-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 -revcomp ex2.html Motif detection

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

Example 2

% ememe crp0.s -mod oops -revcomp -w 20 ex3.html Motif detection

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Example 3

% ememe INO_up800.s -mod anr -revcomp -bfile memenew/yeast.nc.6.freq ex4.html Motif detection

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Go to the output files for this example

Example 4

% ememe lipocalin.s -mod oops -maxw 20 -nmotifs 2 ex5.html Motif detection

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Go to the output files for this example

Example 5

% ememe farntrans5.s -mod anr -maxw 40 -maxsites 50 ex6.html Motif detection

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Go to the output files for this example

Example 6

% ememe farntrans5.s -mod anr -w 10 -maxsites 30 -nmotifs 3 ex7.html Motif detection

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Example 7

% ememe farntrans5.s -mod anr -maxw 12 -nsites 24 -nmotifs 3 ex8.html Motif detection

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Example 8

% ememe adh.s -mod zoops -nmotifs 20 -evt 0.01 ex9.html Motif detection

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Go to the output files for this example

Example 9

% ememe crp0.s -mod oops -pal ex1.html Motif detection

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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.

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. [-outfile] outfile [*.ememe] MEME program output file 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 of 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. (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). (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). (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). (Any integer value) -wnsites float [.8] 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. (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. (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 penalize endgaps). See application documentation for further information. -revcomp boolean [N] Motifs 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 print a progress report 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). (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. (Any numeric value) -spmap selection [uni] 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 is accepted) -maxsize integer [-1] Maximum dataset size in characters (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 is accepted) 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 "-outfile" associated qualifiers -odirectory2 string Output directory General qualifiers: -auto boolean Turn off prompts -stdout boolean Write first file to standard output -filter boolean Read first file from standard input, write first file to standard output -options boolean Prompt for standard and additional values -debug boolean Write debug output to program.dbg -verbose boolean Report some/full command line options -help boolean Report command line options. 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

Standard (Mandatory) qualifiers		Allowed values	Default
[-dataset] (Parameter 1)	User must provide the full filename of a set of sequences, not an indirect reference, e.g. a USA is NOT acceptable.	Readable set of sequences	Required
[-outfile] (Parameter 2)	MEME program output file	Output file	<*>.ememe
Additional (Optional) qualifiers		Allowed values	Default
-bfile	The name of the file containing the background model for sequences. The background model is the model of random sequences used by MEME. The background model is used by MEME 1) during EM as the 'null model', 2) for calculating the log likelihood ratio of a motif, 3) for calculating the significance (E-value) of a motif, and, 4) for creating the position-specific scoring matrix (log-odds matrix). See application documentation for more information.	Input file	Required
-plibfile	The name of the file containing the Dirichlet prior in the format of file prior30.plib	Input file	Required
-mod	If you know how occurrences of motifs are distributed in the training set sequences, you can specify it with these options. The default distribution of motif occurrences is assumed to be zero or one occurrence of per sequence. oops : One Occurrence Per Sequence. MEME assumes that each sequence in the dataset contains exactly one occurrence of each motif. This option is the fastest and most sensitive but the motifs returned by MEME may be 'blurry' if any of the sequences is missing them. zoops : Zero or One Occurrence Per Sequence. MEME assumes that each sequence may contain at most one occurrence of each motif. This option is useful when you suspect that some motifs may be missing from some of the sequences. In that case, the motifs found will be more accurate than using the first option. This option takes more computer time than the first option (about twice as much) and is slightly less sensitive to weak motifs present in all of the sequences. anr : Any Number of Repetitions. MEME assumes each sequence may contain any number of non-overlapping occurrences of each motif. This option is useful when you suspect that motifs repeat multiple times within a single sequence. In that case, the motifs found will be much more accurate than using one of the other options. This option can also be used to discover repeats within a single sequence. This option takes the much more computer time than the first option (about ten times as much) and is somewhat less sensitive to weak motifs which do not repeat within a single sequence than the other two options.	Choose from selection list of values	zoops
-nmotifs	The number of *different* motifs to search for. MEME will search for and output <n> motifs.	Any integer value	1
-text	Default output is in HTML	Boolean value Yes/No	No
-prior	The prior distribution on the model parameters. dirichlet: Simple Dirichlet prior. This is the default for -dna and -alph. It is based on the non-redundant database letter frequencies. dmix: Mixture of Dirichlets prior. This is the default for -protein. mega: Extremely low variance dmix; variance is scaled inversely with the size of the dataset. megap: Mega for all but last iteration of EM; dmix on last iteration. addone: Add +1 to each observed count.	Choose from selection list of values	dirichlet
-evt	Quit looking for motifs if E-value exceeds this value. Has an extremely high default so by default MEME never quits before -nmotifs <n> have been found.	Any numeric value	-1
-nsites	These switches are ignored if mod = oops. The (expected) number of occurrences of each motif. If a value for -nsites is specified, only that number of occurrences is tried. Otherwise, numbers of occurrences between -minsites and -maxsites are tried as initial guesses for the number of motif occurrences. If a value is not specified for -minsites and maxsites then the default hardcoded into MEME, as opposed to the default value given in the ACD file, is used. The hardcoded default value of -minsites is equal to sqrt(number sequences). The hardcoded default value of -maxsites is equal to the number of sequences (zoops) or MIN(5* num.sequences, 50) (anr).	Any integer value	-1
-minsites	These switches are ignored if mod = oops. The (expected) number of occurrences of each motif. If a value for -nsites is specified, only that number of occurrences is tried. Otherwise, numbers of occurrences between -minsites and -maxsites are tried as initial guesses for the number of motif occurrences. If a value is not specified for -minsites and maxsites then the default hardcoded into MEME, as opposed to the default value given in the ACD file, is used. The hardcoded default value of -minsites is equal to sqrt(number sequences). The hardcoded default value of -maxsites is equal to the number of sequences (zoops) or MIN(5 * num.sequences, 50) (anr).	Any integer value	-1
-maxsites	These switches are ignored if mod = oops. The (expected) number of occurrences of each motif. If a value for -nsites is specified, only that number of occurrences is tried. Otherwise, numbers of occurrences between -minsites and -maxsites are tried as initial guesses for the number of motif occurrences. If a value is not specified for -minsites and maxsites then the default hardcoded into MEME, as opposed to the default value given in the ACD file, is used. The hardcoded default value of -minsites is equal to sqrt(number sequences). The hardcoded default value of -maxsites is equal to the number of sequences (zoops) or MIN(5 * num.sequences, 50) (anr).	Any integer value	-1
-wnsites	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.	Any numeric value	.8
-w	The width of the motif(s) to search for. If -w is given, only that width is tried. Otherwise, widths between -minw and -maxw are tried. Note: if width is less than the length of the shortest sequence in the dataset, width is reset by MEME to that value.	Any integer value	-1
-minw	The width of the motif(s) to search for. If -w is given, only that width is tried. Otherwise, widths between -minw and -maxw are tried. Note: if width is less than the length of the shortest sequence in the dataset, width is reset by MEME to that value.	Any integer value	8
-maxw	The width of the motif(s) to search for. If -w is given, only that width is tried. Otherwise, widths between -minw and -maxw are tried. Note: if width is less than the length of the shortest sequence in the dataset, width is reset by MEME to that value.	Any integer value	50
-nomatrim	The -nomatrim, -wg, -ws and -noendgaps switches control trimming (shortening) of motifs using the multiple alignment method. Specifying -nomatrim causes MEME to skip this and causes the other switches to be ignored. The pairwise alignment is controlled by the switches -wg (gap cost), -ws (space cost) and -noendgaps (do not penalize endgaps). See application documentation for further information.	Boolean value Yes/No	No
-wg	The -nomatrim, -wg, -ws and -noendgaps switches control trimming (shortening) of motifs using the multiple alignment method. Specifying -nomatrim causes MEME to skip this and causes the other switches to be ignored. The pairwise alignment is controlled by the switches -wg (gap cost), -ws (space cost) and -noendgaps (do not penalize endgaps). See application documentation for further information.	Any integer value	11
-ws	The -nomatrim, -wg, -ws and -noendgaps switches control trimming (shortening) of motifs using the multiple alignment method. Specifying -nomatrim causes MEME to skip this and causes the other switches to be ignored. The pairwise alignment is controlled by the switches -wg (gap cost), -ws (space cost) and -noendgaps (do not penalize endgaps). See application documentation for further information.	Any integer value	1
-noendgaps	The -nomatrim, -wg, -ws and -noendgaps switches control trimming (shortening) of motifs using the multiple alignment method. Specifying -nomatrim causes MEME to skip this and causes the other switches to be ignored. The pairwise alignment is controlled by the switches -wg (gap cost), -ws (space cost) and -noendgaps (do not penalize endgaps). See application documentation for further information.	Boolean value Yes/No	No
-revcomp	Motifs occurrences may be on the given DNA strand or on its reverse complement. The default is to look for DNA motifs only on the strand given in the training set.	Boolean value Yes/No	No
-pal	Choosing -pal causes MEME to look for palindromes in DNA datasets. MEME averages the letter frequencies in corresponding columns of the motif (PSPM) together. For instance, if the width of the motif is 10, columns 1 and 10, 2 and 9, 3 and 8, etc., are averaged together. The averaging combines the frequency of A in one column with T in the other, and the frequency of C in one column with G in the other.	Boolean value Yes/No	No
-[no]nostatus	Set this option to print a progress report to the terminal.	Boolean value Yes/No	Yes
Advanced (Unprompted) qualifiers		Allowed values	Default
-maxiter	The number of iterations of EM to run from any starting point. EM is run for <n> iterations or until convergence (see -distance, below) from each starting point.	Any integer value	50
-distance	The convergence criterion. MEME stops iterating EM when the change in the motif frequency matrix is less than <a>. (Change is the euclidean distance between two successive frequency matrices.)	Any numeric value	0.001
-b	The strength of the prior on model parameters. A value of 0 means use intrinsic strength of prior if prior = dmix. The default values are 0.01 if prior = dirichlet or 0 if prior = dmix. These defaults are hardcoded into MEME (the value of the default in the ACD file is not used).	Any numeric value	-1.0
-spfuzz	The fuzziness of the mapping. Possible values are greater than 0. Meaning depends on -spmap, see below. See the application documentation for more information.	Any numeric value	-1.0
-spmap	The type of mapping function to use. uni: Use prior when converting a substring to an estimate of theta. Default -spfuzz <a>: 0.5. pam: Use columns of PAM <a> matrix when converting a substring to an estimate of theta. Default -spfuzz <a>: 120 (PAM 120). See the application documentation for more information.	Choose from selection list of values	uni
-cons	Override the sampling of starting points and just use a starting point derived from <string>. This is useful when an actual occurrence of a motif is known and can be used as the starting point for finding the motif. See the application documentation for more information.	Any string is accepted	An empty string is accepted
-maxsize	Maximum dataset size in characters	Any integer value	-1
-p	Only values of >0 will be applied. The -p <np> argument causes a version of MEME compiled for a parallel CPU architecture to be run. (By placing <np> in quotes you may pass installation specific switches to the 'mpirun' command. The number of processors to run on must be the first argument following -p).	Any integer value	0
-time	Only values of more than 0 will be applied.	Any integer value	0
-sf	Print <sf> as name of sequence file	Any string is accepted	An empty string is accepted

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

Input files for usage example 3

File: INO_up800.s

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


  [Part of this file has been deleted for brevity]

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

File: yeast.nc.6.freq

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


  [Part of this file has been deleted for brevity]

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

Input files for usage example 4

File: lipocalin.s

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

Input files for usage example 5

File: farntrans5.s

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

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

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

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

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

Input files for usage example 8

File: adh.s

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


  [Part of this file has been deleted for brevity]

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

Output file format

Output files for usage example

File: ex2.html

Output files for usage example 2

File: ex3.html

Output files for usage example 3

File: ex4.html

Output files for usage example 4

File: ex5.html

Output files for usage example 5

File: ex6.html

Output files for usage example 6

File: ex7.html

Output files for usage example 7

File: ex8.html

Output files for usage example 8

File: ex9.html

Output files for usage example 9

File: ex1.html

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)

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