|
|
ememetext |
Please help by correcting and extending the Wiki pages.
Usage:
ememe [options] dataset outfile
The 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.
The sequences in the dataset should be in
Pearson/FASTA format. For example:
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:
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
In what follows, < n > is an integer, < a > is a decimal number, and < string >
is a string of characters.
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
-mod < string > The type of distribution to assume.
oops zoops anr 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
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:
-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:
Default: 0.8
D) MOTIF WIDTH
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:
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 >
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.
-pal
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 >
-distance < a >
-prior < string > -b < a > -plib < string >
H) SELECTING STARTS FOR EM
The default type of mapping MEME uses is:
Other types of starting points
can be specified using the following switches.
Go to the input files for this example
Example 2
Go to the output files for this example
Example 3
Go to the input files for this example
Example 4
Go to the input files for this example
Example 5
Go to the input files for this example
Example 6
Go to the output files for this example
Example 7
Go to the output files for this example
Example 8
Go to the input files for this example
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 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:
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:
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:
In this example we use -mod anr and -bfile yeast.nc.6.freq. This specifies
that
5) A simple protein example:
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 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:
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:
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:
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.
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.
The MEME results consist of:
The following additional options are provided:
Please read the 'Notes' section below for a description of the differences between the original and EMBASSY MEME, particularly which application command line options are supported.
(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.
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.
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.
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.
>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.
>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
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.
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.
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.
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.
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
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).
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
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.
-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.
-wg < a > (gap cost; default: 11),
-ws < a > (space cost; default 1), and,
-noendgaps (do not penalize endgaps; default:
penalize endgaps).
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
By default, the background model is a 0-order Markov
model based on the letter frequencies in the training
set.
# 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.
Choosing -pal causes MEME to look for palindromes in
DNA datasets.
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
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
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
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
The name of the file containing the Dirichlet prior
in the format of file prior30.plib.
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.
-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)
-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 output files for this example
% ememetext crp0.s -mod oops -revcomp -w 20 ex2.text
Multiple EM for Motif Elicitation. Text file only.
output sequence set [crp0.fasta]:
% 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 output files for this example
% 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 output files for this example
% 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 output files for this example
% 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]:
% 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]:
% 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 output files for this example EXAMPLES:
meme crp0.s -dna -mod oops -pal > ex1.html
meme crp0.s -dna -mod oops -revcomp > ex2.html
meme crp0.s -dna -mod oops -revcomp -w 20 > ex3.html
meme INO_up800.s -dna -mod anr -revcomp -bfile yeast.nc.6.freq > ex4.html
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.
meme lipocalin.s -mod oops -maxw 20 -nmotifs 2 > ex5.html
meme farntrans5.s -mod anr -maxw 40 -maxsites 50 > ex6.html
meme farntrans5.s -mod anr -w 10 -maxsites 30 -nmotifs 3 > ex7.html
meme farntrans5.s -mod anr -maxw 12 -nsites 24 -nmotifs 3 > ex8.html
meme adh.s -mod zoops -nmotifs 20 -evt 0.01 > ex9.html
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.
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 [
Standard (Mandatory) qualifiers
Allowed values
Default
[-dataset]
(Parameter 1)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)MEME program text output file
Output file
<*>.ememetext
[-outseq]
(Parameter 3)Sequence set filename and optional format (output USA)
Writeable sequences
<*>.format
Additional (Optional) qualifiers
Allowed values
Default
-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) 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
The name of the file containing the Dirichlet prior in the format of file prior30.plib
Input file
Required
-mod
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
The number of *different* motifs to search for. MEME will search for and output <n> motifs.
Any integer value
1
-text
Default output is in HTML
Boolean value Yes/No
No
-prior
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Set this option to prevent progress reports to the terminal.
Boolean value Yes/No
Yes
Advanced (Unprompted) qualifiers
Allowed values
Default
-maxiter
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
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
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
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
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
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 is accepted
An empty string is accepted
-maxsize
Maximum dataset size in characters (-1 = use meme default).
Any integer value
-1
-p
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
Only values of more than 0 will be applied.
Any integer value
0
-sf
Print <sf> as name of sequence file
Any string is accepted
An empty string is accepted
-heapsize
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
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
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
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
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 4.0.0 (Release date: 0 PDT 20)
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 1.15 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: emboss6.ebi.ac.uk
********************************************************************************
Output files for usage example 2
File: ex2.text
********************************************************************************
MEME - Motif discovery tool
********************************************************************************
MEME version 4.0.0 (Release date: 0 PDT 20)
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 0.17 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: emboss6.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 4.0.0 (Release date: 0 PDT 20)
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 25.84 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: emboss6.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 4.0.0 (Release date: 0 PDT 20)
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 0.33 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: emboss6.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 4.0.0 (Release date: 0 PDT 20)
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 3.90 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: emboss6.ebi.ac.uk
********************************************************************************
Output files for usage example 6
File: ex6.text
********************************************************************************
MEME - Motif discovery tool
********************************************************************************
MEME version 4.0.0 (Release date: 0 PDT 20)
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 1.17 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: emboss6.ebi.ac.uk
********************************************************************************
Output files for usage example 7
File: ex7.text
********************************************************************************
MEME - Motif discovery tool
********************************************************************************
MEME version 4.0.0 (Release date: 0 PDT 20)
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 1.44 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: emboss6.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 4.0.0 (Release date: 0 PDT 20)
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: emboss6.ebi.ac.uk
********************************************************************************
motif.
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.
outfile : Application output that was normally written to stdout.
Note: ememe makes a temporary local copy of its input sequence data. You must ensure there is sufficient disk space for this in the directory that ememe is run.
2. Installing EMBASSY MEME
The EMBASSY MEME package contains "wrapper" applications providing an EMBOSS-style interface to the applications in the original MEME package version 3.0.14 developed by Timothy L. Bailey. Please read the file README in the EMBASSY MEME package distribution for installation instructions.
3. Installing original MEME
To use EMBASSY MEME, you will first need to download and install the original MEME package:
WWW home: http://meme.sdsc.edu/meme/
Distribution: http://meme.nbcr.net/downloads/old_versions/
Please read the file README in the the original MEME package distribution for installation instructions.
4. Setting up MEME
For the EMBASSY MEME package to work, the directory containing the original MEME executables *must* be in your path. For example if you executables were installed to "/usr/local/meme/bin", then type:
set path=(/usr/local/meme/bin/ $path)
rehash
5. Getting help
Once you have installed the original MEME, type
meme > meme.txt
mast > mast.txt
to retrieve the meme and mast documentation into text files. The same documentation is given here and in the ememe documentation.
References
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.
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 (jison © ebi.ac.uk)
European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
History
Target users
This program is intended to be used by everyone and everything, from naive users to embedded scripts.