences.
In constructing the HMM, Meta-MEME uses information about the order and spacing of motifs within the family. By default, Meta-MEME builds a model with a linear topology, in which the motifs are arranged like beads on a string. It is also possible to request that Meta-MEME build a model in which every motif is connected to every other motif. This completely connected topology allows for the accurate modeling of families containing repeated or shuffled elements.
Step two: Homology detection
A Meta-MEME model can be used to search a sequence database for homologs. The homology detection algorithm assigns to each sequence in the database a score that is proportional to the probability that the sequence was generated by the given model. Meta-MEME can compute two types of scores: the Viterbi score is the probability associated with the single path through the model that is most likely to have generated the given sequence; the total probability score is the sum of the probabilities of all possible paths through the model. By default, Meta-MEME computes the Viterbi score, since this score is less computationally expensive to compute. It is an open question whether Viterbi or total probability scoring produces better homology detection performance. You may request either or both types of scores.Both Viterbi and total probability scores are reported as log-odds scores in bits. An odds score is the ratio of the score of the sequence with respect to the foreground model versus the score of the sequence with respect to the background model. The log-odds score is the log (in base 2) of this ratio. In Meta-MEME, the foreground model is a motif-based HMM, and the background model is a simple linear HMM that roughly captures the features of a typical sequence. If the family in question is small relative to the size of the database being searched, then the odds score is approximately equivalent to the likelihood that the sequence belongs to the family in question divided by the likelihood that it does not. Hence, an odds score of 1 (or a log-odds score of 0) implies equal likelihood that the sequence is a family member or is not.
The threshold for statistical significance of an odds score depends upon the expected number of family members in the database. A sequence can be safely deemed a family member if its odds score is greater than the ratio of the total number of sequences in the database over the expected number of family members. For example, if you are searching a database of 36000 sequences for a family containing approximately 100 sequences, then any sequence that receives an odds score of (36000 / 100) = 360 or higher likely belongs to the family. Since Meta-MEME reports log-odds scores, this threshold corresponds to log2(360) = 8.5.
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