For each motif that it discovers in the training set, MEME prints the
following information: 

    Summary Line

    This line gives the width (`width') and expected number of occurrences in
    the training set (`sites') of the motif. MEME numbers the motifs
    consecutively from one as it finds them. MEME usually finds the most
    statistically significant motifs first. Each motif describes a pattern of 
    a fixed width--no gaps are allowed in MEME motifs. MEME estimates
    the number of places the motif occurs in the training set. This need
    not be an integer value. 

    Simplified Motif Letter-probability Matrix

    MEME motifs are represented by letter-probability matrices that
    specify the probability of each possible letter appearing at each
    possible position in an occurrence of the motif. In order to make it
    easier to see which letters are most likely in each of the columns of
    the motif, the simplified motif shows the letter probabilities multiplied
    by 10 rounded to the nearest integer. Zeros are replaced by ":" (the
    colon) for readability. 

    Information Content Diagram

    The information content diagram provides an idea of which positions
    in the motif are most highly conserved. Each column (position) in a
    motif can be characterized by the amount of information it contains
    (measured in bits). Highly conserved positions in the motif have high
    information; positions where all letters are equally likely have low
    information. The diagram is printed so that each column lines up with
    the same column in the simplified motif letter-probability matrix above
    it. Summing the information content for each position in the motif
    gives the total information content of the motif (shown in parentheses
    to the left of the diagram). This gives a measure of the usefulness of
    the motif for database searches. For a motif to be useful for database
    searches, it must as a rule contain at least log_2(N) bits of
    information where N is the number of sequences in the database
    being searched. For example, to effectively search a database
    containing 100,000 sequences for occurrences of a single motif, the
    motif should have an IC of at least 16.6 bits. Motifs with lower
    information content are still useful when a family of sequences shares
    more than one motif since they can be combined in multiple motif
    searches (using MAST). 

    Multilevel Consensus Sequence

    The multilevel consensus sequence corresponding to the motif is an
    aid in remembering and understanding the motif. It is calculated from
    the motif letter-probability matrix as follows. Separately for each
    column of the motif, the letters in the alphabet are sorted in
    decreasing order by the probability with which they are expected to
    occur in that position of motif occurrences. The sorted letters are
    then printed vertically with the most probable letter on top. Only
    letters with probabilities of 0.2 or higher at that position in the motif
    are printed. As an example, the multilevel consensus sequence of
    motif 2 in the sample output is: 

    Multilevel       LITGAASGIG
    consensus         V  GS    
    sequence              G    

    This multilevel consensus sequence says several things about the
    motif. First, the most likely form of the motif can be read from the top
    line as LITGAASGIG. Second, that only letter L has probability
    more than 0.2 in position 1 of the motif, both I and V have probability
    greater than 0.2 in position 2, etc. Third, a rough approximation of the
    motif can be made by converting the multilevel consensus sequence
    into the Prosite signature
    L-[IV]-T-G-[AG]-[ASG]-S-G-I-G. The multilevel
    consensus sequence is printed so that each column lines up with
    the same column in the simplified motif and information content
    diagrams above it. 

    Motif in BLOCKS or FASTA format
 
    For use with the BLOCKS (http://www.blocks.fhcrc.org/blocks) tools,
    MEME prints the sites in the sequences which were used to construct the
    motif in BLOCKS format.
    The sites reported are, for the different model types:
        OOPS            position with highest z_i in each sequence,
        ZOOPS           position with highest z_i > 0.5 in each sequence,
        TCM             all positions with z_i > 0.5,
    where z_i is the probability that an occurrence of the motif starts
    at position i in the sequence given the sequence and the motif model.
    If you inlcude the  -print_fasta switch on the command line, MEME prints
    the motif sites in FASTA format instead of BLOCKS format.

    Possible Examples of the Motif

    As a further aid in understanding the motif, MEME displays a list of
    possible occurrences of the motif in the training set. This list is made
    by converting the motif letter-probability matrix into a
    position-dependent scoring matrix (log-odds matrix) and using that
    to compute a match score between each position in the training set
    and the motif. All positions which score above a threshold score are
    listed. (The threshold score is chosen by MEME such that the
    expected number of non-motif positions listed in error will equal the
    number of actual motif positions not listed.) The format of the list is
    sequence name, starting position of the (putative) occurrence, match
    score of the position, and the actual sequence including the ten
    positions before and after the motif occurrence (`site'). 

    Position-dependent Scoring Matrix

    The position-dependent scoring matrix corresponding to the motif is
    printed for use by database search programs such as MAST. This
    matrix is a log-odds matrix calculated by taking the log (base 2) of
    the ratio p/f at each position in the motif where p is the probability
    of a particular letter at that position in the motif, and f is the average
    frequency of that letter in the non-redundant database as
    of 9/22/96. The scoring matrix is printed "sideways"--columns correspond 
    to the letters in the alphabet (in the same order as shown in the 
    simplified motif) and rows corresponding to the positions of the motif, 
    position one first.  The scoring matrix is preceded by a line 
    starting with "log-odds matrix:" and containing the length of the alphabet,
    width of the motif, number of characters in the training set and the 
    scoring threshold used in the list of possible motif examples. 

    Motif Letter-probability Matrix

    The motif itself is a position-dependent letter-probability matrix
    giving, for each position in the pattern, the probabilities of each
    possible letter occurring there. The letter-probability matrix is printed
    "sideways"--columns correspond to the letters in the alphabet (in
    the same order as shown in the simplified motif) and rows
    corresponding to the positions of the motif, position one first. The
    motif is preceded by a line starting with "letter-probability matrix:" and
    containing the length of the alphabet, width of the motif and number of
    characters in the training set. 
