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

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

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

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

Usage

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

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

EXAMPLES:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Command line arguments

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

Multiple EM for Motif Elicitation Version: EMBOSS:6.4.0.0 Standard (Mandatory) qualifiers: [-dataset] seqset User must provide the full filename of a set of sequences, not an indirect reference, e.g. a USA is NOT acceptable. [-outdir] outdir [.] MEME program output file output directory Additional (Optional) qualifiers: -bfile infile The name of the file containing the background model for sequences. The background model is the model of random sequences used by MEME. The background model is used by MEME 1) during EM as the 'null model', 2) for calculating the log likelihood ratio of a motif, 3) for calculating the significance (E-value) of a motif, and, 4) for creating the position-specific scoring matrix (log-odds matrix). See application documentation for more information. -plibfile infile The name of the file containing the Dirichlet prior in the format of file prior30.plib -mod selection [zoops] If you know how occurrences of motifs are distributed in the training set sequences, you can specify it with these options. The default distribution of motif occurrences is assumed to be zero or one occurrence per sequence. oops : One Occurrence Per Sequence. MEME assumes that each sequence in the dataset contains exactly one occurrence of each motif. This option is the fastest and most sensitive but the motifs returned by MEME may be 'blurry' if any of the sequences is missing them. zoops : Zero or One Occurrence Per Sequence. MEME assumes that each sequence may contain at most one occurrence of each motif. This option is useful when you suspect that some motifs may be missing from some of the sequences. In that case, the motifs found will be more accurate than using the first option. This option takes more computer time than the first option (about twice as much) and is slightly less sensitive to weak motifs present in all of the sequences. anr : Any Number of Repetitions. MEME assumes each sequence may contain any number of non-overlapping occurrences of each motif. This option is useful when you suspect that motifs repeat multiple times within a single sequence. In that case, the motifs found will be much more accurate than using one of the other options. This option can also be used to discover repeats within a single sequence. This option takes the much more computer time than the first option (about ten times as much) and is somewhat less sensitive to weak motifs which do not repeat within a single sequence than the other two options. -nmotifs integer [1] The number of *different* motifs to search for. MEME will search for and output motifs. (Any integer value) -text boolean [N] Default output is in HTML -prior selection [dirichlet] The prior distribution on the model parameters. dirichlet: Simple Dirichlet prior. This is the default for -dna and -alph. It is based on the non-redundant database letter frequencies. dmix: Mixture of Dirichlets prior. This is the default for -protein. mega: Extremely low variance dmix; variance is scaled inversely with the size of the dataset. megap: Mega for all but last iteration of EM; dmix on last iteration. addone: Add +1 to each observed count. -evt float [-1] Quit looking for motifs if E-value exceeds this value. Has an extremely high default so by default MEME never quits before -nmotifs have been found. A value of -1 here is a shorthand for infinity. (Any numeric value) -nsites integer [-1] These switches are ignored if mod = oops. The (expected) number of occurrences of each motif. If a value for -nsites is specified, only that number of occurrences is tried. Otherwise, numbers of occurrences between -minsites and -maxsites are tried as initial guesses for the number of motif occurrences. If a value is not specified for -minsites and maxsites then the default hardcoded into MEME, as opposed to the default value given in the ACD file, is used. The hardcoded default value of -minsites is equal to sqrt(number sequences). The hardcoded default value of -maxsites is equal to the number of sequences (zoops) or MIN(5* num.sequences, 50) (anr). A value of -1 here represents nsites being unspecified. (Any integer value) -minsites integer [-1] These switches are ignored if mod = oops. The (expected) number of occurrences of each motif. If a value for -nsites is specified, only that number of occurrences is tried. Otherwise, numbers of occurrences between -minsites and -maxsites are tried as initial guesses for the number of motif occurrences. If a value is not specified for -minsites and maxsites then the default hardcoded into MEME, as opposed to the default value given in the ACD file, is used. The hardcoded default value of -minsites is equal to sqrt(number sequences). The hardcoded default value of -maxsites is equal to the number of sequences (zoops) or MIN(5 * num.sequences, 50) (anr). A value of -1 here represents minsites being unspecified. (Any integer value) -maxsites integer [-1] These switches are ignored if mod = oops. The (expected) number of occurrences of each motif. If a value for -nsites is specified, only that number of occurrences is tried. Otherwise, numbers of occurrences between -minsites and -maxsites are tried as initial guesses for the number of motif occurrences. If a value is not specified for -minsites and maxsites then the default hardcoded into MEME, as opposed to the default value given in the ACD file, is used. The hardcoded default value of -minsites is equal to sqrt(number sequences). The hardcoded default value of -maxsites is equal to the number of sequences (zoops) or MIN(5 * num.sequences, 50) (anr). A value of -1 here represents maxsites being unspecified. (Any integer value) -wnsites float [0.8] The weight of the prior on nsites. This controls how strong the bias towards motifs with exactly nsites sites (or between minsites and maxsites sites) is. It is a number in the range [0..1). The larger it is, the stronger the bias towards motifs with exactly nsites occurrences is. (Any numeric value) -w integer [-1] The width of the motif(s) to search for. If -w is given, only that width is tried. Otherwise, widths between -minw and -maxw are tried. Note: if width is less than the length of the shortest sequence in the dataset, width is reset by MEME to that value. A value of -1 here represents -w being unspecified. (Any integer value) -minw integer [8] The width of the motif(s) to search for. If -w is given, only that width is tried. Otherwise, widths between -minw and -maxw are tried. Note: if width is less than the length of the shortest sequence in the dataset, width is reset by MEME to that value. (Any integer value) -maxw integer [50] The width of the motif(s) to search for. If -w is given, only that width is tried. Otherwise, widths between -minw and -maxw are tried. Note: if width is less than the length of the shortest sequence in the dataset, width is reset by MEME to that value. (Any integer value) -nomatrim boolean [N] The -nomatrim, -wg, -ws and -noendgaps switches control trimming (shortening) of motifs using the multiple alignment method. Specifying -nomatrim causes MEME to skip this and causes the other switches to be ignored. The pairwise alignment is controlled by the switches -wg (gap cost), -ws (space cost) and -noendgaps (do not penalize endgaps). See application documentation for further information. -wg integer [11] The -nomatrim, -wg, -ws and -noendgaps switches control trimming (shortening) of motifs using the multiple alignment method. Specifying -nomatrim causes MEME to skip this and causes the other switches to be ignored. The pairwise alignment is controlled by the switches -wg (gap cost), -ws (space cost) and -noendgaps (do not penalize endgaps). See application documentation for further information. (Any integer value) -ws integer [1] The -nomatrim, -wg, -ws and -noendgaps switches control trimming (shortening) of motifs using the multiple alignment method. Specifying -nomatrim causes MEME to skip this and causes the other switches to be ignored. The pairwise alignment is controlled by the switches -wg (gap cost), -ws (space cost) and -noendgaps (do not penalize endgaps). See application documentation for further information. (Any integer value) -noendgaps boolean [N] The -nomatrim, -wg, -ws and -noendgaps switches control trimming (shortening) of motifs using the multiple alignment method. Specifying -nomatrim causes MEME to skip this and causes the other switches to be ignored. The pairwise alignment is controlled by the switches -wg (gap cost), -ws (space cost) and -noendgaps (do not penalise endgaps). See application documentation for further information. -revcomp boolean [N] Motif occurrences may be on the given DNA strand or on its reverse complement. The default is to look for DNA motifs only on the strand given in the training set. -pal boolean [N] Choosing -pal causes MEME to look for palindromes in DNA datasets. MEME averages the letter frequencies in corresponding columns of the motif (PSPM) together. For instance, if the width of the motif is 10, columns 1 and 10, 2 and 9, 3 and 8, etc., are averaged together. The averaging combines the frequency of A in one column with T in the other, and the frequency of C in one column with G in the other. -[no]nostatus boolean [Y] Set this option to prevent progress reports to the terminal. Advanced (Unprompted) qualifiers: -maxiter integer [50] The number of iterations of EM to run from any starting point. EM is run for iterations or until convergence (see -distance, below) from each starting point. (Any integer value) -distance float [0.001] The convergence criterion. MEME stops iterating EM when the change in the motif frequency matrix is less than . (Change is the euclidean distance between two successive frequency matrices.) (Any numeric value) -b float [-1.0] The strength of the prior on model parameters. A value of 0 means use intrinsic strength of prior if prior = dmix. The default values are 0.01 if prior = dirichlet or 0 if prior = dmix. These defaults are hardcoded into MEME (the value of the default in the ACD file is not used). A value of -1 here represents -b being unspecified. (Any numeric value) -spfuzz float [-1.0] The fuzziness of the mapping. Possible values are greater than 0. Meaning depends on -spmap, see below. See the application documentation for more information. A value of -1.0 here represents -spfuzz being unspecified. (Any numeric value) -spmap selection [default] The type of mapping function to use. uni: Use prior when converting a substring to an estimate of theta. Default -spfuzz : 0.5. pam: Use columns of PAM matrix when converting a substring to an estimate of theta. Default -spfuzz : 120 (PAM 120). See the application documentation for more information. -cons string Override the sampling of starting points and just use a starting point derived from . This is useful when an actual occurrence of a motif is known and can be used as the starting point for finding the motif. See the application documentation for more information. (Any string) -maxsize integer [-1] Maximum dataset size in characters (-1 = use meme default). (Any integer value) -p integer [0] Only values of >0 will be applied. The -p argument causes a version of MEME compiled for a parallel CPU architecture to be run. (By placing in quotes you may pass installation specific switches to the 'mpirun' command. The number of processors to run on must be the first argument following -p). (Any integer value) -time integer [0] Only values of more than 0 will be applied. (Any integer value) -sf string Print as name of sequence file (Any string) -heapsize integer [64] The search for good EM starting points can be improved by using a branching search. A branching search begins with a fixed-size heap of best EM starts identified during the search of subsequences from the dataset. These starts are also called seeds. The fixed-size heap of seeds is used as the branch-heap during the first iteration of branching search. See the application documentation for more information. (Any integer value) -xbranch boolean [N] The search for good EM starting points can be improved by using a branching search. A branching search begins with a fixed-size heap of best EM starts identified during the search of subsequences from the dataset. These starts are also called seeds. The fixed-size heap of seeds is used as the branch-heap during the first iteration of branching search. See the application documentation for more information. -wbranch boolean [N] The search for good EM starting points can be improved by using a branching search. A branching search begins with a fixed-size heap of best EM starts identified during the search of subsequences from the dataset. These starts are also called seeds. The fixed-size heap of seeds is used as the branch-heap during the first iteration of branching search. See the application documentation for more information. -bfactor integer [3] The search for good EM starting points can be improved by using a branching search. A branching search begins with a fixed-size heap of best EM starts identified during the search of subsequences from the dataset. These starts are also called seeds. The fixed-size heap of seeds is used as the branch-heap during the first iteration of branching search. See the application documentation for more information. (Any integer value) Associated qualifiers: "-dataset" associated qualifiers -sbegin1 integer Start of each sequence to be used -send1 integer End of each sequence to be used -sreverse1 boolean Reverse (if DNA) -sask1 boolean Ask for begin/end/reverse -snucleotide1 boolean Sequence is nucleotide -sprotein1 boolean Sequence is protein -slower1 boolean Make lower case -supper1 boolean Make upper case -sformat1 string Input sequence format -sdbname1 string Database name -sid1 string Entryname -ufo1 string UFO features -fformat1 string Features format -fopenfile1 string Features file name "-outdir" associated qualifiers -extension2 string Default file extension General qualifiers: -auto boolean Turn off prompts -stdout boolean Write first file to standard output -filter boolean Read first file from standard input, write first file to standard output -options boolean Prompt for standard and additional values -debug boolean Write debug output to program.dbg -verbose boolean Report some/full command line options -help boolean Report command line options and exit. More information on associated and general qualifiers can be found with -help -verbose -warning boolean Report warnings -error boolean Report errors -fatal boolean Report fatal errors -die boolean Report dying program messages -version boolean Report version number and exit

Qualifier	Type	Description	Allowed values	Default
Standard (Mandatory) qualifiers
[-dataset] (Parameter 1)	seqset	User must provide the full filename of a set of sequences, not an indirect reference, e.g. a USA is NOT acceptable.	Readable set of sequences	Required
[-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_dataset	integer	Start of each sequence to be used	Any integer value	0
-send1 -send_dataset	integer	End of each sequence to be used	Any integer value	0
-sreverse1 -sreverse_dataset	boolean	Reverse (if DNA)	Boolean value Yes/No	N
-sask1 -sask_dataset	boolean	Ask for begin/end/reverse	Boolean value Yes/No	N
-snucleotide1 -snucleotide_dataset	boolean	Sequence is nucleotide	Boolean value Yes/No	N
-sprotein1 -sprotein_dataset	boolean	Sequence is protein	Boolean value Yes/No	N
-slower1 -slower_dataset	boolean	Make lower case	Boolean value Yes/No	N
-supper1 -supper_dataset	boolean	Make upper case	Boolean value Yes/No	N
-sformat1 -sformat_dataset	string	Input sequence format	Any string
-sdbname1 -sdbname_dataset	string	Database name	Any string
-sid1 -sid_dataset	string	Entryname	Any string
-ufo1 -ufo_dataset	string	UFO features	Any string
-fformat1 -fformat_dataset	string	Features format	Any string
-fopenfile1 -fopenfile_dataset	string	Features file name	Any string
"-outdir" associated outdir qualifiers
-extension2 -extension_outdir	string	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

Input files for usage example

File: crp0.s

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

Output file format

Output files for usage example

Graphics File: logo1.eps

Graphics File: logo1.png

Graphics File: logo_ssc1.png

Graphics File: logo_ssc1.eps

File: meme.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 4.3.0 (Release date: Sat Sep 26 01:51:56 PDT 2009)

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

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


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

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


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

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


  [Part of this file has been deleted for brevity]

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




Time  0.50 secs.

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


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

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

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


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

CPU: emboss4.ebi.ac.uk

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

File: meme.xml

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

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


  [Part of this file has been deleted for brevity]

<letter_ref letter_id="letter_G"/>
<letter_ref letter_id="letter_T"/>
<letter_ref letter_id="letter_T"/>
<letter_ref letter_id="letter_G"/>
<letter_ref letter_id="letter_A"/>
<letter_ref letter_id="letter_T"/>
<letter_ref letter_id="letter_C"/>
<letter_ref letter_id="letter_G"/>
<letter_ref letter_id="letter_G"/>
</site>
<right_flank>CACG</right_flank>
</contributing_site>
</contributing_sites>
</motif>
</motifs>
<scanned_sites_summary p_thresh="0.0001">
<scanned_sites sequence_id="sequence_ce1cg" pvalue="1.74e-03" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="63" pvalue="1.91e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_ara" pvalue="4.00e-03" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="57" pvalue="4.41e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_bglr1" pvalue="7.85e-03" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="78" pvalue="8.66e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_crp" pvalue="4.37e-03" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="65" pvalue="4.81e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_cya" pvalue="3.66e-03" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="52" pvalue="4.03e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_deop2" pvalue="5.47e-04" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="9" pvalue="6.01e-06"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_gale" pvalue="9.45e-04" num_sites="1"><scanned_site motif_id="motif_1" strand="plus" position="26" pvalue="1.04e-05"/>
</scanned_sites>
<scanned_sites sequence_id="sequence_ilv" pvalue="2.54e-02" num_sites="0"></scanned_sites>
<scanned_sites sequence_id="sequence_lac" 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_male" 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_malk" pvalue="1.35e-02" num_sites="0"></scanned_sites>
<scanned_sites sequence_id="sequence_malt" 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_ompa" 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_tnaa" 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_uxu1" 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_pbr322" 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_trn9cat" pvalue="5.08e-02" num_sites="0"></scanned_sites>
<scanned_sites sequence_id="sequence_tdc" 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>

File: meme.xsl

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

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

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

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

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

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


  [Part of this file has been deleted for brevity]

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

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

</xsl:stylesheet>

The MEME results consist of:

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

Data files

Notes

1. Command-line arguments

-h         : Use -help to get help information.
-dna	   : EMBOSS will specify whether sequences use a DNA alphabet 
             automatically.
-protein   : EMBOSS will specify whether sequences use a protein alphabet 
             automatically.

The following additional options are provided:

outfile    : Application output that was normally written to stdout.

2. Installing EMBASSY MEME

3. Installing original MEME

WWW home:       http://meme.sdsc.edu/meme/
Distribution:   http://meme.nbcr.net/downloads/old_versions/  

4. Setting up MEME

set path=(/usr/local/meme/bin/ $path)
rehash

5. Getting help

meme > meme.txt 
mast > mast.txt 

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

References

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

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

Warnings

Input data

Sequence input

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

Diagnostic Error Messages

Exit status

Known bugs

See also

Author(s)

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

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

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

History

Target users