                                    ememe



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Function

   Multiple EM for Motif Elicitation

Description

   EMBASSY MEME is a suite of application wrappers to the original meme
   v3.0.14 applications written by Timothy Bailey. meme v3.0.14 must be
   installed on the same system as EMBOSS and the location of the meme
   executables must be defined in your path for EMBASSY MEME to work.

   Usage:
   ememe [options] dataset outfile

   The parameter is new to EMBASSY MEME. The output is always written to .
   The name of the input sequences may be specified with the -dataset
   option as normal.

   MEME -- Multiple EM for Motif Elicitation

   MEME is a tool for discovering motifs in a group of related DNA or
   protein sequences.

   A motif is a sequence pattern that occurs repeatedly in a group of
   related protein or DNA sequences. MEME represents motifs as
   position-dependent letter-probability matrices which describe the
   probability of each possible letter at each position in the pattern.
   Individual MEME motifs do not contain gaps. Patterns with
   variable-length gaps are split by MEME into two or more separate
   motifs.

   MEME takes as input a group of DNA or protein sequences (the training
   set) and outputs as many motifs as requested. MEME uses statistical
   modeling techniques to automatically choose the best width, number of
   occurrences, and description for each motif.

   MEME outputs its results as a hypertext (HTML) document.

Algorithm

   Please read the file README distributed with the original MEME.

  REQUIRED ARGUMENTS:

   < dataset >
   The name of the file containing the training set sequences. If <
   dataset > is the word "stdin", MEME reads from standard input.

   The sequences in the dataset should be in Pearson/FASTA format. For
   example:

                        >ICYA_MANSE INSECTICYANIN A FORM (BLUE BILIPROTEIN)
                        GDIFYPGYCPDVKPVNDFDLSAFAGAWHEIAK
                        LPLENENQGKCTIAEYKYDGKKASVYNSFVSNGVKEYMEGDLEIAPDA
                        >LACB_BOVIN BETA-LACTOGLOBULIN PRECURSOR (BETA-LG)
                        MKCLLLALALTCGAQALIVTQTMKGLDI
                        QKVAGTWYSLAMAASDISLLDAQSAPLRVYVEELKPTPEGDLEILLQKW

   Sequences start with a header line followed by sequence lines. A header
   line has the character ">" in position one, followed by an unique name
   without any spaces, followed by (optional) descriptive text. After the
   header line come the actual sequence lines. Spaces and blank lines are
   ignored. Sequences may be in capital or lowercase or both.

   MEME uses the first word in the header line of each sequence, truncated
   to 24 characters if necessary, as the name of the sequence. This name
   must be unique. Sequences with duplicate names will be ignored. (The
   first word in the title line is everything following the ">" up to the
   first blank.)

   Sequence weights may be specified in the dataset file by special header
   lines where the unique name is "WEIGHTS" (all caps) and the descriptive
   text is a list of sequence weights. Sequence weights are numbers in the
   range 0 < w <=1. All weights are assigned in order to the sequences in
   the file. If there are more sequences than weights, the remainder are
   given weight one. Weights must be greater than zero and less than or
   equal to one. Weights may be specified by more than one "WEIGHT" entry
   which may appear anywhere in the file. When weights are used, sequences
   will contribute to motifs in proportion to their weights. Here is an
   example for a file of three sequences where the first two sequences are
   very similar and it is desired to down-weight them:

                        >WEIGHTS 0.5 .5 1.0
                        >seq1
                        GDIFYPGYCPDVKPVNDFDLSAFAGAWHEIAK
                        >seq2
                        GDMFCPGYCPDVKPVGDFDLSAFAGAWHELAK
                        >seq3
                        QKVAGTWYSLAMAASDISLLDAQSAPLRVYVEELKPTPEGDLEILLQKW

  OPTIONAL ARGUMENTS:

   MEME has a large number of optional inputs that can be used to
   fine-tune its behavior. To make these easier to understand they are
   divided into the following categories:

   ALPHABET - control the alphabet for the motifs (patterns) that MEME
   will search for

   DISTRIBUTION - control how MEME assumes the occurrences of the motifs
   are distributed throughout the training set sequences

   SEARCH - control how MEME searches for motifs

   SYSTEM - the -p argument causes a version of MEME compiled for a
   parallel CPU architecture to be run. (By placing < np > in quotes you
   may pass installation specific switches to the 'mpirun' command. The
   number of processors to run on must be the first argument following
   -p).

   In what follows, < n > is an integer, < a > is a decimal number, and <
   string > is a string of characters.

  ALPHABET

   MEME accepts either DNA or protein sequences, but not both in the same
   run. By default, sequences are assumed to be protein. The sequences
   must be in FASTA format.

   DNA sequences must contain only the letters "ACGT", plus the ambiguous
   letters "BDHKMNRSUVWY*-".

   Protein sequences must contain only the letters "ACDEFGHIKLMNPQRSTVWY",
   plus the ambiguous letters "BUXZ*-".

   MEME converts all ambiguous letters to "X", which is treated as
   "unknown".

   -dna Assume sequences are DNA; default: protein sequences

   -protein Assume sequences are protein

  DISTRIBUTION

   If you know how occurrences of motifs are distributed in the training
   set sequences, you can specify it with the following optional switches.
   The default distribution of motif occurrences is assumed to be zero or
   one occurrence of per sequence.

   -mod < string > The type of distribution to assume.

   oops
   One Occurrence Per Sequence
   MEME assumes that each sequence in the dataset contains exactly one
   occurrence of each motif. This option is the fastest and most sensitive
   but the motifs returned by MEME may be "blurry" if any of the sequences
   is missing them.

   zoops
   Zero or One Occurrence Per Sequence
   MEME assumes that each sequence may contain at most one occurrence of
   each motif. This option is useful when you suspect that some motifs may
   be missing from some of the sequences. In that case, the motifs found
   will be more accurate than using the first option. This option takes
   more computer time than the first option (about twice as much) and is
   slightly less sensitive to weak motifs present in all of the sequences.

   anr
   Any Number of Repetitions
   MEME assumes each sequence may contain any number of non-overlapping
   occurrences of each motif. This option is useful when you suspect that
   motifs repeat multiple times within a single sequence. In that case,
   the motifs found will be much more accurate than using one of the other
   options. This option can also be used to discover repeats within a
   single sequence. This option takes the much more computer time than the
   first option (about ten times as much) and is somewhat less sensitive
   to weak motifs which do not repeat within a single sequence than the
   other two options.

  SEARCH

   ------ A) OBJECTIVE FUNCTION

   MEME uses an objective function on motifs to select the "best" motif.
   The objective function is based on the statistical significance of the
   log likelihood ratio (LLR) of the occurrences of the motif. The E-value
   of the motif is an estimate of the number of motifs (with the same
   width and number of occurrences) that would have equal or higher log
   likelihood ratio if the training set sequences had been generated
   randomly according to the (0-order portion of the) background model.

   MEME searches for the motif with the smallest E-value. It searches over
   different motif widths, numbers of occurrences, and positions in the
   training set for the motif occurrences. The user may limit the range of
   motif widths and number of occurrences that MEME tries using the
   switches described below. In addition, MEME trims the motif (using a
   dynamic programming multiple alignment) to eliminate any positions
   where there is a gap in any of the occurrences.

   The log likelihood ratio of a motif is
        llr = log (Pr(sites | motif) / Pr(sites | back))

   and is a measure of how different the sites are from the background
   model. Pr(sites | motif) is the probability of the occurrences given
   the a model consisting of the position-specific probability matrix
   (PSPM) of the motif. (The PSPM is output by MEME).

   Pr(sites | back) is the probability of the occurrences given the
   background model. The background model is an n-order Markov model. By
   default, it is a 0-order model consisting of the frequencies of the
   letters in the training set. A different 0-order Markov model or higher
   order Markov models can be specified to MEME using the -bfile option
   described below.

   The E-value reported by MEME is actually an approximation of the
   E-value of the log likelihood ratio. (An approximation is used because
   it is far more efficient to compute.) The approximation is based on the
   fact that the log likelihood ratio of a motif is the sum of the log
   likelihood ratios of each column of the motif. Instead of computing the
   statistical significance of this sum (its p-value), MEME computes the
   p-value of each column and then computes the significance of their
   product. Although not identical to the significance of the log
   likelihood ratio, this easier to compute objective function works very
   similarly in practice.

   The motif significance is reported as the E-value of the motif.

   The statistical signficance of a motif is computed based on:
    1. the log likelihood ratio,
    2. the width of the motif,
    3. the number of occurrences,
    4. the 0-order portion of the background model,
    5. the size of the training set, and
    6. the type of model (oops, zoops, or anr, which determines the number
       of possible different motifs of the given width and number of
       occurrences).

   MEME searches for motifs by performing Expectation Maximization (EM) on
   a motif model of a fixed width and using an initial estimate of the
   number of sites. It then sorts the possible sites according to their
   probability according to EM. MEME then and calculates the E-values of
   the first n sites in the sorted list for different values of n. This
   procedure (first EM, followed by computing E-values for different
   numbers of sites) is repeated with different widths and different
   initial estimates of the number of sites. MEME outputs the motif with
   the lowest E-value. B) NUMBER OF MOTIFS -nmotifs < n > The number of
   *different* motifs to search for. MEME will search for and output < n >
   motifs. Default: 1

   -evt < p > Quit looking for motifs if E-value exceeds < p >. Default:
   infinite (so by default MEME never quits before -nmotifs < n > have
   been found.) C) NUMBER OF MOTIF OCCURENCES -nsites < n > -minsites < n
   > -maxsites < n > The (expected) number of occurrences of each motif.
   If -nsites is given, only that number of occurrences is tried.
   Otherwise, numbers of occurrences between -minsites and -maxsites are
   tried as initial guesses for the number of motif occurrences. These
   switches are ignored if mod = oops.

   Default:

   -minsites sqrt(number sequences)

   -maxsites Default:
   zoops # of sequences
   anr MIN(5*#sequences, 50) -wnsites < n > The weight on the prior on
   nsites. This controls how strong the bias towards motifs with exactly
   nsites sites (or between minsites and maxsites sites) is. It is a
   number in the range [0..1). The larger it is, the stronger the bias
   towards motifs with exactly nsites occurrences is.

   Default: 0.8 D) MOTIF WIDTH
   -w < n >
   -minw < n >
   -maxw < n >
   The width of the motif(s) to search for. If -w is given, only that
   width is tried. Otherwise, widths between -minw and -maxw are tried.
   Default: -minw 8, -maxw 50 (defined in user.h)
   Note: If < n > is less than the length of the shortest sequence in the
   dataset, < n > is reset by MEME to that value. -nomatrim -wg < a > -ws
   < a > -noendgaps
   These switches control trimming (shortening) of motifs using the
   multiple alignment method. Specifying -nomatrim causes MEME to skip
   this and causes the other switches to be ignored. MEME finds the best
   motif found and then trims (shortens) it using the multiple alignment
   method (described below). The number of occurrences is then adjusted to
   maximize the motif E-value, and then the motif width is further
   shortened to optimize the E-value.

   The multiple alignment method performs a separate pairwise alignment of
   the site with the highest probability and each other possible site.
   (The alignment includes width/2 positions on either side of the sites.)
   The pairwise alignment is controlled by the switches:
   -wg < a > (gap cost; default: 11),
   -ws < a > (space cost; default 1), and,
   -noendgaps (do not penalize endgaps; default: penalize endgaps).

   The pairwise alignments are then combined and the method determines the
   widest section of the motif with no insertions or deletions. If this
   alignment is shorter than < minw >, it tries to find an alignment
   allowing up to one insertion/deletion per motif column. This continues
   (allowing up to 2, 3 ... insertions/deletions per motif column) until
   an alignment of width at least < minw > is found. E) BACKGROUND MODEL
   -bfile < bfile >
   The name of the file containing the background model for sequences. The
   background model is the model of random sequences used by MEME. The
   background model is used by MEME
    1. 1) during EM as the "null model",
    2. 2) for calculating the log likelihood ratio of a motif,
    3. 3) for calculating the significance (E-value) of a motif, and,
    4. 4) for creating the position-specific scoring matrix (log-odds
       matrix).

   By default, the background model is a 0-order Markov model based on the
   letter frequencies in the training set.

   Markov models of any order can be specified in < bfile > by listing
   frequencies of all possible tuples of length up to order+1.

   Note that MEME uses only the 0-order portion (single letter
   frequencies) of the background model for purposes 3) and 4), but uses
   the full-order model for purposes 1) and 2), above.

   Example: To specify a 1-order Markov background model for DNA, < bfile
   > might contain the following lines. Note that optional comment lines
   are by "#" and are ignored by MEME.

                                # tuple   frequency_non_coding
                                a       0.324
                                c       0.176
                                g       0.176
                                t       0.324
                                # tuple   frequency_non_coding
                                aa      0.119
                                ac      0.052
                                ag      0.056
                                at      0.097
                                ca      0.058
                                cc      0.033
                                cg      0.028
                                ct      0.056
                                ga      0.056
                                gc      0.035
                                gg      0.033
                                gt      0.052
                                ta      0.091
                                tc      0.056
                                tg      0.058
                                tt      0.119

   Sample -bfile files are given in directory tests:
   tests/nt.freq (DNA), and
   tests/na.freq (amino acid). F) DNA PALINDROMES AND STRANDS -revcomp
   motifs occurrences may be on the given DNA strand or on its reverse
   complement.
   Default: look for DNA motifs only on the strand given in the training
   set.

   -pal
   Choosing -pal causes MEME to look for palindromes in DNA datasets.

   MEME averages the letter frequencies in corresponding columns of the
   motif (PSPM) together. For instance, if the width of the motif is 10,
   columns 1 and 10, 2 and 9, 3 and 8, etc., are averaged together. The
   averaging combines the frequency of A in one column with T in the
   other, and the frequency of C in one column with G in the other. If
   neither option is not chosen, MEME does not search for DNA palindromes.

   G) EM ALGORITHM

   -maxiter < n >
   The number of iterations of EM to run from any starting point. EM is
   run for < n > iterations or until convergence (see -distance, below)
   from each starting point.
   Default: 50

   -distance < a >
   The convergence criterion. MEME stops iterating EM when the change in
   the motif frequency matrix is less than < a >. (Change is the euclidean
   distance between two successive frequency matrices.)
   Default: 0.001

   -prior < string >
   The prior distribution on the model parameters:
   dirichlet simple Dirichlet prior This is the default for -dna and
   -alph. It is based on the non-redundant database letter frequencies.
   dmix mixture of Dirichlets prior This is the default for -protein.
   mega extremely low variance dmix; variance is scaled inversely with the
   size of the dataset.
   megap mega for all but last iteration of EM; dmix on last iteration.
   addone add +1 to each observed count

   -b < a >
   The strength of the prior on model parameters: < a > = 0 means use
   intrinsic strength of prior for prior = dmix.
   Defaults: 0.01 if prior = dirichlet 0 if prior = dmix

   -plib < string >
   The name of the file containing the Dirichlet prior in the format of
   file prior30.plib.

   H) SELECTING STARTS FOR EM
   The default is for MEME to search the dataset for good starts for EM.
   How the starting points are derived from the dataset is specified by
   the following switches.

   The default type of mapping MEME uses is:
   -spmap uni for -dna and -alph < string >
   -spmap pam for -protein
   -spfuzz < a > The fuzziness of the mapping. Possible values are greater
   than 0. Meaning depends on -spmap, see below.
   -spmap < string > The type of mapping function to use.
   uni Use add-< a > prior when converting a substring to an estimate of
   theta. Default -spfuzz < a >: 0.5 pam Use columns of PAM < a > matrix
   when converting a substring to an estimate of theta. Default -spfuzz <
   a >: 120 (PAM 120)

   Other types of starting points can be specified using the following
   switches.
   -cons < string > Override the sampling of starting points and just use
   a starting point derived from < string >.
   This is useful when an actual occurrence of a motif is known and can be
   used as the starting point for finding the motif.

Usage

   Here is a sample session with ememe


% ememe crp0.s -mod oops
Multiple EM for Motif Elicitation
MEME program output file output directory [.]:


   Go to the input files for this example
   Go to the output files for this example

  EXAMPLES:

   Please note the examples below are unedited excerpts of the original
   MEME documentation. Bear in mind the EMBASSY and original MEME options
   may differ in practice (see "1. Command-line arguments").

   The following examples use data files provided in this release of MEME.
   MEME writes its output to standard output, so you will want to redirect
   it to a file in order for use with MAST.

   1) A simple DNA example:
   meme crp0.s -dna -mod oops -pal > ex1.html

   MEME looks for a single motif in the file crp0.s which contains DNA
   sequences in FASTA format. The OOPS model is used so MEME assumes that
   every sequence contains exactly one occurrence of the motif. The
   palindrome switch is given so the motif model (PSPM) is converted into
   a palindrome by combining corresponding frequency columns. MEME
   automatically chooses the best width for the motif in this example
   since no width was specified.

   2) Searching for motifs on both DNA strands:
   meme crp0.s -dna -mod oops -revcomp > ex2.html

   This is like the previous example except that the -revcomp switch tells
   MEME to consider both DNA strands, and the -pal switch is absent so the
   palindrome conversion is omitted. When DNA uses both DNA strands, motif
   occurrences on the two strands may not overlap. That is, any position
   in the sequence given in the training set may be contained in an
   occurrence of a motif on the positive strand or the negative strand,
   but not both.

   3) A fast DNA example:
   meme crp0.s -dna -mod oops -revcomp -w 20 > ex3.html

   This example differs from example 1) in that MEME is told to only
   consider motifs of width 20. This causes MEME to execute about 10 times
   faster. The -w switch can also be used with protein datasets if the
   width of the motifs are known in advance.

   4) Using a higher-order background model:
   meme INO_up800.s -dna -mod anr -revcomp -bfile yeast.nc.6.freq >
   ex4.html

   In this example we use -mod anr and -bfile yeast.nc.6.freq. This
   specifies that
   a) the motif may have any number of occurrences in each sequence, and,
   b) the Markov model specified in yeast.nc.6.freq is used as the
   background model. This file contains a fifth-order Markov model for the
   non-coding regions in the yeast genome.
   Using a higher order background model can often result in more
   sensitive detection of motifs. This is because the background model
   more accurately models non-motif sequence, allowing MEME to
   discriminate against it and find the true motifs.

   5) A simple protein example:
   meme lipocalin.s -mod oops -maxw 20 -nmotifs 2 > ex5.html

   The -dna switch is absent, so MEME assumes the file lipocalin.s
   contains protein sequences. MEME searches for two motifs each of width
   less than or equal to 20. (Specifying -maxw 20 makes MEME run faster
   since it does not have to consider motifs longer than 20.) Each motif
   is assumed to occur in each of the sequences because the OOPS model is
   specified.

   6) Another simple protein example:
   meme farntrans5.s -mod anr -maxw 40 -maxsites 50 > ex6.html

   MEME searches for a motif of width up to 40 with up to 50 occurrences
   in the entire training set. The ANR sequence model is specified, which
   allows each motif to have any number of occurrences in each sequence.
   This dataset contains motifs with multiple repeats of motifs in each
   sequence. This example is fairly time consuming due to the fact that
   the time required to initiale the motif probability tables is
   proportional to < maxw > times < maxsites >. By default, MEME only
   looks for motifs up to 29 letters wide with a maximum total of number
   of occurrences equal to twice the number of sequences or 30, whichever
   is less.

   7) A much faster protein example:
   meme farntrans5.s -mod anr -w 10 -maxsites 30 -nmotifs 3 > ex7.html

   This time MEME is constrained to search for three motifs of width
   exactly ten. The effect is to break up the long motif found in the
   previous example. The -w switch forces motifs to be *exactly* ten
   letters wide. This example is much faster because, since only one width
   is considered, the time to build the motif probability tables is only
   proportional to < maxsites >.

   8) Splitting the sites into three:
   meme farntrans5.s -mod anr -maxw 12 -nsites 24 -nmotifs 3 > ex8.html

   This forces each motif to have 24 occurrences, exactly, and be up to 12
   letters wide.

   9) A larger protein example with E-value cutoff:
   meme adh.s -mod zoops -nmotifs 20 -evt 0.01 > ex9.html

   In this example, MEME looks for up to 20 motifs, but stops when a motif
   is found with E-value greater than 0.01. Motifs with large E-values are
   likely to be statistical artifacts rather than biologically
   significant.

Command line arguments

   Where possible, the same command-line qualifier names and parameter
   order is used as in the original meme. There are however several
   unavoidable differences and these are clearly documented in the "Notes"
   section below.

   Most of the options in the original meme are given in ACD as "advanced"
   or "additional" options. -options must be specified on the command-line
   in order to be prompted for a value for "additional" options but
   "advanced" options will never be prompted for.

Multiple EM for Motif Elicitation
Version: EMBOSS:6.2.0

   Standard (Mandatory) qualifiers:
  [-dataset]           seqset     User must provide the full filename of a set
                                  of sequences, not an indirect reference,
                                  e.g. a USA is NOT acceptable.
  [-outdir]            outdir     [.] MEME program output file output
                                  directory

   Additional (Optional) qualifiers:
   -bfile              infile     The name of the file containing the
                                  background model for sequences. The
                                  background model is the model of random
                                  sequences used by MEME. The background model
                                  is used by MEME 1) during EM as the 'null
                                  model', 2) for calculating the log
                                  likelihood ratio of a motif, 3) for
                                  calculating the significance (E-value) of a
                                  motif, and, 4) for creating the
                                  position-specific scoring matrix (log-odds
                                  matrix). See application documentation for
                                  more information.
   -plibfile           infile     The name of the file containing the
                                  Dirichlet prior in the format of file
                                  prior30.plib
   -mod                selection  [zoops] If you know how occurrences of
                                  motifs are distributed in the training set
                                  sequences, you can specify it with these
                                  options. The default distribution of motif
                                  occurrences is assumed to be zero or one
                                  occurrence per sequence. oops : One
                                  Occurrence Per Sequence. MEME assumes that
                                  each sequence in the dataset contains
                                  exactly one occurrence of each motif. This
                                  option is the fastest and most sensitive but
                                  the motifs returned by MEME may be 'blurry'
                                  if any of the sequences is missing them.
                                  zoops : Zero or One Occurrence Per Sequence.
                                  MEME assumes that each sequence may contain
                                  at most one occurrence of each motif. This
                                  option is useful when you suspect that some
                                  motifs may be missing from some of the
                                  sequences. In that case, the motifs found
                                  will be more accurate than using the first
                                  option. This option takes more computer time
                                  than the first option (about twice as much)
                                  and is slightly less sensitive to weak
                                  motifs present in all of the sequences. anr
                                  : Any Number of Repetitions. MEME assumes
                                  each sequence may contain any number of
                                  non-overlapping occurrences of each motif.
                                  This option is useful when you suspect that
                                  motifs repeat multiple times within a single
                                  sequence. In that case, the motifs found
                                  will be much more accurate than using one of
                                  the other options. This option can also be
                                  used to discover repeats within a single
                                  sequence. This option takes the much more
                                  computer time than the first option (about
                                  ten times as much) and is somewhat less
                                  sensitive to weak motifs which do not repeat
                                  within a single sequence than the other two
                                  options.
   -nmotifs            integer    [1] The number of *different* motifs to
                                  search for. MEME will search for and output
                                   motifs. (Any integer value)
   -text               boolean    [N] Default output is in HTML
   -prior              selection  [dirichlet] The prior distribution on the
                                  model parameters. dirichlet: Simple
                                  Dirichlet prior. This is the default for
                                  -dna and -alph. It is based on the
                                  non-redundant database letter frequencies.
                                  dmix: Mixture of Dirichlets prior. This is
                                  the default for -protein. mega: Extremely
                                  low variance dmix; variance is scaled
                                  inversely with the size of the dataset.
                                  megap: Mega for all but last iteration of
                                  EM; dmix on last iteration. addone: Add +1
                                  to each observed count.
   -evt                float      [-1] Quit looking for motifs if E-value
                                  exceeds this value. Has an extremely high
                                  default so by default MEME never quits
                                  before -nmotifs  have been found. A value
                                  of -1 here is a shorthand for infinity.
                                  (Any numeric value)
   -nsites             integer    [-1] These switches are ignored if mod =
                                  oops. The (expected) number of occurrences
                                  of each motif. If a value for -nsites is
                                  specified, only that number of occurrences
                                  is tried. Otherwise, numbers of occurrences
                                  between -minsites and -maxsites are tried as
                                  initial guesses for the number of motif
                                  occurrences. If a value is not specified for
                                  -minsites and maxsites then the default
                                  hardcoded into MEME, as opposed to the
                                  default value given in the ACD file, is
                                  used. The hardcoded default value of
                                  -minsites is equal to sqrt(number
                                  sequences). The hardcoded default value of
                                  -maxsites is equal to the number of
                                  sequences (zoops) or MIN(5* num.sequences,
                                  50) (anr). A value of -1 here represents
                                  nsites being unspecified. (Any integer
                                  value)
   -minsites           integer    [-1] These switches are ignored if mod =
                                  oops. The (expected) number of occurrences
                                  of each motif. If a value for -nsites is
                                  specified, only that number of occurrences
                                  is tried. Otherwise, numbers of occurrences
                                  between -minsites and -maxsites are tried as
                                  initial guesses for the number of motif
                                  occurrences. If a value is not specified for
                                  -minsites and maxsites then the default
                                  hardcoded into MEME, as opposed to the
                                  default value given in the ACD file, is
                                  used. The hardcoded default value of
                                  -minsites is equal to sqrt(number
                                  sequences). The hardcoded default value of
                                  -maxsites is equal to the number of
                                  sequences (zoops) or MIN(5 * num.sequences,
                                  50) (anr). A value of -1 here represents
                                  minsites being unspecified. (Any integer
                                  value)
   -maxsites           integer    [-1] These switches are ignored if mod =
                                  oops. The (expected) number of occurrences
                                  of each motif. If a value for -nsites is
                                  specified, only that number of occurrences
                                  is tried. Otherwise, numbers of occurrences
                                  between -minsites and -maxsites are tried as
                                  initial guesses for the number of motif
                                  occurrences. If a value is not specified for
                                  -minsites and maxsites then the default
                                  hardcoded into MEME, as opposed to the
                                  default value given in the ACD file, is
                                  used. The hardcoded default value of
                                  -minsites is equal to sqrt(number
                                  sequences). The hardcoded default value of
                                  -maxsites is equal to the number of
                                  sequences (zoops) or MIN(5 * num.sequences,
                                  50) (anr). A value of -1 here represents
                                  maxsites being unspecified. (Any integer
                                  value)
   -wnsites            float      [0.8] The weight of the prior on nsites.
                                  This controls how strong the bias towards
                                  motifs with exactly nsites sites (or between
                                  minsites and maxsites sites) is. It is a
                                  number in the range [0..1). The larger it
                                  is, the stronger the bias towards motifs
                                  with exactly nsites occurrences is. (Any
                                  numeric value)
   -w                  integer    [-1] The width of the motif(s) to search
                                  for. If -w is given, only that width is
                                  tried. Otherwise, widths between -minw and
                                  -maxw are tried. Note: if width is less than
                                  the length of the shortest sequence in the
                                  dataset, width is reset by MEME to that
                                  value. A value of -1 here represents -w
                                  being unspecified. (Any integer value)
   -minw               integer    [8] The width of the motif(s) to search for.
                                  If -w is given, only that width is tried.
                                  Otherwise, widths between -minw and -maxw
                                  are tried. Note: if width is less than the
                                  length of the shortest sequence in the
                                  dataset, width is reset by MEME to that
                                  value. (Any integer value)
   -maxw               integer    [50] The width of the motif(s) to search
                                  for. If -w is given, only that width is
                                  tried. Otherwise, widths between -minw and
                                  -maxw are tried. Note: if width is less than
                                  the length of the shortest sequence in the
                                  dataset, width is reset by MEME to that
                                  value. (Any integer value)
   -nomatrim           boolean    [N] The -nomatrim, -wg, -ws and -noendgaps
                                  switches control trimming (shortening) of
                                  motifs using the multiple alignment method.
                                  Specifying -nomatrim causes MEME to skip
                                  this and causes the other switches to be
                                  ignored. The pairwise alignment is
                                  controlled by the switches -wg (gap cost),
                                  -ws (space cost) and -noendgaps (do not
                                  penalize endgaps). See application
                                  documentation for further information.
   -wg                 integer    [11] The -nomatrim, -wg, -ws and -noendgaps
                                  switches control trimming (shortening) of
                                  motifs using the multiple alignment method.
                                  Specifying -nomatrim causes MEME to skip
                                  this and causes the other switches to be
                                  ignored. The pairwise alignment is
                                  controlled by the switches -wg (gap cost),
                                  -ws (space cost) and -noendgaps (do not
                                  penalize endgaps). See application
                                  documentation for further information. (Any
                                  integer value)
   -ws                 integer    [1] The -nomatrim, -wg, -ws and -noendgaps
                                  switches control trimming (shortening) of
                                  motifs using the multiple alignment method.
                                  Specifying -nomatrim causes MEME to skip
                                  this and causes the other switches to be
                                  ignored. The pairwise alignment is
                                  controlled by the switches -wg (gap cost),
                                  -ws (space cost) and -noendgaps (do not
                                  penalize endgaps). See application
                                  documentation for further information. (Any
                                  integer value)
   -noendgaps          boolean    [N] The -nomatrim, -wg, -ws and -noendgaps
                                  switches control trimming (shortening) of
                                  motifs using the multiple alignment method.
                                  Specifying -nomatrim causes MEME to skip
                                  this and causes the other switches to be
                                  ignored. The pairwise alignment is
                                  controlled by the switches -wg (gap cost),
                                  -ws (space cost) and -noendgaps (do not
                                  penalise endgaps). See application
                                  documentation for further information.
   -revcomp            boolean    [N] Motif occurrences may be on the given
                                  DNA strand or on its reverse complement. The
                                  default is to look for DNA motifs only on
                                  the strand given in the training set.
   -pal                boolean    [N] Choosing -pal causes MEME to look for
                                  palindromes in DNA datasets. MEME averages
                                  the letter frequencies in corresponding
                                  columns of the motif (PSPM) together. For
                                  instance, if the width of the motif is 10,
                                  columns 1 and 10, 2 and 9, 3 and 8, etc.,
                                  are averaged together. The averaging
                                  combines the frequency of A in one column
                                  with T in the other, and the frequency of C
                                  in one column with G in the other.
   -[no]nostatus       boolean    [Y] Set this option to prevent progress
                                  reports to the terminal.

   Advanced (Unprompted) qualifiers:
   -maxiter            integer    [50] The number of iterations of EM to run
                                  from any starting point. EM is run for
                                  iterations or until convergence (see
                                  -distance, below) from each starting point.
                                  (Any integer value)
   -distance           float      [0.001] The convergence criterion. MEME
                                  stops iterating EM when the change in the
                                  motif frequency matrix is less than .
                                  (Change is the euclidean distance between
                                  two successive frequency matrices.) (Any
                                  numeric value)
   -b                  float      [-1.0] The strength of the prior on model
                                  parameters. A value of 0 means use intrinsic
                                  strength of prior if prior = dmix. The
                                  default values are 0.01 if prior = dirichlet
                                  or 0 if prior = dmix. These defaults are
                                  hardcoded into MEME (the value of the
                                  default in the ACD file is not used). A
                                  value of -1 here represents -b being
                                  unspecified. (Any numeric value)
   -spfuzz             float      [-1.0] The fuzziness of the mapping.
                                  Possible values are greater than 0. Meaning
                                  depends on -spmap, see below. See the
                                  application documentation for more
                                  information. A value of -1.0 here represents
                                  -spfuzz being unspecified. (Any numeric
                                  value)
   -spmap              selection  [default] The type of mapping function to
                                  use. uni: Use prior when converting a
                                  substring to an estimate of theta. Default
                                  -spfuzz : 0.5. pam: Use columns of PAM
                                   matrix when converting a substring to an
                                  estimate of theta. Default -spfuzz : 120
                                  (PAM 120). See the application
                                  documentation for more information.
   -cons               string     Override the sampling of starting points and
                                  just use a starting point derived from
                                  . This is useful when an actual
                                  occurrence of a motif is known and can be
                                  used as the starting point for finding the
                                  motif. See the application documentation for
                                  more information. (Any string)
   -maxsize            integer    [-1] Maximum dataset size in characters (-1
                                  = use meme default). (Any integer value)
   -p                  integer    [0] Only values of >0 will be applied. The
                                  -p  argument causes a version of MEME
                                  compiled for a parallel CPU architecture to
                                  be run. (By placing  in quotes you may
                                  pass installation specific switches to the
                                  'mpirun' command. The number of processors
                                  to run on must be the first argument
                                  following -p). (Any integer value)
   -time               integer    [0] Only values of more than 0 will be
                                  applied. (Any integer value)
   -sf                 string     Print  as name of sequence file (Any
                                  string)
   -heapsize           integer    [64] The search for good EM starting points
                                  can be improved by using a branching search.
                                  A branching search begins with a fixed-size
                                  heap of best EM starts identified during
                                  the search of subsequences from the dataset.
                                  These starts are also called seeds. The
                                  fixed-size heap of seeds is used as the
                                  branch-heap during the first iteration of
                                  branching search. See the application
                                  documentation for more information. (Any
                                  integer value)
   -xbranch            boolean    [N] The search for good EM starting points
                                  can be improved by using a branching search.
                                  A branching search begins with a fixed-size
                                  heap of best EM starts identified during
                                  the search of subsequences from the dataset.
                                  These starts are also called seeds. The
                                  fixed-size heap of seeds is used as the
                                  branch-heap during the first iteration of
                                  branching search. See the application
                                  documentation for more information.
   -wbranch            boolean    [N] The search for good EM starting points
                                  can be improved by using a branching search.
                                  A branching search begins with a fixed-size
                                  heap of best EM starts identified during
                                  the search of subsequences from the dataset.
                                  These starts are also called seeds. The
                                  fixed-size heap of seeds is used as the
                                  branch-heap during the first iteration of
                                  branching search. See the application
                                  documentation for more information.
   -bfactor            integer    [3] The search for good EM starting points
                                  can be improved by using a branching search.
                                  A branching search begins with a fixed-size
                                  heap of best EM starts identified during
                                  the search of subsequences from the dataset.
                                  These starts are also called seeds. The
                                  fixed-size heap of seeds is used as the
                                  branch-heap during the first iteration of
                                  branching search. See the application
                                  documentation for more information. (Any
                                  integer value)

   Associated qualifiers:

   "-dataset" associated qualifiers
   -sbegin1            integer    Start of each sequence to be used
   -send1              integer    End of each sequence to be used
   -sreverse1          boolean    Reverse (if DNA)
   -sask1              boolean    Ask for begin/end/reverse
   -snucleotide1       boolean    Sequence is nucleotide
   -sprotein1          boolean    Sequence is protein
   -slower1            boolean    Make lower case
   -supper1            boolean    Make upper case
   -sformat1           string     Input sequence format
   -sdbname1           string     Database name
   -sid1               string     Entryname
   -ufo1               string     UFO features
   -fformat1           string     Features format
   -fopenfile1         string     Features file name

   General qualifiers:
   -auto               boolean    Turn off prompts
   -stdout             boolean    Write first file to standard output
   -filter             boolean    Read first file from standard input, write
                                  first file to standard output
   -options            boolean    Prompt for standard and additional values
   -debug              boolean    Write debug output to program.dbg
   -verbose            boolean    Report some/full command line options
   -help               boolean    Report command line options and exit. More
                                  information on associated and general
                                  qualifiers can be found with -help -verbose
   -warning            boolean    Report warnings
   -error              boolean    Report errors
   -fatal              boolean    Report fatal errors
   -die                boolean    Report dying program messages
   -version            boolean    Report version number and exit


Input file format

  Sequence formats

   The original MEME only supported input sequences in FASTA format.
   EMBASSY MEME supports all EMBOSS-supported sequence formats. meme reads
   any normal sequence USAs.

  Input files for usage example

  File: crp0.s

>ce1cg
TAATGTTTGTGCTGGTTTTTGTGGCATCGGGCGAGAATAGCGCGTGGTGTGAAAGACTGTTTTTTTGATCGTTTTCACAA
AAATGGAAGTCCACAGTCTTGACAG
>ara
GACAAAAACGCGTAACAAAAGTGTCTATAATCACGGCAGAAAAGTCCACATTGATTATTTGCACGGCGTCACACTTTGCT
ATGCCATAGCATTTTTATCCATAAG
>bglr1
ACAAATCCCAATAACTTAATTATTGGGATTTGTTATATATAACTTTATAAATTCCTAAAATTACACAAAGTTAATAACTG
TGAGCATGGTCATATTTTTATCAAT
>crp
CACAAAGCGAAAGCTATGCTAAAACAGTCAGGATGCTACAGTAATACATTGATGTACTGCATGTATGCAAAGGACGTCAC
ATTACCGTGCAGTACAGTTGATAGC
>cya
ACGGTGCTACACTTGTATGTAGCGCATCTTTCTTTACGGTCAATCAGCAAGGTGTTAAATTGATCACGTTTTAGACCATT
TTTTCGTCGTGAAACTAAAAAAACC
>deop2
AGTGAATTATTTGAACCAGATCGCATTACAGTGATGCAAACTTGTAAGTAGATTTCCTTAATTGTGATGTGTATCGAAGT
GTGTTGCGGAGTAGATGTTAGAATA
>gale
GCGCATAAAAAACGGCTAAATTCTTGTGTAAACGATTCCACTAATTTATTCCATGTCACACTTTTCGCATCTTTGTTATG
CTATGGTTATTTCATACCATAAGCC
>ilv
GCTCCGGCGGGGTTTTTTGTTATCTGCAATTCAGTACAAAACGTGATCAACCCCTCAATTTTCCCTTTGCTGAAAAATTT
TCCATTGTCTCCCCTGTAAAGCTGT
>lac
AACGCAATTAATGTGAGTTAGCTCACTCATTAGGCACCCCAGGCTTTACACTTTATGCTTCCGGCTCGTATGTTGTGTGG
AATTGTGAGCGGATAACAATTTCAC
>male
ACATTACCGCCAATTCTGTAACAGAGATCACACAAAGCGACGGTGGGGCGTAGGGGCAAGGAGGATGGAAAGAGGTTGCC
GTATAAAGAAACTAGAGTCCGTTTA
>malk
GGAGGAGGCGGGAGGATGAGAACACGGCTTCTGTGAACTAAACCGAGGTCATGTAAGGAATTTCGTGATGTTGCTTGCAA
AAATCGTGGCGATTTTATGTGCGCA
>malt
GATCAGCGTCGTTTTAGGTGAGTTGTTAATAAAGATTTGGAATTGTGACACAGTGCAAATTCAGACACATAAAAAAACGT
CATCGCTTGCATTAGAAAGGTTTCT
>ompa
GCTGACAAAAAAGATTAAACATACCTTATACAAGACTTTTTTTTCATATGCCTGACGGAGTTCACACTTGTAAGTTTTCA
ACTACGTTGTAGACTTTACATCGCC
>tnaa
TTTTTTAAACATTAAAATTCTTACGTAATTTATAATCTTTAAAAAAAGCATTTAATATTGCTCCCCGAACGATTGTGATT
CGATTCACATTTAAACAATTTCAGA
>uxu1
CCCATGAGAGTGAAATTGTTGTGATGTGGTTAACCCAATTAGAATTCGGGATTGACATGTCTTACCAAAAGGTAGAACTT
ATACGCCATCTCATCCGATGCAAGC
>pbr322
CTGGCTTAACTATGCGGCATCAGAGCAGATTGTACTGAGAGTGCACCATATGCGGTGTGAAATACCGCACAGATGCGTAA
GGAGAAAATACCGCATCAGGCGCTC
>trn9cat
CTGTGACGGAAGATCACTTCGCAGAATAAATAAATCCTGGTGTCCCTGTTGATACCGGGAAGCCCTGGGCCAACTTTTGG
CGAAAATGAGACGTTGATCGGCACG
>tdc
GATTTTTATACTTTAACTTGTTGATATTTAAAGGTATTTAATTGTAATAACGATACTCTGGAAAGTATTGAAAGTTAATT
TGTGAGTGGTCGCACATATCCTGTT

Output file format

  Output files for usage example

  Graphics File: logo1.eps

   [ememe results]

  File: logo1.png

   This file contains non-printing characters and so cannot be displayed
   here.

  Graphics File: logo_ssc1.eps

   [ememe results]

  File: logo_ssc1.png

   This file contains non-printing characters and so cannot be displayed
   here.

  File: meme.html

   <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
   "http://www.w3.org/TR/html4/loose.dtd"> <html> <head> <meta
   http-equiv="Content-Type" content="text/html; charset=UTF-8">
   <title>MEME</title> <style type="text/css"> td.left {text-align: left;}
   td.right {text-align: right; padding-right: 1cm;} </style> </head>
   <body bgcolor="#D5F0FF"><form
   enctype="application/x-www-form-urlencoded" method="post" target="_new"
   action="http://emboss4.ebi.ac.uk/meme/cgi-bin/process_request.cgi"> <a
   name="top_buttons"></a><hr> <table summary="buttons" align="left"
   cellspacing="0"><tr> <td bgcolor="#DDDDFF"><a
   href="#command"><b>Command line</b></a></td> <td bgcolor="#00FFFF"><a
   href="#sequences"><b>Training Set</b></a></td> <td bgcolor="#DDFFDD"><a
   href="#summary1"><b>First Motif</b></a></td> <td bgcolor="#FFDDFF"><a
   href="#motif-summary"><b>Summary of Motifs</b></a></td> <td
   bgcolor="#00FF00"><a href="#stopped"><b>Termination</b></a></td> <td
   bgcolor="#FFFF00"><a href="#explanation"><b>Explanation</b></a></td>
   </tr></table> <br clear="left"><b><br><input type="submit"
   name="action" value="MAST"> Search sequence databases for the best
   combined matches with these motifs using <a
   href="http://emboss4.ebi.ac.uk/meme/mast-intro.html">MAST.</a><br><inpu
   t type="submit" name="action" value="FIMO"> Search sequence databases
   for all matches with these motifs using <a
   href="http://emboss4.ebi.ac.uk/meme/fimo-intro.html">FIMO</a>.
   <br><input type="submit" name="action" value="GOMO"> Find Genome
   Ontology terms associated with upstream sequences matching these motifs
   using <a href="http://emboss4.ebi.ac.uk/meme/gomo-intro.html">GOMO</a>.
   <br><input type="submit" name="action" value="BLOCKS"> Submit these
   motifs to <a href="http://blocks.fhcrc.org/blocks/process_blocks.html">
   BLOCKS multiple alignment processor. </a><br></b><br clear="left"><hr>
   <center> <a name="version"></a><big><b><a href="#version_doc">MEME -
   Motif discovery tool</a></b></big> </center> <hr> <p> MEME version
   4.2.0 (Release date: Wed Jul 22 01:12:17 PDT 2009) </p> <p> For further
   information on how to interpret these results or to get a copy of the
   MEME software please access <a
   href="http://meme.sdsc.edu">http://meme.sdsc.edu</a>. </p> <p> This
   file may be used as input to the MAST algorithm for searching sequence
   databases for matches to groups of motifs. MAST is available for
   interactive use and downloading at [Part of this file has been deleted
   for brevity] The motif is preceded by a line starting with
   "letter-probability matrix:" and containing the length of the alphabet,
   width of the motif, number of occurrences of the motif, and the
   <i>E</i>-value of the motif. <p><b>Note:</b> Earlier versions of MEME
   gave the posterior probabilities--the probability after applying a
   prior on letter frequencies--rather than the observed frequencies.
   These versions of MEME also gave the number of <i>possible</i>
   positions for the motif rather than the actual number of occurrences.
   The output from these earlier versions of MEME can be distinguished by
   "n=" rather than "nsites=" in the line preceding the matrix. </p> </li>
   <li> <h4><a name="regular_expression_doc2"
   href="#regular_expression1">Regular Expression</a></h4> This is a
   regular expression (RE) describing the motif. In each column, all
   letters with observed frequencies greater than 0.2 are shown;
   less-frequent letters are not included in the RE. MEME regular
   expressions are interpreted as follows: single letters match that
   letter; groups of letters in square brackets match any of the letters
   in the group. Regular expressions can be used for searching for the
   motif in sequences (using, for example, <a
   href="http://nar.oxfordjournals.org/cgi/content/full/33/suppl_2/W262">
   PatMatch </a> ) but the search accuracy will usually be better with the
   PSSM (using, for example <a
   href="http://meme.nbcr.net/meme/mast-intro.html">MAST</a>.) </li> <li>
   <h4><a name="motif-summary-doc2" href="#motif-summary">Motif Summary
   Tiling</a></h4> The motif summary tiling is done using the same
   algorithm as used by <a
   href="http://meme.nbcr.net/meme/mast-intro.html">MAST</a>. The motif
   occurrences shown in the motif summary <b>may not be exactly the same
   as those reported in each motif section</b> because only motifs with a
   position <em>p</em>-value of 0.0001 that don't overlap other, more
   significant motif occurrences are shown. The format of the machine
   readable motif-summary is: <pre> [sequence_name
   combined_<em>p</em>-value number_of_motif_occurrences [motif_number
   start_of_motif position_<em>p</em>-value]+]+ </pre> See the
   documentation for <a
   href="http://meme.nbcr.net/meme/mast-output.html">MAST output</a> for
   the definition of position and combined <em>p</em>-values. </li> </ul>
   </form></body> </html>

  File: meme.fasta

>ce1cg
TAATGTTTGTGCTGGTTTTTGTGGCATCGGGCGAGAATAGCGCGTGGTGTGAAAGACTGT
TTTTTTGATCGTTTTCACAAAAATGGAAGTCCACAGTCTTGACAG
>ara
GACAAAAACGCGTAACAAAAGTGTCTATAATCACGGCAGAAAAGTCCACATTGATTATTT
GCACGGCGTCACACTTTGCTATGCCATAGCATTTTTATCCATAAG
>bglr1
ACAAATCCCAATAACTTAATTATTGGGATTTGTTATATATAACTTTATAAATTCCTAAAA
TTACACAAAGTTAATAACTGTGAGCATGGTCATATTTTTATCAAT
>crp
CACAAAGCGAAAGCTATGCTAAAACAGTCAGGATGCTACAGTAATACATTGATGTACTGC
ATGTATGCAAAGGACGTCACATTACCGTGCAGTACAGTTGATAGC
>cya
ACGGTGCTACACTTGTATGTAGCGCATCTTTCTTTACGGTCAATCAGCAAGGTGTTAAAT
TGATCACGTTTTAGACCATTTTTTCGTCGTGAAACTAAAAAAACC
>deop2
AGTGAATTATTTGAACCAGATCGCATTACAGTGATGCAAACTTGTAAGTAGATTTCCTTA
ATTGTGATGTGTATCGAAGTGTGTTGCGGAGTAGATGTTAGAATA
>gale
GCGCATAAAAAACGGCTAAATTCTTGTGTAAACGATTCCACTAATTTATTCCATGTCACA
CTTTTCGCATCTTTGTTATGCTATGGTTATTTCATACCATAAGCC
>ilv
GCTCCGGCGGGGTTTTTTGTTATCTGCAATTCAGTACAAAACGTGATCAACCCCTCAATT
TTCCCTTTGCTGAAAAATTTTCCATTGTCTCCCCTGTAAAGCTGT
>lac
AACGCAATTAATGTGAGTTAGCTCACTCATTAGGCACCCCAGGCTTTACACTTTATGCTT
CCGGCTCGTATGTTGTGTGGAATTGTGAGCGGATAACAATTTCAC
>male
ACATTACCGCCAATTCTGTAACAGAGATCACACAAAGCGACGGTGGGGCGTAGGGGCAAG
GAGGATGGAAAGAGGTTGCCGTATAAAGAAACTAGAGTCCGTTTA
>malk
GGAGGAGGCGGGAGGATGAGAACACGGCTTCTGTGAACTAAACCGAGGTCATGTAAGGAA
TTTCGTGATGTTGCTTGCAAAAATCGTGGCGATTTTATGTGCGCA
>malt
GATCAGCGTCGTTTTAGGTGAGTTGTTAATAAAGATTTGGAATTGTGACACAGTGCAAAT
TCAGACACATAAAAAAACGTCATCGCTTGCATTAGAAAGGTTTCT
>ompa
GCTGACAAAAAAGATTAAACATACCTTATACAAGACTTTTTTTTCATATGCCTGACGGAG
TTCACACTTGTAAGTTTTCAACTACGTTGTAGACTTTACATCGCC
>tnaa
TTTTTTAAACATTAAAATTCTTACGTAATTTATAATCTTTAAAAAAAGCATTTAATATTG
CTCCCCGAACGATTGTGATTCGATTCACATTTAAACAATTTCAGA
>uxu1
CCCATGAGAGTGAAATTGTTGTGATGTGGTTAACCCAATTAGAATTCGGGATTGACATGT
CTTACCAAAAGGTAGAACTTATACGCCATCTCATCCGATGCAAGC
>pbr322
CTGGCTTAACTATGCGGCATCAGAGCAGATTGTACTGAGAGTGCACCATATGCGGTGTGA
AATACCGCACAGATGCGTAAGGAGAAAATACCGCATCAGGCGCTC
>trn9cat
CTGTGACGGAAGATCACTTCGCAGAATAAATAAATCCTGGTGTCCCTGTTGATACCGGGA
AGCCCTGGGCCAACTTTTGGCGAAAATGAGACGTTGATCGGCACG
>tdc
GATTTTTATACTTTAACTTGTTGATATTTAAAGGTATTTAATTGTAATAACGATACTCTG
GAAAGTATTGAAAGTTAATTTGTGAGTGGTCGCACATATCCTGTT

  File: meme.txt

********************************************************************************
MEME - Motif discovery tool
********************************************************************************
MEME version 4.2.0 (Release date: Wed Jul 22 01:12:17 PDT 2009)

For further information on how to interpret these results or to get
a copy of the MEME software please access http://meme.nbcr.net.

This file may be used as input to the MAST algorithm for searching
sequence databases for matches to groups of motifs.  MAST is available
for interactive use and downloading at http://meme.nbcr.net.
********************************************************************************


********************************************************************************
REFERENCE
********************************************************************************
If you use this program in your research, please cite:

Timothy L. Bailey and Charles Elkan,
"Fitting a mixture model by expectation maximization to discover
motifs in biopolymers", Proceedings of the Second International
Conference on Intelligent Systems for Molecular Biology, pp. 28-36,
AAAI Press, Menlo Park, California, 1994.
********************************************************************************


********************************************************************************
TRAINING SET
********************************************************************************
DATAFILE= ./meme.fasta
ALPHABET= ACGT
Sequence name            Weight Length  Sequence name            Weight Length
-------------            ------ ------  -------------            ------ ------
ce1cg                    1.0000    105  ara                      1.0000    105
bglr1                    1.0000    105  crp                      1.0000    105
cya                      1.0000    105  deop2                    1.0000    105
gale                     1.0000    105  ilv                      1.0000    105
lac                      1.0000    105  male                     1.0000    105
malk                     1.0000    105  malt                     1.0000    105
ompa                     1.0000    105  tnaa                     1.0000    105
uxu1                     1.0000    105  pbr322                   1.0000    105
trn9cat                  1.0000    105  tdc                      1.0000    105
********************************************************************************

********************************************************************************
COMMAND LINE SUMMARY
********************************************************************************
This information can also be useful in the event you wish to report a
problem with the MEME software.


  [Part of this file has been deleted for brevity]

--------------------------------------------------------------------------------
TGTGA[ACT][CAG][GT][AGT][GC][TAC]TCAC
--------------------------------------------------------------------------------




Time  0.30 secs.

********************************************************************************


********************************************************************************
SUMMARY OF MOTIFS
********************************************************************************

--------------------------------------------------------------------------------
        Combined block diagrams: non-overlapping sites with p-value < 0.0001
--------------------------------------------------------------------------------
SEQUENCE NAME            COMBINED P-VALUE  MOTIF DIAGRAM
-------------            ----------------  -------------
ce1cg                            1.74e-03  63_[1(1.91e-05)]_27
ara                              4.00e-03  57_[1(4.41e-05)]_33
bglr1                            7.85e-03  78_[1(8.66e-05)]_12
crp                              4.37e-03  65_[1(4.81e-05)]_25
cya                              3.66e-03  52_[1(4.03e-05)]_38
deop2                            5.47e-04  9_[1(6.01e-06)]_81
gale                             9.45e-04  26_[1(1.04e-05)]_64
ilv                              2.54e-02  105
lac                              5.39e-05  11_[1(5.92e-07)]_79
male                             2.12e-04  16_[1(2.33e-06)]_74
malk                             1.35e-02  105
malt                             2.55e-03  43_[1(2.80e-05)]_47
ompa                             1.42e-03  50_[1(1.57e-05)]_40
tnaa                             2.11e-03  73_[1(2.32e-05)]_17
uxu1                             3.35e-03  19_[1(3.69e-05)]_71
pbr322                           2.12e-04  55_[1(2.33e-06)]_35
trn9cat                          5.08e-02  105
tdc                              1.57e-03  80_[1(1.73e-05)]_10
--------------------------------------------------------------------------------

********************************************************************************


********************************************************************************
Stopped because nmotifs = 1 reached.
********************************************************************************

CPU: emboss4.ebi.ac.uk

********************************************************************************

  File: meme.xml

<?xml version='1.0' encoding='UTF-8' standalone='yes'?>
<?xml-stylesheet type='text/xsl' href='meme.xsl'?>
<!-- Document definition -->
<!DOCTYPE MEME[
<!ELEMENT MEME (
  training_set,
  model,
  motifs,
  scanned_sites_summary?
)>
<!ATTLIST MEME
  version CDATA #REQUIRED
  release CDATA #REQUIRED
>
<!-- Training-set elements -->
<!ELEMENT training_set (alphabet, ambigs, sequence+, letter_frequencies)>
<!ATTLIST training_set datafile CDATA #REQUIRED length CDATA #REQUIRED>
<!ELEMENT alphabet (letter+)>
<!ATTLIST alphabet id (amino-acid|nucleotide) #REQUIRED
                   length CDATA #REQUIRED>
<!ELEMENT ambigs (letter+)>
<!ELEMENT letter EMPTY>
<!ATTLIST letter id ID #REQUIRED>
<!ATTLIST letter symbol CDATA #REQUIRED>
<!ELEMENT sequence EMPTY>
<!ATTLIST sequence id ID #REQUIRED
                   name CDATA #REQUIRED
                   length CDATA #REQUIRED
                   weight CDATA #REQUIRED
>
<!ELEMENT letter_frequencies (alphabet_array)>

<!-- Model elements -->
<!ELEMENT model (
  command_line,
  host,
  type,
  nmotifs,
  evalue_threshold,
  object_function,
  min_width,
  max_width,
  minic,
  wg,
  ws,
  endgaps,
  minsites,
  maxsites,
  wnsites,
  prob,


  [Part of this file has been deleted for brevity]

<letter_ref letter_id="letter_G"/>
<letter_ref letter_id="letter_T"/>
<letter_ref letter_id="letter_T"/>
<letter_ref letter_id="letter_G"/>
<letter_ref letter_id="letter_A"/>
<letter_ref letter_id="letter_T"/>
<letter_ref letter_id="letter_C"/>
<letter_ref letter_id="letter_G"/>
<letter_ref letter_id="letter_G"/>
</site>
<right_flank>CACG</right_flank>
</contributing_site>
</contributing_sites>
</motif>
</motifs>
<scanned_sites_summary p_thresh="0.0001">
<scanned_sites sequence_id="sequence_ce1cg" pvalue="1.74e-03" num_sites="1"><sca
nned_site motif_id="motif_1" strand="plus" position="63" pvalue="1.91e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_ara" pvalue="4.00e-03" num_sites="1"><scann
ed_site motif_id="motif_1" strand="plus" position="57" pvalue="4.41e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_bglr1" pvalue="7.85e-03" num_sites="1"><sca
nned_site motif_id="motif_1" strand="plus" position="78" pvalue="8.66e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_crp" pvalue="4.37e-03" num_sites="1"><scann
ed_site motif_id="motif_1" strand="plus" position="65" pvalue="4.81e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_cya" pvalue="3.66e-03" num_sites="1"><scann
ed_site motif_id="motif_1" strand="plus" position="52" pvalue="4.03e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_deop2" pvalue="5.47e-04" num_sites="1"><sca
nned_site motif_id="motif_1" strand="plus" position="9" pvalue="6.01e-06"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_gale" pvalue="9.45e-04" num_sites="1"><scan
ned_site motif_id="motif_1" strand="plus" position="26" pvalue="1.04e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_ilv" pvalue="2.54e-02" num_sites="0"></scan
ned_sites>
<scanned_sites sequence_id="sequence_lac" pvalue="5.39e-05" num_sites="1"><scann
ed_site motif_id="motif_1" strand="plus" position="11" pvalue="5.92e-07"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_male" pvalue="2.12e-04" num_sites="1"><scan
ned_site motif_id="motif_1" strand="plus" position="16" pvalue="2.33e-06"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_malk" pvalue="1.35e-02" num_sites="0"></sca
nned_sites>
<scanned_sites sequence_id="sequence_malt" pvalue="2.55e-03" num_sites="1"><scan
ned_site motif_id="motif_1" strand="plus" position="43" pvalue="2.80e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_ompa" pvalue="1.42e-03" num_sites="1"><scan
ned_site motif_id="motif_1" strand="plus" position="50" pvalue="1.57e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_tnaa" pvalue="2.11e-03" num_sites="1"><scan
ned_site motif_id="motif_1" strand="plus" position="73" pvalue="2.32e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_uxu1" pvalue="3.35e-03" num_sites="1"><scan
ned_site motif_id="motif_1" strand="plus" position="19" pvalue="3.69e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_pbr322" pvalue="2.12e-04" num_sites="1"><sc
anned_site motif_id="motif_1" strand="plus" position="55" pvalue="2.33e-06"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_trn9cat" pvalue="5.08e-02" num_sites="0"></
scanned_sites>
<scanned_sites sequence_id="sequence_tdc" pvalue="1.57e-03" num_sites="1"><scann
ed_site motif_id="motif_1" strand="plus" position="80" pvalue="1.73e-05"/>
</scanned_sites>
</scanned_sites_summary>
</MEME>

  File: meme.xsl

<?xml version="1.0" encoding="ISO-8859-1"?>
<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">

<!--
  This file is automatically built from meme.xsl.in at MAKE time.

  This stylesheet transforms the XML output of MEME into HTML closely matching
  the appearance of the HTML generated by previous versions of MEME.
-->

  <xsl:output method="html" indent="yes"
    doctype-public="-//W3C//DTD HTML 4.01 Transitional//EN"
    doctype-system="http://www.w3.org/TR/html4/loose.dtd"
  />

  <!-- Stylesheet processing starts here -->
  <xsl:template match="/MEME">
    <html>
      <xsl:call-template name="html-head"/>
      <body bgcolor='#D5F0FF'>
        <form
          enctype="application/x-www-form-urlencoded"
          method = "post"
          target = "_new"
          action = "http://emboss4.ebi.ac.uk/meme/cgi-bin/process_request.cgi"
        >
        <!-- Create the various sub-sections of the document -->
        <xsl:call-template name="top-buttons"/>
        <xsl:call-template name="meme-version"/>
        <xsl:call-template name="reference"/>
        <xsl:call-template name="training-set"/>
        <xsl:call-template name="model"/>
        <xsl:call-template name="motifs"/>
        <xsl:call-template name="scanned_site_summary"/>
        <xsl:call-template name="explanation"/>
      </form>
      </body>
    </html>
  </xsl:template>

  <xsl:template name="html-head">
    <!-- This template prints the HTML head block, including the document level
CSS. -->
    <head>
      <meta http-equiv="Content-Type" content="text/html; charset=ISO-8859-1"/>
      <title>MEME</title>
      <style type="text/css">
        td.left {text-align: left;}
        td.right {text-align: right; padding-right: 1cm;}
      </style>
    </head>


  [Part of this file has been deleted for brevity]

    <xsl:text>&#10;</xsl:text>
    <big><b><a href="#pspm_doc">Motif <xsl:value-of select="$motif_index"/> posi
tion-specific probability matrix</a>
    <xsl:text>&#10;</xsl:text>
    </b></big></center><hr/>
    <xsl:variable name="pspm">
letter-probability matrix: alength= <xsl:value-of
select="/MEME/training_set/alphabet/@length"/> w= <xsl:value-of select="@width"/
> nsites= <xsl:value-of select="@sites"/> E= <xsl:value-of select="@e_value"/> <
xsl:text>&#10;</xsl:text>
      <xsl:for-each select="/MEME/motifs/motif[$motif_index]/probabilities/alpha
bet_matrix/alphabet_array">
        <xsl:for-each select="value">
          <xsl:value-of select="."/><xsl:text> </xsl:text>
        </xsl:for-each>
      <xsl:text>&#10;</xsl:text>
      </xsl:for-each>
    </xsl:variable>
    <xsl:text>&#10;</xsl:text>
    <input type="hidden" name="pspm{$motif_index}" value="{$pspm} "/>
    <xsl:text>&#10;</xsl:text>
</xsl:template>

<xsl:template name="pssm">
  <xsl:param name="motif_index"/>
  <xsl:text>&#10;</xsl:text>
  <hr/><center><a name="pssm{$motif_index}"/>
  <big><b><a href="#pssm_doc">Motif <xsl:value-of select="$motif_index"/> positi
on-specific scoring matrix</a>
  </b></big></center><hr/>
    <xsl:variable name="pssm">
log-odds matrix: alength= <xsl:value-of select="/MEME/training_set/alphabet/@len
gth"/> w= <xsl:value-of select="@width"/> n= <xsl:value-of select="@sites"/> bay
es= <xsl:value-of select="@bayes_threshold"/> E= <xsl:value-of select="@e_value"
/>
      <xsl:text>&#10;</xsl:text>
      <xsl:for-each select="/MEME/motifs/motif[$motif_index]/scores/alphabet_mat
rix/alphabet_array">
        <xsl:for-each select="value">
          <xsl:value-of select="."/><xsl:text> </xsl:text>
        </xsl:for-each>
        <xsl:text>&#10;</xsl:text>
      </xsl:for-each>
    </xsl:variable>
    <xsl:variable name="motifblock">
BL   MOTIF <xsl:value-of select="@name"/> width=<xsl:value-of select="@width"/>
seqs=<xsl:value-of select="@sites"/>
    </xsl:variable>
    <xsl:text>&#10;</xsl:text>
<!-- motif Block entry -->
    <input type="hidden" name="motifblock{$motif_index}" value="
{$motifblock}
"/>
<!-- don't change the next 3 lines or make_logodds breaks -->
    <input type="hidden" name="pssm{$motif_index}" value="
{$pssm}
"/>
    <xsl:text>&#10;</xsl:text>
</xsl:template>

</xsl:stylesheet>

   The MEME results consist of:
     * The version of MEME and the date it was released.
     * The reference to cite if you use MEME in your research.
     * A description of the sequences you submitted (the "training set")
       showing the name, "weight" and length of each sequence.
     * The command line summary detailing the parameters with which you
       ran MEME.
     * Information on each of the motifs MEME discovered, including:
         1. 1.A summary line showing the width, number of occurrences, log
            likelihood ratio and statistical significance of the motif.
         2. 2.A simplified position-specific probability matrix.
         3. 3.A diagram showing the degree of conservation at each motif
            position.
         4. 4.A multilevel consensus sequence showing the most conserved
            letter(s) at each motif position.
         5. 5.The occurrences of the motif sorted by p-value and aligned
            with each other.
         6. 6.Block diagrams of the occurrences of the motif within each
            sequence in the training set.
         7. 7.The motif in BLOCKS format.
         8. 8.A position-specific scoring matrix (PSSM) for use by the
            MAST database search program.
         9. 9.The position specific probability matrix (PSPM) describing
            the
       motif.
     * A summary of motifs showing an optimized (non-overlapping) tiling
       of all of the motifs onto each of the sequences in the training
       set.
     * The reason why MEME stopped and the name of the CPU on which it
       ran.
     * This explanation of how to interpret MEME results.

Data files

   None.

Notes

  1. Command-line arguments

   The following original MEME options are not supported:
-h         : Use -help to get help information.
-dna       : EMBOSS will specify whether sequences use a DNA alphabet
             automatically.
-protein   : EMBOSS will specify whether sequences use a protein alphabet
             automatically.

   The following additional options are provided:
outfile    : Application output that was normally written to stdout.

   Note: ememe makes a temporary local copy of its input sequence data.
   You must ensure there is sufficient disk space for this in the
   directory that ememe is run.

  2. Installing EMBASSY MEME

   The EMBASSY MEME package contains "wrapper" applications providing an
   EMBOSS-style interface to the applications in the original MEME package
   version 3.0.14 developed by Timothy L. Bailey. Please read the file
   README in the EMBASSY MEME package distribution for installation
   instructions.

  3. Installing original MEME

   To use EMBASSY MEME, you will first need to download and install the
   original MEME package:
WWW home:       http://meme.sdsc.edu/meme/
Distribution:   http://meme.nbcr.net/downloads/old_versions/

   Please read the file README in the the original MEME package
   distribution for installation instructions.

  4. Setting up MEME

   For the EMBASSY MEME package to work, the directory containing the
   original MEME executables *must* be in your path. For example if you
   executables were installed to "/usr/local/meme/bin", then type:
set path=(/usr/local/meme/bin/ $path)
rehash

  5. Getting help

   Once you have installed the original MEME, type
meme > meme.txt
mast > mast.txt

   to retrieve the meme and mast documentation into text files. The same
   documentation is given here and in the ememe documentation.

   Please read the 'Notes' section below for a description of the
   differences between the original and EMBASSY MEME, particularly which
   application command line options are supported.

References

   (MEME) Timothy L. Bailey and Charles Elkan, "Fitting a mixture model by
   expectation maximization to discover motifs in biopolymers",
   Proceedings of the Second International Conference on Intelligent
   Systems for Molecular Biology, pp. 28-36, AAAI Press, Menlo Park,
   California, 1994.

   (MAST) Timothy L. Bailey and Michael Gribskov, "Combining evidence
   using p-values: application to sequence homology searches",
   Bioinformatics, Vol. 14, pp. 48-54, 1998.

Warnings

  Input data

  Sequence input

   Note: ememe makes a temporary local copy of its input sequence data.
   You must ensure there is sufficient disk space for this in the
   directory that ememe is run.

   The user must provide the full filename of a sequence database for the
   sequence input ("seqset" ACD option), not an indirect reference, e.g. a
   USA is NOT acceptable. This is because meme (which ememe wraps) does
   not support USAs, and a full sequence database is too big to write to a
   temporary file that the original meme would understand.

Diagnostic Error Messages

   None.

Exit status

   It always exits with status 0.

Known bugs

   None.

See also

   Program name     Description
   antigenic        Finds antigenic sites in proteins
   digest           Reports on protein proteolytic enzyme or reagent cleavage sites
   echlorop         Reports presence of chloroplast transit peptides
   eiprscan         Motif detection
   elipop           Prediction of lipoproteins
   emast            Motif detection
   ememetext        Multiple EM for Motif Elicitation. Text file only
   enetnglyc        Reports N-glycosylation sites in human proteins
   enetoglyc        Reports mucin type GalNAc O-glycosylation sites in mammalian
                    proteins
   enetphos         Reports ser, thr and tyr phosphorylation sites in eukaryotic
                    proteins
   epestfind        Finds PEST motifs as potential proteolytic cleavage sites
   eprop            Reports propeptide cleavage sites in proteins
   esignalp         Reports protein signal cleavage sites
   etmhmm           Reports transmembrane helices
   eyinoyang        Reports O-(beta)-GlcNAc attachment sites
   fuzzpro          Search for patterns in protein sequences
   fuzztran         Search for patterns in protein sequences (translated)
   helixturnhelix   Identify nucleic acid-binding motifs in protein
                    sequences
   oddcomp          Identify proteins with specified sequence word composition
   omeme            Motif detection
   patmatdb         Searches protein sequences with a sequence motif
   patmatmotifs     Scan a protein sequence with motifs from the PROSITE
                    database
   pepcoil          Predicts coiled coil regions in protein sequences
   preg             Regular expression search of protein sequence(s)
   pscan            Scans protein sequence(s) with fingerprints from the PRINTS
                    database
   sigcleave        Reports on signal cleavage sites in a protein sequence

Author(s)

   Jon Ison (jison (c) ebi.ac.uk)
   European Bioinformatics Institute, Wellcome Trust Genome Campus,
   Hinxton, Cambridge CB10 1SD, UK

   This program is an EMBASSY wrapper to a program written by Timothy L.
   Bailey as part of his meme package.

   Please report any bugs to the EMBOSS bug team in the first instance,
   not to Timothy L. Bailey.

History

   None.

Target users

   This program is intended to be used by everyone and everything, from
   naive users to embedded scripts.
