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Function
Multiple EM for Motif ElicitationDescription
EMBASSY MEME is a suite of application wrappers to the original meme v3.0.14 applications written by Timothy Bailey. meme v3.0.14 must be installed on the same system as EMBOSS and the location of the meme executables must be defined in your path for EMBASSY MEME to work.Usage:
ememe [options] dataset outfile
The 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
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.
Please report all bugs to the EMBOSS bug team (emboss-bug © emboss.open-bio.org) not to the original author.
This program is an EMBASSY wrapper to a program written by Timothy L. Bailey as part of his meme package.
Please report any bugs to the EMBOSS bug team in the first instance, not to Timothy L. Bailey.
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 ememe
% ememe crp0.s -mod oops
Multiple EM for Motif Elicitation
MEME program output file output directory [.]:
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.
Multiple EM for Motif Elicitation
Version: EMBOSS:6.3.0
Standard (Mandatory) qualifiers:
[-dataset] seqset User must provide the full filename of a set
of sequences, not an indirect reference,
e.g. a USA is NOT acceptable.
[-outdir] outdir [.] MEME program output file output
directory
Additional (Optional) qualifiers:
-bfile infile The name of the file containing the
background model for sequences. The
background model is the model of random
sequences used by MEME. The background model
is used by MEME 1) during EM as the 'null
model', 2) for calculating the log
likelihood ratio of a motif, 3) for
calculating the significance (E-value) of a
motif, and, 4) for creating the
position-specific scoring matrix (log-odds
matrix). See application documentation for
more information.
-plibfile infile The name of the file containing the
Dirichlet prior in the format of file
prior30.plib
-mod selection [zoops] If you know how occurrences of
motifs are distributed in the training set
sequences, you can specify it with these
options. The default distribution of motif
occurrences is assumed to be zero or one
occurrence per sequence. oops : One
Occurrence Per Sequence. MEME assumes that
each sequence in the dataset contains
exactly one occurrence of each motif. This
option is the fastest and most sensitive but
the motifs returned by MEME may be 'blurry'
if any of the sequences is missing them.
zoops : Zero or One Occurrence Per Sequence.
MEME assumes that each sequence may contain
at most one occurrence of each motif. This
option is useful when you suspect that some
motifs may be missing from some of the
sequences. In that case, the motifs found
will be more accurate than using the first
option. This option takes more computer time
than the first option (about twice as much)
and is slightly less sensitive to weak
motifs present in all of the sequences. anr
: Any Number of Repetitions. MEME assumes
each sequence may contain any number of
non-overlapping occurrences of each motif.
This option is useful when you suspect that
motifs repeat multiple times within a single
sequence. In that case, the motifs found
will be much more accurate than using one of
the other options. This option can also be
used to discover repeats within a single
sequence. This option takes the much more
computer time than the first option (about
ten times as much) and is somewhat less
sensitive to weak motifs which do not repeat
within a single sequence than the other two
options.
-nmotifs integer [1] The number of *different* motifs to
search for. MEME will search for and output
Qualifier
Type
Description
Allowed values
Default
Standard (Mandatory) qualifiers
[-dataset]
(Parameter 1)seqset
User must provide the full filename of a set of sequences, not an indirect reference, e.g. a USA is NOT acceptable.
Readable set of sequences
Required
[-outdir]
(Parameter 2)outdir
MEME program output file output directory
Output directory
.
Additional (Optional) qualifiers
-bfile
infile
The name of the file containing the background model for sequences. The background model is the model of random sequences used by MEME. The background model is used by MEME 1) during EM as the 'null model', 2) for calculating the log likelihood ratio of a motif, 3) for calculating the significance (E-value) of a motif, and, 4) for creating the position-specific scoring matrix (log-odds matrix). See application documentation for more information.
Input file
Required
-plibfile
infile
The name of the file containing the Dirichlet prior in the format of file prior30.plib
Input file
Required
-mod
selection
If you know how occurrences of motifs are distributed in the training set sequences, you can specify it with these options. The default distribution of motif occurrences is assumed to be zero or one occurrence per sequence. oops : One Occurrence Per Sequence. MEME assumes that each sequence in the dataset contains exactly one occurrence of each motif. This option is the fastest and most sensitive but the motifs returned by MEME may be 'blurry' if any of the sequences is missing them. zoops : Zero or One Occurrence Per Sequence. MEME assumes that each sequence may contain at most one occurrence of each motif. This option is useful when you suspect that some motifs may be missing from some of the sequences. In that case, the motifs found will be more accurate than using the first option. This option takes more computer time than the first option (about twice as much) and is slightly less sensitive to weak motifs present in all of the sequences. anr : Any Number of Repetitions. MEME assumes each sequence may contain any number of non-overlapping occurrences of each motif. This option is useful when you suspect that motifs repeat multiple times within a single sequence. In that case, the motifs found will be much more accurate than using one of the other options. This option can also be used to discover repeats within a single sequence. This option takes the much more computer time than the first option (about ten times as much) and is somewhat less sensitive to weak motifs which do not repeat within a single sequence than the other two options.
Choose from selection list of values
zoops
-nmotifs
integer
The number of *different* motifs to search for. MEME will search for and output <n> motifs.
Any integer value
1
-text
boolean
Default output is in HTML
Boolean value Yes/No
No
-prior
selection
The prior distribution on the model parameters. dirichlet: Simple Dirichlet prior. This is the default for -dna and -alph. It is based on the non-redundant database letter frequencies. dmix: Mixture of Dirichlets prior. This is the default for -protein. mega: Extremely low variance dmix; variance is scaled inversely with the size of the dataset. megap: Mega for all but last iteration of EM; dmix on last iteration. addone: Add +1 to each observed count.
Choose from selection list of values
dirichlet
-evt
float
Quit looking for motifs if E-value exceeds this value. Has an extremely high default so by default MEME never quits before -nmotifs <n> have been found. A value of -1 here is a shorthand for infinity.
Any numeric value
-1
-nsites
integer
These switches are ignored if mod = oops. The (expected) number of occurrences of each motif. If a value for -nsites is specified, only that number of occurrences is tried. Otherwise, numbers of occurrences between -minsites and -maxsites are tried as initial guesses for the number of motif occurrences. If a value is not specified for -minsites and maxsites then the default hardcoded into MEME, as opposed to the default value given in the ACD file, is used. The hardcoded default value of -minsites is equal to sqrt(number sequences). The hardcoded default value of -maxsites is equal to the number of sequences (zoops) or MIN(5* num.sequences, 50) (anr). A value of -1 here represents nsites being unspecified.
Any integer value
-1
-minsites
integer
These switches are ignored if mod = oops. The (expected) number of occurrences of each motif. If a value for -nsites is specified, only that number of occurrences is tried. Otherwise, numbers of occurrences between -minsites and -maxsites are tried as initial guesses for the number of motif occurrences. If a value is not specified for -minsites and maxsites then the default hardcoded into MEME, as opposed to the default value given in the ACD file, is used. The hardcoded default value of -minsites is equal to sqrt(number sequences). The hardcoded default value of -maxsites is equal to the number of sequences (zoops) or MIN(5 * num.sequences, 50) (anr). A value of -1 here represents minsites being unspecified.
Any integer value
-1
-maxsites
integer
These switches are ignored if mod = oops. The (expected) number of occurrences of each motif. If a value for -nsites is specified, only that number of occurrences is tried. Otherwise, numbers of occurrences between -minsites and -maxsites are tried as initial guesses for the number of motif occurrences. If a value is not specified for -minsites and maxsites then the default hardcoded into MEME, as opposed to the default value given in the ACD file, is used. The hardcoded default value of -minsites is equal to sqrt(number sequences). The hardcoded default value of -maxsites is equal to the number of sequences (zoops) or MIN(5 * num.sequences, 50) (anr). A value of -1 here represents maxsites being unspecified.
Any integer value
-1
-wnsites
float
The weight of the prior on nsites. This controls how strong the bias towards motifs with exactly nsites sites (or between minsites and maxsites sites) is. It is a number in the range [0..1). The larger it is, the stronger the bias towards motifs with exactly nsites occurrences is.
Any numeric value
0.8
-w
integer
The width of the motif(s) to search for. If -w is given, only that width is tried. Otherwise, widths between -minw and -maxw are tried. Note: if width is less than the length of the shortest sequence in the dataset, width is reset by MEME to that value. A value of -1 here represents -w being unspecified.
Any integer value
-1
-minw
integer
The width of the motif(s) to search for. If -w is given, only that width is tried. Otherwise, widths between -minw and -maxw are tried. Note: if width is less than the length of the shortest sequence in the dataset, width is reset by MEME to that value.
Any integer value
8
-maxw
integer
The width of the motif(s) to search for. If -w is given, only that width is tried. Otherwise, widths between -minw and -maxw are tried. Note: if width is less than the length of the shortest sequence in the dataset, width is reset by MEME to that value.
Any integer value
50
-nomatrim
boolean
The -nomatrim, -wg, -ws and -noendgaps switches control trimming (shortening) of motifs using the multiple alignment method. Specifying -nomatrim causes MEME to skip this and causes the other switches to be ignored. The pairwise alignment is controlled by the switches -wg (gap cost), -ws (space cost) and -noendgaps (do not penalize endgaps). See application documentation for further information.
Boolean value Yes/No
No
-wg
integer
The -nomatrim, -wg, -ws and -noendgaps switches control trimming (shortening) of motifs using the multiple alignment method. Specifying -nomatrim causes MEME to skip this and causes the other switches to be ignored. The pairwise alignment is controlled by the switches -wg (gap cost), -ws (space cost) and -noendgaps (do not penalize endgaps). See application documentation for further information.
Any integer value
11
-ws
integer
The -nomatrim, -wg, -ws and -noendgaps switches control trimming (shortening) of motifs using the multiple alignment method. Specifying -nomatrim causes MEME to skip this and causes the other switches to be ignored. The pairwise alignment is controlled by the switches -wg (gap cost), -ws (space cost) and -noendgaps (do not penalize endgaps). See application documentation for further information.
Any integer value
1
-noendgaps
boolean
The -nomatrim, -wg, -ws and -noendgaps switches control trimming (shortening) of motifs using the multiple alignment method. Specifying -nomatrim causes MEME to skip this and causes the other switches to be ignored. The pairwise alignment is controlled by the switches -wg (gap cost), -ws (space cost) and -noendgaps (do not penalise endgaps). See application documentation for further information.
Boolean value Yes/No
No
-revcomp
boolean
Motif occurrences may be on the given DNA strand or on its reverse complement. The default is to look for DNA motifs only on the strand given in the training set.
Boolean value Yes/No
No
-pal
boolean
Choosing -pal causes MEME to look for palindromes in DNA datasets. MEME averages the letter frequencies in corresponding columns of the motif (PSPM) together. For instance, if the width of the motif is 10, columns 1 and 10, 2 and 9, 3 and 8, etc., are averaged together. The averaging combines the frequency of A in one column with T in the other, and the frequency of C in one column with G in the other.
Boolean value Yes/No
No
-[no]nostatus
boolean
Set this option to prevent progress reports to the terminal.
Boolean value Yes/No
Yes
Advanced (Unprompted) qualifiers
-maxiter
integer
The number of iterations of EM to run from any starting point. EM is run for <n> iterations or until convergence (see -distance, below) from each starting point.
Any integer value
50
-distance
float
The convergence criterion. MEME stops iterating EM when the change in the motif frequency matrix is less than <a>. (Change is the euclidean distance between two successive frequency matrices.)
Any numeric value
0.001
-b
float
The strength of the prior on model parameters. A value of 0 means use intrinsic strength of prior if prior = dmix. The default values are 0.01 if prior = dirichlet or 0 if prior = dmix. These defaults are hardcoded into MEME (the value of the default in the ACD file is not used). A value of -1 here represents -b being unspecified.
Any numeric value
-1.0
-spfuzz
float
The fuzziness of the mapping. Possible values are greater than 0. Meaning depends on -spmap, see below. See the application documentation for more information. A value of -1.0 here represents -spfuzz being unspecified.
Any numeric value
-1.0
-spmap
selection
The type of mapping function to use. uni: Use prior when converting a substring to an estimate of theta. Default -spfuzz <a>: 0.5. pam: Use columns of PAM <a> matrix when converting a substring to an estimate of theta. Default -spfuzz <a>: 120 (PAM 120). See the application documentation for more information.
Choose from selection list of values
default
-cons
string
Override the sampling of starting points and just use a starting point derived from <string>. This is useful when an actual occurrence of a motif is known and can be used as the starting point for finding the motif. See the application documentation for more information.
Any string
-maxsize
integer
Maximum dataset size in characters (-1 = use meme default).
Any integer value
-1
-p
integer
Only values of >0 will be applied. The -p <np> argument causes a version of MEME compiled for a parallel CPU architecture to be run. (By placing <np> in quotes you may pass installation specific switches to the 'mpirun' command. The number of processors to run on must be the first argument following -p).
Any integer value
0
-time
integer
Only values of more than 0 will be applied.
Any integer value
0
-sf
string
Print <sf> as name of sequence file
Any string
-heapsize
integer
The search for good EM starting points can be improved by using a branching search. A branching search begins with a fixed-size heap of best EM starts identified during the search of subsequences from the dataset. These starts are also called seeds. The fixed-size heap of seeds is used as the branch-heap during the first iteration of branching search. See the application documentation for more information.
Any integer value
64
-xbranch
boolean
The search for good EM starting points can be improved by using a branching search. A branching search begins with a fixed-size heap of best EM starts identified during the search of subsequences from the dataset. These starts are also called seeds. The fixed-size heap of seeds is used as the branch-heap during the first iteration of branching search. See the application documentation for more information.
Boolean value Yes/No
No
-wbranch
boolean
The search for good EM starting points can be improved by using a branching search. A branching search begins with a fixed-size heap of best EM starts identified during the search of subsequences from the dataset. These starts are also called seeds. The fixed-size heap of seeds is used as the branch-heap during the first iteration of branching search. See the application documentation for more information.
Boolean value Yes/No
No
-bfactor
integer
The search for good EM starting points can be improved by using a branching search. A branching search begins with a fixed-size heap of best EM starts identified during the search of subsequences from the dataset. These starts are also called seeds. The fixed-size heap of seeds is used as the branch-heap during the first iteration of branching search. See the application documentation for more information.
Any integer value
3
Associated qualifiers
"-dataset" associated seqset qualifiers
-sbegin1
-sbegin_datasetinteger
Start of each sequence to be used
Any integer value
0
-send1
-send_datasetinteger
End of each sequence to be used
Any integer value
0
-sreverse1
-sreverse_datasetboolean
Reverse (if DNA)
Boolean value Yes/No
N
-sask1
-sask_datasetboolean
Ask for begin/end/reverse
Boolean value Yes/No
N
-snucleotide1
-snucleotide_datasetboolean
Sequence is nucleotide
Boolean value Yes/No
N
-sprotein1
-sprotein_datasetboolean
Sequence is protein
Boolean value Yes/No
N
-slower1
-slower_datasetboolean
Make lower case
Boolean value Yes/No
N
-supper1
-supper_datasetboolean
Make upper case
Boolean value Yes/No
N
-sformat1
-sformat_datasetstring
Input sequence format
Any string
-sdbname1
-sdbname_datasetstring
Database name
Any string
-sid1
-sid_datasetstring
Entryname
Any string
-ufo1
-ufo_datasetstring
UFO features
Any string
-fformat1
-fformat_datasetstring
Features format
Any string
-fopenfile1
-fopenfile_datasetstring
Features file name
Any string
"-outdir" associated outdir qualifiers
-extension2
-extension_outdirstring
Default file extension
Any string
General qualifiers
-auto
boolean
Turn off prompts
Boolean value Yes/No
N
-stdout
boolean
Write first file to standard output
Boolean value Yes/No
N
-filter
boolean
Read first file from standard input, write first file to standard output
Boolean value Yes/No
N
-options
boolean
Prompt for standard and additional values
Boolean value Yes/No
N
-debug
boolean
Write debug output to program.dbg
Boolean value Yes/No
N
-verbose
boolean
Report some/full command line options
Boolean value Yes/No
Y
-help
boolean
Report command line options and exit. More information on associated and general qualifiers can be found with -help -verbose
Boolean value Yes/No
N
-warning
boolean
Report warnings
Boolean value Yes/No
Y
-error
boolean
Report errors
Boolean value Yes/No
Y
-fatal
boolean
Report fatal errors
Boolean value Yes/No
Y
-die
boolean
Report dying program messages
Boolean value Yes/No
Y
-version
boolean
Report version number and exit
Boolean value Yes/No
N
Input file format
Sequence formats
The original MEME only supported input sequences in FASTA format. EMBASSY MEME supports all EMBOSS-supported sequence formats.
meme reads any normal sequence USAs.
Input files for usage example
File: crp0.s
>ce1cg
TAATGTTTGTGCTGGTTTTTGTGGCATCGGGCGAGAATAGCGCGTGGTGTGAAAGACTGTTTTTTTGATCGTTTTCACAA
AAATGGAAGTCCACAGTCTTGACAG
>ara
GACAAAAACGCGTAACAAAAGTGTCTATAATCACGGCAGAAAAGTCCACATTGATTATTTGCACGGCGTCACACTTTGCT
ATGCCATAGCATTTTTATCCATAAG
>bglr1
ACAAATCCCAATAACTTAATTATTGGGATTTGTTATATATAACTTTATAAATTCCTAAAATTACACAAAGTTAATAACTG
TGAGCATGGTCATATTTTTATCAAT
>crp
CACAAAGCGAAAGCTATGCTAAAACAGTCAGGATGCTACAGTAATACATTGATGTACTGCATGTATGCAAAGGACGTCAC
ATTACCGTGCAGTACAGTTGATAGC
>cya
ACGGTGCTACACTTGTATGTAGCGCATCTTTCTTTACGGTCAATCAGCAAGGTGTTAAATTGATCACGTTTTAGACCATT
TTTTCGTCGTGAAACTAAAAAAACC
>deop2
AGTGAATTATTTGAACCAGATCGCATTACAGTGATGCAAACTTGTAAGTAGATTTCCTTAATTGTGATGTGTATCGAAGT
GTGTTGCGGAGTAGATGTTAGAATA
>gale
GCGCATAAAAAACGGCTAAATTCTTGTGTAAACGATTCCACTAATTTATTCCATGTCACACTTTTCGCATCTTTGTTATG
CTATGGTTATTTCATACCATAAGCC
>ilv
GCTCCGGCGGGGTTTTTTGTTATCTGCAATTCAGTACAAAACGTGATCAACCCCTCAATTTTCCCTTTGCTGAAAAATTT
TCCATTGTCTCCCCTGTAAAGCTGT
>lac
AACGCAATTAATGTGAGTTAGCTCACTCATTAGGCACCCCAGGCTTTACACTTTATGCTTCCGGCTCGTATGTTGTGTGG
AATTGTGAGCGGATAACAATTTCAC
>male
ACATTACCGCCAATTCTGTAACAGAGATCACACAAAGCGACGGTGGGGCGTAGGGGCAAGGAGGATGGAAAGAGGTTGCC
GTATAAAGAAACTAGAGTCCGTTTA
>malk
GGAGGAGGCGGGAGGATGAGAACACGGCTTCTGTGAACTAAACCGAGGTCATGTAAGGAATTTCGTGATGTTGCTTGCAA
AAATCGTGGCGATTTTATGTGCGCA
>malt
GATCAGCGTCGTTTTAGGTGAGTTGTTAATAAAGATTTGGAATTGTGACACAGTGCAAATTCAGACACATAAAAAAACGT
CATCGCTTGCATTAGAAAGGTTTCT
>ompa
GCTGACAAAAAAGATTAAACATACCTTATACAAGACTTTTTTTTCATATGCCTGACGGAGTTCACACTTGTAAGTTTTCA
ACTACGTTGTAGACTTTACATCGCC
>tnaa
TTTTTTAAACATTAAAATTCTTACGTAATTTATAATCTTTAAAAAAAGCATTTAATATTGCTCCCCGAACGATTGTGATT
CGATTCACATTTAAACAATTTCAGA
>uxu1
CCCATGAGAGTGAAATTGTTGTGATGTGGTTAACCCAATTAGAATTCGGGATTGACATGTCTTACCAAAAGGTAGAACTT
ATACGCCATCTCATCCGATGCAAGC
>pbr322
CTGGCTTAACTATGCGGCATCAGAGCAGATTGTACTGAGAGTGCACCATATGCGGTGTGAAATACCGCACAGATGCGTAA
GGAGAAAATACCGCATCAGGCGCTC
>trn9cat
CTGTGACGGAAGATCACTTCGCAGAATAAATAAATCCTGGTGTCCCTGTTGATACCGGGAAGCCCTGGGCCAACTTTTGG
CGAAAATGAGACGTTGATCGGCACG
>tdc
GATTTTTATACTTTAACTTGTTGATATTTAAAGGTATTTAATTGTAATAACGATACTCTGGAAAGTATTGAAAGTTAATT
TGTGAGTGGTCGCACATATCCTGTT
Output file format
Output files for usage example
Graphics File: help.gif
![[ememe results]](ememe.1.help.gif)
Graphics File: logo1.eps
![[ememe results]](ememe.1.logo1.e.gif){kind=logo}
Graphics File: logo1.png
![[ememe results]](ememe.1.logo1.png){kind=logo}
Graphics File: logo_rc1.eps
![[ememe results]](ememe.1.logo_rc1.e.gif){kind=logo}
Graphics File: logo_rc1.png
![[ememe results]](ememe.1.logo_rc1.png){kind=logo}
File: meme.html
<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>MEME</title>
<style type="text/css">
/* The following is the content of meme.css */
body { background-color:white; font-size: 12px; font-family: Verdana, Arial, Helvetica, sans-serif;}
div.help {
display:inline-block;
margin:0px;
padding:0px;
width:12px;
height:13px;
background-image: url("help.gif");
background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAANAQMAAACn5x0BAAAAAXNSR0IArs4c6QAAAAZQTFRFAAAAnp6eqp814gAAAAF0Uk5TAEDm2GYAAAABYktHRACIBR1IAAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH2gMJBQgGYqhNZQAAACZJREFUCNdj+P+BoUGAoV+AYeYEEGoWYGgTYGgRAAm2gRGQ8f8DAOnhC2lYnqs6AAAAAElFTkSuQmCC");
}
p.spaced { line-height: 1.8em;}
p.pad { padding-left: 30px; padding-top: 5px; padding-bottom: 10px;}
td.jump { font-size: 13px; color: #ffffff; background-color: #00666a;
font-family: Georgia, "Times New Roman", Times, serif;}
a.jump { margin: 15px 0 0; font-style: normal; font-variant: small-caps;
font-weight: bolder; font-family: Georgia, "Times New Roman", Times, serif;}
h2.mainh {font-size: 1.5em; font-style: normal; margin: 15px 0 0;
font-variant: small-caps; font-family: Georgia, "Times New Roman", Times, serif;}
h2.line {border-bottom: 1px solid #CCCCCC; font-size: 1.5em; font-style: normal;
margin: 15px 0 0; padding-bottom: 3px; font-variant: small-caps;
font-family: Georgia, "Times New Roman", Times, serif;}
h4 {border-bottom: 1px solid #CCCCCC; font-size: 1.2em; font-style: normal;
margin: 10px 0 0; padding-bottom: 3px; font-family: Georgia, "Times New Roman", Times, serif;}
h5 {margin: 0px}
a.help { font-size: 9px; font-style: normal; text-transform: uppercase;
font-family: Georgia, "Times New Roman", Times, serif;}
div.pad { padding-left: 30px; padding-top: 5px; padding-bottom: 10px;}
div.pad1 { margin: 10px 5px;}
div.pad2 { margin: 25px 5px 5px;}
h2.pad2 { padding: 25px 5px 5px;}
[Part of this file has been deleted for brevity]
For use with <a href="http://blocks.fhcrc.org/blocks">BLOCKS tools</a>.
</dd>
<dt>
<a name="format_FASTA_doc"></a>FASTA Format</dt>
<dd>
The FASTA format as described <a href="http://meme.nbcr.net/meme/doc/fasta-format.html">here</a>.
</dd>
<dt>
<a name="format_raw_doc"></a>Raw Format</dt>
<dd>
Just the sites of the sequences that contributed to the motif. One site per line.
</dd>
</dl>
</div>
<a name="sites_doc"></a><h5 class="doc">Sites</h5>
<div class="doc"><p>
MEME displays the occurrences (sites) of the motif in the training set. The sites are shown aligned with each other, and the ten sequence
positions preceding and following each site are also shown. Each site is identified by the name of the sequence where it occurs,
the strand (if both strands of DNA sequences are being used), and the position in the sequence where the site begins. When the DNA strand
is specified, '+' means the sequence in the training set, and '-' means the reverse complement of the training set sequence.
(For '-' strands, the 'start' position is actually the position on the <b>positive</b> strand where the site ends.) The sites are <b>listed
in order of increasing statistical significance</b> (<i>p</i>-value). The <i>p</i>-value of a site is computed from the the match score of
the site with the <a href="#format_PSSM_doc">position specific scoring matrix</a> for the motif. The <i>p</i>-value gives the probability of a
random string (generated from the background letter frequencies) having the same match score or higher. (This is referred to as the
<b>position <i>p</i>-value</b> by the MAST algorithm.)
</p></div>
<a name="diagrams_doc"></a><h5 class="doc">Block Diagrams</h5>
<div class="doc"><p>
The occurrences of the motif in the training set sequences are shown as coloured blocks on a line. One diagram is printed for each
sequence showing all the sites contributating to that motif in that sequence. The sequences are <b>listed in the same order as in the input</b>
to make it easier to compare multiple block diagrams. Additionally the best <i>p</i>-value for the sequence/motif combination is
listed though this may not be in ascending order as with the sites. The <i>p</i>-value of an occurrence is the probability of a single
random subsequence the length of the motif, generated according to the 0-order background model, having a score at least as high as
the score of the occurrence. When the DNA strand is specified '+', it means the motif appears from left to right on the sequence, and '-'
means the motif appears from right to left on the complementary strand. A sequence position scale is shown at the end of each table of
block diagrams.
</p></div>
<a name="combined_doc"></a><h5>Combined Block Diagrams</h5>
<div class="doc">
<p>
The motif occurrences shown in the motif summary <b>may not be exactly the same as those reported in each motif section</b> because
only motifs with a position <em>p</em>-value of 0.0001 that don't overlap other, more significant motif occurrences are shown.
</p>
<p>
See the documentation for <a href="http://meme.nbcr.net/meme/mast-output.html">MAST output</a> for the definition of position and
combined <em>p</em>-values.
</p>
</div>
</div></span><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
</form></body>
</html>
File: meme.fasta
>ce1cg
TAATGTTTGTGCTGGTTTTTGTGGCATCGGGCGAGAATAGCGCGTGGTGTGAAAGACTGT
TTTTTTGATCGTTTTCACAAAAATGGAAGTCCACAGTCTTGACAG
>ara
GACAAAAACGCGTAACAAAAGTGTCTATAATCACGGCAGAAAAGTCCACATTGATTATTT
GCACGGCGTCACACTTTGCTATGCCATAGCATTTTTATCCATAAG
>bglr1
ACAAATCCCAATAACTTAATTATTGGGATTTGTTATATATAACTTTATAAATTCCTAAAA
TTACACAAAGTTAATAACTGTGAGCATGGTCATATTTTTATCAAT
>crp
CACAAAGCGAAAGCTATGCTAAAACAGTCAGGATGCTACAGTAATACATTGATGTACTGC
ATGTATGCAAAGGACGTCACATTACCGTGCAGTACAGTTGATAGC
>cya
ACGGTGCTACACTTGTATGTAGCGCATCTTTCTTTACGGTCAATCAGCAAGGTGTTAAAT
TGATCACGTTTTAGACCATTTTTTCGTCGTGAAACTAAAAAAACC
>deop2
AGTGAATTATTTGAACCAGATCGCATTACAGTGATGCAAACTTGTAAGTAGATTTCCTTA
ATTGTGATGTGTATCGAAGTGTGTTGCGGAGTAGATGTTAGAATA
>gale
GCGCATAAAAAACGGCTAAATTCTTGTGTAAACGATTCCACTAATTTATTCCATGTCACA
CTTTTCGCATCTTTGTTATGCTATGGTTATTTCATACCATAAGCC
>ilv
GCTCCGGCGGGGTTTTTTGTTATCTGCAATTCAGTACAAAACGTGATCAACCCCTCAATT
TTCCCTTTGCTGAAAAATTTTCCATTGTCTCCCCTGTAAAGCTGT
>lac
AACGCAATTAATGTGAGTTAGCTCACTCATTAGGCACCCCAGGCTTTACACTTTATGCTT
CCGGCTCGTATGTTGTGTGGAATTGTGAGCGGATAACAATTTCAC
>male
ACATTACCGCCAATTCTGTAACAGAGATCACACAAAGCGACGGTGGGGCGTAGGGGCAAG
GAGGATGGAAAGAGGTTGCCGTATAAAGAAACTAGAGTCCGTTTA
>malk
GGAGGAGGCGGGAGGATGAGAACACGGCTTCTGTGAACTAAACCGAGGTCATGTAAGGAA
TTTCGTGATGTTGCTTGCAAAAATCGTGGCGATTTTATGTGCGCA
>malt
GATCAGCGTCGTTTTAGGTGAGTTGTTAATAAAGATTTGGAATTGTGACACAGTGCAAAT
TCAGACACATAAAAAAACGTCATCGCTTGCATTAGAAAGGTTTCT
>ompa
GCTGACAAAAAAGATTAAACATACCTTATACAAGACTTTTTTTTCATATGCCTGACGGAG
TTCACACTTGTAAGTTTTCAACTACGTTGTAGACTTTACATCGCC
>tnaa
TTTTTTAAACATTAAAATTCTTACGTAATTTATAATCTTTAAAAAAAGCATTTAATATTG
CTCCCCGAACGATTGTGATTCGATTCACATTTAAACAATTTCAGA
>uxu1
CCCATGAGAGTGAAATTGTTGTGATGTGGTTAACCCAATTAGAATTCGGGATTGACATGT
CTTACCAAAAGGTAGAACTTATACGCCATCTCATCCGATGCAAGC
>pbr322
CTGGCTTAACTATGCGGCATCAGAGCAGATTGTACTGAGAGTGCACCATATGCGGTGTGA
AATACCGCACAGATGCGTAAGGAGAAAATACCGCATCAGGCGCTC
>trn9cat
CTGTGACGGAAGATCACTTCGCAGAATAAATAAATCCTGGTGTCCCTGTTGATACCGGGA
AGCCCTGGGCCAACTTTTGGCGAAAATGAGACGTTGATCGGCACG
>tdc
GATTTTTATACTTTAACTTGTTGATATTTAAAGGTATTTAATTGTAATAACGATACTCTG
GAAAGTATTGAAAGTTAATTTGTGAGTGGTCGCACATATCCTGTT
File: meme.txt
********************************************************************************
MEME - Motif discovery tool
********************************************************************************
MEME version 4.4.0 (Release date: Tue Apr 27 10:09:30 EST 2010)
For further information on how to interpret these results or to get
a copy of the MEME software please access http://meme.nbcr.net.
This file may be used as input to the MAST algorithm for searching
sequence databases for matches to groups of motifs. MAST is available
for interactive use and downloading at http://meme.nbcr.net.
********************************************************************************
********************************************************************************
REFERENCE
********************************************************************************
If you use this program in your research, please cite:
Timothy L. Bailey and Charles Elkan,
"Fitting a mixture model by expectation maximization to discover
motifs in biopolymers", Proceedings of the Second International
Conference on Intelligent Systems for Molecular Biology, pp. 28-36,
AAAI Press, Menlo Park, California, 1994.
********************************************************************************
********************************************************************************
TRAINING SET
********************************************************************************
DATAFILE= ./meme.fasta
ALPHABET= ACGT
Sequence name Weight Length Sequence name Weight Length
------------- ------ ------ ------------- ------ ------
ce1cg 1.0000 105 ara 1.0000 105
bglr1 1.0000 105 crp 1.0000 105
cya 1.0000 105 deop2 1.0000 105
gale 1.0000 105 ilv 1.0000 105
lac 1.0000 105 male 1.0000 105
malk 1.0000 105 malt 1.0000 105
ompa 1.0000 105 tnaa 1.0000 105
uxu1 1.0000 105 pbr322 1.0000 105
trn9cat 1.0000 105 tdc 1.0000 105
********************************************************************************
********************************************************************************
COMMAND LINE SUMMARY
********************************************************************************
This information can also be useful in the event you wish to report a
problem with the MEME software.
[Part of this file has been deleted for brevity]
--------------------------------------------------------------------------------
TGTGA[ACT][CAG][GT][AGT][GC][TAC]TCAC
--------------------------------------------------------------------------------
Time 0.29 secs.
********************************************************************************
********************************************************************************
SUMMARY OF MOTIFS
********************************************************************************
--------------------------------------------------------------------------------
Combined block diagrams: non-overlapping sites with p-value < 0.0001
--------------------------------------------------------------------------------
SEQUENCE NAME COMBINED P-VALUE MOTIF DIAGRAM
------------- ---------------- -------------
ce1cg 1.74e-03 63_[1(1.91e-05)]_27
ara 4.00e-03 57_[1(4.41e-05)]_33
bglr1 7.85e-03 78_[1(8.66e-05)]_12
crp 4.37e-03 65_[1(4.81e-05)]_25
cya 3.66e-03 52_[1(4.03e-05)]_38
deop2 5.47e-04 9_[1(6.01e-06)]_81
gale 9.45e-04 26_[1(1.04e-05)]_64
ilv 2.54e-02 105
lac 5.39e-05 11_[1(5.92e-07)]_79
male 2.12e-04 16_[1(2.33e-06)]_74
malk 1.35e-02 105
malt 2.55e-03 43_[1(2.80e-05)]_47
ompa 1.42e-03 50_[1(1.57e-05)]_40
tnaa 2.11e-03 73_[1(2.32e-05)]_17
uxu1 3.35e-03 19_[1(3.69e-05)]_71
pbr322 2.12e-04 55_[1(2.33e-06)]_35
trn9cat 5.08e-02 105
tdc 1.57e-03 80_[1(1.73e-05)]_10
--------------------------------------------------------------------------------
********************************************************************************
********************************************************************************
Stopped because nmotifs = 1 reached.
********************************************************************************
CPU: emboss4.ebi.ac.uk
********************************************************************************
File: meme.xml
<?xml version='1.0' encoding='UTF-8' standalone='yes'?>
<!-- Document definition -->
<!DOCTYPE MEME[
<!ELEMENT MEME (
training_set,
model,
motifs,
scanned_sites_summary?
)>
<!ATTLIST MEME
version CDATA #REQUIRED
release CDATA #REQUIRED
>
<!-- Training-set elements -->
<!ELEMENT training_set (alphabet, ambigs, sequence+, letter_frequencies)>
<!ATTLIST training_set datafile CDATA #REQUIRED length CDATA #REQUIRED>
<!ELEMENT alphabet (letter+)>
<!ATTLIST alphabet id (amino-acid|nucleotide) #REQUIRED
length CDATA #REQUIRED>
<!ELEMENT ambigs (letter+)>
<!ELEMENT letter EMPTY>
<!ATTLIST letter id ID #REQUIRED>
<!ATTLIST letter symbol CDATA #REQUIRED>
<!ELEMENT sequence EMPTY>
<!ATTLIST sequence id ID #REQUIRED
name CDATA #REQUIRED
length CDATA #REQUIRED
weight CDATA #REQUIRED
>
<!ELEMENT letter_frequencies (alphabet_array)>
<!-- Model elements -->
<!ELEMENT model (
command_line,
host,
type,
nmotifs,
evalue_threshold,
object_function,
min_width,
max_width,
minic,
wg,
ws,
endgaps,
minsites,
maxsites,
wnsites,
prob,
spmap,
[Part of this file has been deleted for brevity]
<letter_ref letter_id="letter_G"/>
<letter_ref letter_id="letter_T"/>
<letter_ref letter_id="letter_T"/>
<letter_ref letter_id="letter_G"/>
<letter_ref letter_id="letter_A"/>
<letter_ref letter_id="letter_T"/>
<letter_ref letter_id="letter_C"/>
<letter_ref letter_id="letter_G"/>
<letter_ref letter_id="letter_G"/>
</site>
<right_flank>CACG</right_flank>
</contributing_site>
</contributing_sites>
</motif>
</motifs>
<scanned_sites_summary p_thresh="0.0001">
<scanned_sites sequence_id="sequence_0" pvalue="1.74e-03" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="63" pvalue="1.91e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_1" pvalue="4.00e-03" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="57" pvalue="4.41e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_2" pvalue="7.85e-03" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="78" pvalue="8.66e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_3" pvalue="4.37e-03" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="65" pvalue="4.81e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_4" pvalue="3.66e-03" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="52" pvalue="4.03e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_5" pvalue="5.47e-04" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="9" pvalue="6.01e-06"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_6" pvalue="9.45e-04" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="26" pvalue="1.04e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_7" pvalue="2.54e-02" num_sites="0"></scanned_sites>
<scanned_sites sequence_id="sequence_8" pvalue="5.39e-05" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="11" pvalue="5.92e-07"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_9" pvalue="2.12e-04" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="16" pvalue="2.33e-06"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_10" pvalue="1.35e-02" num_sites="0"></scanned_sites>
<scanned_sites sequence_id="sequence_11" pvalue="2.55e-03" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="43" pvalue="2.80e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_12" pvalue="1.42e-03" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="50" pvalue="1.57e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_13" pvalue="2.11e-03" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="73" pvalue="2.32e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_14" pvalue="3.35e-03" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="19" pvalue="3.69e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_15" pvalue="2.12e-04" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="55" pvalue="2.33e-06"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_16" pvalue="5.08e-02" num_sites="0"></scanned_sites>
<scanned_sites sequence_id="sequence_17" pvalue="1.57e-03" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="80" pvalue="1.73e-05"/>
</scanned_sites>
</scanned_sites_summary>
</MEME>
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 MEMENEW package contains "wrapper" applications providing an EMBOSS-style interface to the applications in the original MEME package version 4.4.0 developed by Timothy L. Bailey. Please read the file README in the EMBASSY MEMENEW package distribution for installation instructions.
3. Installing original MEME
To use EMBASSY MEMENEW, you will first need to download and install the original MEME package:
WWW home: http://meme.sdsc.edu/meme/
Distribution: http://meme.nbcr.net/downloads/old_versions/
Please read the file README in the the original MEMENEW package distribution for installation instructions.
4. Setting up MEME
For the EMBASSY MEMENEW package to work, the directory containing the original MEME executables *must* be in your path. For example if you executables were installed to "/usr/local/meme/bin", then type:
set path=(/usr/local/meme/bin/ $path)
rehash
5. Getting help
Once you have installed the original MEME, type
meme > meme.txt
mast > mast.txt
to retrieve the meme and mast documentation into text files. The same documentation is given here and in the ememe documentation.
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
ememetext
Multiple EM for Motif Elicitation. Text file only
enetnglyc
Reports N-glycosylation sites in human proteins
enetoglyc
Reports mucin type GalNAc O-glycosylation sites in mammalian proteins
enetphos
Reports ser, thr and tyr phosphorylation sites in eukaryotic proteins
epestfind
Finds PEST motifs as potential proteolytic cleavage sites
eprop
Reports propeptide cleavage sites in proteins
esignalp
Reports protein signal cleavage sites
etmhmm
Reports transmembrane helices
eyinoyang
Reports O-(beta)-GlcNAc attachment sites
fuzzpro
Search for patterns in protein sequences
fuzztran
Search for patterns in protein sequences (translated)
helixturnhelix
Identify nucleic acid-binding motifs in protein sequences
oddcomp
Identify proteins with specified sequence word composition
omeme
Motif detection
patmatdb
Searches protein sequences with a sequence motif
patmatmotifs
Scan a protein sequence with motifs from the PROSITE database
pepcoil
Predicts coiled coil regions in protein sequences
preg
Regular expression search of protein sequence(s)
pscan
Scans protein sequence(s) with fingerprints from the PRINTS database
sigcleave
Reports on signal cleavage sites in a protein sequence
Author(s)
Jon Ison
European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
History
None.
Target users
This program is intended to be used by everyone and everything, from naive users to embedded scripts.