CSC

 
 
Tehdyt toimenpiteet
EMBOSS: ememetext
ememetext

 

Wiki

The master copies of EMBOSS documentation are available at http://emboss.open-bio.org/wiki/Appdocs on the EMBOSS Wiki.

Please help by correcting and extending the Wiki pages.

Function

Multiple EM for Motif Elicitation. Text file only

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 ememetext 


% ememetext crp0.s  -mod oops -revcomp ex.text 
Multiple EM for Motif Elicitation. Text file only.
output sequence set [crp0.fasta]:

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

Example 2 


% ememetext crp0.s -mod oops -revcomp -w 20 ex2.text 
Multiple EM for Motif Elicitation. Text file only.
output sequence set [crp0.fasta]: 
w set, setting max and min to 20#######

Go to the output files for this example

Example 3 


% ememetext INO_up800.s -mod anr -revcomp -bfile memenew/yeast.nc.6.freq ex3.text 
Multiple EM for Motif Elicitation. Text file only.
output sequence set [ino_up800.fasta]:

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

Example 4 


% ememetext lipocalin.s -mod oops -maxw 20 -nmotifs 2 ex4.text 
Multiple EM for Motif Elicitation. Text file only.
output sequence set [lipocalin.fasta]:

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

Example 5 


% ememetext farntrans5.s -mod anr -maxw 40 -maxsites 50 ex5.text 
Multiple EM for Motif Elicitation. Text file only.
output sequence set [farntrans5.fasta]:

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

Example 6 


% ememetext farntrans5.s -mod anr -w 10 -maxsites 30 -nmotifs 3 ex6.text 
Multiple EM for Motif Elicitation. Text file only.
output sequence set [farntrans5.fasta]: 
w set, setting max and min to 10#######

Go to the output files for this example

Example 7 


% ememetext farntrans5.s -mod anr -maxw 12 -nsites 24 -nmotifs 3 ex7.text 
Multiple EM for Motif Elicitation. Text file only.
output sequence set [farntrans5.fasta]:

Go to the output files for this example

Example 8 


% ememetext adh.s -mod zoops -nmotifs 20 -evt 0.01 ex8.text 
Multiple EM for Motif Elicitation. Text file only.
output sequence set [adh.fasta]:

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. Text file only.
Version: EMBOSS:6.3.0

   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.
  [-outtext]           outfile    [*.ememetext] MEME program text output file
  [-outseq]            seqoutset  [.] Sequence set filename
                                  and optional format (output USA)

   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

   "-outtext" associated qualifiers
   -odirectory2        string     Output directory

   "-outseq" associated qualifiers
   -osformat3          string     Output seq format
   -osextension3       string     File name extension
   -osname3            string     Base file name
   -osdirectory3       string     Output directory
   -osdbname3          string     Database name to add
   -ossingle3          boolean    Separate file for each entry
   -oufo3              string     UFO features
   -offormat3          string     Features format
   -ofname3            string     Features file name
   -ofdirectory3       string     Output directory

   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

Qualifier Type Description Allowed values Default
Standard (Mandatory) qualifiers
[-dataset]
(Parameter 1)		 seqset User must provide the full filename of a set of sequences, not an indirect reference, e.g. a USA is NOT acceptable. Readable set of sequences Required
[-outtext]
(Parameter 2)		 outfile MEME program text output file Output file <*>.ememetext
[-outseq]
(Parameter 3)		 seqoutset Sequence set filename and optional format (output USA) Writeable sequences <*>.format
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. Input file Required
-plibfile infile The name of the file containing the Dirichlet prior in the format of file prior30.plib Input file Required
-mod selection 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. Choose from selection list of values zoops
-nmotifs integer The number of *different* motifs to search for. MEME will search for and output <n> motifs. Any integer value 1
-text boolean Default output is in HTML Boolean value Yes/No No
-prior selection 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. Choose from selection list of values dirichlet
-evt float Quit looking for motifs if E-value exceeds this value. Has an extremely high default so by default MEME never quits before -nmotifs <n> have been found. A value of -1 here is a shorthand for infinity. Any numeric value -1
-nsites integer 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 -1
-minsites integer 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 -1
-maxsites integer 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 -1
-wnsites float 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 0.8
-w integer 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 -1
-minw integer 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 8
-maxw integer 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 50
-nomatrim boolean 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. Boolean value Yes/No No
-wg integer 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 11
-ws integer 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 1
-noendgaps boolean 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. Boolean value Yes/No No
-revcomp boolean 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. Boolean value Yes/No No
-pal boolean 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. Boolean value Yes/No No
-[no]nostatus boolean Set this option to prevent progress reports to the terminal. Boolean value Yes/No Yes
Advanced (Unprompted) qualifiers
-maxiter integer 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. Any integer value 50
-distance float 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.) Any numeric value 0.001
-b float 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 -1.0
-spfuzz float 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 -1.0
-spmap selection The type of mapping function to use. uni: Use 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). See the application documentation for more information. Choose from selection list of values default
-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. See the application documentation for more information. Any string  
-maxsize integer Maximum dataset size in characters (-1 = use meme default). Any integer value -1
-p integer Only values of >0 will be applied. The -p <np> 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). Any integer value 0
-time integer Only values of more than 0 will be applied. Any integer value 0
-sf string Print <sf> as name of sequence file Any string  
-heapsize integer 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 64
-xbranch boolean 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. Boolean value Yes/No No
-wbranch boolean 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. Boolean value Yes/No No
-bfactor integer 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 3
Associated qualifiers
"-dataset" associated seqset qualifiers
-sbegin1
-sbegin_dataset		 integer Start of each sequence to be used Any integer value 0
-send1
-send_dataset		 integer End of each sequence to be used Any integer value 0
-sreverse1
-sreverse_dataset		 boolean Reverse (if DNA) Boolean value Yes/No N
-sask1
-sask_dataset		 boolean Ask for begin/end/reverse Boolean value Yes/No N
-snucleotide1
-snucleotide_dataset		 boolean Sequence is nucleotide Boolean value Yes/No N
-sprotein1
-sprotein_dataset		 boolean Sequence is protein Boolean value Yes/No N
-slower1
-slower_dataset		 boolean Make lower case Boolean value Yes/No N
-supper1
-supper_dataset		 boolean Make upper case Boolean value Yes/No N
-sformat1
-sformat_dataset		 string Input sequence format Any string  
-sdbname1
-sdbname_dataset		 string Database name Any string  
-sid1
-sid_dataset		 string Entryname Any string  
-ufo1
-ufo_dataset		 string UFO features Any string  
-fformat1
-fformat_dataset		 string Features format Any string  
-fopenfile1
-fopenfile_dataset		 string Features file name Any string  
"-outtext" associated outfile qualifiers
-odirectory2
-odirectory_outtext		 string Output directory Any string  
"-outseq" associated seqoutset qualifiers
-osformat3
-osformat_outseq		 string Output seq format Any string  
-osextension3
-osextension_outseq		 string File name extension Any string  
-osname3
-osname_outseq		 string Base file name Any string  
-osdirectory3
-osdirectory_outseq		 string Output directory Any string  
-osdbname3
-osdbname_outseq		 string Database name to add Any string  
-ossingle3
-ossingle_outseq		 boolean Separate file for each entry Boolean value Yes/No N
-oufo3
-oufo_outseq		 string UFO features Any string  
-offormat3
-offormat_outseq		 string Features format Any string  
-ofname3
-ofname_outseq		 string Features file name Any string  
-ofdirectory3
-ofdirectory_outseq		 string Output directory Any string  
General qualifiers
-auto boolean Turn off prompts Boolean value Yes/No N
-stdout boolean Write first file to standard output Boolean value Yes/No N
-filter boolean Read first file from standard input, write first file to standard output Boolean value Yes/No N
-options boolean Prompt for standard and additional values Boolean value Yes/No N
-debug boolean Write debug output to program.dbg Boolean value Yes/No N
-verbose boolean Report some/full command line options Boolean value Yes/No Y
-help boolean Report command line options and exit. More information on associated and general qualifiers can be found with -help -verbose Boolean value Yes/No N
-warning boolean Report warnings Boolean value Yes/No Y
-error boolean Report errors Boolean value Yes/No Y
-fatal boolean Report fatal errors Boolean value Yes/No Y
-die boolean Report dying program messages Boolean value Yes/No Y
-version boolean Report version number and exit Boolean value Yes/No N

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

Input files for usage example 3

File: INO_up800.s

>CHO1	 sequence of the region upstream from YER026C
CCGACCCAAATGTAATGGAACAATATTATTTGACACTTGATCAGCAGCAAAATAATCACC
AAAATATGGCCTGGTTGACTCCTCCACAACTGCCACCTCATTTAGAAAACGTCATTTTGA
ATAGTTACTCAAACGCGCAAACTGATAATACGTCTGGCGCCCTTCCCATTCCGAACCATG
TTATATTGAACCATCTGGCGACAAGCAGTATTAAGCATAATACATTATGTGTCGCATCCA
TTGTTAGGTATAAACAAAAATACGTGACCCAAATACTGTATACACCATTGCAATAGATAT
GATTATAGAGCTTATAGCTACATCTTTTTAGATAAAAGCGAAGATGTTTCTGCGATTTTT
CCATTATAGCTCTCCATGATACTAAATATCAAGGTCTACATGTAAGTATTTGTATATATG
GGTTGGAATGTATATACGTATATACGTACGTACGTACGTATATGCACATAATTGTTACGG
GATGTATATATAAATTAGTAGCATTATAGAAGATATCCCTAACATCAATCCCCACTCCTT
CTCAATGTGTGCAGACTTCTGTGCCAGACACTGAATATATATCAGTAATTGGTCAAAATC
ACTTTGAACGTTCACACGGCACCCTCACGCCTTTGAGCTTTCACATGGACCCATCTAAAG
ATGAAGATCCGTATTTTATAGGAAACATTATAAATAAGGAAAGAGAGATACACCTATTTT
TTTCATTTTGTGGGTGATTGTCATTTTTAGTTGTCTATTTGATTCAATCAAAAAACAAAA
ATAAAACTATATATTAAAAA
>CHO2	 sequence of the region upstream from YGR157W
ACCCTCTAACGCGAATAAAGCGAATGACAGCGGCACCATTAATATGGCGAAACTGCAATT
ACTACCTGAAAACCAACAAGATATGATCAAACAAGTTCTTACTTTGACACCTGCCCAGAT
CCAAAGTTTACCAAGTGACCAGCAACTTATGGTGGAAAACTTTAGAAAAGAATATATAAT
CTAAGTAATCAGAGCCATAGCGTATCAGAAAACCACACCTAATTAGATGGTTCTTGCATC
TGTACCTCTTATCACTAAAAGCGGCACTAAACTTCCAACATTAAATGTTTGCCTTGTTAA
ATATATATTTTTGCCTTGGTTTAAATTGGTCAAGACAGTCAATTGCCACACTTTTCTCAT
GCCGCATTCATTATTCGCGAAGTTTTCCACACAAAACTGTGAAAATGAACGGCGATGCCA
GAAACGGCAAAACCTCAAATGTTAGATAACGTGGATCTCCGACACATGTGAATTTATAAG
TAGGCATATGAAAATACAGATTCTTTCCACTGTGTTCCCTTTTATTCCCTTCTCATGTGA
AGAGTTCACACCAAATCTTCAAAATATAACTAATATAGTAGAGTTTGATTCAAAGGACCT
TTTTTTTTGCCTCTTTGATTAGTTTATCTTCTTTTCTTCATTTTATCCCCTAATTTTATA
CGTTAGTTCAACCTAACAATCCAGGATTTCATTAACAAGAAAGGTAAAAGTAACCTATCA
AGGCTATTTTGAAAAAAAAAATTCCGCCCTGAATATTTCGAGTGATTTTCTTAGTGACAA
AGCTTTTTCTTCATCTGTAG
>FAS1	 sequence of the region upstream from YKL182W
CCGGGTTATAGCAGCGTCTGCTCCGCATCACGATACACGAGGTGCAGGCACGGTTCACTA
CTCCCCTGGCCTCCAACAAACGACGGCCAAAAACTTCACATGCCGCCCAGCCAAGCATAA
TTACGCAACAGCGATCTTTCCGTCGCACAAGTTAAAAGAAATTGTTGAAAAATACAAATA
ATCGCGAACAATACGTTGTTGCTATTTAACGCTTTTGGTCTGACAGTAAGTGTGCCTTTC
CCAATCACCGAAAAGTGTTGAACGATTCACTGCGACAATAATCAGAGATTACAGTCGGCA
TTTTGGCATTTTTGGCATACTTTTTATCGATTGAACCATCTTCTCCAAACACTTTTCCTT
TTTCCTTCTATTCTGCAGGACCAACTAAAACTGGGTATATATATCATTATCTATATATAT
AAACGGCTTTCAACAAAGTTATAGGGGAAAACTAAAAATATAAGAAAAAAAAAGGTATTG
ATTGATAAGGAAAAAGAACCAAGGGAAAAATATAAAAAAGTACATTGGGCCTTTTCATAC
TTGTTATCACTTACATTACAAAGAAGAACAAACAACTTTTTTAAACGAATTTTCTTTCTT
CCTTTTTCAATTTATTAATTCTTTTTTTCCATACAATTCAAGGTCAAATATATTCTTATA
TGCTCTTTGAATATTTCTGAAAAATATATAAAGAAAAGAAACTACAAGAACATCATCCGG
AAAATCAGATTATAGACTAGGATTCCGCTCTTTTTAGTATATTTATTCGCCACACCTAAC
TGCTCTATTATTCGCTCATT
>FAS2	 sequence of the region upstream from YPL231W
TCCAGGCAAGGCACCAAGAGTTATTGAAACTAGAAAAATCCATGGCAGAACTTACTCAAT
TGTTTAATGACATGGAAGAACTGGTAATAGAACAACAAGAAAACGTAGACGTCATCGACA
AGAACGTTGAAGACGCTCAACTCGACGTAGAACAGGGTGTCGGTCATACCGATAAAGCCG
TCAAGAGTGCCAGAAAAGCAAGAAAGAACAAGATTAGATGTTGGTTGATTGTATTCGCCA


  [Part of this file has been deleted for brevity]

CTCTTCCTAAAAATACATTGGGCATTACCCGCAAACTAACCCATCGCTTAGCAAAATCCA
ACCATTTTTTTTTTATCTCCCGCGTTTTCACATGCTACCTCATTCGCCTCGTAACGTTAC
GACCGAAATCTCACTAAGGCACGGTTTGTTGGGCAGTTTACAGATGTTGGATAACCAGTT
GTTTCTAAACGGTTATGCCTCATATATAACTTGTTAACTGAAGGTTACACAAGACCACAT
CACCACTGTCGTGCTTTTCTAATAACCGCTATATTAGACGTTTAAAGGGCTACAGCAACA
CCAATTGAAATACCATCATT
>ACC1	 sequence of the region upstream from YNR016C
TATCCAAAGGGGAATGCTTCATCTTGTTGAACAACGCCCAACAATTTCCACTGCCCACCG
AATCGTTGCGCCCGTTAAAATCTTCACATGGCCCGGCCGCGCGCGCGTTGTGCCAACAAG
TCGCAGTCGAAATTCAACCGCTCATTGCCACTCTCTCTACTGCTTGGTGAACTAGGCTAT
ACGCTCAATCAGCGCCAAGATATATAAGAAGAACAGCACTCCCAGTCGTATTCTGGCACA
GTATAGCCTAGCACAATCACTGTCACAATTGTTATCGGTTCTACAATTGTTCTGCTCTCT
TCAATTTTCCTTTCCTTATTCTACTCTTTTTATCCCTTTCGTACAGTTTACCTGAAGATA
AAAAACAACAAAGCCAATTCCCTAATTTGCAATCGCCATTTGCATCTATATATATATATT
TGTTGTGCCATTTTTTTATCCTCTGTGAGTGATCGGTGCATGTGTTTATAAAAGTTTATT
CATTCTACTATACGAACTTTTCCCTCTGCCCTTCCCTCCCGCTTCATCCTTATTTTTGGA
CAATAAACTAGAGAACAATTTGAACTTGAATTGGAATTCAGATTCAGAGCAAGAGACAAG
AAACTTCCCTTTTTCTTCTCCACATATTATTATTTATTCGTGTATTTTCTTTTAACGATA
CGATACGATACGACACGATACGATACGACACGCTACTATACTATACAAATATAATAGTAT
AATAACCGATTCGTCTTCTAGCTTAATTTTTTTCCGTTCCCGAAACAGCGCAGAAAATTA
GAAAAAATCAAGTTTCTACC
>INO1	 sequence of the region upstream from YJL153C
AGCAAACAACCAAATATAATTTAGAAATGGACAGAGACCATATTAATGACCATGACCATC
GAATGAGCTATTCCATCAACAAGGACGACTTGTTGTTAATGGTTTTGGCGGTTTTCATTC
CCCCAGTGGCCGTCTGGAAGCGTAAGGGTATGTTCAACAGGGATACACTATTGAACTTAC
TTCTCTTCCTACTGTTATTCTTCCCAGCAATCATTCACGCTTGCTACGTTGTATATGAAA
CGAGTAGTGAACGTTCGTACGATCTTTCACGCAGACATGCGACTGCGCCCGCCGTAGACC
GTGACCTGGAAGCTCACCCTGCAGAGGAATCTCAAGCACAGCCTCCAGCATATGATGAAG
ACGATGAGGCCGGTGCCGATGTGCCCTTGATGGACAACAAACAACAGCTCTCTTCCGGCC
GTACTTAGTGATCGGAACGAGCTCTTTATCACCGTAGTTCTAAATAACACATAGAGTAAA
TTATTGCCTTTTTCTTCGTTCCTTTTGTTCTTCACGTCCTTTTTATGAAATACGTGCCGG
TGTTCCGGGGTTGGATGCGGAATCGAAAGTGTTGAATGTGAAATATGCGGAGGCCAAGTA
TGCGCTTCGGCGGCTAAATGCGGCATGTGAAAAGTATTGTCTATTTTATCTTCATCCTTC
TTTCCCAGAATATTGAACTTATTTAATTCACATGGAGCAGAGAAAGCGCACCTCTGCGTT
GGCGGCAATGTTAATTTGAGACGTATATAAATTGGAGCTTTCGTCACCTTTTTTTGGCTT
GTTCTGTTGTCGGGTTCCTA
>OPI3	 sequence of the region upstream from YJR073C
GTGTCCACAACGTGAAACTTCCGTACCATTTCTTGCAACAATTGGTAAACAGCATGACAT
CTTGCAGGCAACTCTTTGTTGCTTGCTTGCGACGCCTCCTCCTTTGTCAAAGGTACATTA
ATGGAGATGACCACATCCGTGTCAAACTGGGTTAATCTGATCAACGCTACGCCGATGACA
ACGGTCTGTGCCAGATCTGGTTTTCCCCACTTATTTGCTACTTCCATAACGAGTCCGGTG
AACTTGGTTCCTTGCTGAACAGTGTCTTCTTGTAAAGCTTCCCATTTGGTGGTCCCGTTC
AACTCCGTCAGGTCTTCCACGTGGAACTGCCAAGCCTCCTTCAGATCGCTCTTGTCGACC
GTCTCCAAGAGATCCACGATAATGCTTTCATTGGTGGCTAGTCCATCTTCGAATTCTTCT
TCATCGCGACGGGAATTGACGTACACCTCCTGTGTATCGGGGACTTCTCTTAGAGTAGAA
GCGTCTATAAACCCAGGTGGGACGACAGTAGTGATGGCGCCGCCGTATAATTCGACTTCC
TTGTTGTTCATGCTTCCTTGATGACCAGGGTAGGTGTCAATGAGAGTGCATGTGGAAAGT
TGCACCGGTTGTGAAATATGAGAAGCCTTTTCAATCTTCATATGCAAACCCACACATGCA
TCGTTGGTTTCTGTCCACTGCCACTGCAATGACCACTGGATAAGGGGTCTTTATAAGAGA
ACACATATGAAGAACATGAACGTTCTTGGACAGAGCCATAAACAGCAATTGAAGACAACA
AGAATAGCGCAAGTCAAGCG

File: yeast.nc.6.freq

# seq	frequency_non_coding
a	0.32442758667668
c	0.175572413323319
g	0.175572413323319
t	0.32442758667668
# seq	frequency_non_coding
aa	0.118982244161714
ac	0.0521182743409142
ag	0.0559273922850834
at	0.0973159523835682
ca	0.0584827538751812
cc	0.0326990007534392
cg	0.0284473890701011
ct	0.0559273922850834
ga	0.0559247902310797
gc	0.0348909421343666
gg	0.0326990007534392
gt	0.0521182743409142
ta	0.0910768051171416
tc	0.0559247902310797
tg	0.0584827538751812
tt	0.118982244161714
# seq	observed_freq
aaa	0.049152768651441
aac	0.0174036386740962
aag	0.0213094373095717
aat	0.0313483273294989
aca	0.0183651016732642
acc	0.00948257362793872
acg	0.00868125792953577
act	0.0156686613162602
aga	0.0191771324713567
agc	0.0105445268863571
agg	0.0105978127875158
agt	0.0157042817827957
ata	0.0333561053334843
atc	0.0152910264515268
atg	0.0174586621589883
att	0.0311913655989118
caa	0.0201461250000362
cac	0.0104918201797762
cag	0.0104046513958155
cat	0.0175637859748612
cca	0.0105905728552932
ccc	0.0063256735815742
ccg	0.00537550487667355
cct	0.0106563114398748
cga	0.00831404856720293
cgc	0.00609312695858266
cgg	0.00532859011587077


  [Part of this file has been deleted for brevity]

tttatc	0.000598827491406134
tttatg	0.000612506661319178
tttatt	0.00158183592505095
tttcaa	0.000947937370357122
tttcac	0.000474696300599468
tttcag	0.000478625423872363
tttcat	0.000873720597424649
tttcca	0.000523301010716029
tttccc	0.000362352479611488
tttccg	0.00028871779901574
tttcct	0.000716701189593004
tttcga	0.000341251632405197
tttcgc	0.000242004888993536
tttcgg	0.000211736087483821
tttcgt	0.000410229574307143
tttcta	0.000718884035855724
tttctc	0.000684977157241476
tttctg	0.00052009950286404
tttctt	0.00171891867034976
tttgaa	0.000813910609826126
tttgac	0.000305161907528229
tttgag	0.000387236927006494
tttgat	0.000670424848823344
tttgca	0.000441080468153583
tttgcc	0.000306471615285861
tttgcg	0.000215228641504173
tttgct	0.000500599409583743
tttgga	0.000346635986519906
tttggc	0.000271400551998163
tttggg	0.000238366811889003
tttggt	0.000427110252072176
tttgta	0.000642920985913074
tttgtc	0.000363807710453302
tttgtg	0.000376613741861258
tttgtt	0.00102200862020541
ttttaa	0.00107774396144686
ttttac	0.00076588799204629
ttttag	0.000618473107770613
ttttat	0.00164935863611109
ttttca	0.00119867364440154
ttttcc	0.000846944349935286
ttttcg	0.000516897995012051
ttttct	0.00167235128341174
ttttga	0.00088157884397044
ttttgc	0.000600137199163766
ttttgg	0.000542364534743782
ttttgt	0.00103670645170773
ttttta	0.00171950076268648
tttttc	0.00190678897202784
tttttg	0.00124276713890848
tttttt	0.00570057577663487

Input files for usage example 4

File: lipocalin.s

>ICYA_MANSE
GDIFYPGYCPDVKPVNDFDLSAFAGAWHEIAKLPLENENQGKCTIAEYKYDGKKASVYNSFVSNGVKEYMEGDLEIAPDA
KYTKQGKYVMTFKFGQRVVNLVPWVLATDYKNYAINYNCDYHPDKKAHSIHAWILSKSKVLEGNTKEVVDNVLKTFSHLI
DASKFISNDFSEAACQYSTTYSLTGPDRH
>LACB_BOVIN
MKCLLLALALTCGAQALIVTQTMKGLDIQKVAGTWYSLAMAASDISLLDAQSAPLRVYVEELKPTPEGDLEILLQKWENG
ECAQKKIIAEKTKIPAVFKIDALNENKVLVLDTDYKKYLLFCMENSAEPEQSLACQCLVRTPEVDDEALEKFDKALKALP
MHIRLSFNPTQLEEQCHI
>BBP_PIEBR
NVYHDGACPEVKPVDNFDWSNYHGKWWEVAKYPNSVEKYGKCGWAEYTPEGKSVKVSNYHVIHGKEYFIEGTAYPVGDSK
IGKIYHKLTYGGVTKENVFNVLSTDNKNYIIGYYCKYDEDKKGHQDFVWVLSRSKVLTGEAKTAVENYLIGSPVVDSQKL
VYSDFSEAACKVN
>RETB_BOVIN
ERDCRVSSFRVKENFDKARFAGTWYAMAKKDPEGLFLQDNIVAEFSVDENGHMSATAKGRVRLLNNWDVCADMVGTFTDT
EDPAKFKMKYWGVASFLQKGNDDHWIIDTDYETFAVQYSCRLLNLDGTCADSYSFVFARDPSGFSPEVQKIVRQRQEELC
LARQYRLIPHNGYCDGKSERNIL
>MUP2_MOUSE
MKMLLLLCLGLTLVCVHAEEASSTGRNFNVEKINGEWHTIILASDKREKIEDNGNFRLFLEQIHVLEKSLVLKFHTVRDE
ECSELSMVADKTEKAGEYSVTYDGFNTFTIPKTDYDNFLMAHLINEKDGETFQLMGLYGREPDLSSDIKERFAKLCEEHG
ILRENIIDLSNANRCLQARE

Input files for usage example 5

File: farntrans5.s

>RAM1_YEAST PROTEIN FARNESYLTRANSFERASE BETA SUBUNIT (EC 2.5.1.-) (CAAX FARN
MRQRVGRSIA RAKFINTALL GRKRPVMERV VDIAHVDSSK AIQPLMKELE TDTTEARYKV
LQSVLEIYDD EKNIEPALTK EFHKMYLDVA FEISLPPQMT ALDASQPWML YWIANSLKVM
DRDWLSDDTK RKIVVKLFTI SPSGGPFGGG PGQLSHLAST YAAINALSLC DNIDGCWDRI
DRKGIYQWLI SLKEPNGGFK TCLEVGEVDT RGIYCALSIA TLLNILTEEL TEGVLNYLKN
CQNYEGGFGS CPHVDEAHGG YTFCATASLA ILRSMDQINV EKLLEWSSAR QLQEERGFCG
RSNKLVDGCY SFWVGGSAAI LEAFGYGQCF NKHALRDYIL YCCQEKEQPG LRDKPGAHSD
FYHTNYCLLG LAVAESSYSC TPNDSPHNIK CTPDRLIGSS KLTDVNPVYG LPIENVRKII
HYFKSNLSSP S

>PFTB_RAT PROTEIN FARNESYLTRANSFERASE BETA SUBUNIT (EC 2.5.1.-) (CAAX FARNES
MASSSSFTYY CPPSSSPVWS EPLYSLRPEH ARERLQDDSV ETVTSIEQAK VEEKIQEVFS
SYKFNHLVPR LVLQREKHFH YLKRGLRQLT DAYECLDASR PWLCYWILHS LELLDEPIPQ
IVATDVCQFL ELCQSPDGGF GGGPGQYPHL APTYAAVNAL CIIGTEEAYN VINREKLLQY
LYSLKQPDGS FLMHVGGEVD VRSAYCAASV ASLTNIITPD LFEGTAEWIA RCQNWEGGIG
GVPGMEAHGG YTFCGLAALV ILKKERSLNL KSLLQWVTSR QMRFEGGFQG RCNKLVDGCY
SFWQAGLLPL LHRALHAQGD PALSMSHWMF HQQALQEYIL MCCQCPAGGL LDKPGKSRDF
YHTCYCLSGL SIAQHFGSGA MLHDVVMGVP ENVLQPTHPV YNIGPDKVIQ ATTHFLQKPV
PGFEECEDAV TSDPATD

>BET2_YEAST YPT1/SEC4 PROTEINS GERANYLGERANYLTRANSFERASE BETA SUBUNIT (EC 2.
MSGSLTLLKE KHIRYIESLD TNKHNFEYWL TEHLRLNGIY WGLTALCVLD SPETFVKEEV
ISFVLSCWDD KYGAFAPFPR HDAHLLTTLS AVQILATYDA LDVLGKDRKV RLISFIRGNQ
LEDGSFQGDR FGEVDTRFVY TALSALSILG ELTSEVVDPA VDFVLKCYNF DGGFGLCPNA
ESHAAQAFTC LGALAIANKL DMLSDDQLEE IGWWLCERQL PEGGLNGRPS KLPDVCYSWW
VLSSLAIIGR LDWINYEKLT EFILKCQDEK KGGISDRPEN EVDVFHTVFG VAGLSLMGYD
NLVPIDPIYC MPKSVTSKFK KYPYK

>RATRABGERB Rat rab geranylgeranyl transferase beta-subunit
MGTQQKDVTIKSDAPDTLLLEKHADYIASYGSKKDDYEYCMSEY
LRMSGVYWGLTVMDLMGQLHRMNKEEILVFIKSCQHECGGVSASIGHDPHLLYTLSAV
QILTLYDSIHVINVDKVVAYVQSLQKEDGSFAGDIWGEIDTRFSFCAVATLALLGKLD
AINVEKAIEFVLSCMNFDGGFGCRPGSESHAGQIYCCTGFLAITSQLHQVNSDLLGWW
LCERQLPSGGLNGRPEKLPDVCYSWWVLASLKIIGRLHWIDREKLRSFILACQDEETG
GFADRPGDMVDPFHTLFGIAGLSLLGEEQIKPVSPVFCMPEEVLQRVNVQPELVS

>CAL1_YEAST RAS PROTEINS GERANYLGERANYLTRANSFERASE (EC 2.5.1.-) (PROTEIN GER
MCQATNGPSR VVTKKHRKFF ERHLQLLPSS HQGHDVNRMA IIFYSISGLS IFDVNVSAKY
GDHLGWMRKH YIKTVLDDTE NTVISGFVGS LVMNIPHATT INLPNTLFAL LSMIMLRDYE
YFETILDKRS LARFVSKCQR PDRGSFVSCL DYKTNCGSSV DSDDLRFCYI AVAILYICGC
RSKEDFDEYI DTEKLLGYIM SQQCYNGAFG AHNEPHSGYT SCALSTLALL SSLEKLSDKF
KEDTITWLLH RQVSSHGCMK FESELNASYD QSDDGGFQGR ENKFADTCYA FWCLNSLHLL
TKDWKMLCQT ELVTNYLLDR TQKTLTGGFS KNDEEDADLY HSCLGSAALA LIEGKFNGEL
CIPQEIFNDF SKRCCF

Input files for usage example 8

File: adh.s

>2BHD_STREX 20-BETA-HYDROXYSTEROID DEHYDROGENASE (EC 1.1.1.53)
MNDLSGKTVIITGGARGLGAEAARQAVAAGARVVLADVLDEEGAATARELGDAARYQHLDVTIEEDWQRVVAYAREEFGSVDGLVNNAGISTGMFLETESVERFRKVVDINLTGVFIGMKTVIPAMKDAGGGSIVNISSAAGLMGLALTSSYGASKWGVRGLSKLAAVELGTDRIRVNSVHPGMTYTPMTAETGIRQGEGNYPNTPMGRVGNEPGEIAGAVVKLLSDTSSYVTGAELAVDGGWTTGPTVKYVMGQ
>3BHD_COMTE 3-BETA-HYDROXYSTEROID DEHYDROGENASE (EC 1.1.1.51)
TNRLQGKVALVTGGASGVGLEVVKLLLGEGAKVAFSDINEAAGQQLAAELGERSMFVRHDVSSEADWTLVMAAVQRRLGTLNVLVNNAGILLPGDMETGRLEDFSRLLKINTESVFIGCQQGIAAMKETGGSIINMASVSSWLPIEQYAGYSASKAAVSALTRAAALSCRKQGYAIRVNSIHPDGIYTPMMQASLPKGVSKEMVLHDPKLNRAGRAYMPERIAQLVLFLASDESSVMSGGELHADNSILGMGL
>ADH_DROME ALCOHOL DEHYDROGENASE (EC 1.1.1.1)
SFTLTNKNVIFVAGLGGIGLDTSKELLKRDLKNLVILDRIENPAAIAELKAINPKVTVTFYPYDVTVPIAETTKLLKTIFAQLKTVDVLINGAGILDDHQIERTIAVNYTGLVNTTTAILDFWDKRKGGPGGIICNIGSVTGFNAIYQVPVYSGTKAAVVNFTSSLAKLAPITGVTAYTVNPGITRTTLVHKFNSWLDVEPQVAEKLLAHPTQPSLACAENFVKAIELNQNGAIWKLDLGTLEAIQWTKHWDSGI
>AP27_MOUSE ADIPOCYTE P27 PROTEIN (AP27)
MKLNFSGLRALVTGAGKGIGRDTVKALHASGAKVVAVTRTNSDLVSLAKECPGIEPVCVDLGDWDATEKALGGIGPVDLLVNNAALVIMQPFLEVTKEAFDRSFSVNLRSVFQVSQMVARDMINRGVPGSIVNVSSMVAHVTFPNLITYSSTKGAMTMLTKAMAMELGPHKIRVNSVNPTVVLTDMGKKVSADPEFARKLKERHPLRKFAEVEDVVNSILFLLSDRSASTSGGGILVDAGYLAS
>BA72_EUBSP 7-ALPHA-HYDROXYSTEROID DEHYDROGENASE (EC 1.1.1.159) (BILE ACID 7-DEHYDROXYLASE) (BILE ACID-INDUCIBLE PROTEIN)
MNLVQDKVTIITGGTRGIGFAAAKIFIDNGAKVSIFGETQEEVDTALAQLKELYPEEEVLGFAPDLTSRDAVMAAVGQVAQKYGRLDVMINNAGITSNNVFSRVSEEEFKHIMDINVTGVFNGAWCAYQCMKDAKKGVIINTASVTGIFGSLSGVGYPASKASVIGLTHGLGREIIRKNIRVVGVAPGVVNTDMTNGNPPEIMEGYLKALPMKRMLEPEEIANVYLFLASDLASGITATTVSVDGAYRP
>BDH_HUMAN D-BETA-HYDROXYBUTYRATE DEHYDROGENASE PRECURSOR (EC 1.1.1.30) (BDH) (3-HYDROXYBUTYRATE DEHYDROGENASE) (FRAGMENT)
GLRPPPPGRFSRLPGKTLSACDRENGARRPLLLGSTSFIPIGRRTYASAAEPVGSKAVLVTGCDSGFGFSLAKHLHSKGFLVFAGCLMKDKGHDGVKELDSLNSDRLRTVQLNVFRSEEVEKVVGDCPFEPEGPEKGMWGLVNNAGISTFGEVEFTSLETYKQVAEVNLWGTVRMTKSFLPLIRRAKGRVVNISSMLGRMANPARSPYCITKFGVEAFSDCLRYEMYPLGVKVSVVEPGNFIAATSLYNPESIQAIAKKMWEELPEVVRKDYGKKYFDEKIAKMETYCSSGSTDTSPVIDAVTHALTATTPYTRYHPMDYYWWLRMQIMTHLPGAISDMIYIR
>BPHB_PSEPS BIPHENYL-CIS-DIOL DEHYDROGENASE (EC 1.3.1.-)
MKLKGEAVLITGGASGLGRALVDRFVAEAKVAVLDKSAERLAELETDLGDNVLGIVGDVRSLEDQKQAASRCVARFGKIDTLIPNAGIWDYSTALVDLPEESLDAAFDEVFHINVKGYIHAVKALPALVASRGNVIFTISNAGFYPNGGGPLYTAAKQAIVGLVRELAFELAPYVRVNGVGPGGMNSDMRGPSSLGMGSKAISTVPLADMLKSVLPIGRMPEVEEYTGAYVFFATRGDAAPASGALVNYDGGLGVRGFFSGAGGNDLLEQLNIHP
>BUDC_KLETE ACETOIN(DIACETYL) REDUCTASE (EC 1.1.1.5) (ACETOIN DEHYDROGENASE)
MQKVALVTGAGQGIGKAIALRLVKDGFAVAIADYNDATATAVAAEINQAGGRAVAIKVDVSRRDQVFAAVEQARKALGGFNVIVNNAGIAPSTPIESITEEIVDRVYNINVKGVIWGMQAAVEAFKKEGHGGKIVNACSQAGHVGNPELAVYSSSKFAVRGLTQTAARDLAPLGITVNGFCPGIVKTPMWAEIDRQCRKRRANRWATARLNLPNASPLAACRSLKTSPPACRSSPARIPTI
>DHES_HUMAN ESTRADIOL 17 BETA-DEHYDROGENASE (EC 1.1.1.62) (20 ALPHA-HYDROXYSTEROID DEHYDROGENASE) (E2DH) (17-BETA-HSD) (PLACENTAL 17-BETA-HYDROXYSTEROID DEHYDROGENASE)
ARTVVLITGCSSGIGLHLAVRLASDPSQSFKVYATLRDLKTQGRLWEAARALACPPGSLETLQLDVRDSKSVAAARERVTEGRVDVLVCNAGLGLLGPLEALGEDAVASVLDVNVVGTVRMLQAFLPDMKRRGSGRVLVTGSVGGLMGLPFNDVYCASKFALEGLCESLAVLLLPFGVHLSLIECGPVHTAFMEKVLGSPEEVLDRTDIHTFHRFYQYLAHSKQVFREAAQNPEEVAEVFLTALRAPKPTLRYFTTERFLPLLRMRLDDPSGSNYVTAMHREVFGDVPAKAEAGAEAGGGAGPGAEDEAGRSAVGDPELGDPPAAPQ
>DHGB_BACME GLUCOSE 1-DEHYDROGENASE B (EC 1.1.1.47)
MYKDLEGKVVVITGSSTGLGKSMAIRFATEKAKVVVNYRSKEDEANSVLEEEIKKVGGEAIAVKGDVTVESDVINLVQSAIKEFGKLDVMINNAGMENPVSSHEMSLSDWNKVIDTNLTGAFLGSREAIKYFVENDIKGTVINMSSVHEWKIPWPLFVHYAASKGGMKLMTETLALEYAPKGIRVNNIGPGAINTPINAEKFADPEQRADVESMIPMGYIGEPEEIAAVAWLASSEASYVTGITLFADGGMTQYPSFQAGRG
>DHII_HUMAN CORTICOSTEROID 11-BETA-DEHYDROGENASE (EC 1.1.1.146) (11-DH) (11-BETA- HYDROXYSTEROID DEHYDROGENASE) (11-BETA-HSD)
MAFMKKYLLPILGLFMAYYYYSANEEFRPEMLQGKKVIVTGASKGIGREMAYHLAKMGAHVVVTARSKETLQKVVSHCLELGAASAHYIAGTMEDMTFAEQFVAQAGKLMGGLDMLILNHITNTSLNLFHDDIHHVRKSMEVNFLSYVVLTVAALPMLKQSNGSIVVVSSLAGKVAYPMVAAYSASKFALDGFFSSIRKEYSVSRVNVSITLCVLGLIDTETAMKAVSGIVHMQAAPKEECALEIIKGGALRQEEVYYDSSLWTTLLIRNPCRKILEFLYSTSYNMDRFINK
>DHMA_FLAS1 N-ACYLMANNOSAMINE 1-DEHYDROGENASE (EC 1.1.1.233) (NAM-DH) 
TTAGVSRRPGRLAGKAAIVTGAAGGIGRATVEAYLREGASVVAMDLAPRLAATRYEEPGAIPIACDLADRAAIDAAMADAVARLGGLDILVAGGALKGGTGNFLDLSDADWDRYVDVNMTGTFLTCRAGARMAVAAGAGKDGRSARIITIGSVNSFMAEPEAAAYVAAKGGVAMLTRAMAVDLARHGILVNMIAPGPVDVTGNNTGYSEPRLAEQVLDEVALGRPGLPEEVATAAVFLAEDGSSFITGSTITIDGGLSAMIFGGMREGRR
>ENTA_ECOLI 2,3-DIHYDRO-2,3-DIHYDROXYBENZOATE DEHYDROGENASE (EC 1.3.1.28)
MDFSGKNVWVTGAGKGIGYATALAFVEAGAKVTGFDQAFTQEQYPFATEVMDVADAAQVAQVCQRLLAETERLDALVNAAGILRMGATDQLSKEDWQQTFAVNVGGAFNLFQQTMNQFRRQRGGAIVTVASDAAHTPRIGMSAYGASKAALKSLALSVGLELAGSGVRCNVVSPGSTDTDMQRTLWVSDDAEEQRIRGFGEQFKLGIPLGKIARPQEIANTILFLASDLASHITLQDIVVDGGSTLGA
>FIXR_BRAJA FIXR PROTEIN
MGLDLPNDNLIRGPLPEAHLDRLVDAVNARVDRGEPKVMLLTGASRGIGHATAKLFSEAGWRIISCARQPFDGERCPWEAGNDDHFQVDLGDHRMLPRAITEVKKRLAGAPLHALVNNAGVSPKTPTGDRMTSLTTSTDTWMRVFHLNLVAPILLAQGLFDELRAASGSIVNVTSIAGSRVHPFAGSAYATSKAALASLTRELAHDYAPHGIRVNAIAPGEIRTDMLSPDAEARVVASIPLRRVGTPDEVAKVIFFLCSDAASYVTGAEVPINGGQHL
>GUTD_ECOLI SORBITOL-6-PHOSPHATE 2-DEHYDROGENASE (EC 1.1.1.140) (GLUCITOL-6- PHOSPHATE DEHYDROGENASE) (KETOSEPHOSPHATE REDUCTASE)
MNQVAVVIGGGQTLGAFLCHGLAAEGYRVAVVDIQSDKAANVAQEINAEYGESMAYGFGADATSEQSCLALSRGVDEIFGRVDLLVYSAGIAKAAFISDFQLGDFDRSLQVNLVGYFLCAREFSRLMIRDGIQGRIIQINSKSGKVGSKHNSGYSAAKFGGVGLTQSLALDLAEYGITVHSLMLGNLLKSPMFQSLLPQYATKLGIKPDQVEQYYIDKVPLKRGCDYQDVLNMLLFYASPKASYCTGQSINVTGGQVMF
>HDE_CANTR HYDRATASE-DEHYDROGENASE-EPIMERASE (HDE)
MSPVDFKDKVVIITGAGGGLGKYYSLEFAKLGAKVVVNDLGGALNGQGGNSKAADVVVDEIVKNGGVAVADYNNVLDGDKIVETAVKNFGTVHVIINNAGILRDASMKKMTEKDYKLVIDVHLNGAFAVTKAAWPYFQKQKYGRIVNTSSPAGLYGNFGQANYASAKSALLGFAETLAKEGAKYNIKANAIAPLARSRMTESILPPPMLEKLGPEKVAPLVLYLSSAENELTGQFFEVAAGFYAQIRWERSGGVLFKPDQSFTAEVVAKRFSEILDYDDSRKPEYLKNQYPFMLNDYATLTNE
ARKLPANDASGAPTVSLKDKVVLITGAGAGLGKEYAKWFAKYGAKVVVNDFKDATKTVDEIKAAGGEAWPDQHDVAKDSEAIIKNVIDKYGTIDILVNNAGILRDRSFAKMSKQEWDSVQQVHLIGTFNLSRLAWPYFVEKQFGRIINITSTSGIYGNFGQANYSSSKAGILGLSKTMAIEGAKNNIKVNIVAPHAETAMTLTIFREQDKNLYHADQVAPLLVYLGTDDVPVTGETSEIGGGWIGNTRWQRAKGAVSHDEHTTVEFIKEHLNEITDFTTDTENPKSTTESSMAILSAVGGDDD
DDDEDEEEDEGDEEEDEEDEEEDDPVWRFDDRDVILYNIALGATTKQLKYVYENDSDFQVIPTFGHLITFNSGKSQNSFAKLLRNFNPMLLLHGEHYLKVHSWPPPTEGEIKTTFEPIATTPKGTNVVIVHGSKSVDNKSGELIYSNEATYFIRNCQADNKVYADRPAFATNQFLAPKRAPDYQVDVPVSEDLAALYRLSGDRNPLHIDPNFAKGAKFPKPILHGMCTYGLSAKALIDKFGMFNEIKARFTGIVFPGETLRVLAWKESDDTIVFQTHVVDRGTIAINNAAIKLVGDKAKI
>HDHA_ECOLI 7-ALPHA-HYDROXYSTEROID DEHYDROGENASE (EC 1.1.1.159) (HSDH)
MFNSDNLRLDGKCAIITGAGAGIGKEIAITFATAGASVVVSDINADAANHVVDEIQQLGGQAFACRCDITSEQELSALADFAISKLGKVDILVNNAGGGGPKPFDMPMADFRRAYELNVFSFFHLSQLVAPEMEKNGGGVILTITSMAAENKNINMTSYASSKAAASHLVRNMAFDLGEKNIRVNGIAPGAILTDALKSVITPEIEQKMLQHTPIRRLGQPQDIANAALFLCSPAASWVSGQILTVSGGGVQELN
>LIGD_PSEPA C ALPHA-DEHYDROGENASE (EC -.-.-.-)
MKDFQDQVAFITGGASGAGFGQAKVFGQAGAKIVVADVRAEAVEKAVAELEGLGITAHGIVLDIMDREAYARAADEVEAVFGQAPTLLSNTAGVNSFGPIEKTTYDDFDWIIGVNLNGVINGMVTFVPRMIASGRPGHIVTVSSLGGFMGSALAGPYSAAKAASINLMEGYRQGLEKYGIGVSVCTPANIKSNIAEASRLRPAKYGTSGYVENEESIASLHSIHQHGLEPEKLAEAIKKGVEDNALYIIPYPEVREGLEKHFQAIIDSVAPMESDPEGARQRVEALMAWGRDRTRVFAEGDKKGA
>NODG_RHIME NODULATION PROTEIN G (HOST-SPECIFICITY OF NODULATION PROTEIN C)
MFELTGRKALVTGASGAIGGAIARVLHAQGAIVGLHGTQIEKLETLATELGDRVKLFPANLANRDEVKALGQRAEADLEGVDILVNNAGITKDGLFLHMADPDWDIVLEVNLTAMFRLTREITQQMIRRRNGRIINVTSVAGAIGNPGQTNYCASKAGMIGFSKSLAQEIATRNITVNCVAPGFIESAMTDKLNHKQKEKIMVAIPIHRMGTGTEVASAVAYLASDHAAYVTGQTIHVNGGMAMI
>RIDH_KLEAE RIBITOL 2-DEHYDROGENASE (EC 1.1.1.56) (RDH)
MKHSVSSMNTSLSGKVAAITGAASGIGLECARTLLGAGAKVVLIDREGEKLNKLVAELGENAFALQVDLMQADQVDNLLQGILQLTGRLDIFHANAGAYIGGPVAEGDPDVWDRVLHLNINAAFRCVRSVLPHLIAQKSGDIIFTAVIAGVVPVIWEPVYTASKFAVQAFVHTTRRQVAQYGVRVGAVLPGPVVTALLDDWPKAKMDEALANGSLMQPIEVAESVLFMVTRSKNVTVRDIVILPNSVDL
>YINL_LISMO HYPOTHETICAL 26.8 KD PROTEIN IN INLA 5'REGION (ORFA)
MTIKNKVIIITGASSGIGKATALLLAEKGAKLVLAARRVEKLEKIVQIIKANSGEAIFAKTDVTKREDNKKLVELAIERYGKVDAIFLNAGIMPNSPLSALKEDEWEQMIDINIKGVLNGIAAVLPSFIAQKSGHIIATSSVAGLKAYPGGAVYGATKWAVRDLMEVLRMESAQEGTNIRTATIYPAAINTELLETITDKETEQGMTSLYKQYGITPDRIASIVAYAIDQPEDVNVNEFTVGPTSQPW
>YRTP_BACSU HYPOTHETICAL 25.3 KD PROTEIN IN RTP 5'REGION (ORF238)
MQSLQHKTALITGGGRGIGRATALALAKEGVNIGLIGRTSANVEKVAEEVKALGVKAAFAAADVKDADQVNQAVAQVKEQLGDIDILINNAGISKFGGFLDLSADEWENIIQVNLMGVYHVTRAVLPEMIERKAGDIINISSTAGQRGAAVTSAYSASKFAVLGLTESLMQEVRKHNIRVSALTPSTVASDMSIELNLTDGNPEKVMQPEDLAEYMVAQLKLDPRIFIKTAGLWSTNP
>CSGA_MYXXA no comment
MRAFATNVCTGPVDVLINNAGVSGLWCALGDVDYADMARTFTINALGPLR
VTSAMLPGLRQGALRRVAHVTSRMGSLAANTDGGAYAYRMSKAALNMAVR
SMSTDLRPEGFVTVLLHPGWVQTDMGGPDATLPAPDSVRGMLRVIDGLNP


  [Part of this file has been deleted for brevity]

FSIAAMNELELK
>FVT1_HUMAN no comment
MLLLAAAFLVAFVLLLYMVSPLISPKPLALPGAHVVVTGGSSGIGKCIAI
ECYKQGAFITLVARNEDKLLQAKKEIEMHSINDKQVVLCISVDVSQDYNQ
VENVIKQAQEKLGPVDMLVNCAGMAVSGKFEDLEVSTFERLMSINYLGSV
YPSRAVITTMKERRVGRIVFVSSQAGQLGLFGFTAYSASKFAIRGLAEAL
QMEVKPYNVYITVAYPPDTDTPGFAEENRTKPLETRLISETTSVCKPEQV
AKQIVKDAIQGNFNSSLGSDGYMLSALTCGMAPVTSITEGLQQVVTMGLF
RTIALFYLGSFDSIVRRCMMQREKSENADKTA
>HMTR_LEIMA no comment
MTAPTVPVALVTGAAKRLGRSIAEGLHAEGYAVCLHYHRSAAEANALSAT
LNARRPNSAITVQADLSNVATAPVSGADGSAPVTLFTRCAELVAACYTHW
GRCDVLVNNASSFYPTPLLRNDEDGHEPCVGDREAMETATADLFGSNAIA
PYFLIKAFAHRSRHPSQASRTNYSIINMVDAMTNQPLLGYTIYTMAKGAL
EGLTRSAALELAPLQIRVNGVGPGLSVLVDDMPPAVWEGHRSKVPLYQRD
SSAAEVSDVVIFLCSSKAKYITGTCVKVDGGYSLTRA
>MAS1_AGRRA no comment
MHQLWAYDVGTLGCVSYHALPDIKRHSPKSGHLYLNKPSLRSFILQCPSL
ARTLVLPSHQPVSRSSTSSAMVQPISTRKKCTCKVKNIGVCRAPARTSVS
MELANAKRFSPATFSANFLSXSVVCSPLLRAIQTALIANIGFLCFDIDED
LKERDFGKHEGGYGPLKMFEDNYPDCEDTEMFSLRVAKALTHAKNENTLF
VSHGGVLRVIAALLGVDLTKEHTNNGRVLHFRRGFSHWTVEIHQSPVILV
SGSNRGVGKAIAEDLIAHGYRLSLGARKVKDLEVAFGPQDEWLHYARFDA
EDHGTMAAWVTAAVEKFGRIDGLVNNAGYGEPVNLDKHVDYQRFHLQWYI
NCVAPLRMTELCLPHLYETGSGRIVNINSMSGQRVLNPLVGYNMTKHALG
GLTKTTQHVGWDRRCAAIDICLGFVATDMSAWTDLIASKDMIQPEDIAKL
VREAIERPNRAYVPRSEVMCIKEATR
>PCR_PEA no comment
MALQTASMLPASFSIPKEGKIGASLKDSTLFGVSSLSDSLKGDFTSSALR
CKELRQKVGAVRAETAAPATPAVNKSSSEGKKTLRKGNVVITGASSGLGL
ATAKALAESGKWHVIMACRDYLKAARAAKSAGLAKENYTIMHLDLASLDS
VRQFVDNFRRSEMPLDVLINNAAVYFPTAKEPSFTADGFEISVGTNHLGH
FLLSRLLLEDLKKSDYPSKRLIIVGSITGNTNTLAGNVPPKANLGDLRGL
AGGLTGLNSSAMIDGGDFDGAKAYKDSKVCNMLTMQEFHRRYHEETGITF
ASLYPGCIATTGLFREHIPLFRTLFPPFQKYITKGYVSEEESGKRLAQVV
SDPSLTKSGVYWSWNNASASFENQLSQEASDAEKARKVWEVSEKLVGLA
>RFBB_NEIGO no comment
MQTEGKKNILVTGGAGFIGSAVVRHIIQNTRDSVVNLDKLTYAGNLESLT
DIADNPRYAFEQVDICDRAELDRVFAQYRPDAVMHLAAESHVDRAIGSAG
EFIRTNIVGTFDLLEAARAYWQQMPSEKREAFRFHHISTDEVYGDLHGTD
DLFTETTPYAPSSPYSASKAAADHLVRAWQRTYRLPSIVSNCSNNYGPRQ
FPEKLIPLMILNALSGKPLPVYGDGAQIRDWLFVEDHARALYQVVTEGVV
GETYNIGGHNEKTNLEVVKTICALLEELAPEKPAGVARYEDLITFVQDRP
GHDARYAVDAAKIRRDLGWLPLETFESGLRKTVQWYLDNKTRRQNA
>YURA_MYXXA no comment
RQHTGGLHGGDELPDGVGDGCLQRPGTRAGAVARQAGVRVFAAGRRLPQL
QAADEAPGGRRHRGARGVDVTKADATLERIRALDAEAGGLDLVVANAGVG
GTTNAKRLPWERVRGIIDTNVTGAAATLSAVLPQMVERKRGHLVGVSSLA
GFRGLPATRYSASKAFLSTFMESLRVDLRGTGVRVTCIYPGFVKSELTAT
NNFPMPFLMETHDAVELMGKGIVRGDAEVSFPWQLAVPTRMAKVLPNPLF
DAAARRLR

Output file format

Output files for usage example

File: crp0.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: ex.text

********************************************************************************
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= crp0.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]

--------------------------------------------------------------------------------
GTGA[TC][CG][TC][ATG][GT][TC]TCACA
--------------------------------------------------------------------------------




Time  0.47 secs.

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


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

--------------------------------------------------------------------------------
	Combined block diagrams: non-overlapping sites with p-value < 0.0001
--------------------------------------------------------------------------------
SEQUENCE NAME            COMBINED P-VALUE  MOTIF DIAGRAM
-------------            ----------------  -------------
ce1cg                            1.94e-03  64_[+1(1.07e-05)]_26
ara                              5.19e-04  57_[-1(2.85e-06)]_33
bglr1                            1.76e-03  78_[-1(9.67e-06)]_12
crp                              2.34e-03  65_[-1(1.29e-05)]_25
cya                              8.88e-04  52_[-1(4.88e-06)]_38
deop2                            1.76e-03  9_[-1(9.67e-06)]_81
gale                             1.06e-02  54_[+1(5.85e-05)]_36
ilv                              2.85e-02  105
lac                              2.93e-04  11_[-1(1.61e-06)]_79
male                             2.80e-03  16_[-1(1.54e-05)]_74
malk                             9.85e-04  64_[+1(5.41e-06)]_26
malt                             2.12e-03  44_[+1(1.17e-05)]_46
ompa                             4.19e-04  51_[+1(2.30e-06)]_39
tnaa                             7.20e-04  74_[+1(3.95e-06)]_16
uxu1                             2.80e-03  20_[+1(1.54e-05)]_70
pbr322                           9.85e-04  55_[-1(5.41e-06)]_35
trn9cat                          4.18e-02  105
tdc                              3.35e-03  81_[+1(1.84e-05)]_9
--------------------------------------------------------------------------------

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


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

CPU: emboss4.ebi.ac.uk

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

Output files for usage example 2

File: ex2.text

********************************************************************************
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= crp0.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]

--------------------------------------------------------------------------------
[TA][AT]AT[GT]T[GA][AC][AGT]C[CTAGA]A[CTG][GAC]TCACA[AC]
--------------------------------------------------------------------------------




Time  0.07 secs.

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


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

--------------------------------------------------------------------------------
	Combined block diagrams: non-overlapping sites with p-value < 0.0001
--------------------------------------------------------------------------------
SEQUENCE NAME            COMBINED P-VALUE  MOTIF DIAGRAM
-------------            ----------------  -------------
ce1cg                            1.25e-03  60_[+1(7.30e-06)]_25
ara                              1.68e-06  54_[+1(9.77e-09)]_31
bglr1                            1.93e-03  77_[-1(1.12e-05)]_8
crp                              8.74e-04  62_[+1(5.08e-06)]_23
cya                              2.47e-03  51_[-1(1.44e-05)]_34
deop2                            3.29e-04  6_[+1(1.91e-06)]_79
gale                             1.23e-04  41_[+1(7.15e-07)]_44
ilv                              4.96e-03  38_[+1(2.89e-05)]_47
lac                              2.67e-04  8_[+1(1.55e-06)]_77
male                             4.93e-04  13_[+1(2.86e-06)]_72
malk                             2.47e-03  62_[-1(1.44e-05)]_23
malt                             4.09e-05  42_[-1(2.38e-07)]_43
ompa                             9.58e-04  49_[-1(5.57e-06)]_36
tnaa                             1.38e-04  72_[-1(8.02e-07)]_13
uxu1                             7.96e-04  18_[-1(4.63e-06)]_67
pbr322                           4.03e-04  54_[-1(2.34e-06)]_31
trn9cat                          6.32e-02  105
tdc                              4.03e-04  79_[-1(2.34e-06)]_6
--------------------------------------------------------------------------------

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


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

CPU: emboss4.ebi.ac.uk

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

Output files for usage example 3

File: ino_up800.fasta

>CHO1 sequence of the region upstream from YER026C
CCGACCCAAATGTAATGGAACAATATTATTTGACACTTGATCAGCAGCAAAATAATCACC
AAAATATGGCCTGGTTGACTCCTCCACAACTGCCACCTCATTTAGAAAACGTCATTTTGA
ATAGTTACTCAAACGCGCAAACTGATAATACGTCTGGCGCCCTTCCCATTCCGAACCATG
TTATATTGAACCATCTGGCGACAAGCAGTATTAAGCATAATACATTATGTGTCGCATCCA
TTGTTAGGTATAAACAAAAATACGTGACCCAAATACTGTATACACCATTGCAATAGATAT
GATTATAGAGCTTATAGCTACATCTTTTTAGATAAAAGCGAAGATGTTTCTGCGATTTTT
CCATTATAGCTCTCCATGATACTAAATATCAAGGTCTACATGTAAGTATTTGTATATATG
GGTTGGAATGTATATACGTATATACGTACGTACGTACGTATATGCACATAATTGTTACGG
GATGTATATATAAATTAGTAGCATTATAGAAGATATCCCTAACATCAATCCCCACTCCTT
CTCAATGTGTGCAGACTTCTGTGCCAGACACTGAATATATATCAGTAATTGGTCAAAATC
ACTTTGAACGTTCACACGGCACCCTCACGCCTTTGAGCTTTCACATGGACCCATCTAAAG
ATGAAGATCCGTATTTTATAGGAAACATTATAAATAAGGAAAGAGAGATACACCTATTTT
TTTCATTTTGTGGGTGATTGTCATTTTTAGTTGTCTATTTGATTCAATCAAAAAACAAAA
ATAAAACTATATATTAAAAA
>CHO2 sequence of the region upstream from YGR157W
ACCCTCTAACGCGAATAAAGCGAATGACAGCGGCACCATTAATATGGCGAAACTGCAATT
ACTACCTGAAAACCAACAAGATATGATCAAACAAGTTCTTACTTTGACACCTGCCCAGAT
CCAAAGTTTACCAAGTGACCAGCAACTTATGGTGGAAAACTTTAGAAAAGAATATATAAT
CTAAGTAATCAGAGCCATAGCGTATCAGAAAACCACACCTAATTAGATGGTTCTTGCATC
TGTACCTCTTATCACTAAAAGCGGCACTAAACTTCCAACATTAAATGTTTGCCTTGTTAA
ATATATATTTTTGCCTTGGTTTAAATTGGTCAAGACAGTCAATTGCCACACTTTTCTCAT
GCCGCATTCATTATTCGCGAAGTTTTCCACACAAAACTGTGAAAATGAACGGCGATGCCA
GAAACGGCAAAACCTCAAATGTTAGATAACGTGGATCTCCGACACATGTGAATTTATAAG
TAGGCATATGAAAATACAGATTCTTTCCACTGTGTTCCCTTTTATTCCCTTCTCATGTGA
AGAGTTCACACCAAATCTTCAAAATATAACTAATATAGTAGAGTTTGATTCAAAGGACCT
TTTTTTTTGCCTCTTTGATTAGTTTATCTTCTTTTCTTCATTTTATCCCCTAATTTTATA
CGTTAGTTCAACCTAACAATCCAGGATTTCATTAACAAGAAAGGTAAAAGTAACCTATCA
AGGCTATTTTGAAAAAAAAAATTCCGCCCTGAATATTTCGAGTGATTTTCTTAGTGACAA
AGCTTTTTCTTCATCTGTAG
>FAS1 sequence of the region upstream from YKL182W
CCGGGTTATAGCAGCGTCTGCTCCGCATCACGATACACGAGGTGCAGGCACGGTTCACTA
CTCCCCTGGCCTCCAACAAACGACGGCCAAAAACTTCACATGCCGCCCAGCCAAGCATAA
TTACGCAACAGCGATCTTTCCGTCGCACAAGTTAAAAGAAATTGTTGAAAAATACAAATA
ATCGCGAACAATACGTTGTTGCTATTTAACGCTTTTGGTCTGACAGTAAGTGTGCCTTTC
CCAATCACCGAAAAGTGTTGAACGATTCACTGCGACAATAATCAGAGATTACAGTCGGCA
TTTTGGCATTTTTGGCATACTTTTTATCGATTGAACCATCTTCTCCAAACACTTTTCCTT
TTTCCTTCTATTCTGCAGGACCAACTAAAACTGGGTATATATATCATTATCTATATATAT
AAACGGCTTTCAACAAAGTTATAGGGGAAAACTAAAAATATAAGAAAAAAAAAGGTATTG
ATTGATAAGGAAAAAGAACCAAGGGAAAAATATAAAAAAGTACATTGGGCCTTTTCATAC
TTGTTATCACTTACATTACAAAGAAGAACAAACAACTTTTTTAAACGAATTTTCTTTCTT
CCTTTTTCAATTTATTAATTCTTTTTTTCCATACAATTCAAGGTCAAATATATTCTTATA
TGCTCTTTGAATATTTCTGAAAAATATATAAAGAAAAGAAACTACAAGAACATCATCCGG
AAAATCAGATTATAGACTAGGATTCCGCTCTTTTTAGTATATTTATTCGCCACACCTAAC
TGCTCTATTATTCGCTCATT
>FAS2 sequence of the region upstream from YPL231W
TCCAGGCAAGGCACCAAGAGTTATTGAAACTAGAAAAATCCATGGCAGAACTTACTCAAT
TGTTTAATGACATGGAAGAACTGGTAATAGAACAACAAGAAAACGTAGACGTCATCGACA
AGAACGTTGAAGACGCTCAACTCGACGTAGAACAGGGTGTCGGTCATACCGATAAAGCCG
TCAAGAGTGCCAGAAAAGCAAGAAAGAACAAGATTAGATGTTGGTTGATTGTATTCGCCA


  [Part of this file has been deleted for brevity]

CTCTTCCTAAAAATACATTGGGCATTACCCGCAAACTAACCCATCGCTTAGCAAAATCCA
ACCATTTTTTTTTTATCTCCCGCGTTTTCACATGCTACCTCATTCGCCTCGTAACGTTAC
GACCGAAATCTCACTAAGGCACGGTTTGTTGGGCAGTTTACAGATGTTGGATAACCAGTT
GTTTCTAAACGGTTATGCCTCATATATAACTTGTTAACTGAAGGTTACACAAGACCACAT
CACCACTGTCGTGCTTTTCTAATAACCGCTATATTAGACGTTTAAAGGGCTACAGCAACA
CCAATTGAAATACCATCATT
>ACC1 sequence of the region upstream from YNR016C
TATCCAAAGGGGAATGCTTCATCTTGTTGAACAACGCCCAACAATTTCCACTGCCCACCG
AATCGTTGCGCCCGTTAAAATCTTCACATGGCCCGGCCGCGCGCGCGTTGTGCCAACAAG
TCGCAGTCGAAATTCAACCGCTCATTGCCACTCTCTCTACTGCTTGGTGAACTAGGCTAT
ACGCTCAATCAGCGCCAAGATATATAAGAAGAACAGCACTCCCAGTCGTATTCTGGCACA
GTATAGCCTAGCACAATCACTGTCACAATTGTTATCGGTTCTACAATTGTTCTGCTCTCT
TCAATTTTCCTTTCCTTATTCTACTCTTTTTATCCCTTTCGTACAGTTTACCTGAAGATA
AAAAACAACAAAGCCAATTCCCTAATTTGCAATCGCCATTTGCATCTATATATATATATT
TGTTGTGCCATTTTTTTATCCTCTGTGAGTGATCGGTGCATGTGTTTATAAAAGTTTATT
CATTCTACTATACGAACTTTTCCCTCTGCCCTTCCCTCCCGCTTCATCCTTATTTTTGGA
CAATAAACTAGAGAACAATTTGAACTTGAATTGGAATTCAGATTCAGAGCAAGAGACAAG
AAACTTCCCTTTTTCTTCTCCACATATTATTATTTATTCGTGTATTTTCTTTTAACGATA
CGATACGATACGACACGATACGATACGACACGCTACTATACTATACAAATATAATAGTAT
AATAACCGATTCGTCTTCTAGCTTAATTTTTTTCCGTTCCCGAAACAGCGCAGAAAATTA
GAAAAAATCAAGTTTCTACC
>INO1 sequence of the region upstream from YJL153C
AGCAAACAACCAAATATAATTTAGAAATGGACAGAGACCATATTAATGACCATGACCATC
GAATGAGCTATTCCATCAACAAGGACGACTTGTTGTTAATGGTTTTGGCGGTTTTCATTC
CCCCAGTGGCCGTCTGGAAGCGTAAGGGTATGTTCAACAGGGATACACTATTGAACTTAC
TTCTCTTCCTACTGTTATTCTTCCCAGCAATCATTCACGCTTGCTACGTTGTATATGAAA
CGAGTAGTGAACGTTCGTACGATCTTTCACGCAGACATGCGACTGCGCCCGCCGTAGACC
GTGACCTGGAAGCTCACCCTGCAGAGGAATCTCAAGCACAGCCTCCAGCATATGATGAAG
ACGATGAGGCCGGTGCCGATGTGCCCTTGATGGACAACAAACAACAGCTCTCTTCCGGCC
GTACTTAGTGATCGGAACGAGCTCTTTATCACCGTAGTTCTAAATAACACATAGAGTAAA
TTATTGCCTTTTTCTTCGTTCCTTTTGTTCTTCACGTCCTTTTTATGAAATACGTGCCGG
TGTTCCGGGGTTGGATGCGGAATCGAAAGTGTTGAATGTGAAATATGCGGAGGCCAAGTA
TGCGCTTCGGCGGCTAAATGCGGCATGTGAAAAGTATTGTCTATTTTATCTTCATCCTTC
TTTCCCAGAATATTGAACTTATTTAATTCACATGGAGCAGAGAAAGCGCACCTCTGCGTT
GGCGGCAATGTTAATTTGAGACGTATATAAATTGGAGCTTTCGTCACCTTTTTTTGGCTT
GTTCTGTTGTCGGGTTCCTA
>OPI3 sequence of the region upstream from YJR073C
GTGTCCACAACGTGAAACTTCCGTACCATTTCTTGCAACAATTGGTAAACAGCATGACAT
CTTGCAGGCAACTCTTTGTTGCTTGCTTGCGACGCCTCCTCCTTTGTCAAAGGTACATTA
ATGGAGATGACCACATCCGTGTCAAACTGGGTTAATCTGATCAACGCTACGCCGATGACA
ACGGTCTGTGCCAGATCTGGTTTTCCCCACTTATTTGCTACTTCCATAACGAGTCCGGTG
AACTTGGTTCCTTGCTGAACAGTGTCTTCTTGTAAAGCTTCCCATTTGGTGGTCCCGTTC
AACTCCGTCAGGTCTTCCACGTGGAACTGCCAAGCCTCCTTCAGATCGCTCTTGTCGACC
GTCTCCAAGAGATCCACGATAATGCTTTCATTGGTGGCTAGTCCATCTTCGAATTCTTCT
TCATCGCGACGGGAATTGACGTACACCTCCTGTGTATCGGGGACTTCTCTTAGAGTAGAA
GCGTCTATAAACCCAGGTGGGACGACAGTAGTGATGGCGCCGCCGTATAATTCGACTTCC
TTGTTGTTCATGCTTCCTTGATGACCAGGGTAGGTGTCAATGAGAGTGCATGTGGAAAGT
TGCACCGGTTGTGAAATATGAGAAGCCTTTTCAATCTTCATATGCAAACCCACACATGCA
TCGTTGGTTTCTGTCCACTGCCACTGCAATGACCACTGGATAAGGGGTCTTTATAAGAGA
ACACATATGAAGAACATGAACGTTCTTGGACAGAGCCATAAACAGCAATTGAAGACAACA
AGAATAGCGCAAGTCAAGCG

File: ex3.text

********************************************************************************
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= ino_up800.fasta
ALPHABET= ACGT
Sequence name            Weight Length  Sequence name            Weight Length  
-------------            ------ ------  -------------            ------ ------  
CHO1                     1.0000    800  CHO2                     1.0000    800  
FAS1                     1.0000    800  FAS2                     1.0000    800  
ACC1                     1.0000    800  INO1                     1.0000    800  
OPI3                     1.0000    800  
********************************************************************************

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

command: meme ino_up800.fasta -bfile ../../data/memenew/yeast.nc.6.freq -mod anr -prior dirichlet -revcomp -nostatus -dna -text 

model:  mod=           anr    nmotifs=         1    evt=           inf
object function=  E-value of product of p-values


  [Part of this file has been deleted for brevity]

 0.000000  0.714286  0.285714  0.000000 
 0.428571  0.500000  0.000000  0.071429 
 0.357143  0.214286  0.357143  0.071429 
 0.214286  0.714286  0.000000  0.071429 
 0.357143  0.571429  0.071429  0.000000 
 0.071429  0.428571  0.142857  0.357143 
 0.142857  0.428571  0.000000  0.428571 
--------------------------------------------------------------------------------

--------------------------------------------------------------------------------
	Motif 1 regular expression
--------------------------------------------------------------------------------
TTCACATG[CG][CA][AGC][CA][CA][CT][CT]
--------------------------------------------------------------------------------




Time 10.22 secs.

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


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

--------------------------------------------------------------------------------
	Combined block diagrams: non-overlapping sites with p-value < 0.0001
--------------------------------------------------------------------------------
SEQUENCE NAME            COMBINED P-VALUE  MOTIF DIAGRAM
-------------            ----------------  -------------
CHO1                             3.16e-04  162_[+1(3.61e-06)]_351_[+1(9.87e-05)]_67_[+1(2.01e-07)]_14_[+1(7.50e-07)]_146
CHO2                             9.08e-04  353_[+1(5.77e-07)]_109_[-1(7.24e-06)]_308
FAS1                             9.60e-06  94_[+1(6.11e-09)]_691
FAS2                             2.82e-04  566_[+1(1.80e-07)]_219
ACC1                             6.55e-04  82_[+1(4.17e-07)]_703
INO1                             4.14e-05  546_[-1(2.94e-06)]_6_[-1(8.23e-07)]_34_[-1(2.64e-08)]_55_[+1(1.09e-06)]_99
OPI3                             1.57e-03  581_[-1(1.82e-06)]_40_[+1(1.00e-06)]_149
--------------------------------------------------------------------------------

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


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

CPU: emboss4.ebi.ac.uk

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

Output files for usage example 4

File: lipocalin.fasta

>ICYA_MANSE
GDIFYPGYCPDVKPVNDFDLSAFAGAWHEIAKLPLENENQGKCTIAEYKYDGKKASVYNS
FVSNGVKEYMEGDLEIAPDAKYTKQGKYVMTFKFGQRVVNLVPWVLATDYKNYAINYNCD
YHPDKKAHSIHAWILSKSKVLEGNTKEVVDNVLKTFSHLIDASKFISNDFSEAACQYSTT
YSLTGPDRH
>LACB_BOVIN
MKCLLLALALTCGAQALIVTQTMKGLDIQKVAGTWYSLAMAASDISLLDAQSAPLRVYVE
ELKPTPEGDLEILLQKWENGECAQKKIIAEKTKIPAVFKIDALNENKVLVLDTDYKKYLL
FCMENSAEPEQSLACQCLVRTPEVDDEALEKFDKALKALPMHIRLSFNPTQLEEQCHI
>BBP_PIEBR
NVYHDGACPEVKPVDNFDWSNYHGKWWEVAKYPNSVEKYGKCGWAEYTPEGKSVKVSNYH
VIHGKEYFIEGTAYPVGDSKIGKIYHKLTYGGVTKENVFNVLSTDNKNYIIGYYCKYDED
KKGHQDFVWVLSRSKVLTGEAKTAVENYLIGSPVVDSQKLVYSDFSEAACKVN
>RETB_BOVIN
ERDCRVSSFRVKENFDKARFAGTWYAMAKKDPEGLFLQDNIVAEFSVDENGHMSATAKGR
VRLLNNWDVCADMVGTFTDTEDPAKFKMKYWGVASFLQKGNDDHWIIDTDYETFAVQYSC
RLLNLDGTCADSYSFVFARDPSGFSPEVQKIVRQRQEELCLARQYRLIPHNGYCDGKSER
NIL
>MUP2_MOUSE
MKMLLLLCLGLTLVCVHAEEASSTGRNFNVEKINGEWHTIILASDKREKIEDNGNFRLFL
EQIHVLEKSLVLKFHTVRDEECSELSMVADKTEKAGEYSVTYDGFNTFTIPKTDYDNFLM
AHLINEKDGETFQLMGLYGREPDLSSDIKERFAKLCEEHGILRENIIDLSNANRCLQARE

File: ex4.text

********************************************************************************
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= lipocalin.fasta
ALPHABET= ACDEFGHIKLMNPQRSTVWY
Sequence name            Weight Length  Sequence name            Weight Length  
-------------            ------ ------  -------------            ------ ------  
ICYA_MANSE               1.0000    189  LACB_BOVIN               1.0000    178  
BBP_PIEBR                1.0000    173  RETB_BOVIN               1.0000    183  
MUP2_MOUSE               1.0000    180  
********************************************************************************

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

command: meme lipocalin.fasta -mod oops -nmotifs 2 -prior dirichlet -maxw 20 -nostatus -protein -text 

model:  mod=          oops    nmotifs=         2    evt=           inf
object function=  E-value of product of p-values
width:  minw=            8    maxw=           20    minic=        0.00


  [Part of this file has been deleted for brevity]

 0.000000  0.000000  0.200000  0.200000  0.000000  0.000000  0.000000  0.000000  0.600000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000 
 0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.200000  0.000000  0.000000  0.600000  0.000000  0.000000  0.000000  0.000000  0.200000  0.000000  0.000000  0.000000 
 0.000000  0.000000  0.000000  0.000000  0.400000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.600000 
 0.400000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.200000  0.000000  0.400000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000 
 0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.400000  0.000000  0.200000  0.200000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.200000  0.000000  0.000000 
 0.200000  0.000000  0.000000  0.000000  0.200000  0.200000  0.000000  0.000000  0.000000  0.000000  0.000000  0.200000  0.000000  0.200000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000 
 0.000000  0.200000  0.000000  0.000000  0.000000  0.000000  0.200000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.600000 
 0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.200000  0.200000  0.200000  0.000000  0.000000  0.000000  0.200000  0.000000  0.000000  0.000000  0.200000 
 0.000000  0.600000  0.000000  0.200000  0.000000  0.000000  0.000000  0.200000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000 
--------------------------------------------------------------------------------

--------------------------------------------------------------------------------
	Motif 2 regular expression
--------------------------------------------------------------------------------
[ENF][NDL][VDKT][FHPV][WLNT][VI][LIP][DAKS]TD[YN][KDE][NKT][YF][ALI][ILMV][AFGNQ][YCH][LMNSY][CEI]
--------------------------------------------------------------------------------




Time  0.12 secs.

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


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

--------------------------------------------------------------------------------
	Combined block diagrams: non-overlapping sites with p-value < 0.0001
--------------------------------------------------------------------------------
SEQUENCE NAME            COMBINED P-VALUE  MOTIF DIAGRAM
-------------            ----------------  -------------
ICYA_MANSE                       5.85e-32  13_[1(1.17e-18)]_67_[2(2.23e-20)]_70
LACB_BOVIN                       2.65e-27  21_[1(4.11e-17)]_64_[2(3.82e-17)]_18_[1(7.85e-05)]_17
BBP_PIEBR                        3.66e-31  12_[1(6.04e-19)]_64_[2(3.37e-19)]_58
RETB_BOVIN                       1.46e-29  10_[1(6.49e-18)]_71_[2(1.16e-18)]_63
MUP2_MOUSE                       2.28e-27  23_[1(1.21e-16)]_62_[2(1.09e-17)]_56
--------------------------------------------------------------------------------

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


********************************************************************************
Stopped because nmotifs = 2 reached.
********************************************************************************

CPU: emboss4.ebi.ac.uk

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

Output files for usage example 5

File: farntrans5.fasta

>RAM1_YEAST PROTEIN FARNESYLTRANSFERASE BETA SUBUNIT (EC 2.5.1.-) (CAAX FARN
MRQRVGRSIARAKFINTALLGRKRPVMERVVDIAHVDSSKAIQPLMKELETDTTEARYKV
LQSVLEIYDDEKNIEPALTKEFHKMYLDVAFEISLPPQMTALDASQPWMLYWIANSLKVM
DRDWLSDDTKRKIVVKLFTISPSGGPFGGGPGQLSHLASTYAAINALSLCDNIDGCWDRI
DRKGIYQWLISLKEPNGGFKTCLEVGEVDTRGIYCALSIATLLNILTEELTEGVLNYLKN
CQNYEGGFGSCPHVDEAHGGYTFCATASLAILRSMDQINVEKLLEWSSARQLQEERGFCG
RSNKLVDGCYSFWVGGSAAILEAFGYGQCFNKHALRDYILYCCQEKEQPGLRDKPGAHSD
FYHTNYCLLGLAVAESSYSCTPNDSPHNIKCTPDRLIGSSKLTDVNPVYGLPIENVRKII
HYFKSNLSSPS
>PFTB_RAT PROTEIN FARNESYLTRANSFERASE BETA SUBUNIT (EC 2.5.1.-) (CAAX FARNES
MASSSSFTYYCPPSSSPVWSEPLYSLRPEHARERLQDDSVETVTSIEQAKVEEKIQEVFS
SYKFNHLVPRLVLQREKHFHYLKRGLRQLTDAYECLDASRPWLCYWILHSLELLDEPIPQ
IVATDVCQFLELCQSPDGGFGGGPGQYPHLAPTYAAVNALCIIGTEEAYNVINREKLLQY
LYSLKQPDGSFLMHVGGEVDVRSAYCAASVASLTNIITPDLFEGTAEWIARCQNWEGGIG
GVPGMEAHGGYTFCGLAALVILKKERSLNLKSLLQWVTSRQMRFEGGFQGRCNKLVDGCY
SFWQAGLLPLLHRALHAQGDPALSMSHWMFHQQALQEYILMCCQCPAGGLLDKPGKSRDF
YHTCYCLSGLSIAQHFGSGAMLHDVVMGVPENVLQPTHPVYNIGPDKVIQATTHFLQKPV
PGFEECEDAVTSDPATD
>BET2_YEAST YPT1/SEC4 PROTEINS GERANYLGERANYLTRANSFERASE BETA SUBUNIT (EC 2.
MSGSLTLLKEKHIRYIESLDTNKHNFEYWLTEHLRLNGIYWGLTALCVLDSPETFVKEEV
ISFVLSCWDDKYGAFAPFPRHDAHLLTTLSAVQILATYDALDVLGKDRKVRLISFIRGNQ
LEDGSFQGDRFGEVDTRFVYTALSALSILGELTSEVVDPAVDFVLKCYNFDGGFGLCPNA
ESHAAQAFTCLGALAIANKLDMLSDDQLEEIGWWLCERQLPEGGLNGRPSKLPDVCYSWW
VLSSLAIIGRLDWINYEKLTEFILKCQDEKKGGISDRPENEVDVFHTVFGVAGLSLMGYD
NLVPIDPIYCMPKSVTSKFKKYPYK
>RATRABGERB Rat rab geranylgeranyl transferase beta-subunit
MGTQQKDVTIKSDAPDTLLLEKHADYIASYGSKKDDYEYCMSEYLRMSGVYWGLTVMDLM
GQLHRMNKEEILVFIKSCQHECGGVSASIGHDPHLLYTLSAVQILTLYDSIHVINVDKVV
AYVQSLQKEDGSFAGDIWGEIDTRFSFCAVATLALLGKLDAINVEKAIEFVLSCMNFDGG
FGCRPGSESHAGQIYCCTGFLAITSQLHQVNSDLLGWWLCERQLPSGGLNGRPEKLPDVC
YSWWVLASLKIIGRLHWIDREKLRSFILACQDEETGGFADRPGDMVDPFHTLFGIAGLSL
LGEEQIKPVSPVFCMPEEVLQRVNVQPELVS
>CAL1_YEAST RAS PROTEINS GERANYLGERANYLTRANSFERASE (EC 2.5.1.-) (PROTEIN GER
MCQATNGPSRVVTKKHRKFFERHLQLLPSSHQGHDVNRMAIIFYSISGLSIFDVNVSAKY
GDHLGWMRKHYIKTVLDDTENTVISGFVGSLVMNIPHATTINLPNTLFALLSMIMLRDYE
YFETILDKRSLARFVSKCQRPDRGSFVSCLDYKTNCGSSVDSDDLRFCYIAVAILYICGC
RSKEDFDEYIDTEKLLGYIMSQQCYNGAFGAHNEPHSGYTSCALSTLALLSSLEKLSDKF
KEDTITWLLHRQVSSHGCMKFESELNASYDQSDDGGFQGRENKFADTCYAFWCLNSLHLL
TKDWKMLCQTELVTNYLLDRTQKTLTGGFSKNDEEDADLYHSCLGSAALALIEGKFNGEL
CIPQEIFNDFSKRCCF

File: ex5.text

********************************************************************************
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= farntrans5.fasta
ALPHABET= ACDEFGHIKLMNPQRSTVWY
Sequence name            Weight Length  Sequence name            Weight Length  
-------------            ------ ------  -------------            ------ ------  
RAM1_YEAST               1.0000    431  PFTB_RAT                 1.0000    437  
BET2_YEAST               1.0000    325  RATRABGERB               1.0000    331  
CAL1_YEAST               1.0000    376  
********************************************************************************

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

command: meme farntrans5.fasta -mod anr -prior dirichlet -maxsites 50 -maxw 40 -nostatus -protein -text 

model:  mod=           anr    nmotifs=         1    evt=           inf
object function=  E-value of product of p-values
width:  minw=            8    maxw=           40    minic=        0.00


  [Part of this file has been deleted for brevity]

 0.000000  0.000000  0.000000  0.166667  0.055556  0.388889  0.000000  0.000000  0.000000  0.000000  0.000000  0.222222  0.000000  0.000000  0.000000  0.055556  0.000000  0.055556  0.055556  0.000000 
 0.111111  0.000000  0.111111  0.055556  0.000000  0.166667  0.000000  0.000000  0.333333  0.000000  0.055556  0.055556  0.000000  0.055556  0.000000  0.055556  0.000000  0.000000  0.000000  0.000000 
 0.000000  0.000000  0.055556  0.444444  0.055556  0.000000  0.055556  0.000000  0.000000  0.222222  0.055556  0.000000  0.000000  0.000000  0.000000  0.055556  0.000000  0.000000  0.000000  0.055556 
 0.222222  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.055556  0.000000  0.000000  0.000000  0.000000  0.166667  0.000000  0.055556  0.166667  0.000000  0.333333  0.000000  0.000000 
 0.000000  0.000000  0.722222  0.000000  0.000000  0.000000  0.277778  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000 
 0.111111  0.000000  0.000000  0.000000  0.111111  0.222222  0.000000  0.000000  0.000000  0.111111  0.000000  0.000000  0.055556  0.000000  0.000000  0.000000  0.166667  0.222222  0.000000  0.000000 
 0.111111  0.277778  0.000000  0.000000  0.111111  0.166667  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.166667  0.000000  0.000000  0.000000  0.000000  0.166667 
 0.000000  0.000000  0.000000  0.000000  0.111111  0.000000  0.277778  0.000000  0.000000  0.000000  0.000000  0.000000  0.055556  0.111111  0.000000  0.055556  0.000000  0.000000  0.000000  0.388889 
 0.166667  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.055556  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.333333  0.388889  0.055556  0.000000  0.000000 
--------------------------------------------------------------------------------

--------------------------------------------------------------------------------
	Motif 1 regular expression
--------------------------------------------------------------------------------
Qx[EP][DE]GG[FL]G[GD]RP[GN]K[EL][VA][DH][GV]C[YH][TS]
--------------------------------------------------------------------------------




Time  1.49 secs.

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


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

--------------------------------------------------------------------------------
	Combined block diagrams: non-overlapping sites with p-value < 0.0001
--------------------------------------------------------------------------------
SEQUENCE NAME            COMBINED P-VALUE  MOTIF DIAGRAM
-------------            ----------------  -------------
RAM1_YEAST                       1.98e-11  140_[1(3.83e-06)]_82_[1(3.85e-11)]_29_[1(4.81e-14)]_33_[1(3.01e-12)]_67
PFTB_RAT                         2.50e-14  133_[1(5.98e-14)]_31_[1(1.26e-12)]_28_[1(5.88e-16)]_29_[1(5.97e-17)]_42_[1(1.38e-13)]_74
BET2_YEAST                       5.50e-14  119_[1(1.69e-13)]_28_[1(3.03e-13)]_31_[1(1.80e-16)]_29_[1(5.98e-14)]_38
RATRABGERB                       8.82e-14  126_[1(1.53e-13)]_28_[1(9.50e-15)]_28_[1(2.83e-16)]_29_[1(2.05e-15)]_40
CAL1_YEAST                       2.42e-13  270_[1(6.78e-16)]_32_[1(4.48e-11)]_34
--------------------------------------------------------------------------------

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


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

CPU: emboss4.ebi.ac.uk

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

Output files for usage example 6

File: ex6.text

********************************************************************************
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= farntrans5.fasta
ALPHABET= ACDEFGHIKLMNPQRSTVWY
Sequence name            Weight Length  Sequence name            Weight Length  
-------------            ------ ------  -------------            ------ ------  
RAM1_YEAST               1.0000    431  PFTB_RAT                 1.0000    437  
BET2_YEAST               1.0000    325  RATRABGERB               1.0000    331  
CAL1_YEAST               1.0000    376  
********************************************************************************

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

command: meme farntrans5.fasta -mod anr -nmotifs 3 -prior dirichlet -maxsites 30 -w 10 -nostatus -protein -text 

model:  mod=           anr    nmotifs=         3    evt=           inf
object function=  E-value of product of p-values
width:  minw=           10    maxw=           10    minic=        0.00


  [Part of this file has been deleted for brevity]

 0.000000  0.000000  0.142857  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.142857  0.000000  0.571429  0.000000  0.071429  0.000000  0.000000  0.000000  0.071429  0.000000  0.000000 
 0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.285714  0.071429  0.000000  0.000000  0.000000  0.000000  0.214286  0.000000  0.071429  0.285714  0.000000  0.071429 
 0.000000  0.000000  0.071429  0.785714  0.000000  0.000000  0.071429  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.071429  0.000000  0.000000  0.000000  0.000000  0.000000 
 0.071429  0.000000  0.000000  0.142857  0.000000  0.000000  0.000000  0.000000  0.785714  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000 
 0.071429  0.000000  0.000000  0.000000  0.000000  0.000000  0.214286  0.142857  0.000000  0.428571  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.142857  0.000000  0.000000 
 0.071429  0.000000  0.000000  0.000000  0.071429  0.000000  0.000000  0.285714  0.000000  0.285714  0.000000  0.000000  0.000000  0.000000  0.142857  0.000000  0.071429  0.071429  0.000000  0.000000 
 0.071429  0.000000  0.142857  0.214286  0.000000  0.071429  0.142857  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.071429  0.071429  0.142857  0.000000  0.071429  0.000000  0.000000 
 0.000000  0.000000  0.000000  0.000000  0.357143  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.071429  0.571429 
 0.000000  0.000000  0.000000  0.000000  0.071429  0.000000  0.000000  0.500000  0.000000  0.142857  0.000000  0.000000  0.000000  0.000000  0.000000  0.071429  0.000000  0.214286  0.000000  0.000000 
--------------------------------------------------------------------------------

--------------------------------------------------------------------------------
	Motif 3 regular expression
--------------------------------------------------------------------------------
[IL]N[KVR]EK[LH][IL]E[YF][IV]
--------------------------------------------------------------------------------




Time  0.47 secs.

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


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

--------------------------------------------------------------------------------
	Combined block diagrams: non-overlapping sites with p-value < 0.0001
--------------------------------------------------------------------------------
SEQUENCE NAME            COMBINED P-VALUE  MOTIF DIAGRAM
-------------            ----------------  -------------
RAM1_YEAST                       1.28e-15  109_[2(1.99e-06)]_24_[1(9.95e-09)]_6_[2(3.56e-08)]_43_[2(6.10e-07)]_2_[3(1.62e-05)]_10_[1(6.34e-09)]_7_[2(6.90e-10)]_6_[3(7.11e-09)]_7_[1(3.91e-09)]_6_[2(8.06e-07)]_9_[3(4.43e-08)]_24_[2(1.85e-06)]_40_[3(3.31e-08)]_8
PFTB_RAT                         1.38e-16  72_[3(4.86e-08)]_21_[2(7.36e-07)]_23_[1(2.07e-10)]_6_[2(1.20e-08)]_9_[3(2.23e-09)]_22_[2(1.35e-06)]_22_[1(2.12e-09)]_6_[2(2.28e-08)]_23_[1(6.68e-11)]_68_[2(8.11e-08)]_65
BET2_YEAST                       3.95e-16  6_[3(6.29e-09)]_22_[2(2.41e-07)]_6_[3(1.97e-07)]_74_[2(1.05e-07)]_6_[3(5.91e-05)]_6_[1(3.56e-09)]_6_[2(8.11e-08)]_25_[1(1.39e-09)]_6_[2(1.03e-08)]_6_[3(9.33e-10)]_7_[1(3.44e-08)]_6_[2(1.46e-06)]_29
RATRABGERB                       3.89e-16  17_[3(1.70e-07)]_38_[3(2.44e-08)]_38_[3(5.33e-08)]_22_[2(5.42e-08)]_6_[3(5.01e-10)]_6_[1(6.01e-10)]_6_[2(9.24e-08)]_22_[1(3.56e-09)]_6_[2(4.12e-08)]_6_[3(2.91e-09)]_7_[1(6.95e-09)]_6_[2(2.83e-06)]_31
CAL1_YEAST                       5.03e-15  41_[2(7.36e-07)]_74_[3(3.01e-05)]_32_[2(8.06e-07)]_12_[3(2.20e-08)]_20_[2(1.92e-07)]_44_[1(1.82e-10)]_6_[2(3.07e-08)]_77
--------------------------------------------------------------------------------

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


********************************************************************************
Stopped because nmotifs = 3 reached.
********************************************************************************

CPU: emboss4.ebi.ac.uk

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

Output files for usage example 7

File: ex7.text

********************************************************************************
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= farntrans5.fasta
ALPHABET= ACDEFGHIKLMNPQRSTVWY
Sequence name            Weight Length  Sequence name            Weight Length  
-------------            ------ ------  -------------            ------ ------  
RAM1_YEAST               1.0000    431  PFTB_RAT                 1.0000    437  
BET2_YEAST               1.0000    325  RATRABGERB               1.0000    331  
CAL1_YEAST               1.0000    376  
********************************************************************************

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

command: meme farntrans5.fasta -mod anr -nmotifs 3 -prior dirichlet -nsites 24 -maxw 12 -nostatus -protein -text 

model:  mod=           anr    nmotifs=         3    evt=           inf
object function=  E-value of product of p-values
width:  minw=            8    maxw=           12    minic=        0.00


  [Part of this file has been deleted for brevity]

 0.000000  0.000000  0.125000  0.583333  0.000000  0.000000  0.041667  0.000000  0.125000  0.000000  0.000000  0.000000  0.000000  0.041667  0.041667  0.041667  0.000000  0.000000  0.000000  0.000000 
 0.083333  0.000000  0.000000  0.083333  0.000000  0.083333  0.000000  0.000000  0.625000  0.041667  0.000000  0.000000  0.041667  0.000000  0.000000  0.041667  0.000000  0.000000  0.000000  0.000000 
 0.125000  0.000000  0.000000  0.000000  0.000000  0.000000  0.166667  0.166667  0.000000  0.333333  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.208333  0.000000  0.000000 
 0.041667  0.000000  0.000000  0.000000  0.041667  0.000000  0.000000  0.250000  0.000000  0.250000  0.000000  0.000000  0.000000  0.083333  0.125000  0.000000  0.083333  0.083333  0.000000  0.041667 
 0.041667  0.000000  0.125000  0.208333  0.000000  0.041667  0.083333  0.000000  0.041667  0.000000  0.000000  0.083333  0.000000  0.208333  0.041667  0.083333  0.000000  0.041667  0.000000  0.000000 
 0.041667  0.000000  0.000000  0.000000  0.291667  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.041667  0.000000  0.000000  0.000000  0.000000  0.041667  0.125000  0.458333 
 0.000000  0.000000  0.000000  0.000000  0.125000  0.000000  0.000000  0.333333  0.000000  0.250000  0.000000  0.000000  0.000000  0.000000  0.000000  0.041667  0.041667  0.208333  0.000000  0.000000 
 0.041667  0.000000  0.000000  0.083333  0.000000  0.000000  0.000000  0.041667  0.166667  0.333333  0.083333  0.000000  0.000000  0.041667  0.000000  0.083333  0.083333  0.000000  0.000000  0.041667 
 0.083333  0.000000  0.041667  0.000000  0.000000  0.000000  0.041667  0.000000  0.125000  0.000000  0.041667  0.041667  0.000000  0.000000  0.083333  0.500000  0.000000  0.000000  0.000000  0.041667 
--------------------------------------------------------------------------------

--------------------------------------------------------------------------------
	Motif 3 regular expression
--------------------------------------------------------------------------------
INVEK[LV][IL][EQ][YF][ILV]LS
--------------------------------------------------------------------------------




Time  0.58 secs.

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


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

--------------------------------------------------------------------------------
	Combined block diagrams: non-overlapping sites with p-value < 0.0001
--------------------------------------------------------------------------------
SEQUENCE NAME            COMBINED P-VALUE  MOTIF DIAGRAM
-------------            ----------------  -------------
RAM1_YEAST                       2.42e-16  35_[3(3.87e-06)]_62_[1(1.43e-07)]_23_[2(2.96e-09)]_3_[1(4.98e-09)]_8_[3(4.87e-07)]_21_[1(3.26e-08)]_4_[3(6.95e-07)]_5_[2(3.84e-07)]_4_[1(6.42e-10)]_4_[3(2.63e-08)]_6_[2(7.99e-09)]_3_[1(2.34e-07)]_7_[3(2.04e-07)]_7_[2(2.81e-07)]_15_[2(1.16e-06)]_26_[3(1.79e-09)]_6
PFTB_RAT                         3.08e-19  49_[3(1.45e-06)]_11_[3(3.82e-08)]_19_[1(4.06e-08)]_22_[2(1.38e-10)]_3_[1(9.07e-10)]_7_[3(5.77e-11)]_5_[2(8.29e-08)]_3_[1(9.97e-08)]_21_[2(1.99e-09)]_3_[1(8.60e-09)]_4_[3(8.26e-07)]_6_[2(5.90e-11)]_32_[3(1.82e-06)]_6_[2(4.62e-08)]_3_[1(1.31e-07)]_28_[3(4.11e-06)]_23
BET2_YEAST                       9.82e-18  6_[3(7.95e-09)]_20_[1(7.52e-09)]_4_[3(3.82e-08)]_6_[2(4.17e-08)]_39_[2(5.11e-08)]_3_[1(1.27e-09)]_4_[3(2.11e-06)]_5_[2(5.63e-10)]_3_[1(2.32e-08)]_24_[2(1.99e-09)]_3_[1(6.42e-10)]_4_[3(7.88e-10)]_6_[2(8.71e-10)]_3_[1(6.15e-08)]_27
RATRABGERB                       2.86e-20  17_[3(4.04e-07)]_20_[1(1.20e-07)]_4_[3(2.99e-08)]_5_[2(1.09e-07)]_19_[3(1.57e-08)]_5_[2(1.31e-07)]_3_[1(2.05e-09)]_4_[3(6.10e-12)]_5_[2(4.94e-11)]_3_[1(7.50e-08)]_21_[2(2.25e-10)]_3_[1(2.39e-09)]_4_[3(5.99e-09)]_6_[2(8.34e-11)]_3_[1(1.99e-07)]_29
CAL1_YEAST                       2.39e-16  10_[3(4.04e-07)]_19_[1(2.94e-07)]_79_[2(6.23e-06)]_23_[1(7.50e-08)]_10_[3(7.88e-10)]_5_[2(4.15e-07)]_1_[1(5.56e-08)]_43_[2(7.55e-10)]_3_[1(8.60e-09)]_6_[3(3.19e-06)]_7_[2(1.56e-07)]_38
--------------------------------------------------------------------------------

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


********************************************************************************
Stopped because nmotifs = 3 reached.
********************************************************************************

CPU: emboss4.ebi.ac.uk

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

Output files for usage example 8

File: adh.fasta

>2BHD_STREX 20-BETA-HYDROXYSTEROID DEHYDROGENASE (EC 1.1.1.53)
MNDLSGKTVIITGGARGLGAEAARQAVAAGARVVLADVLDEEGAATARELGDAARYQHLD
VTIEEDWQRVVAYAREEFGSVDGLVNNAGISTGMFLETESVERFRKVVDINLTGVFIGMK
TVIPAMKDAGGGSIVNISSAAGLMGLALTSSYGASKWGVRGLSKLAAVELGTDRIRVNSV
HPGMTYTPMTAETGIRQGEGNYPNTPMGRVGNEPGEIAGAVVKLLSDTSSYVTGAELAVD
GGWTTGPTVKYVMGQ
>3BHD_COMTE 3-BETA-HYDROXYSTEROID DEHYDROGENASE (EC 1.1.1.51)
TNRLQGKVALVTGGASGVGLEVVKLLLGEGAKVAFSDINEAAGQQLAAELGERSMFVRHD
VSSEADWTLVMAAVQRRLGTLNVLVNNAGILLPGDMETGRLEDFSRLLKINTESVFIGCQ
QGIAAMKETGGSIINMASVSSWLPIEQYAGYSASKAAVSALTRAAALSCRKQGYAIRVNS
IHPDGIYTPMMQASLPKGVSKEMVLHDPKLNRAGRAYMPERIAQLVLFLASDESSVMSGG
ELHADNSILGMGL
>ADH_DROME ALCOHOL DEHYDROGENASE (EC 1.1.1.1)
SFTLTNKNVIFVAGLGGIGLDTSKELLKRDLKNLVILDRIENPAAIAELKAINPKVTVTF
YPYDVTVPIAETTKLLKTIFAQLKTVDVLINGAGILDDHQIERTIAVNYTGLVNTTTAIL
DFWDKRKGGPGGIICNIGSVTGFNAIYQVPVYSGTKAAVVNFTSSLAKLAPITGVTAYTV
NPGITRTTLVHKFNSWLDVEPQVAEKLLAHPTQPSLACAENFVKAIELNQNGAIWKLDLG
TLEAIQWTKHWDSGI
>AP27_MOUSE ADIPOCYTE P27 PROTEIN (AP27)
MKLNFSGLRALVTGAGKGIGRDTVKALHASGAKVVAVTRTNSDLVSLAKECPGIEPVCVD
LGDWDATEKALGGIGPVDLLVNNAALVIMQPFLEVTKEAFDRSFSVNLRSVFQVSQMVAR
DMINRGVPGSIVNVSSMVAHVTFPNLITYSSTKGAMTMLTKAMAMELGPHKIRVNSVNPT
VVLTDMGKKVSADPEFARKLKERHPLRKFAEVEDVVNSILFLLSDRSASTSGGGILVDAG
YLAS
>BA72_EUBSP 7-ALPHA-HYDROXYSTEROID DEHYDROGENASE (EC 1.1.1.159) (BILE ACID 7-DEHYDROXYLASE) (BILE ACID-INDUCIBLE PROTEIN)
MNLVQDKVTIITGGTRGIGFAAAKIFIDNGAKVSIFGETQEEVDTALAQLKELYPEEEVL
GFAPDLTSRDAVMAAVGQVAQKYGRLDVMINNAGITSNNVFSRVSEEEFKHIMDINVTGV
FNGAWCAYQCMKDAKKGVIINTASVTGIFGSLSGVGYPASKASVIGLTHGLGREIIRKNI
RVVGVAPGVVNTDMTNGNPPEIMEGYLKALPMKRMLEPEEIANVYLFLASDLASGITATT
VSVDGAYRP
>BDH_HUMAN D-BETA-HYDROXYBUTYRATE DEHYDROGENASE PRECURSOR (EC 1.1.1.30) (BDH) (3-HYDROXYBUTYRATE DEHYDROGENASE) (FRAGMENT)
GLRPPPPGRFSRLPGKTLSACDRENGARRPLLLGSTSFIPIGRRTYASAAEPVGSKAVLV
TGCDSGFGFSLAKHLHSKGFLVFAGCLMKDKGHDGVKELDSLNSDRLRTVQLNVFRSEEV
EKVVGDCPFEPEGPEKGMWGLVNNAGISTFGEVEFTSLETYKQVAEVNLWGTVRMTKSFL
PLIRRAKGRVVNISSMLGRMANPARSPYCITKFGVEAFSDCLRYEMYPLGVKVSVVEPGN
FIAATSLYNPESIQAIAKKMWEELPEVVRKDYGKKYFDEKIAKMETYCSSGSTDTSPVID
AVTHALTATTPYTRYHPMDYYWWLRMQIMTHLPGAISDMIYIR
>BPHB_PSEPS BIPHENYL-CIS-DIOL DEHYDROGENASE (EC 1.3.1.-)
MKLKGEAVLITGGASGLGRALVDRFVAEAKVAVLDKSAERLAELETDLGDNVLGIVGDVR
SLEDQKQAASRCVARFGKIDTLIPNAGIWDYSTALVDLPEESLDAAFDEVFHINVKGYIH
AVKALPALVASRGNVIFTISNAGFYPNGGGPLYTAAKQAIVGLVRELAFELAPYVRVNGV
GPGGMNSDMRGPSSLGMGSKAISTVPLADMLKSVLPIGRMPEVEEYTGAYVFFATRGDAA
PASGALVNYDGGLGVRGFFSGAGGNDLLEQLNIHP
>BUDC_KLETE ACETOIN(DIACETYL) REDUCTASE (EC 1.1.1.5) (ACETOIN DEHYDROGENASE)
MQKVALVTGAGQGIGKAIALRLVKDGFAVAIADYNDATATAVAAEINQAGGRAVAIKVDV
SRRDQVFAAVEQARKALGGFNVIVNNAGIAPSTPIESITEEIVDRVYNINVKGVIWGMQA
AVEAFKKEGHGGKIVNACSQAGHVGNPELAVYSSSKFAVRGLTQTAARDLAPLGITVNGF
CPGIVKTPMWAEIDRQCRKRRANRWATARLNLPNASPLAACRSLKTSPPACRSSPARIPT
I
>DHES_HUMAN ESTRADIOL 17 BETA-DEHYDROGENASE (EC 1.1.1.62) (20 ALPHA-HYDROXYSTEROID DEHYDROGENASE) (E2DH) (17-BETA-HSD) (PLACENTAL 17-BETA-HYDROXYSTEROID DEHYDROGENASE)


  [Part of this file has been deleted for brevity]

GVHQKEGWPSSAYGVTKIGVTVLSRIHARKLSEQRKGDKILLNACCPGWVRTDMAGPKAT
KSPEEGAETPVYLALLPPDAEGPHGQFVSEKRVEQW
>FABI_ECOLI no comment
MGFLSGKRILVTGVASKLSIAYGIAQAMHREGAELAFTYQNDKLKGRVEEFAAQLGSDIV
LQCDVAEDASIDTMFAELGKVWPKFDGFVHSIGFAPGDQLDGDYVNAVTREGFKIAHDIS
SYSFVAMAKACRSMLNPGSALLTLSYLGAERAIPNYNVMGLAKASLEANVRYMANAMGPE
GVRVNAISAGPIRTLAASGIKDFRKMLAHCEAVTPIRRTVTIEDVGNSAAFLCSDLSAGI
SGEVVHVDGGFSIAAMNELELK
>FVT1_HUMAN no comment
MLLLAAAFLVAFVLLLYMVSPLISPKPLALPGAHVVVTGGSSGIGKCIAIECYKQGAFIT
LVARNEDKLLQAKKEIEMHSINDKQVVLCISVDVSQDYNQVENVIKQAQEKLGPVDMLVN
CAGMAVSGKFEDLEVSTFERLMSINYLGSVYPSRAVITTMKERRVGRIVFVSSQAGQLGL
FGFTAYSASKFAIRGLAEALQMEVKPYNVYITVAYPPDTDTPGFAEENRTKPLETRLISE
TTSVCKPEQVAKQIVKDAIQGNFNSSLGSDGYMLSALTCGMAPVTSITEGLQQVVTMGLF
RTIALFYLGSFDSIVRRCMMQREKSENADKTA
>HMTR_LEIMA no comment
MTAPTVPVALVTGAAKRLGRSIAEGLHAEGYAVCLHYHRSAAEANALSATLNARRPNSAI
TVQADLSNVATAPVSGADGSAPVTLFTRCAELVAACYTHWGRCDVLVNNASSFYPTPLLR
NDEDGHEPCVGDREAMETATADLFGSNAIAPYFLIKAFAHRSRHPSQASRTNYSIINMVD
AMTNQPLLGYTIYTMAKGALEGLTRSAALELAPLQIRVNGVGPGLSVLVDDMPPAVWEGH
RSKVPLYQRDSSAAEVSDVVIFLCSSKAKYITGTCVKVDGGYSLTRA
>MAS1_AGRRA no comment
MHQLWAYDVGTLGCVSYHALPDIKRHSPKSGHLYLNKPSLRSFILQCPSLARTLVLPSHQ
PVSRSSTSSAMVQPISTRKKCTCKVKNIGVCRAPARTSVSMELANAKRFSPATFSANFLS
XSVVCSPLLRAIQTALIANIGFLCFDIDEDLKERDFGKHEGGYGPLKMFEDNYPDCEDTE
MFSLRVAKALTHAKNENTLFVSHGGVLRVIAALLGVDLTKEHTNNGRVLHFRRGFSHWTV
EIHQSPVILVSGSNRGVGKAIAEDLIAHGYRLSLGARKVKDLEVAFGPQDEWLHYARFDA
EDHGTMAAWVTAAVEKFGRIDGLVNNAGYGEPVNLDKHVDYQRFHLQWYINCVAPLRMTE
LCLPHLYETGSGRIVNINSMSGQRVLNPLVGYNMTKHALGGLTKTTQHVGWDRRCAAIDI
CLGFVATDMSAWTDLIASKDMIQPEDIAKLVREAIERPNRAYVPRSEVMCIKEATR
>PCR_PEA no comment
MALQTASMLPASFSIPKEGKIGASLKDSTLFGVSSLSDSLKGDFTSSALRCKELRQKVGA
VRAETAAPATPAVNKSSSEGKKTLRKGNVVITGASSGLGLATAKALAESGKWHVIMACRD
YLKAARAAKSAGLAKENYTIMHLDLASLDSVRQFVDNFRRSEMPLDVLINNAAVYFPTAK
EPSFTADGFEISVGTNHLGHFLLSRLLLEDLKKSDYPSKRLIIVGSITGNTNTLAGNVPP
KANLGDLRGLAGGLTGLNSSAMIDGGDFDGAKAYKDSKVCNMLTMQEFHRRYHEETGITF
ASLYPGCIATTGLFREHIPLFRTLFPPFQKYITKGYVSEEESGKRLAQVVSDPSLTKSGV
YWSWNNASASFENQLSQEASDAEKARKVWEVSEKLVGLA
>RFBB_NEIGO no comment
MQTEGKKNILVTGGAGFIGSAVVRHIIQNTRDSVVNLDKLTYAGNLESLTDIADNPRYAF
EQVDICDRAELDRVFAQYRPDAVMHLAAESHVDRAIGSAGEFIRTNIVGTFDLLEAARAY
WQQMPSEKREAFRFHHISTDEVYGDLHGTDDLFTETTPYAPSSPYSASKAAADHLVRAWQ
RTYRLPSIVSNCSNNYGPRQFPEKLIPLMILNALSGKPLPVYGDGAQIRDWLFVEDHARA
LYQVVTEGVVGETYNIGGHNEKTNLEVVKTICALLEELAPEKPAGVARYEDLITFVQDRP
GHDARYAVDAAKIRRDLGWLPLETFESGLRKTVQWYLDNKTRRQNA
>YURA_MYXXA no comment
RQHTGGLHGGDELPDGVGDGCLQRPGTRAGAVARQAGVRVFAAGRRLPQLQAADEAPGGR
RHRGARGVDVTKADATLERIRALDAEAGGLDLVVANAGVGGTTNAKRLPWERVRGIIDTN
VTGAAATLSAVLPQMVERKRGHLVGVSSLAGFRGLPATRYSASKAFLSTFMESLRVDLRG
TGVRVTCIYPGFVKSELTATNNFPMPFLMETHDAVELMGKGIVRGDAEVSFPWQLAVPTR
MAKVLPNPLFDAAARRLR

File: ex8.text

********************************************************************************
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= adh.fasta
ALPHABET= ACDEFGHIKLMNPQRSTVWY
Sequence name            Weight Length  Sequence name            Weight Length  
-------------            ------ ------  -------------            ------ ------  
2BHD_STREX               1.0000    255  3BHD_COMTE               1.0000    253  
ADH_DROME                1.0000    255  AP27_MOUSE               1.0000    244  
BA72_EUBSP               1.0000    249  BDH_HUMAN                1.0000    343  
BPHB_PSEPS               1.0000    275  BUDC_KLETE               1.0000    241  
DHES_HUMAN               1.0000    327  DHGB_BACME               1.0000    262  
DHII_HUMAN               1.0000    292  DHMA_FLAS1               1.0000    270  
ENTA_ECOLI               1.0000    248  FIXR_BRAJA               1.0000    278  
GUTD_ECOLI               1.0000    259  HDE_CANTR                1.0000    906  
HDHA_ECOLI               1.0000    255  LIGD_PSEPA               1.0000    305  
NODG_RHIME               1.0000    245  RIDH_KLEAE               1.0000    249  
YINL_LISMO               1.0000    248  YRTP_BACSU               1.0000    238  
CSGA_MYXXA               1.0000    166  DHB2_HUMAN               1.0000    387  
DHB3_HUMAN               1.0000    310  DHCA_HUMAN               1.0000    276  
FABI_ECOLI               1.0000    262  FVT1_HUMAN               1.0000    332  
HMTR_LEIMA               1.0000    287  MAS1_AGRRA               1.0000    476  
PCR_PEA                  1.0000    399  RFBB_NEIGO               1.0000    346  


  [Part of this file has been deleted for brevity]


--------------------------------------------------------------------------------
	Combined block diagrams: non-overlapping sites with p-value < 0.0001
--------------------------------------------------------------------------------
SEQUENCE NAME            COMBINED P-VALUE  MOTIF DIAGRAM
-------------            ----------------  -------------
2BHD_STREX                       3.00e-81  5_[2(6.76e-13)]_2_[8(2.79e-13)]_24_[3(3.26e-12)]_12_[4(1.64e-13)]_2_[6(1.48e-15)]_5_[1(8.10e-19)]_[7(4.84e-10)]_24_[5(1.29e-21)]_13
3BHD_COMTE                       4.50e-74  5_[2(6.53e-15)]_2_[8(6.48e-16)]_24_[3(4.42e-12)]_12_[4(1.98e-11)]_1_[6(3.58e-11)]_5_[1(1.62e-15)]_2_[7(1.89e-08)]_28_[5(5.31e-21)]_6
ADH_DROME                        2.38e-37  5_[2(3.69e-11)]_56_[3(1.89e-10)]_4_[4(2.17e-11)]_5_[6(1.44e-11)]_5_[1(4.20e-13)]_[7(2.82e-07)]_66
AP27_MOUSE                       6.69e-75  6_[2(1.73e-14)]_2_[8(5.45e-13)]_19_[3(4.79e-10)]_12_[4(7.74e-13)]_3_[6(1.19e-11)]_5_[1(3.16e-22)]_[7(9.85e-08)]_25_[5(3.17e-19)]_4
BA72_EUBSP                       1.68e-81  5_[2(3.44e-14)]_2_[8(8.85e-13)]_29_[3(1.25e-13)]_12_[4(2.96e-14)]_2_[6(2.51e-14)]_5_[1(1.55e-16)]_[7(3.30e-09)]_23_[5(3.54e-23)]_3
BDH_HUMAN                        1.27e-45  54_[2(9.49e-15)]_59_[3(1.36e-10)]_12_[4(4.70e-13)]_1_[6(3.80e-14)]_5_[1(6.62e-18)]_107
BPHB_PSEPS                       3.73e-42  4_[2(5.94e-14)]_1_[8(3.23e-06)]_24_[3(9.73e-11)]_17_[4(1.11e-11)]_[6(1.24e-10)]_5_[1(4.44e-14)]_94
BUDC_KLETE                       3.15e-66  1_[2(1.49e-17)]_2_[8(5.08e-13)]_27_[3(1.52e-10)]_12_[4(1.59e-12)]_3_[6(1.82e-13)]_5_[1(2.03e-21)]_[7(5.92e-10)]_52
DHES_HUMAN                       2.57e-42  1_[2(5.94e-14)]_58_[3(2.01e-11)]_12_[4(8.18e-12)]_2_[6(4.83e-13)]_5_[1(2.45e-17)]_144
DHGB_BACME                       3.04e-66  6_[2(8.39e-15)]_56_[3(1.76e-12)]_12_[4(2.54e-14)]_3_[6(6.03e-10)]_6_[1(9.72e-20)]_[7(3.36e-07)]_24_[5(2.28e-20)]_12
DHII_HUMAN                       1.93e-53  33_[2(4.63e-17)]_2_[8(1.21e-15)]_28_[3(1.70e-08)]_12_[4(6.26e-11)]_1_[6(1.10e-13)]_5_[1(7.62e-16)]_81
DHMA_FLAS1                       8.76e-61  13_[2(8.39e-15)]_49_[3(5.34e-08)]_13_[4(3.17e-15)]_8_[6(3.28e-11)]_5_[1(6.62e-18)]_34_[5(1.77e-22)]_14
ENTA_ECOLI                       3.09e-68  4_[2(1.11e-16)]_44_[3(5.83e-10)]_12_[4(6.04e-13)]_2_[6(2.09e-11)]_5_[1(1.55e-16)]_[7(4.26e-08)]_33_[5(2.99e-25)]_5
FIXR_BRAJA                       9.12e-69  35_[2(3.91e-15)]_52_[3(2.72e-09)]_18_[4(2.86e-11)]_1_[6(9.83e-12)]_6_[1(3.46e-21)]_[7(5.02e-09)]_20_[5(5.45e-24)]_3
GUTD_ECOLI                       1.30e-71  1_[2(4.40e-11)]_2_[8(6.15e-15)]_29_[3(3.92e-10)]_12_[4(3.17e-15)]_3_[6(6.62e-12)]_5_[1(5.21e-19)]_44_[5(1.77e-22)]_4
HDE_CANTR                        1.58e-58  7_[2(1.53e-11)]_60_[3(4.28e-11)]_12_[4(3.59e-08)]_2_[6(1.14e-07)]_5_[1(1.97e-12)]_21_[5(5.78e-05)]_80_[2(5.54e-17)]_50_[3(9.64e-14)]_12_[4(6.17e-14)]_2_[6(3.31e-14)]_5_[1(5.78e-18)]_57_[8(3.01e-13)]_329
HDHA_ECOLI                       5.20e-81  10_[2(2.96e-16)]_2_[8(3.51e-15)]_27_[3(9.10e-12)]_11_[4(1.78e-12)]_2_[6(4.26e-11)]_5_[1(6.04e-19)]_[7(4.32e-07)]_24_[5(7.10e-25)]_6
LIGD_PSEPA                       3.19e-45  5_[2(1.34e-12)]_2_[8(8.35e-16)]_53_[4(2.15e-13)]_3_[6(3.81e-13)]_5_[1(1.18e-15)]_120
NODG_RHIME                       2.04e-87  5_[2(1.72e-12)]_2_[8(9.46e-16)]_24_[3(1.76e-12)]_12_[4(2.54e-14)]_2_[6(1.18e-16)]_5_[1(4.63e-22)]_[7(4.68e-07)]_23_[5(2.47e-23)]_4
RIDH_KLEAE                       2.13e-56  13_[2(1.14e-15)]_2_[8(5.42e-20)]_24_[3(4.46e-09)]_12_[4(4.70e-13)]_2_[6(1.34e-10)]_5_[1(4.60e-17)]_61
YINL_LISMO                       1.43e-58  4_[2(2.66e-17)]_2_[8(7.36e-16)]_27_[3(1.24e-09)]_12_[4(9.87e-13)]_2_[6(2.06e-13)]_5_[1(5.04e-15)]_2_[7(5.94e-07)]_55
YRTP_BACSU                       3.25e-69  5_[2(2.15e-16)]_2_[8(5.11e-14)]_27_[3(2.07e-12)]_12_[4(5.23e-15)]_2_[6(5.95e-15)]_5_[1(5.59e-22)]_[7(1.07e-06)]_46
CSGA_MYXXA                       2.43e-28  9_[3(1.51e-12)]_13_[4(3.03e-10)]_31_[1(1.25e-13)]_[7(1.33e-11)]_41
DHB2_HUMAN                       1.75e-51  81_[2(2.62e-15)]_55_[3(5.65e-09)]_13_[4(9.87e-13)]_1_[6(6.62e-12)]_5_[1(8.10e-19)]_1_[8(2.58e-13)]_101
DHB3_HUMAN                       1.82e-48  47_[2(3.44e-14)]_2_[8(5.51e-15)]_26_[3(6.73e-08)]_14_[4(3.14e-12)]_2_[6(5.41e-12)]_5_[1(4.56e-15)]_84
DHCA_HUMAN                       3.85e-44  3_[2(1.54e-14)]_3_[8(1.21e-05)]_27_[3(1.10e-14)]_12_[4(4.78e-05)]_[6(2.51e-11)]_46_[1(7.01e-12)]_4_[7(1.11e-12)]_42
FABI_ECOLI                       3.60e-30  5_[2(8.23e-11)]_132_[1(1.74e-13)]_34_[5(2.46e-22)]_12
FVT1_HUMAN                       2.52e-62  31_[2(1.36e-14)]_2_[8(1.50e-16)]_32_[3(6.81e-12)]_12_[4(3.91e-12)]_2_[6(7.76e-16)]_5_[1(1.13e-17)]_[7(5.08e-07)]_63_[4(2.64e-05)]_25
HMTR_LEIMA                       2.44e-44  5_[2(1.23e-12)]_73_[3(8.68e-11)]_80_[1(1.14e-19)]_31_[5(1.29e-21)]_6
MAS1_AGRRA                       2.00e-27  172_[7(1.01e-05)]_63_[2(4.05e-12)]_51_[3(3.78e-12)]_19_[1(6.98e-11)]_43_[7(2.41e-08)]_47
PCR_PEA                          6.40e-31  25_[1(2.02e-10)]_31_[2(1.54e-14)]_55_[3(2.10e-10)]_13_[4(5.76e-11)]_95_[7(8.04e-08)]_87
RFBB_NEIGO                       7.66e-16  5_[2(1.72e-12)]_138_[1(5.57e-15)]_153
YURA_MYXXA                       5.59e-32  35_[8(6.92e-05)]_26_[3(6.11e-09)]_12_[4(7.46e-06)]_2_[6(2.64e-13)]_4_[1(2.11e-19)]_[7(2.35e-07)]_61
--------------------------------------------------------------------------------

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


********************************************************************************
Stopped because motif E-value > 1.00e-02.
********************************************************************************

CPU: emboss4.ebi.ac.uk

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

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 MEMENEW

The EMBASSY MEMENEW package contains "wrapper" applications providing an EMBOSS-style interface to the applications in the original MEME package version 4.4.0 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 MEMENEW, 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 MEMENEW 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 MEMENEW, 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
ememe Multiple EM for Motif Elicitation
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
European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK

Please report all bugs to the EMBOSS bug team (emboss-bug © emboss.open-bio.org) not to the original author.

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

Target users

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