ememe Wiki The master copies of EMBOSS documentation are available at http://emboss.open-bio.org/wiki/Appdocs on the EMBOSS Wiki. Please help by correcting and extending the Wiki pages. Function Multiple EM for motif elicitation Description EMBASSY MEME is a suite of application wrappers to the original meme v3.0.14 applications written by Timothy Bailey. meme v3.0.14 must be installed on the same system as EMBOSS and the location of the meme executables must be defined in your path for EMBASSY MEME to work. Usage: ememe [options] dataset outfile The parameter is new to EMBASSY MEME. The output is always written to . The name of the input sequences may be specified with the -dataset option as normal. MEME -- Multiple EM for Motif Elicitation MEME is a tool for discovering motifs in a group of related DNA or protein sequences. A motif is a sequence pattern that occurs repeatedly in a group of related protein or DNA sequences. MEME represents motifs as position-dependent letter-probability matrices which describe the probability of each possible letter at each position in the pattern. Individual MEME motifs do not contain gaps. Patterns with variable-length gaps are split by MEME into two or more separate motifs. MEME takes as input a group of DNA or protein sequences (the training set) and outputs as many motifs as requested. MEME uses statistical modeling techniques to automatically choose the best width, number of occurrences, and description for each motif. MEME outputs its results as a hypertext (HTML) document. Algorithm Please read the file README distributed with the original MEME. REQUIRED ARGUMENTS: < dataset > The name of the file containing the training set sequences. If < dataset > is the word "stdin", MEME reads from standard input. The sequences in the dataset should be in Pearson/FASTA format. For example: >ICYA_MANSE INSECTICYANIN A FORM (BLUE BILIPROTEIN) GDIFYPGYCPDVKPVNDFDLSAFAGAWHEIAK LPLENENQGKCTIAEYKYDGKKASVYNSFVSNGVKEYMEGDLEIAPDA >LACB_BOVIN BETA-LACTOGLOBULIN PRECURSOR (BETA-LG) MKCLLLALALTCGAQALIVTQTMKGLDI QKVAGTWYSLAMAASDISLLDAQSAPLRVYVEELKPTPEGDLEILLQKW Sequences start with a header line followed by sequence lines. A header line has the character ">" in position one, followed by an unique name without any spaces, followed by (optional) descriptive text. After the header line come the actual sequence lines. Spaces and blank lines are ignored. Sequences may be in capital or lowercase or both. MEME uses the first word in the header line of each sequence, truncated to 24 characters if necessary, as the name of the sequence. This name must be unique. Sequences with duplicate names will be ignored. (The first word in the title line is everything following the ">" up to the first blank.) Sequence weights may be specified in the dataset file by special header lines where the unique name is "WEIGHTS" (all caps) and the descriptive text is a list of sequence weights. Sequence weights are numbers in the range 0 < w <=1. All weights are assigned in order to the sequences in the file. If there are more sequences than weights, the remainder are given weight one. Weights must be greater than zero and less than or equal to one. Weights may be specified by more than one "WEIGHT" entry which may appear anywhere in the file. When weights are used, sequences will contribute to motifs in proportion to their weights. Here is an example for a file of three sequences where the first two sequences are very similar and it is desired to down-weight them: >WEIGHTS 0.5 .5 1.0 >seq1 GDIFYPGYCPDVKPVNDFDLSAFAGAWHEIAK >seq2 GDMFCPGYCPDVKPVGDFDLSAFAGAWHELAK >seq3 QKVAGTWYSLAMAASDISLLDAQSAPLRVYVEELKPTPEGDLEILLQKW OPTIONAL ARGUMENTS: MEME has a large number of optional inputs that can be used to fine-tune its behavior. To make these easier to understand they are divided into the following categories: ALPHABET - control the alphabet for the motifs (patterns) that MEME will search for DISTRIBUTION - control how MEME assumes the occurrences of the motifs are distributed throughout the training set sequences SEARCH - control how MEME searches for motifs SYSTEM - the -p argument causes a version of MEME compiled for a parallel CPU architecture to be run. (By placing < np > in quotes you may pass installation specific switches to the 'mpirun' command. The number of processors to run on must be the first argument following -p). In what follows, < n > is an integer, < a > is a decimal number, and < string > is a string of characters. ALPHABET MEME accepts either DNA or protein sequences, but not both in the same run. By default, sequences are assumed to be protein. The sequences must be in FASTA format. DNA sequences must contain only the letters "ACGT", plus the ambiguous letters "BDHKMNRSUVWY*-". Protein sequences must contain only the letters "ACDEFGHIKLMNPQRSTVWY", plus the ambiguous letters "BUXZ*-". MEME converts all ambiguous letters to "X", which is treated as "unknown". -dna Assume sequences are DNA; default: protein sequences -protein Assume sequences are protein DISTRIBUTION If you know how occurrences of motifs are distributed in the training set sequences, you can specify it with the following optional switches. The default distribution of motif occurrences is assumed to be zero or one occurrence of per sequence. -mod < string > The type of distribution to assume. oops One Occurrence Per Sequence MEME assumes that each sequence in the dataset contains exactly one occurrence of each motif. This option is the fastest and most sensitive but the motifs returned by MEME may be "blurry" if any of the sequences is missing them. zoops Zero or One Occurrence Per Sequence MEME assumes that each sequence may contain at most one occurrence of each motif. This option is useful when you suspect that some motifs may be missing from some of the sequences. In that case, the motifs found will be more accurate than using the first option. This option takes more computer time than the first option (about twice as much) and is slightly less sensitive to weak motifs present in all of the sequences. anr Any Number of Repetitions MEME assumes each sequence may contain any number of non-overlapping occurrences of each motif. This option is useful when you suspect that motifs repeat multiple times within a single sequence. In that case, the motifs found will be much more accurate than using one of the other options. This option can also be used to discover repeats within a single sequence. This option takes the much more computer time than the first option (about ten times as much) and is somewhat less sensitive to weak motifs which do not repeat within a single sequence than the other two options. SEARCH ------ A) OBJECTIVE FUNCTION MEME uses an objective function on motifs to select the "best" motif. The objective function is based on the statistical significance of the log likelihood ratio (LLR) of the occurrences of the motif. The E-value of the motif is an estimate of the number of motifs (with the same width and number of occurrences) that would have equal or higher log likelihood ratio if the training set sequences had been generated randomly according to the (0-order portion of the) background model. MEME searches for the motif with the smallest E-value. It searches over different motif widths, numbers of occurrences, and positions in the training set for the motif occurrences. The user may limit the range of motif widths and number of occurrences that MEME tries using the switches described below. In addition, MEME trims the motif (using a dynamic programming multiple alignment) to eliminate any positions where there is a gap in any of the occurrences. The log likelihood ratio of a motif is llr = log (Pr(sites | motif) / Pr(sites | back)) and is a measure of how different the sites are from the background model. Pr(sites | motif) is the probability of the occurrences given the a model consisting of the position-specific probability matrix (PSPM) of the motif. (The PSPM is output by MEME). Pr(sites | back) is the probability of the occurrences given the background model. The background model is an n-order Markov model. By default, it is a 0-order model consisting of the frequencies of the letters in the training set. A different 0-order Markov model or higher order Markov models can be specified to MEME using the -bfile option described below. The E-value reported by MEME is actually an approximation of the E-value of the log likelihood ratio. (An approximation is used because it is far more efficient to compute.) The approximation is based on the fact that the log likelihood ratio of a motif is the sum of the log likelihood ratios of each column of the motif. Instead of computing the statistical significance of this sum (its p-value), MEME computes the p-value of each column and then computes the significance of their product. Although not identical to the significance of the log likelihood ratio, this easier to compute objective function works very similarly in practice. The motif significance is reported as the E-value of the motif. The statistical signficance of a motif is computed based on: 1. the log likelihood ratio, 2. the width of the motif, 3. the number of occurrences, 4. the 0-order portion of the background model, 5. the size of the training set, and 6. the type of model (oops, zoops, or anr, which determines the number of possible different motifs of the given width and number of occurrences). MEME searches for motifs by performing Expectation Maximization (EM) on a motif model of a fixed width and using an initial estimate of the number of sites. It then sorts the possible sites according to their probability according to EM. MEME then and calculates the E-values of the first n sites in the sorted list for different values of n. This procedure (first EM, followed by computing E-values for different numbers of sites) is repeated with different widths and different initial estimates of the number of sites. MEME outputs the motif with the lowest E-value. B) NUMBER OF MOTIFS -nmotifs < n > The number of *different* motifs to search for. MEME will search for and output < n > motifs. Default: 1 -evt < p > Quit looking for motifs if E-value exceeds < p >. Default: infinite (so by default MEME never quits before -nmotifs < n > have been found.) C) NUMBER OF MOTIF OCCURENCES -nsites < n > -minsites < n > -maxsites < n > The (expected) number of occurrences of each motif. If -nsites is given, only that number of occurrences is tried. Otherwise, numbers of occurrences between -minsites and -maxsites are tried as initial guesses for the number of motif occurrences. These switches are ignored if mod = oops. Default: -minsites sqrt(number sequences) -maxsites Default: zoops # of sequences anr MIN(5*#sequences, 50) -wnsites < n > The weight on the prior on nsites. This controls how strong the bias towards motifs with exactly nsites sites (or between minsites and maxsites sites) is. It is a number in the range [0..1). The larger it is, the stronger the bias towards motifs with exactly nsites occurrences is. Default: 0.8 D) MOTIF WIDTH -w < n > -minw < n > -maxw < n > The width of the motif(s) to search for. If -w is given, only that width is tried. Otherwise, widths between -minw and -maxw are tried. Default: -minw 8, -maxw 50 (defined in user.h) Note: If < n > is less than the length of the shortest sequence in the dataset, < n > is reset by MEME to that value. -nomatrim -wg < a > -ws < a > -noendgaps These switches control trimming (shortening) of motifs using the multiple alignment method. Specifying -nomatrim causes MEME to skip this and causes the other switches to be ignored. MEME finds the best motif found and then trims (shortens) it using the multiple alignment method (described below). The number of occurrences is then adjusted to maximize the motif E-value, and then the motif width is further shortened to optimize the E-value. The multiple alignment method performs a separate pairwise alignment of the site with the highest probability and each other possible site. (The alignment includes width/2 positions on either side of the sites.) The pairwise alignment is controlled by the switches: -wg < a > (gap cost; default: 11), -ws < a > (space cost; default 1), and, -noendgaps (do not penalize endgaps; default: penalize endgaps). The pairwise alignments are then combined and the method determines the widest section of the motif with no insertions or deletions. If this alignment is shorter than < minw >, it tries to find an alignment allowing up to one insertion/deletion per motif column. This continues (allowing up to 2, 3 ... insertions/deletions per motif column) until an alignment of width at least < minw > is found. E) BACKGROUND MODEL -bfile < bfile > The name of the file containing the background model for sequences. The background model is the model of random sequences used by MEME. The background model is used by MEME 1. 1) during EM as the "null model", 2. 2) for calculating the log likelihood ratio of a motif, 3. 3) for calculating the significance (E-value) of a motif, and, 4. 4) for creating the position-specific scoring matrix (log-odds matrix). By default, the background model is a 0-order Markov model based on the letter frequencies in the training set. Markov models of any order can be specified in < bfile > by listing frequencies of all possible tuples of length up to order+1. Note that MEME uses only the 0-order portion (single letter frequencies) of the background model for purposes 3) and 4), but uses the full-order model for purposes 1) and 2), above. Example: To specify a 1-order Markov background model for DNA, < bfile > might contain the following lines. Note that optional comment lines are by "#" and are ignored by MEME. # tuple frequency_non_coding a 0.324 c 0.176 g 0.176 t 0.324 # tuple frequency_non_coding aa 0.119 ac 0.052 ag 0.056 at 0.097 ca 0.058 cc 0.033 cg 0.028 ct 0.056 ga 0.056 gc 0.035 gg 0.033 gt 0.052 ta 0.091 tc 0.056 tg 0.058 tt 0.119 Sample -bfile files are given in directory tests: tests/nt.freq (DNA), and tests/na.freq (amino acid). F) DNA PALINDROMES AND STRANDS -revcomp motifs occurrences may be on the given DNA strand or on its reverse complement. Default: look for DNA motifs only on the strand given in the training set. -pal Choosing -pal causes MEME to look for palindromes in DNA datasets. MEME averages the letter frequencies in corresponding columns of the motif (PSPM) together. For instance, if the width of the motif is 10, columns 1 and 10, 2 and 9, 3 and 8, etc., are averaged together. The averaging combines the frequency of A in one column with T in the other, and the frequency of C in one column with G in the other. If neither option is not chosen, MEME does not search for DNA palindromes. G) EM ALGORITHM -maxiter < n > The number of iterations of EM to run from any starting point. EM is run for < n > iterations or until convergence (see -distance, below) from each starting point. Default: 50 -distance < a > The convergence criterion. MEME stops iterating EM when the change in the motif frequency matrix is less than < a >. (Change is the euclidean distance between two successive frequency matrices.) Default: 0.001 -prior < string > The prior distribution on the model parameters: dirichlet simple Dirichlet prior This is the default for -dna and -alph. It is based on the non-redundant database letter frequencies. dmix mixture of Dirichlets prior This is the default for -protein. mega extremely low variance dmix; variance is scaled inversely with the size of the dataset. megap mega for all but last iteration of EM; dmix on last iteration. addone add +1 to each observed count -b < a > The strength of the prior on model parameters: < a > = 0 means use intrinsic strength of prior for prior = dmix. Defaults: 0.01 if prior = dirichlet 0 if prior = dmix -plib < string > The name of the file containing the Dirichlet prior in the format of file prior30.plib. H) SELECTING STARTS FOR EM The default is for MEME to search the dataset for good starts for EM. How the starting points are derived from the dataset is specified by the following switches. The default type of mapping MEME uses is: -spmap uni for -dna and -alph < string > -spmap pam for -protein -spfuzz < a > The fuzziness of the mapping. Possible values are greater than 0. Meaning depends on -spmap, see below. -spmap < string > The type of mapping function to use. uni Use add-< a > prior when converting a substring to an estimate of theta. Default -spfuzz < a >: 0.5 pam Use columns of PAM < a > matrix when converting a substring to an estimate of theta. Default -spfuzz < a >: 120 (PAM 120) Other types of starting points can be specified using the following switches. -cons < string > Override the sampling of starting points and just use a starting point derived from < string >. This is useful when an actual occurrence of a motif is known and can be used as the starting point for finding the motif. Usage Here is a sample session with ememe % 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 Where possible, the same command-line qualifier names and parameter order is used as in the original meme. There are however several unavoidable differences and these are clearly documented in the "Notes" section below. Most of the options in the original meme are given in ACD as "advanced" or "additional" options. -options must be specified on the command-line in order to be prompted for a value for "additional" options but "advanced" options will never be prompted for. Multiple EM for motif elicitation Version: EMBOSS:6.6.0.0 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 -scircular1 boolean Sequence is circular -squick1 boolean Read id and sequence only -sformat1 string Input sequence format -iquery1 string Input query fields or ID list -ioffset1 integer Input start position offset -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 Input file format Sequence formats The original MEME only supported input sequences in FASTA format. EMBASSY MEME supports all EMBOSS-supported sequence formats. meme reads any normal sequence USAs. Input files for usage example File: crp0.s >ce1cg TAATGTTTGTGCTGGTTTTTGTGGCATCGGGCGAGAATAGCGCGTGGTGTGAAAGACTGTTTTTTTGATCGTTTTCACAA AAATGGAAGTCCACAGTCTTGACAG >ara GACAAAAACGCGTAACAAAAGTGTCTATAATCACGGCAGAAAAGTCCACATTGATTATTTGCACGGCGTCACACTTTGCT ATGCCATAGCATTTTTATCCATAAG >bglr1 ACAAATCCCAATAACTTAATTATTGGGATTTGTTATATATAACTTTATAAATTCCTAAAATTACACAAAGTTAATAACTG TGAGCATGGTCATATTTTTATCAAT >crp CACAAAGCGAAAGCTATGCTAAAACAGTCAGGATGCTACAGTAATACATTGATGTACTGCATGTATGCAAAGGACGTCAC ATTACCGTGCAGTACAGTTGATAGC >cya ACGGTGCTACACTTGTATGTAGCGCATCTTTCTTTACGGTCAATCAGCAAGGTGTTAAATTGATCACGTTTTAGACCATT TTTTCGTCGTGAAACTAAAAAAACC >deop2 AGTGAATTATTTGAACCAGATCGCATTACAGTGATGCAAACTTGTAAGTAGATTTCCTTAATTGTGATGTGTATCGAAGT GTGTTGCGGAGTAGATGTTAGAATA >gale GCGCATAAAAAACGGCTAAATTCTTGTGTAAACGATTCCACTAATTTATTCCATGTCACACTTTTCGCATCTTTGTTATG CTATGGTTATTTCATACCATAAGCC >ilv GCTCCGGCGGGGTTTTTTGTTATCTGCAATTCAGTACAAAACGTGATCAACCCCTCAATTTTCCCTTTGCTGAAAAATTT TCCATTGTCTCCCCTGTAAAGCTGT >lac AACGCAATTAATGTGAGTTAGCTCACTCATTAGGCACCCCAGGCTTTACACTTTATGCTTCCGGCTCGTATGTTGTGTGG AATTGTGAGCGGATAACAATTTCAC >male ACATTACCGCCAATTCTGTAACAGAGATCACACAAAGCGACGGTGGGGCGTAGGGGCAAGGAGGATGGAAAGAGGTTGCC GTATAAAGAAACTAGAGTCCGTTTA >malk GGAGGAGGCGGGAGGATGAGAACACGGCTTCTGTGAACTAAACCGAGGTCATGTAAGGAATTTCGTGATGTTGCTTGCAA AAATCGTGGCGATTTTATGTGCGCA >malt GATCAGCGTCGTTTTAGGTGAGTTGTTAATAAAGATTTGGAATTGTGACACAGTGCAAATTCAGACACATAAAAAAACGT CATCGCTTGCATTAGAAAGGTTTCT >ompa GCTGACAAAAAAGATTAAACATACCTTATACAAGACTTTTTTTTCATATGCCTGACGGAGTTCACACTTGTAAGTTTTCA ACTACGTTGTAGACTTTACATCGCC >tnaa TTTTTTAAACATTAAAATTCTTACGTAATTTATAATCTTTAAAAAAAGCATTTAATATTGCTCCCCGAACGATTGTGATT CGATTCACATTTAAACAATTTCAGA >uxu1 CCCATGAGAGTGAAATTGTTGTGATGTGGTTAACCCAATTAGAATTCGGGATTGACATGTCTTACCAAAAGGTAGAACTT ATACGCCATCTCATCCGATGCAAGC >pbr322 CTGGCTTAACTATGCGGCATCAGAGCAGATTGTACTGAGAGTGCACCATATGCGGTGTGAAATACCGCACAGATGCGTAA GGAGAAAATACCGCATCAGGCGCTC >trn9cat CTGTGACGGAAGATCACTTCGCAGAATAAATAAATCCTGGTGTCCCTGTTGATACCGGGAAGCCCTGGGCCAACTTTTGG CGAAAATGAGACGTTGATCGGCACG >tdc GATTTTTATACTTTAACTTGTTGATATTTAAAGGTATTTAATTGTAATAACGATACTCTGGAAAGTATTGAAAGTTAATT TGTGAGTGGTCGCACATATCCTGTT Output file format Output files for usage example Graphics File: help.gif [ememe results] Graphics File: logo1.eps [ememe results] Graphics File: logo1.png [ememe results] Graphics File: logo_rc1.eps [ememe results] Graphics File: logo_rc1.png [ememe results] File: meme.html MEME