ememetext 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, text file only 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 ememetext % ememetext crp0.s -mod oops -revcomp ex.text Multiple EM for motif elicitation, text file only. output sequence set [crp0.fasta]: Go to the input files for this example Go to the output files for this example Example 2 % ememetext crp0.s -mod oops -revcomp -w 20 ex2.text Multiple EM for motif elicitation, text file only. output sequence set [crp0.fasta]: w set, setting max and min to 20####### Go to the output files for this example Example 3 % ememetext INO_up800.s -mod anr -revcomp -bfile memenew/yeast.nc.6.freq ex3.tex t Multiple EM for motif elicitation, text file only. output sequence set [ino_up800.fasta]: Go to the input files for this example Go to the output files for this example Example 4 % ememetext lipocalin.s -mod oops -maxw 20 -nmotifs 2 ex4.text Multiple EM for motif elicitation, text file only. output sequence set [lipocalin.fasta]: Go to the input files for this example Go to the output files for this example Example 5 % ememetext farntrans5.s -mod anr -maxw 40 -maxsites 50 ex5.text Multiple EM for motif elicitation, text file only. output sequence set [farntrans5.fasta]: Go to the input files for this example Go to the output files for this example Example 6 % ememetext farntrans5.s -mod anr -w 10 -maxsites 30 -nmotifs 3 ex6.text Multiple EM for motif elicitation, text file only. output sequence set [farntrans5.fasta]: w set, setting max and min to 10####### Go to the output files for this example Example 7 % ememetext farntrans5.s -mod anr -maxw 12 -nsites 24 -nmotifs 3 ex7.text Multiple EM for motif elicitation, text file only. output sequence set [farntrans5.fasta]: Go to the output files for this example Example 8 % ememetext adh.s -mod zoops -nmotifs 20 -evt 0.01 ex8.text Multiple EM for motif elicitation, text file only. output sequence set [adh.fasta]: Go to the 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, text file only. 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. [-outtext] outfile [*.ememetext] MEME program text output file [-outseq] seqoutset [.] Sequence set filename and optional format (output USA) 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 "-outtext" associated qualifiers -odirectory2 string Output directory "-outseq" associated qualifiers -osformat3 string Output seq format -osextension3 string File name extension -osname3 string Base file name -osdirectory3 string Output directory -osdbname3 string Database name to add -ossingle3 boolean Separate file for each entry -oufo3 string UFO features -offormat3 string Features format -ofname3 string Features file name -ofdirectory3 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 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 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) MNDLSGKTVIITGGARGLGAEAARQAVAAGARVVLADVLDEEGAATARELGDAARYQHLDVTIEEDWQRVVAYAREEFGS VDGLVNNAGISTGMFLETESVERFRKVVDINLTGVFIGMKTVIPAMKDAGGGSIVNISSAAGLMGLALTSSYGASKWGVR GLSKLAAVELGTDRIRVNSVHPGMTYTPMTAETGIRQGEGNYPNTPMGRVGNEPGEIAGAVVKLLSDTSSYVTGAELAVD GGWTTGPTVKYVMGQ >3BHD_COMTE 3-BETA-HYDROXYSTEROID DEHYDROGENASE (EC 1.1.1.51) TNRLQGKVALVTGGASGVGLEVVKLLLGEGAKVAFSDINEAAGQQLAAELGERSMFVRHDVSSEADWTLVMAAVQRRLGT LNVLVNNAGILLPGDMETGRLEDFSRLLKINTESVFIGCQQGIAAMKETGGSIINMASVSSWLPIEQYAGYSASKAAVSA LTRAAALSCRKQGYAIRVNSIHPDGIYTPMMQASLPKGVSKEMVLHDPKLNRAGRAYMPERIAQLVLFLASDESSVMSGG ELHADNSILGMGL >ADH_DROME ALCOHOL DEHYDROGENASE (EC 1.1.1.1) SFTLTNKNVIFVAGLGGIGLDTSKELLKRDLKNLVILDRIENPAAIAELKAINPKVTVTFYPYDVTVPIAETTKLLKTIF AQLKTVDVLINGAGILDDHQIERTIAVNYTGLVNTTTAILDFWDKRKGGPGGIICNIGSVTGFNAIYQVPVYSGTKAAVV NFTSSLAKLAPITGVTAYTVNPGITRTTLVHKFNSWLDVEPQVAEKLLAHPTQPSLACAENFVKAIELNQNGAIWKLDLG TLEAIQWTKHWDSGI >AP27_MOUSE ADIPOCYTE P27 PROTEIN (AP27) MKLNFSGLRALVTGAGKGIGRDTVKALHASGAKVVAVTRTNSDLVSLAKECPGIEPVCVDLGDWDATEKALGGIGPVDLL VNNAALVIMQPFLEVTKEAFDRSFSVNLRSVFQVSQMVARDMINRGVPGSIVNVSSMVAHVTFPNLITYSSTKGAMTMLT KAMAMELGPHKIRVNSVNPTVVLTDMGKKVSADPEFARKLKERHPLRKFAEVEDVVNSILFLLSDRSASTSGGGILVDAG YLAS >BA72_EUBSP 7-ALPHA-HYDROXYSTEROID DEHYDROGENASE (EC 1.1.1.159) (BILE ACID 7-DEH YDROXYLASE) (BILE ACID-INDUCIBLE PROTEIN) MNLVQDKVTIITGGTRGIGFAAAKIFIDNGAKVSIFGETQEEVDTALAQLKELYPEEEVLGFAPDLTSRDAVMAAVGQVA QKYGRLDVMINNAGITSNNVFSRVSEEEFKHIMDINVTGVFNGAWCAYQCMKDAKKGVIINTASVTGIFGSLSGVGYPAS KASVIGLTHGLGREIIRKNIRVVGVAPGVVNTDMTNGNPPEIMEGYLKALPMKRMLEPEEIANVYLFLASDLASGITATT VSVDGAYRP >BDH_HUMAN D-BETA-HYDROXYBUTYRATE DEHYDROGENASE PRECURSOR (EC 1.1.1.30) (BDH) (3 -HYDROXYBUTYRATE DEHYDROGENASE) (FRAGMENT) GLRPPPPGRFSRLPGKTLSACDRENGARRPLLLGSTSFIPIGRRTYASAAEPVGSKAVLVTGCDSGFGFSLAKHLHSKGF LVFAGCLMKDKGHDGVKELDSLNSDRLRTVQLNVFRSEEVEKVVGDCPFEPEGPEKGMWGLVNNAGISTFGEVEFTSLET YKQVAEVNLWGTVRMTKSFLPLIRRAKGRVVNISSMLGRMANPARSPYCITKFGVEAFSDCLRYEMYPLGVKVSVVEPGN FIAATSLYNPESIQAIAKKMWEELPEVVRKDYGKKYFDEKIAKMETYCSSGSTDTSPVIDAVTHALTATTPYTRYHPMDY YWWLRMQIMTHLPGAISDMIYIR >BPHB_PSEPS BIPHENYL-CIS-DIOL DEHYDROGENASE (EC 1.3.1.-) MKLKGEAVLITGGASGLGRALVDRFVAEAKVAVLDKSAERLAELETDLGDNVLGIVGDVRSLEDQKQAASRCVARFGKID TLIPNAGIWDYSTALVDLPEESLDAAFDEVFHINVKGYIHAVKALPALVASRGNVIFTISNAGFYPNGGGPLYTAAKQAI VGLVRELAFELAPYVRVNGVGPGGMNSDMRGPSSLGMGSKAISTVPLADMLKSVLPIGRMPEVEEYTGAYVFFATRGDAA PASGALVNYDGGLGVRGFFSGAGGNDLLEQLNIHP >BUDC_KLETE ACETOIN(DIACETYL) REDUCTASE (EC 1.1.1.5) (ACETOIN DEHYDROGENASE) MQKVALVTGAGQGIGKAIALRLVKDGFAVAIADYNDATATAVAAEINQAGGRAVAIKVDVSRRDQVFAAVEQARKALGGF NVIVNNAGIAPSTPIESITEEIVDRVYNINVKGVIWGMQAAVEAFKKEGHGGKIVNACSQAGHVGNPELAVYSSSKFAVR GLTQTAARDLAPLGITVNGFCPGIVKTPMWAEIDRQCRKRRANRWATARLNLPNASPLAACRSLKTSPPACRSSPARIPT I >DHES_HUMAN ESTRADIOL 17 BETA-DEHYDROGENASE (EC 1.1.1.62) (20 ALPHA-HYDROXYSTERO ID DEHYDROGENASE) (E2DH) (17-BETA-HSD) (PLACENTAL 17-BETA-HYDROXYSTEROID DEHYDRO GENASE) ARTVVLITGCSSGIGLHLAVRLASDPSQSFKVYATLRDLKTQGRLWEAARALACPPGSLETLQLDVRDSKSVAAARERVT EGRVDVLVCNAGLGLLGPLEALGEDAVASVLDVNVVGTVRMLQAFLPDMKRRGSGRVLVTGSVGGLMGLPFNDVYCASKF ALEGLCESLAVLLLPFGVHLSLIECGPVHTAFMEKVLGSPEEVLDRTDIHTFHRFYQYLAHSKQVFREAAQNPEEVAEVF LTALRAPKPTLRYFTTERFLPLLRMRLDDPSGSNYVTAMHREVFGDVPAKAEAGAEAGGGAGPGAEDEAGRSAVGDPELG DPPAAPQ >DHGB_BACME GLUCOSE 1-DEHYDROGENASE B (EC 1.1.1.47) MYKDLEGKVVVITGSSTGLGKSMAIRFATEKAKVVVNYRSKEDEANSVLEEEIKKVGGEAIAVKGDVTVESDVINLVQSA IKEFGKLDVMINNAGMENPVSSHEMSLSDWNKVIDTNLTGAFLGSREAIKYFVENDIKGTVINMSSVHEWKIPWPLFVHY AASKGGMKLMTETLALEYAPKGIRVNNIGPGAINTPINAEKFADPEQRADVESMIPMGYIGEPEEIAAVAWLASSEASYV TGITLFADGGMTQYPSFQAGRG >DHII_HUMAN CORTICOSTEROID 11-BETA-DEHYDROGENASE (EC 1.1.1.146) (11-DH) (11-BETA - HYDROXYSTEROID DEHYDROGENASE) (11-BETA-HSD) MAFMKKYLLPILGLFMAYYYYSANEEFRPEMLQGKKVIVTGASKGIGREMAYHLAKMGAHVVVTARSKETLQKVVSHCLE LGAASAHYIAGTMEDMTFAEQFVAQAGKLMGGLDMLILNHITNTSLNLFHDDIHHVRKSMEVNFLSYVVLTVAALPMLKQ SNGSIVVVSSLAGKVAYPMVAAYSASKFALDGFFSSIRKEYSVSRVNVSITLCVLGLIDTETAMKAVSGIVHMQAAPKEE CALEIIKGGALRQEEVYYDSSLWTTLLIRNPCRKILEFLYSTSYNMDRFINK >DHMA_FLAS1 N-ACYLMANNOSAMINE 1-DEHYDROGENASE (EC 1.1.1.233) (NAM-DH) TTAGVSRRPGRLAGKAAIVTGAAGGIGRATVEAYLREGASVVAMDLAPRLAATRYEEPGAIPIACDLADRAAIDAAMADA VARLGGLDILVAGGALKGGTGNFLDLSDADWDRYVDVNMTGTFLTCRAGARMAVAAGAGKDGRSARIITIGSVNSFMAEP EAAAYVAAKGGVAMLTRAMAVDLARHGILVNMIAPGPVDVTGNNTGYSEPRLAEQVLDEVALGRPGLPEEVATAAVFLAE DGSSFITGSTITIDGGLSAMIFGGMREGRR >ENTA_ECOLI 2,3-DIHYDRO-2,3-DIHYDROXYBENZOATE DEHYDROGENASE (EC 1.3.1.28) MDFSGKNVWVTGAGKGIGYATALAFVEAGAKVTGFDQAFTQEQYPFATEVMDVADAAQVAQVCQRLLAETERLDALVNAA GILRMGATDQLSKEDWQQTFAVNVGGAFNLFQQTMNQFRRQRGGAIVTVASDAAHTPRIGMSAYGASKAALKSLALSVGL ELAGSGVRCNVVSPGSTDTDMQRTLWVSDDAEEQRIRGFGEQFKLGIPLGKIARPQEIANTILFLASDLASHITLQDIVV DGGSTLGA >FIXR_BRAJA FIXR PROTEIN MGLDLPNDNLIRGPLPEAHLDRLVDAVNARVDRGEPKVMLLTGASRGIGHATAKLFSEAGWRIISCARQPFDGERCPWEA GNDDHFQVDLGDHRMLPRAITEVKKRLAGAPLHALVNNAGVSPKTPTGDRMTSLTTSTDTWMRVFHLNLVAPILLAQGLF DELRAASGSIVNVTSIAGSRVHPFAGSAYATSKAALASLTRELAHDYAPHGIRVNAIAPGEIRTDMLSPDAEARVVASIP LRRVGTPDEVAKVIFFLCSDAASYVTGAEVPINGGQHL >GUTD_ECOLI SORBITOL-6-PHOSPHATE 2-DEHYDROGENASE (EC 1.1.1.140) (GLUCITOL-6- PHO SPHATE DEHYDROGENASE) (KETOSEPHOSPHATE REDUCTASE) MNQVAVVIGGGQTLGAFLCHGLAAEGYRVAVVDIQSDKAANVAQEINAEYGESMAYGFGADATSEQSCLALSRGVDEIFG RVDLLVYSAGIAKAAFISDFQLGDFDRSLQVNLVGYFLCAREFSRLMIRDGIQGRIIQINSKSGKVGSKHNSGYSAAKFG GVGLTQSLALDLAEYGITVHSLMLGNLLKSPMFQSLLPQYATKLGIKPDQVEQYYIDKVPLKRGCDYQDVLNMLLFYASP KASYCTGQSINVTGGQVMF >HDE_CANTR HYDRATASE-DEHYDROGENASE-EPIMERASE (HDE) MSPVDFKDKVVIITGAGGGLGKYYSLEFAKLGAKVVVNDLGGALNGQGGNSKAADVVVDEIVKNGGVAVADYNNVLDGDK IVETAVKNFGTVHVIINNAGILRDASMKKMTEKDYKLVIDVHLNGAFAVTKAAWPYFQKQKYGRIVNTSSPAGLYGNFGQ ANYASAKSALLGFAETLAKEGAKYNIKANAIAPLARSRMTESILPPPMLEKLGPEKVAPLVLYLSSAENELTGQFFEVAA GFYAQIRWERSGGVLFKPDQSFTAEVVAKRFSEILDYDDSRKPEYLKNQYPFMLNDYATLTNE ARKLPANDASGAPTVSLKDKVVLITGAGAGLGKEYAKWFAKYGAKVVVNDFKDATKTVDEIKAAGGEAWPDQHDVAKDSE AIIKNVIDKYGTIDILVNNAGILRDRSFAKMSKQEWDSVQQVHLIGTFNLSRLAWPYFVEKQFGRIINITSTSGIYGNFG QANYSSSKAGILGLSKTMAIEGAKNNIKVNIVAPHAETAMTLTIFREQDKNLYHADQVAPLLVYLGTDDVPVTGETSEIG GGWIGNTRWQRAKGAVSHDEHTTVEFIKEHLNEITDFTTDTENPKSTTESSMAILSAVGGDDD DDDEDEEEDEGDEEEDEEDEEEDDPVWRFDDRDVILYNIALGATTKQLKYVYENDSDFQVIPTFGHLITFNSGKSQNSFA KLLRNFNPMLLLHGEHYLKVHSWPPPTEGEIKTTFEPIATTPKGTNVVIVHGSKSVDNKSGELIYSNEATYFIRNCQADN KVYADRPAFATNQFLAPKRAPDYQVDVPVSEDLAALYRLSGDRNPLHIDPNFAKGAKFPKPILHGMCTYGLSAKALIDKF GMFNEIKARFTGIVFPGETLRVLAWKESDDTIVFQTHVVDRGTIAINNAAIKLVGDKAKI >HDHA_ECOLI 7-ALPHA-HYDROXYSTEROID DEHYDROGENASE (EC 1.1.1.159) (HSDH) MFNSDNLRLDGKCAIITGAGAGIGKEIAITFATAGASVVVSDINADAANHVVDEIQQLGGQAFACRCDITSEQELSALAD FAISKLGKVDILVNNAGGGGPKPFDMPMADFRRAYELNVFSFFHLSQLVAPEMEKNGGGVILTITSMAAENKNINMTSYA SSKAAASHLVRNMAFDLGEKNIRVNGIAPGAILTDALKSVITPEIEQKMLQHTPIRRLGQPQDIANAALFLCSPAASWVS GQILTVSGGGVQELN >LIGD_PSEPA C ALPHA-DEHYDROGENASE (EC -.-.-.-) MKDFQDQVAFITGGASGAGFGQAKVFGQAGAKIVVADVRAEAVEKAVAELEGLGITAHGIVLDIMDREAYARAADEVEAV FGQAPTLLSNTAGVNSFGPIEKTTYDDFDWIIGVNLNGVINGMVTFVPRMIASGRPGHIVTVSSLGGFMGSALAGPYSAA KAASINLMEGYRQGLEKYGIGVSVCTPANIKSNIAEASRLRPAKYGTSGYVENEESIASLHSIHQHGLEPEKLAEAIKKG VEDNALYIIPYPEVREGLEKHFQAIIDSVAPMESDPEGARQRVEALMAWGRDRTRVFAEGDKKGA >NODG_RHIME NODULATION PROTEIN G (HOST-SPECIFICITY OF NODULATION PROTEIN C) MFELTGRKALVTGASGAIGGAIARVLHAQGAIVGLHGTQIEKLETLATELGDRVKLFPANLANRDEVKALGQRAEADLEG VDILVNNAGITKDGLFLHMADPDWDIVLEVNLTAMFRLTREITQQMIRRRNGRIINVTSVAGAIGNPGQTNYCASKAGMI GFSKSLAQEIATRNITVNCVAPGFIESAMTDKLNHKQKEKIMVAIPIHRMGTGTEVASAVAYLASDHAAYVTGQTIHVNG GMAMI >RIDH_KLEAE RIBITOL 2-DEHYDROGENASE (EC 1.1.1.56) (RDH) MKHSVSSMNTSLSGKVAAITGAASGIGLECARTLLGAGAKVVLIDREGEKLNKLVAELGENAFALQVDLMQADQVDNLLQ GILQLTGRLDIFHANAGAYIGGPVAEGDPDVWDRVLHLNINAAFRCVRSVLPHLIAQKSGDIIFTAVIAGVVPVIWEPVY TASKFAVQAFVHTTRRQVAQYGVRVGAVLPGPVVTALLDDWPKAKMDEALANGSLMQPIEVAESVLFMVTRSKNVTVRDI VILPNSVDL >YINL_LISMO HYPOTHETICAL 26.8 KD PROTEIN IN INLA 5'REGION (ORFA) MTIKNKVIIITGASSGIGKATALLLAEKGAKLVLAARRVEKLEKIVQIIKANSGEAIFAKTDVTKREDNKKLVELAIERY GKVDAIFLNAGIMPNSPLSALKEDEWEQMIDINIKGVLNGIAAVLPSFIAQKSGHIIATSSVAGLKAYPGGAVYGATKWA VRDLMEVLRMESAQEGTNIRTATIYPAAINTELLETITDKETEQGMTSLYKQYGITPDRIASIVAYAIDQPEDVNVNEFT VGPTSQPW >YRTP_BACSU HYPOTHETICAL 25.3 KD PROTEIN IN RTP 5'REGION (ORF238) MQSLQHKTALITGGGRGIGRATALALAKEGVNIGLIGRTSANVEKVAEEVKALGVKAAFAAADVKDADQVNQAVAQVKEQ LGDIDILINNAGISKFGGFLDLSADEWENIIQVNLMGVYHVTRAVLPEMIERKAGDIINISSTAGQRGAAVTSAYSASKF AVLGLTESLMQEVRKHNIRVSALTPSTVASDMSIELNLTDGNPEKVMQPEDLAEYMVAQLKLDPRIFIKTAGLWSTNP >CSGA_MYXXA no comment MRAFATNVCTGPVDVLINNAGVSGLWCALGDVDYADMARTFTINALGPLR VTSAMLPGLRQGALRRVAHVTSRMGSLAANTDGGAYAYRMSKAALNMAVR SMSTDLRPEGFVTVLLHPGWVQTDMGGPDATLPAPDSVRGMLRVIDGLNP [Part of this file has been deleted for brevity] FSIAAMNELELK >FVT1_HUMAN no comment MLLLAAAFLVAFVLLLYMVSPLISPKPLALPGAHVVVTGGSSGIGKCIAI ECYKQGAFITLVARNEDKLLQAKKEIEMHSINDKQVVLCISVDVSQDYNQ VENVIKQAQEKLGPVDMLVNCAGMAVSGKFEDLEVSTFERLMSINYLGSV YPSRAVITTMKERRVGRIVFVSSQAGQLGLFGFTAYSASKFAIRGLAEAL QMEVKPYNVYITVAYPPDTDTPGFAEENRTKPLETRLISETTSVCKPEQV AKQIVKDAIQGNFNSSLGSDGYMLSALTCGMAPVTSITEGLQQVVTMGLF RTIALFYLGSFDSIVRRCMMQREKSENADKTA >HMTR_LEIMA no comment MTAPTVPVALVTGAAKRLGRSIAEGLHAEGYAVCLHYHRSAAEANALSAT LNARRPNSAITVQADLSNVATAPVSGADGSAPVTLFTRCAELVAACYTHW GRCDVLVNNASSFYPTPLLRNDEDGHEPCVGDREAMETATADLFGSNAIA PYFLIKAFAHRSRHPSQASRTNYSIINMVDAMTNQPLLGYTIYTMAKGAL EGLTRSAALELAPLQIRVNGVGPGLSVLVDDMPPAVWEGHRSKVPLYQRD SSAAEVSDVVIFLCSSKAKYITGTCVKVDGGYSLTRA >MAS1_AGRRA no comment MHQLWAYDVGTLGCVSYHALPDIKRHSPKSGHLYLNKPSLRSFILQCPSL ARTLVLPSHQPVSRSSTSSAMVQPISTRKKCTCKVKNIGVCRAPARTSVS MELANAKRFSPATFSANFLSXSVVCSPLLRAIQTALIANIGFLCFDIDED LKERDFGKHEGGYGPLKMFEDNYPDCEDTEMFSLRVAKALTHAKNENTLF VSHGGVLRVIAALLGVDLTKEHTNNGRVLHFRRGFSHWTVEIHQSPVILV SGSNRGVGKAIAEDLIAHGYRLSLGARKVKDLEVAFGPQDEWLHYARFDA EDHGTMAAWVTAAVEKFGRIDGLVNNAGYGEPVNLDKHVDYQRFHLQWYI NCVAPLRMTELCLPHLYETGSGRIVNINSMSGQRVLNPLVGYNMTKHALG GLTKTTQHVGWDRRCAAIDICLGFVATDMSAWTDLIASKDMIQPEDIAKL VREAIERPNRAYVPRSEVMCIKEATR >PCR_PEA no comment MALQTASMLPASFSIPKEGKIGASLKDSTLFGVSSLSDSLKGDFTSSALR CKELRQKVGAVRAETAAPATPAVNKSSSEGKKTLRKGNVVITGASSGLGL ATAKALAESGKWHVIMACRDYLKAARAAKSAGLAKENYTIMHLDLASLDS VRQFVDNFRRSEMPLDVLINNAAVYFPTAKEPSFTADGFEISVGTNHLGH FLLSRLLLEDLKKSDYPSKRLIIVGSITGNTNTLAGNVPPKANLGDLRGL AGGLTGLNSSAMIDGGDFDGAKAYKDSKVCNMLTMQEFHRRYHEETGITF ASLYPGCIATTGLFREHIPLFRTLFPPFQKYITKGYVSEEESGKRLAQVV SDPSLTKSGVYWSWNNASASFENQLSQEASDAEKARKVWEVSEKLVGLA >RFBB_NEIGO no comment MQTEGKKNILVTGGAGFIGSAVVRHIIQNTRDSVVNLDKLTYAGNLESLT DIADNPRYAFEQVDICDRAELDRVFAQYRPDAVMHLAAESHVDRAIGSAG EFIRTNIVGTFDLLEAARAYWQQMPSEKREAFRFHHISTDEVYGDLHGTD DLFTETTPYAPSSPYSASKAAADHLVRAWQRTYRLPSIVSNCSNNYGPRQ FPEKLIPLMILNALSGKPLPVYGDGAQIRDWLFVEDHARALYQVVTEGVV GETYNIGGHNEKTNLEVVKTICALLEELAPEKPAGVARYEDLITFVQDRP GHDARYAVDAAKIRRDLGWLPLETFESGLRKTVQWYLDNKTRRQNA >YURA_MYXXA no comment RQHTGGLHGGDELPDGVGDGCLQRPGTRAGAVARQAGVRVFAAGRRLPQL QAADEAPGGRRHRGARGVDVTKADATLERIRALDAEAGGLDLVVANAGVG GTTNAKRLPWERVRGIIDTNVTGAAATLSAVLPQMVERKRGHLVGVSSLA GFRGLPATRYSASKAFLSTFMESLRVDLRGTGVRVTCIYPGFVKSELTAT NNFPMPFLMETHDAVELMGKGIVRGDAEVSFPWQLAVPTRMAKVLPNPLF DAAARRLR Output file format Output files for usage example File: crp0.fasta >ce1cg TAATGTTTGTGCTGGTTTTTGTGGCATCGGGCGAGAATAGCGCGTGGTGTGAAAGACTGT TTTTTTGATCGTTTTCACAAAAATGGAAGTCCACAGTCTTGACAG >ara GACAAAAACGCGTAACAAAAGTGTCTATAATCACGGCAGAAAAGTCCACATTGATTATTT GCACGGCGTCACACTTTGCTATGCCATAGCATTTTTATCCATAAG >bglr1 ACAAATCCCAATAACTTAATTATTGGGATTTGTTATATATAACTTTATAAATTCCTAAAA TTACACAAAGTTAATAACTGTGAGCATGGTCATATTTTTATCAAT >crp CACAAAGCGAAAGCTATGCTAAAACAGTCAGGATGCTACAGTAATACATTGATGTACTGC ATGTATGCAAAGGACGTCACATTACCGTGCAGTACAGTTGATAGC >cya ACGGTGCTACACTTGTATGTAGCGCATCTTTCTTTACGGTCAATCAGCAAGGTGTTAAAT TGATCACGTTTTAGACCATTTTTTCGTCGTGAAACTAAAAAAACC >deop2 AGTGAATTATTTGAACCAGATCGCATTACAGTGATGCAAACTTGTAAGTAGATTTCCTTA ATTGTGATGTGTATCGAAGTGTGTTGCGGAGTAGATGTTAGAATA >gale GCGCATAAAAAACGGCTAAATTCTTGTGTAAACGATTCCACTAATTTATTCCATGTCACA CTTTTCGCATCTTTGTTATGCTATGGTTATTTCATACCATAAGCC >ilv GCTCCGGCGGGGTTTTTTGTTATCTGCAATTCAGTACAAAACGTGATCAACCCCTCAATT TTCCCTTTGCTGAAAAATTTTCCATTGTCTCCCCTGTAAAGCTGT >lac AACGCAATTAATGTGAGTTAGCTCACTCATTAGGCACCCCAGGCTTTACACTTTATGCTT CCGGCTCGTATGTTGTGTGGAATTGTGAGCGGATAACAATTTCAC >male ACATTACCGCCAATTCTGTAACAGAGATCACACAAAGCGACGGTGGGGCGTAGGGGCAAG GAGGATGGAAAGAGGTTGCCGTATAAAGAAACTAGAGTCCGTTTA >malk GGAGGAGGCGGGAGGATGAGAACACGGCTTCTGTGAACTAAACCGAGGTCATGTAAGGAA TTTCGTGATGTTGCTTGCAAAAATCGTGGCGATTTTATGTGCGCA >malt GATCAGCGTCGTTTTAGGTGAGTTGTTAATAAAGATTTGGAATTGTGACACAGTGCAAAT TCAGACACATAAAAAAACGTCATCGCTTGCATTAGAAAGGTTTCT >ompa GCTGACAAAAAAGATTAAACATACCTTATACAAGACTTTTTTTTCATATGCCTGACGGAG TTCACACTTGTAAGTTTTCAACTACGTTGTAGACTTTACATCGCC >tnaa TTTTTTAAACATTAAAATTCTTACGTAATTTATAATCTTTAAAAAAAGCATTTAATATTG CTCCCCGAACGATTGTGATTCGATTCACATTTAAACAATTTCAGA >uxu1 CCCATGAGAGTGAAATTGTTGTGATGTGGTTAACCCAATTAGAATTCGGGATTGACATGT CTTACCAAAAGGTAGAACTTATACGCCATCTCATCCGATGCAAGC >pbr322 CTGGCTTAACTATGCGGCATCAGAGCAGATTGTACTGAGAGTGCACCATATGCGGTGTGA AATACCGCACAGATGCGTAAGGAGAAAATACCGCATCAGGCGCTC >trn9cat CTGTGACGGAAGATCACTTCGCAGAATAAATAAATCCTGGTGTCCCTGTTGATACCGGGA AGCCCTGGGCCAACTTTTGGCGAAAATGAGACGTTGATCGGCACG >tdc GATTTTTATACTTTAACTTGTTGATATTTAAAGGTATTTAATTGTAATAACGATACTCTG GAAAGTATTGAAAGTTAATTTGTGAGTGGTCGCACATATCCTGTT File: ex.text ******************************************************************************** MEME - Motif discovery tool ******************************************************************************** MEME version 4.7.0 (Release date: Wed Sep 28 17:30:10 EST 2011) For further information on how to interpret these results or to get a copy of the MEME software please access http://meme.nbcr.net. This file may be used as input to the MAST algorithm for searching sequence databases for matches to groups of motifs. MAST is available for interactive use and downloading at http://meme.nbcr.net. ******************************************************************************** ******************************************************************************** REFERENCE ******************************************************************************** If you use this program in your research, please cite: Timothy L. Bailey and Charles Elkan, "Fitting a mixture model by expectation maximization to discover motifs in biopolymers", Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, pp. 28-36, AAAI Press, Menlo Park, California, 1994. ******************************************************************************** ******************************************************************************** TRAINING SET ******************************************************************************** DATAFILE= crp0.fasta ALPHABET= ACGT Sequence name Weight Length Sequence name Weight Length ------------- ------ ------ ------------- ------ ------ ce1cg 1.0000 105 ara 1.0000 105 bglr1 1.0000 105 crp 1.0000 105 cya 1.0000 105 deop2 1.0000 105 gale 1.0000 105 ilv 1.0000 105 lac 1.0000 105 male 1.0000 105 malk 1.0000 105 malt 1.0000 105 ompa 1.0000 105 tnaa 1.0000 105 uxu1 1.0000 105 pbr322 1.0000 105 trn9cat 1.0000 105 tdc 1.0000 105 ******************************************************************************** ******************************************************************************** COMMAND LINE SUMMARY ******************************************************************************** This information can also be useful in the event you wish to report a problem with the MEME software. [Part of this file has been deleted for brevity] -------------------------------------------------------------------------------- GTGA[TC][CG][TC][ATG][GT][TC]TCACA -------------------------------------------------------------------------------- Time 0.50 secs. ******************************************************************************** ******************************************************************************** SUMMARY OF MOTIFS ******************************************************************************** -------------------------------------------------------------------------------- Combined block diagrams: non-overlapping sites with p-value < 0.0001 -------------------------------------------------------------------------------- SEQUENCE NAME COMBINED P-VALUE MOTIF DIAGRAM ------------- ---------------- ------------- ce1cg 1.94e-03 64_[+1(1.07e-05)]_26 ara 5.19e-04 57_[-1(2.85e-06)]_33 bglr1 1.76e-03 78_[-1(9.67e-06)]_12 crp 2.34e-03 65_[-1(1.29e-05)]_25 cya 8.88e-04 52_[-1(4.88e-06)]_38 deop2 1.76e-03 9_[-1(9.67e-06)]_81 gale 1.06e-02 54_[+1(5.85e-05)]_36 ilv 2.85e-02 105 lac 2.93e-04 11_[-1(1.61e-06)]_79 male 2.80e-03 16_[-1(1.54e-05)]_74 malk 9.85e-04 64_[+1(5.41e-06)]_26 malt 2.12e-03 44_[+1(1.17e-05)]_46 ompa 4.19e-04 51_[+1(2.30e-06)]_39 tnaa 7.20e-04 74_[+1(3.95e-06)]_16 uxu1 2.80e-03 20_[+1(1.54e-05)]_70 pbr322 9.85e-04 55_[-1(5.41e-06)]_35 trn9cat 4.18e-02 105 tdc 3.35e-03 81_[+1(1.84e-05)]_9 -------------------------------------------------------------------------------- ******************************************************************************** ******************************************************************************** Stopped because nmotifs = 1 reached. ******************************************************************************** CPU: peterlenovo ******************************************************************************** Output files for usage example 2 File: ex2.text ******************************************************************************** MEME - Motif discovery tool ******************************************************************************** MEME version 4.7.0 (Release date: Wed Sep 28 17:30:10 EST 2011) For further information on how to interpret these results or to get a copy of the MEME software please access http://meme.nbcr.net. This file may be used as input to the MAST algorithm for searching sequence databases for matches to groups of motifs. MAST is available for interactive use and downloading at http://meme.nbcr.net. ******************************************************************************** ******************************************************************************** REFERENCE ******************************************************************************** If you use this program in your research, please cite: Timothy L. Bailey and Charles Elkan, "Fitting a mixture model by expectation maximization to discover motifs in biopolymers", Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, pp. 28-36, AAAI Press, Menlo Park, California, 1994. ******************************************************************************** ******************************************************************************** TRAINING SET ******************************************************************************** DATAFILE= crp0.fasta ALPHABET= ACGT Sequence name Weight Length Sequence name Weight Length ------------- ------ ------ ------------- ------ ------ ce1cg 1.0000 105 ara 1.0000 105 bglr1 1.0000 105 crp 1.0000 105 cya 1.0000 105 deop2 1.0000 105 gale 1.0000 105 ilv 1.0000 105 lac 1.0000 105 male 1.0000 105 malk 1.0000 105 malt 1.0000 105 ompa 1.0000 105 tnaa 1.0000 105 uxu1 1.0000 105 pbr322 1.0000 105 trn9cat 1.0000 105 tdc 1.0000 105 ******************************************************************************** ******************************************************************************** COMMAND LINE SUMMARY ******************************************************************************** This information can also be useful in the event you wish to report a problem with the MEME software. [Part of this file has been deleted for brevity] -------------------------------------------------------------------------------- [TA][AT]AT[GT]T[GA][AC][AGT]C[CTAGA]A[CTG][GAC]TCACA[AC] -------------------------------------------------------------------------------- Time 0.50 secs. ******************************************************************************** ******************************************************************************** SUMMARY OF MOTIFS ******************************************************************************** -------------------------------------------------------------------------------- Combined block diagrams: non-overlapping sites with p-value < 0.0001 -------------------------------------------------------------------------------- SEQUENCE NAME COMBINED P-VALUE MOTIF DIAGRAM ------------- ---------------- ------------- ce1cg 1.25e-03 60_[+1(7.30e-06)]_25 ara 1.68e-06 54_[+1(9.77e-09)]_31 bglr1 1.93e-03 77_[-1(1.12e-05)]_8 crp 8.74e-04 62_[+1(5.08e-06)]_23 cya 2.47e-03 51_[-1(1.44e-05)]_34 deop2 3.29e-04 6_[+1(1.91e-06)]_79 gale 1.23e-04 41_[+1(7.15e-07)]_44 ilv 4.96e-03 38_[+1(2.89e-05)]_47 lac 2.67e-04 8_[+1(1.55e-06)]_77 male 4.93e-04 13_[+1(2.86e-06)]_72 malk 2.47e-03 62_[-1(1.44e-05)]_23 malt 4.09e-05 42_[-1(2.38e-07)]_43 ompa 9.58e-04 49_[-1(5.57e-06)]_36 tnaa 1.38e-04 72_[-1(8.02e-07)]_13 uxu1 7.96e-04 18_[-1(4.63e-06)]_67 pbr322 4.03e-04 54_[-1(2.34e-06)]_31 trn9cat 6.32e-02 105 tdc 4.03e-04 79_[-1(2.34e-06)]_6 -------------------------------------------------------------------------------- ******************************************************************************** ******************************************************************************** Stopped because nmotifs = 1 reached. ******************************************************************************** CPU: peterlenovo ******************************************************************************** Output files for usage example 3 File: ino_up800.fasta >CHO1 sequence of the region upstream from YER026C CCGACCCAAATGTAATGGAACAATATTATTTGACACTTGATCAGCAGCAAAATAATCACC AAAATATGGCCTGGTTGACTCCTCCACAACTGCCACCTCATTTAGAAAACGTCATTTTGA ATAGTTACTCAAACGCGCAAACTGATAATACGTCTGGCGCCCTTCCCATTCCGAACCATG TTATATTGAACCATCTGGCGACAAGCAGTATTAAGCATAATACATTATGTGTCGCATCCA TTGTTAGGTATAAACAAAAATACGTGACCCAAATACTGTATACACCATTGCAATAGATAT GATTATAGAGCTTATAGCTACATCTTTTTAGATAAAAGCGAAGATGTTTCTGCGATTTTT CCATTATAGCTCTCCATGATACTAAATATCAAGGTCTACATGTAAGTATTTGTATATATG GGTTGGAATGTATATACGTATATACGTACGTACGTACGTATATGCACATAATTGTTACGG GATGTATATATAAATTAGTAGCATTATAGAAGATATCCCTAACATCAATCCCCACTCCTT CTCAATGTGTGCAGACTTCTGTGCCAGACACTGAATATATATCAGTAATTGGTCAAAATC ACTTTGAACGTTCACACGGCACCCTCACGCCTTTGAGCTTTCACATGGACCCATCTAAAG ATGAAGATCCGTATTTTATAGGAAACATTATAAATAAGGAAAGAGAGATACACCTATTTT TTTCATTTTGTGGGTGATTGTCATTTTTAGTTGTCTATTTGATTCAATCAAAAAACAAAA ATAAAACTATATATTAAAAA >CHO2 sequence of the region upstream from YGR157W ACCCTCTAACGCGAATAAAGCGAATGACAGCGGCACCATTAATATGGCGAAACTGCAATT ACTACCTGAAAACCAACAAGATATGATCAAACAAGTTCTTACTTTGACACCTGCCCAGAT CCAAAGTTTACCAAGTGACCAGCAACTTATGGTGGAAAACTTTAGAAAAGAATATATAAT CTAAGTAATCAGAGCCATAGCGTATCAGAAAACCACACCTAATTAGATGGTTCTTGCATC TGTACCTCTTATCACTAAAAGCGGCACTAAACTTCCAACATTAAATGTTTGCCTTGTTAA ATATATATTTTTGCCTTGGTTTAAATTGGTCAAGACAGTCAATTGCCACACTTTTCTCAT GCCGCATTCATTATTCGCGAAGTTTTCCACACAAAACTGTGAAAATGAACGGCGATGCCA GAAACGGCAAAACCTCAAATGTTAGATAACGTGGATCTCCGACACATGTGAATTTATAAG TAGGCATATGAAAATACAGATTCTTTCCACTGTGTTCCCTTTTATTCCCTTCTCATGTGA AGAGTTCACACCAAATCTTCAAAATATAACTAATATAGTAGAGTTTGATTCAAAGGACCT TTTTTTTTGCCTCTTTGATTAGTTTATCTTCTTTTCTTCATTTTATCCCCTAATTTTATA CGTTAGTTCAACCTAACAATCCAGGATTTCATTAACAAGAAAGGTAAAAGTAACCTATCA AGGCTATTTTGAAAAAAAAAATTCCGCCCTGAATATTTCGAGTGATTTTCTTAGTGACAA AGCTTTTTCTTCATCTGTAG >FAS1 sequence of the region upstream from YKL182W CCGGGTTATAGCAGCGTCTGCTCCGCATCACGATACACGAGGTGCAGGCACGGTTCACTA CTCCCCTGGCCTCCAACAAACGACGGCCAAAAACTTCACATGCCGCCCAGCCAAGCATAA TTACGCAACAGCGATCTTTCCGTCGCACAAGTTAAAAGAAATTGTTGAAAAATACAAATA ATCGCGAACAATACGTTGTTGCTATTTAACGCTTTTGGTCTGACAGTAAGTGTGCCTTTC CCAATCACCGAAAAGTGTTGAACGATTCACTGCGACAATAATCAGAGATTACAGTCGGCA TTTTGGCATTTTTGGCATACTTTTTATCGATTGAACCATCTTCTCCAAACACTTTTCCTT TTTCCTTCTATTCTGCAGGACCAACTAAAACTGGGTATATATATCATTATCTATATATAT AAACGGCTTTCAACAAAGTTATAGGGGAAAACTAAAAATATAAGAAAAAAAAAGGTATTG ATTGATAAGGAAAAAGAACCAAGGGAAAAATATAAAAAAGTACATTGGGCCTTTTCATAC TTGTTATCACTTACATTACAAAGAAGAACAAACAACTTTTTTAAACGAATTTTCTTTCTT CCTTTTTCAATTTATTAATTCTTTTTTTCCATACAATTCAAGGTCAAATATATTCTTATA TGCTCTTTGAATATTTCTGAAAAATATATAAAGAAAAGAAACTACAAGAACATCATCCGG AAAATCAGATTATAGACTAGGATTCCGCTCTTTTTAGTATATTTATTCGCCACACCTAAC TGCTCTATTATTCGCTCATT >FAS2 sequence of the region upstream from YPL231W TCCAGGCAAGGCACCAAGAGTTATTGAAACTAGAAAAATCCATGGCAGAACTTACTCAAT TGTTTAATGACATGGAAGAACTGGTAATAGAACAACAAGAAAACGTAGACGTCATCGACA AGAACGTTGAAGACGCTCAACTCGACGTAGAACAGGGTGTCGGTCATACCGATAAAGCCG TCAAGAGTGCCAGAAAAGCAAGAAAGAACAAGATTAGATGTTGGTTGATTGTATTCGCCA [Part of this file has been deleted for brevity] CTCTTCCTAAAAATACATTGGGCATTACCCGCAAACTAACCCATCGCTTAGCAAAATCCA ACCATTTTTTTTTTATCTCCCGCGTTTTCACATGCTACCTCATTCGCCTCGTAACGTTAC GACCGAAATCTCACTAAGGCACGGTTTGTTGGGCAGTTTACAGATGTTGGATAACCAGTT GTTTCTAAACGGTTATGCCTCATATATAACTTGTTAACTGAAGGTTACACAAGACCACAT CACCACTGTCGTGCTTTTCTAATAACCGCTATATTAGACGTTTAAAGGGCTACAGCAACA CCAATTGAAATACCATCATT >ACC1 sequence of the region upstream from YNR016C TATCCAAAGGGGAATGCTTCATCTTGTTGAACAACGCCCAACAATTTCCACTGCCCACCG AATCGTTGCGCCCGTTAAAATCTTCACATGGCCCGGCCGCGCGCGCGTTGTGCCAACAAG TCGCAGTCGAAATTCAACCGCTCATTGCCACTCTCTCTACTGCTTGGTGAACTAGGCTAT ACGCTCAATCAGCGCCAAGATATATAAGAAGAACAGCACTCCCAGTCGTATTCTGGCACA GTATAGCCTAGCACAATCACTGTCACAATTGTTATCGGTTCTACAATTGTTCTGCTCTCT TCAATTTTCCTTTCCTTATTCTACTCTTTTTATCCCTTTCGTACAGTTTACCTGAAGATA AAAAACAACAAAGCCAATTCCCTAATTTGCAATCGCCATTTGCATCTATATATATATATT TGTTGTGCCATTTTTTTATCCTCTGTGAGTGATCGGTGCATGTGTTTATAAAAGTTTATT CATTCTACTATACGAACTTTTCCCTCTGCCCTTCCCTCCCGCTTCATCCTTATTTTTGGA CAATAAACTAGAGAACAATTTGAACTTGAATTGGAATTCAGATTCAGAGCAAGAGACAAG AAACTTCCCTTTTTCTTCTCCACATATTATTATTTATTCGTGTATTTTCTTTTAACGATA CGATACGATACGACACGATACGATACGACACGCTACTATACTATACAAATATAATAGTAT AATAACCGATTCGTCTTCTAGCTTAATTTTTTTCCGTTCCCGAAACAGCGCAGAAAATTA GAAAAAATCAAGTTTCTACC >INO1 sequence of the region upstream from YJL153C AGCAAACAACCAAATATAATTTAGAAATGGACAGAGACCATATTAATGACCATGACCATC GAATGAGCTATTCCATCAACAAGGACGACTTGTTGTTAATGGTTTTGGCGGTTTTCATTC CCCCAGTGGCCGTCTGGAAGCGTAAGGGTATGTTCAACAGGGATACACTATTGAACTTAC TTCTCTTCCTACTGTTATTCTTCCCAGCAATCATTCACGCTTGCTACGTTGTATATGAAA CGAGTAGTGAACGTTCGTACGATCTTTCACGCAGACATGCGACTGCGCCCGCCGTAGACC GTGACCTGGAAGCTCACCCTGCAGAGGAATCTCAAGCACAGCCTCCAGCATATGATGAAG ACGATGAGGCCGGTGCCGATGTGCCCTTGATGGACAACAAACAACAGCTCTCTTCCGGCC GTACTTAGTGATCGGAACGAGCTCTTTATCACCGTAGTTCTAAATAACACATAGAGTAAA TTATTGCCTTTTTCTTCGTTCCTTTTGTTCTTCACGTCCTTTTTATGAAATACGTGCCGG TGTTCCGGGGTTGGATGCGGAATCGAAAGTGTTGAATGTGAAATATGCGGAGGCCAAGTA TGCGCTTCGGCGGCTAAATGCGGCATGTGAAAAGTATTGTCTATTTTATCTTCATCCTTC TTTCCCAGAATATTGAACTTATTTAATTCACATGGAGCAGAGAAAGCGCACCTCTGCGTT GGCGGCAATGTTAATTTGAGACGTATATAAATTGGAGCTTTCGTCACCTTTTTTTGGCTT GTTCTGTTGTCGGGTTCCTA >OPI3 sequence of the region upstream from YJR073C GTGTCCACAACGTGAAACTTCCGTACCATTTCTTGCAACAATTGGTAAACAGCATGACAT CTTGCAGGCAACTCTTTGTTGCTTGCTTGCGACGCCTCCTCCTTTGTCAAAGGTACATTA ATGGAGATGACCACATCCGTGTCAAACTGGGTTAATCTGATCAACGCTACGCCGATGACA ACGGTCTGTGCCAGATCTGGTTTTCCCCACTTATTTGCTACTTCCATAACGAGTCCGGTG AACTTGGTTCCTTGCTGAACAGTGTCTTCTTGTAAAGCTTCCCATTTGGTGGTCCCGTTC AACTCCGTCAGGTCTTCCACGTGGAACTGCCAAGCCTCCTTCAGATCGCTCTTGTCGACC GTCTCCAAGAGATCCACGATAATGCTTTCATTGGTGGCTAGTCCATCTTCGAATTCTTCT TCATCGCGACGGGAATTGACGTACACCTCCTGTGTATCGGGGACTTCTCTTAGAGTAGAA GCGTCTATAAACCCAGGTGGGACGACAGTAGTGATGGCGCCGCCGTATAATTCGACTTCC TTGTTGTTCATGCTTCCTTGATGACCAGGGTAGGTGTCAATGAGAGTGCATGTGGAAAGT TGCACCGGTTGTGAAATATGAGAAGCCTTTTCAATCTTCATATGCAAACCCACACATGCA TCGTTGGTTTCTGTCCACTGCCACTGCAATGACCACTGGATAAGGGGTCTTTATAAGAGA ACACATATGAAGAACATGAACGTTCTTGGACAGAGCCATAAACAGCAATTGAAGACAACA AGAATAGCGCAAGTCAAGCG File: ex3.text ******************************************************************************** MEME - Motif discovery tool ******************************************************************************** MEME version 4.7.0 (Release date: Wed Sep 28 17:30:10 EST 2011) For further information on how to interpret these results or to get a copy of the MEME software please access http://meme.nbcr.net. This file may be used as input to the MAST algorithm for searching sequence databases for matches to groups of motifs. MAST is available for interactive use and downloading at http://meme.nbcr.net. ******************************************************************************** ******************************************************************************** REFERENCE ******************************************************************************** If you use this program in your research, please cite: Timothy L. Bailey and Charles Elkan, "Fitting a mixture model by expectation maximization to discover motifs in biopolymers", Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, pp. 28-36, AAAI Press, Menlo Park, California, 1994. ******************************************************************************** ******************************************************************************** TRAINING SET ******************************************************************************** DATAFILE= ino_up800.fasta ALPHABET= ACGT Sequence name Weight Length Sequence name Weight Length ------------- ------ ------ ------------- ------ ------ CHO1 1.0000 800 CHO2 1.0000 800 FAS1 1.0000 800 FAS2 1.0000 800 ACC1 1.0000 800 INO1 1.0000 800 OPI3 1.0000 800 ******************************************************************************** ******************************************************************************** COMMAND LINE SUMMARY ******************************************************************************** This information can also be useful in the event you wish to report a problem with the MEME software. command: meme ino_up800.fasta -bfile ../../data/memenew/yeast.nc.6.freq -mod anr -prior dirichlet -revcomp -nostatus -dna -text model: mod= anr nmotifs= 1 evt= inf object function= E-value of product of p-values [Part of this file has been deleted for brevity] 0.000000 0.714286 0.285714 0.000000 0.428571 0.500000 0.000000 0.071429 0.357143 0.214286 0.357143 0.071429 0.214286 0.714286 0.000000 0.071429 0.357143 0.571429 0.071429 0.000000 0.071429 0.428571 0.142857 0.357143 0.142857 0.428571 0.000000 0.428571 -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Motif 1 regular expression -------------------------------------------------------------------------------- TTCACATG[CG][CA][AGC][CA][CA][CT][CT] -------------------------------------------------------------------------------- Time 7.41 secs. ******************************************************************************** ******************************************************************************** SUMMARY OF MOTIFS ******************************************************************************** -------------------------------------------------------------------------------- Combined block diagrams: non-overlapping sites with p-value < 0.0001 -------------------------------------------------------------------------------- SEQUENCE NAME COMBINED P-VALUE MOTIF DIAGRAM ------------- ---------------- ------------- CHO1 3.16e-04 162_[+1(3.61e-06)]_351_[+1(9.87e-05)] _67_[+1(2.01e-07)]_14_[+1(7.50e-07)]_146 CHO2 9.08e-04 353_[+1(5.77e-07)]_109_[-1(7.24e-06)] _308 FAS1 9.60e-06 94_[+1(6.11e-09)]_691 FAS2 2.82e-04 566_[+1(1.80e-07)]_219 ACC1 6.55e-04 82_[+1(4.17e-07)]_703 INO1 4.14e-05 546_[-1(2.94e-06)]_6_[-1(8.23e-07)]_3 4_[-1(2.64e-08)]_55_[+1(1.09e-06)]_99 OPI3 1.57e-03 581_[-1(1.82e-06)]_40_[+1(1.00e-06)]_ 149 -------------------------------------------------------------------------------- ******************************************************************************** ******************************************************************************** Stopped because nmotifs = 1 reached. ******************************************************************************** CPU: peterlenovo ******************************************************************************** Output files for usage example 4 File: lipocalin.fasta >ICYA_MANSE GDIFYPGYCPDVKPVNDFDLSAFAGAWHEIAKLPLENENQGKCTIAEYKYDGKKASVYNS FVSNGVKEYMEGDLEIAPDAKYTKQGKYVMTFKFGQRVVNLVPWVLATDYKNYAINYNCD YHPDKKAHSIHAWILSKSKVLEGNTKEVVDNVLKTFSHLIDASKFISNDFSEAACQYSTT YSLTGPDRH >LACB_BOVIN MKCLLLALALTCGAQALIVTQTMKGLDIQKVAGTWYSLAMAASDISLLDAQSAPLRVYVE ELKPTPEGDLEILLQKWENGECAQKKIIAEKTKIPAVFKIDALNENKVLVLDTDYKKYLL FCMENSAEPEQSLACQCLVRTPEVDDEALEKFDKALKALPMHIRLSFNPTQLEEQCHI >BBP_PIEBR NVYHDGACPEVKPVDNFDWSNYHGKWWEVAKYPNSVEKYGKCGWAEYTPEGKSVKVSNYH VIHGKEYFIEGTAYPVGDSKIGKIYHKLTYGGVTKENVFNVLSTDNKNYIIGYYCKYDED KKGHQDFVWVLSRSKVLTGEAKTAVENYLIGSPVVDSQKLVYSDFSEAACKVN >RETB_BOVIN ERDCRVSSFRVKENFDKARFAGTWYAMAKKDPEGLFLQDNIVAEFSVDENGHMSATAKGR VRLLNNWDVCADMVGTFTDTEDPAKFKMKYWGVASFLQKGNDDHWIIDTDYETFAVQYSC RLLNLDGTCADSYSFVFARDPSGFSPEVQKIVRQRQEELCLARQYRLIPHNGYCDGKSER NIL >MUP2_MOUSE MKMLLLLCLGLTLVCVHAEEASSTGRNFNVEKINGEWHTIILASDKREKIEDNGNFRLFL EQIHVLEKSLVLKFHTVRDEECSELSMVADKTEKAGEYSVTYDGFNTFTIPKTDYDNFLM AHLINEKDGETFQLMGLYGREPDLSSDIKERFAKLCEEHGILRENIIDLSNANRCLQARE File: ex4.text ******************************************************************************** MEME - Motif discovery tool ******************************************************************************** MEME version 4.7.0 (Release date: Wed Sep 28 17:30:10 EST 2011) For further information on how to interpret these results or to get a copy of the MEME software please access http://meme.nbcr.net. This file may be used as input to the MAST algorithm for searching sequence databases for matches to groups of motifs. MAST is available for interactive use and downloading at http://meme.nbcr.net. ******************************************************************************** ******************************************************************************** REFERENCE ******************************************************************************** If you use this program in your research, please cite: Timothy L. Bailey and Charles Elkan, "Fitting a mixture model by expectation maximization to discover motifs in biopolymers", Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, pp. 28-36, AAAI Press, Menlo Park, California, 1994. ******************************************************************************** ******************************************************************************** TRAINING SET ******************************************************************************** DATAFILE= lipocalin.fasta ALPHABET= ACDEFGHIKLMNPQRSTVWY Sequence name Weight Length Sequence name Weight Length ------------- ------ ------ ------------- ------ ------ ICYA_MANSE 1.0000 189 LACB_BOVIN 1.0000 178 BBP_PIEBR 1.0000 173 RETB_BOVIN 1.0000 183 MUP2_MOUSE 1.0000 180 ******************************************************************************** ******************************************************************************** COMMAND LINE SUMMARY ******************************************************************************** This information can also be useful in the event you wish to report a problem with the MEME software. command: meme lipocalin.fasta -mod oops -nmotifs 2 -prior dirichlet -maxw 20 -no status -protein -text model: mod= oops nmotifs= 2 evt= inf object function= E-value of product of p-values width: minw= 8 maxw= 20 minic= 0.00 [Part of this file has been deleted for brevity] 0.000000 0.000000 0.200000 0.200000 0.000000 0.000000 0.000000 0.000000 0.600000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.200000 0.000000 0.000000 0.600000 0.000000 0.000000 0.000000 0.000000 0.200000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.400000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.600000 0.400000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.200000 0.000000 0.400000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.400000 0.000000 0.200000 0.200000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.200000 0.000000 0.000000 0.200000 0.000000 0.000000 0.000000 0.200000 0.200000 0.000000 0.000000 0.000000 0.000000 0.000000 0.200000 0.000000 0.200000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.200000 0.000000 0.000000 0.000000 0.000000 0.200000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.600000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.200000 0.200000 0.200000 0.000000 0.000000 0.000000 0.200000 0.000000 0.000000 0.000000 0.200000 0.000000 0.600000 0.000000 0.200000 0.000000 0.000000 0.000000 0.200000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Motif 2 regular expression -------------------------------------------------------------------------------- [ENF][NDL][VDKT][FHPV][WLNT][VI][LIP][DAKS]TD[YN][KDE][NKT][YF][ALI][ILMV][AFGNQ ][YCH][LMNSY][CEI] -------------------------------------------------------------------------------- Time 0.50 secs. ******************************************************************************** ******************************************************************************** SUMMARY OF MOTIFS ******************************************************************************** -------------------------------------------------------------------------------- Combined block diagrams: non-overlapping sites with p-value < 0.0001 -------------------------------------------------------------------------------- SEQUENCE NAME COMBINED P-VALUE MOTIF DIAGRAM ------------- ---------------- ------------- ICYA_MANSE 5.85e-32 13_[1(1.17e-18)]_67_[2(2.23e-20)]_70 LACB_BOVIN 2.65e-27 21_[1(4.11e-17)]_64_[2(3.82e-17)]_18_ [1(7.85e-05)]_17 BBP_PIEBR 3.66e-31 12_[1(6.04e-19)]_64_[2(3.37e-19)]_58 RETB_BOVIN 1.46e-29 10_[1(6.49e-18)]_71_[2(1.16e-18)]_63 MUP2_MOUSE 2.28e-27 23_[1(1.21e-16)]_62_[2(1.09e-17)]_56 -------------------------------------------------------------------------------- ******************************************************************************** ******************************************************************************** Stopped because nmotifs = 2 reached. ******************************************************************************** CPU: peterlenovo ******************************************************************************** Output files for usage example 5 File: farntrans5.fasta >RAM1_YEAST PROTEIN FARNESYLTRANSFERASE BETA SUBUNIT (EC 2.5.1.-) (CAAX FARN MRQRVGRSIARAKFINTALLGRKRPVMERVVDIAHVDSSKAIQPLMKELETDTTEARYKV LQSVLEIYDDEKNIEPALTKEFHKMYLDVAFEISLPPQMTALDASQPWMLYWIANSLKVM DRDWLSDDTKRKIVVKLFTISPSGGPFGGGPGQLSHLASTYAAINALSLCDNIDGCWDRI DRKGIYQWLISLKEPNGGFKTCLEVGEVDTRGIYCALSIATLLNILTEELTEGVLNYLKN CQNYEGGFGSCPHVDEAHGGYTFCATASLAILRSMDQINVEKLLEWSSARQLQEERGFCG RSNKLVDGCYSFWVGGSAAILEAFGYGQCFNKHALRDYILYCCQEKEQPGLRDKPGAHSD FYHTNYCLLGLAVAESSYSCTPNDSPHNIKCTPDRLIGSSKLTDVNPVYGLPIENVRKII HYFKSNLSSPS >PFTB_RAT PROTEIN FARNESYLTRANSFERASE BETA SUBUNIT (EC 2.5.1.-) (CAAX FARNES MASSSSFTYYCPPSSSPVWSEPLYSLRPEHARERLQDDSVETVTSIEQAKVEEKIQEVFS SYKFNHLVPRLVLQREKHFHYLKRGLRQLTDAYECLDASRPWLCYWILHSLELLDEPIPQ IVATDVCQFLELCQSPDGGFGGGPGQYPHLAPTYAAVNALCIIGTEEAYNVINREKLLQY LYSLKQPDGSFLMHVGGEVDVRSAYCAASVASLTNIITPDLFEGTAEWIARCQNWEGGIG GVPGMEAHGGYTFCGLAALVILKKERSLNLKSLLQWVTSRQMRFEGGFQGRCNKLVDGCY SFWQAGLLPLLHRALHAQGDPALSMSHWMFHQQALQEYILMCCQCPAGGLLDKPGKSRDF YHTCYCLSGLSIAQHFGSGAMLHDVVMGVPENVLQPTHPVYNIGPDKVIQATTHFLQKPV PGFEECEDAVTSDPATD >BET2_YEAST YPT1/SEC4 PROTEINS GERANYLGERANYLTRANSFERASE BETA SUBUNIT (EC 2. MSGSLTLLKEKHIRYIESLDTNKHNFEYWLTEHLRLNGIYWGLTALCVLDSPETFVKEEV ISFVLSCWDDKYGAFAPFPRHDAHLLTTLSAVQILATYDALDVLGKDRKVRLISFIRGNQ LEDGSFQGDRFGEVDTRFVYTALSALSILGELTSEVVDPAVDFVLKCYNFDGGFGLCPNA ESHAAQAFTCLGALAIANKLDMLSDDQLEEIGWWLCERQLPEGGLNGRPSKLPDVCYSWW VLSSLAIIGRLDWINYEKLTEFILKCQDEKKGGISDRPENEVDVFHTVFGVAGLSLMGYD NLVPIDPIYCMPKSVTSKFKKYPYK >RATRABGERB Rat rab geranylgeranyl transferase beta-subunit MGTQQKDVTIKSDAPDTLLLEKHADYIASYGSKKDDYEYCMSEYLRMSGVYWGLTVMDLM GQLHRMNKEEILVFIKSCQHECGGVSASIGHDPHLLYTLSAVQILTLYDSIHVINVDKVV AYVQSLQKEDGSFAGDIWGEIDTRFSFCAVATLALLGKLDAINVEKAIEFVLSCMNFDGG FGCRPGSESHAGQIYCCTGFLAITSQLHQVNSDLLGWWLCERQLPSGGLNGRPEKLPDVC YSWWVLASLKIIGRLHWIDREKLRSFILACQDEETGGFADRPGDMVDPFHTLFGIAGLSL LGEEQIKPVSPVFCMPEEVLQRVNVQPELVS >CAL1_YEAST RAS PROTEINS GERANYLGERANYLTRANSFERASE (EC 2.5.1.-) (PROTEIN GER MCQATNGPSRVVTKKHRKFFERHLQLLPSSHQGHDVNRMAIIFYSISGLSIFDVNVSAKY GDHLGWMRKHYIKTVLDDTENTVISGFVGSLVMNIPHATTINLPNTLFALLSMIMLRDYE YFETILDKRSLARFVSKCQRPDRGSFVSCLDYKTNCGSSVDSDDLRFCYIAVAILYICGC RSKEDFDEYIDTEKLLGYIMSQQCYNGAFGAHNEPHSGYTSCALSTLALLSSLEKLSDKF KEDTITWLLHRQVSSHGCMKFESELNASYDQSDDGGFQGRENKFADTCYAFWCLNSLHLL TKDWKMLCQTELVTNYLLDRTQKTLTGGFSKNDEEDADLYHSCLGSAALALIEGKFNGEL CIPQEIFNDFSKRCCF File: ex5.text ******************************************************************************** MEME - Motif discovery tool ******************************************************************************** MEME version 4.7.0 (Release date: Wed Sep 28 17:30:10 EST 2011) For further information on how to interpret these results or to get a copy of the MEME software please access http://meme.nbcr.net. This file may be used as input to the MAST algorithm for searching sequence databases for matches to groups of motifs. MAST is available for interactive use and downloading at http://meme.nbcr.net. ******************************************************************************** ******************************************************************************** REFERENCE ******************************************************************************** If you use this program in your research, please cite: Timothy L. Bailey and Charles Elkan, "Fitting a mixture model by expectation maximization to discover motifs in biopolymers", Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, pp. 28-36, AAAI Press, Menlo Park, California, 1994. ******************************************************************************** ******************************************************************************** TRAINING SET ******************************************************************************** DATAFILE= farntrans5.fasta ALPHABET= ACDEFGHIKLMNPQRSTVWY Sequence name Weight Length Sequence name Weight Length ------------- ------ ------ ------------- ------ ------ RAM1_YEAST 1.0000 431 PFTB_RAT 1.0000 437 BET2_YEAST 1.0000 325 RATRABGERB 1.0000 331 CAL1_YEAST 1.0000 376 ******************************************************************************** ******************************************************************************** COMMAND LINE SUMMARY ******************************************************************************** This information can also be useful in the event you wish to report a problem with the MEME software. command: meme farntrans5.fasta -mod anr -prior dirichlet -maxsites 50 -maxw 40 - nostatus -protein -text model: mod= anr nmotifs= 1 evt= inf object function= E-value of product of p-values width: minw= 8 maxw= 40 minic= 0.00 [Part of this file has been deleted for brevity] 0.000000 0.000000 0.000000 0.166667 0.055556 0.388889 0.000000 0.000000 0.000000 0.000000 0.000000 0.222222 0.000000 0.000000 0.000000 0.055556 0.000000 0.055556 0.055556 0.000000 0.111111 0.000000 0.111111 0.055556 0.000000 0.166667 0.000000 0.000000 0.333333 0.000000 0.055556 0.055556 0.000000 0.055556 0.000000 0.055556 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.055556 0.444444 0.055556 0.000000 0.055556 0.000000 0.000000 0.222222 0.055556 0.000000 0.000000 0.000000 0.000000 0.055556 0.000000 0.000000 0.000000 0.055556 0.222222 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.055556 0.000000 0.000000 0.000000 0.000000 0.166667 0.000000 0.055556 0.166667 0.000000 0.333333 0.000000 0.000000 0.000000 0.000000 0.722222 0.000000 0.000000 0.000000 0.277778 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.111111 0.000000 0.000000 0.000000 0.111111 0.222222 0.000000 0.000000 0.000000 0.111111 0.000000 0.000000 0.055556 0.000000 0.000000 0.000000 0.166667 0.222222 0.000000 0.000000 0.111111 0.277778 0.000000 0.000000 0.111111 0.166667 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.166667 0.000000 0.000000 0.000000 0.000000 0.166667 0.000000 0.000000 0.000000 0.000000 0.111111 0.000000 0.277778 0.000000 0.000000 0.000000 0.000000 0.000000 0.055556 0.111111 0.000000 0.055556 0.000000 0.000000 0.000000 0.388889 0.166667 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.055556 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.333333 0.388889 0.055556 0.000000 0.000000 -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Motif 1 regular expression -------------------------------------------------------------------------------- Qx[EP][DE]GG[FL]G[GD]RP[GN]K[EL][VA][DH][GV]C[YH][TS] -------------------------------------------------------------------------------- Time 1.50 secs. ******************************************************************************** ******************************************************************************** SUMMARY OF MOTIFS ******************************************************************************** -------------------------------------------------------------------------------- Combined block diagrams: non-overlapping sites with p-value < 0.0001 -------------------------------------------------------------------------------- SEQUENCE NAME COMBINED P-VALUE MOTIF DIAGRAM ------------- ---------------- ------------- RAM1_YEAST 1.98e-11 140_[1(3.83e-06)]_82_[1(3.85e-11)]_29 _[1(4.81e-14)]_33_[1(3.01e-12)]_67 PFTB_RAT 2.50e-14 133_[1(5.98e-14)]_31_[1(1.26e-12)]_28 _[1(5.88e-16)]_29_[1(5.97e-17)]_42_[1(1.38e-13)]_74 BET2_YEAST 5.50e-14 119_[1(1.69e-13)]_28_[1(3.03e-13)]_31 _[1(1.80e-16)]_29_[1(5.98e-14)]_38 RATRABGERB 8.82e-14 126_[1(1.53e-13)]_28_[1(9.50e-15)]_28 _[1(2.83e-16)]_29_[1(2.05e-15)]_40 CAL1_YEAST 2.42e-13 270_[1(6.78e-16)]_32_[1(4.48e-11)]_34 -------------------------------------------------------------------------------- ******************************************************************************** ******************************************************************************** Stopped because nmotifs = 1 reached. ******************************************************************************** CPU: peterlenovo ******************************************************************************** Output files for usage example 6 File: ex6.text ******************************************************************************** MEME - Motif discovery tool ******************************************************************************** MEME version 4.7.0 (Release date: Wed Sep 28 17:30:10 EST 2011) For further information on how to interpret these results or to get a copy of the MEME software please access http://meme.nbcr.net. This file may be used as input to the MAST algorithm for searching sequence databases for matches to groups of motifs. MAST is available for interactive use and downloading at http://meme.nbcr.net. ******************************************************************************** ******************************************************************************** REFERENCE ******************************************************************************** If you use this program in your research, please cite: Timothy L. Bailey and Charles Elkan, "Fitting a mixture model by expectation maximization to discover motifs in biopolymers", Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, pp. 28-36, AAAI Press, Menlo Park, California, 1994. ******************************************************************************** ******************************************************************************** TRAINING SET ******************************************************************************** DATAFILE= farntrans5.fasta ALPHABET= ACDEFGHIKLMNPQRSTVWY Sequence name Weight Length Sequence name Weight Length ------------- ------ ------ ------------- ------ ------ RAM1_YEAST 1.0000 431 PFTB_RAT 1.0000 437 BET2_YEAST 1.0000 325 RATRABGERB 1.0000 331 CAL1_YEAST 1.0000 376 ******************************************************************************** ******************************************************************************** COMMAND LINE SUMMARY ******************************************************************************** This information can also be useful in the event you wish to report a problem with the MEME software. command: meme farntrans5.fasta -mod anr -nmotifs 3 -prior dirichlet -maxsites 30 -w 10 -nostatus -protein -text model: mod= anr nmotifs= 3 evt= inf object function= E-value of product of p-values width: minw= 10 maxw= 10 minic= 0.00 [Part of this file has been deleted for brevity] 0.000000 0.000000 0.142857 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.142857 0.000000 0.571429 0.000000 0.071429 0.000000 0.000000 0.000000 0.071429 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.285714 0.071429 0.000000 0.000000 0.000000 0.000000 0.214286 0.000000 0.071429 0.285714 0.000000 0.071429 0.000000 0.000000 0.071429 0.785714 0.000000 0.000000 0.071429 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.071429 0.000000 0.000000 0.000000 0.000000 0.000000 0.071429 0.000000 0.000000 0.142857 0.000000 0.000000 0.000000 0.000000 0.785714 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.071429 0.000000 0.000000 0.000000 0.000000 0.000000 0.214286 0.142857 0.000000 0.428571 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.142857 0.000000 0.000000 0.071429 0.000000 0.000000 0.000000 0.071429 0.000000 0.000000 0.285714 0.000000 0.285714 0.000000 0.000000 0.000000 0.000000 0.142857 0.000000 0.071429 0.071429 0.000000 0.000000 0.071429 0.000000 0.142857 0.214286 0.000000 0.071429 0.142857 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.071429 0.071429 0.142857 0.000000 0.071429 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.357143 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.071429 0.571429 0.000000 0.000000 0.000000 0.000000 0.071429 0.000000 0.000000 0.500000 0.000000 0.142857 0.000000 0.000000 0.000000 0.000000 0.000000 0.071429 0.000000 0.214286 0.000000 0.000000 -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Motif 3 regular expression -------------------------------------------------------------------------------- [IL]N[KVR]EK[LH][IL]E[YF][IV] -------------------------------------------------------------------------------- Time 0.50 secs. ******************************************************************************** ******************************************************************************** SUMMARY OF MOTIFS ******************************************************************************** -------------------------------------------------------------------------------- Combined block diagrams: non-overlapping sites with p-value < 0.0001 -------------------------------------------------------------------------------- SEQUENCE NAME COMBINED P-VALUE MOTIF DIAGRAM ------------- ---------------- ------------- RAM1_YEAST 1.28e-15 109_[2(1.99e-06)]_24_[1(9.95e-09)]_6_ [2(3.56e-08)]_43_[2(6.10e-07)]_2_[3(1.62e-05)]_10_[1(6.34e-09)]_7_[2(6.90e-10)]_ 6_[3(7.11e-09)]_7_[1(3.91e-09)]_6_[2(8.06e-07)]_9_[3(4.43e-08)]_24_[2(1.85e-06)] _40_[3(3.31e-08)]_8 PFTB_RAT 1.38e-16 72_[3(4.86e-08)]_21_[2(7.36e-07)]_23_ [1(2.07e-10)]_6_[2(1.20e-08)]_9_[3(2.23e-09)]_22_[2(1.35e-06)]_22_[1(2.12e-09)]_ 6_[2(2.28e-08)]_23_[1(6.68e-11)]_68_[2(8.11e-08)]_65 BET2_YEAST 3.95e-16 6_[3(6.29e-09)]_22_[2(2.41e-07)]_6_[3 (1.97e-07)]_74_[2(1.05e-07)]_6_[3(5.91e-05)]_6_[1(3.56e-09)]_6_[2(8.11e-08)]_25_ [1(1.39e-09)]_6_[2(1.03e-08)]_6_[3(9.33e-10)]_7_[1(3.44e-08)]_6_[2(1.46e-06)]_29 RATRABGERB 3.89e-16 17_[3(1.70e-07)]_38_[3(2.44e-08)]_38_ [3(5.33e-08)]_22_[2(5.42e-08)]_6_[3(5.01e-10)]_6_[1(6.01e-10)]_6_[2(9.24e-08)]_2 2_[1(3.56e-09)]_6_[2(4.12e-08)]_6_[3(2.91e-09)]_7_[1(6.95e-09)]_6_[2(2.83e-06)]_ 31 CAL1_YEAST 5.03e-15 41_[2(7.36e-07)]_74_[3(3.01e-05)]_32_ [2(8.06e-07)]_12_[3(2.20e-08)]_20_[2(1.92e-07)]_44_[1(1.82e-10)]_6_[2(3.07e-08)] _77 -------------------------------------------------------------------------------- ******************************************************************************** ******************************************************************************** Stopped because nmotifs = 3 reached. ******************************************************************************** CPU: peterlenovo ******************************************************************************** Output files for usage example 7 File: ex7.text ******************************************************************************** MEME - Motif discovery tool ******************************************************************************** MEME version 4.7.0 (Release date: Wed Sep 28 17:30:10 EST 2011) For further information on how to interpret these results or to get a copy of the MEME software please access http://meme.nbcr.net. This file may be used as input to the MAST algorithm for searching sequence databases for matches to groups of motifs. MAST is available for interactive use and downloading at http://meme.nbcr.net. ******************************************************************************** ******************************************************************************** REFERENCE ******************************************************************************** If you use this program in your research, please cite: Timothy L. Bailey and Charles Elkan, "Fitting a mixture model by expectation maximization to discover motifs in biopolymers", Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, pp. 28-36, AAAI Press, Menlo Park, California, 1994. ******************************************************************************** ******************************************************************************** TRAINING SET ******************************************************************************** DATAFILE= farntrans5.fasta ALPHABET= ACDEFGHIKLMNPQRSTVWY Sequence name Weight Length Sequence name Weight Length ------------- ------ ------ ------------- ------ ------ RAM1_YEAST 1.0000 431 PFTB_RAT 1.0000 437 BET2_YEAST 1.0000 325 RATRABGERB 1.0000 331 CAL1_YEAST 1.0000 376 ******************************************************************************** ******************************************************************************** COMMAND LINE SUMMARY ******************************************************************************** This information can also be useful in the event you wish to report a problem with the MEME software. command: meme farntrans5.fasta -mod anr -nmotifs 3 -prior dirichlet -nsites 24 - maxw 12 -nostatus -protein -text model: mod= anr nmotifs= 3 evt= inf object function= E-value of product of p-values width: minw= 8 maxw= 12 minic= 0.00 [Part of this file has been deleted for brevity] 0.000000 0.000000 0.125000 0.583333 0.000000 0.000000 0.041667 0.000000 0.125000 0.000000 0.000000 0.000000 0.000000 0.041667 0.041667 0.041667 0.000000 0.000000 0.000000 0.000000 0.083333 0.000000 0.000000 0.083333 0.000000 0.083333 0.000000 0.000000 0.625000 0.041667 0.000000 0.000000 0.041667 0.000000 0.000000 0.041667 0.000000 0.000000 0.000000 0.000000 0.125000 0.000000 0.000000 0.000000 0.000000 0.000000 0.166667 0.166667 0.000000 0.333333 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.208333 0.000000 0.000000 0.041667 0.000000 0.000000 0.000000 0.041667 0.000000 0.000000 0.250000 0.000000 0.250000 0.000000 0.000000 0.000000 0.083333 0.125000 0.000000 0.083333 0.083333 0.000000 0.041667 0.041667 0.000000 0.125000 0.208333 0.000000 0.041667 0.083333 0.000000 0.041667 0.000000 0.000000 0.083333 0.000000 0.208333 0.041667 0.083333 0.000000 0.041667 0.000000 0.000000 0.041667 0.000000 0.000000 0.000000 0.291667 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.041667 0.000000 0.000000 0.000000 0.000000 0.041667 0.125000 0.458333 0.000000 0.000000 0.000000 0.000000 0.125000 0.000000 0.000000 0.333333 0.000000 0.250000 0.000000 0.000000 0.000000 0.000000 0.000000 0.041667 0.041667 0.208333 0.000000 0.000000 0.041667 0.000000 0.000000 0.083333 0.000000 0.000000 0.000000 0.041667 0.166667 0.333333 0.083333 0.000000 0.000000 0.041667 0.000000 0.083333 0.083333 0.000000 0.000000 0.041667 0.083333 0.000000 0.041667 0.000000 0.000000 0.000000 0.041667 0.000000 0.125000 0.000000 0.041667 0.041667 0.000000 0.000000 0.083333 0.500000 0.000000 0.000000 0.000000 0.041667 -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Motif 3 regular expression -------------------------------------------------------------------------------- INVEK[LV][IL][EQ][YF][ILV]LS -------------------------------------------------------------------------------- Time 0.50 secs. ******************************************************************************** ******************************************************************************** SUMMARY OF MOTIFS ******************************************************************************** -------------------------------------------------------------------------------- Combined block diagrams: non-overlapping sites with p-value < 0.0001 -------------------------------------------------------------------------------- SEQUENCE NAME COMBINED P-VALUE MOTIF DIAGRAM ------------- ---------------- ------------- RAM1_YEAST 2.42e-16 35_[3(3.87e-06)]_62_[1(1.43e-07)]_23_ [2(2.96e-09)]_3_[1(4.98e-09)]_8_[3(4.87e-07)]_21_[1(3.26e-08)]_4_[3(6.95e-07)]_5 _[2(3.84e-07)]_4_[1(6.42e-10)]_4_[3(2.63e-08)]_6_[2(7.99e-09)]_3_[1(2.34e-07)]_7 _[3(2.04e-07)]_7_[2(2.81e-07)]_15_[2(1.16e-06)]_26_[3(1.79e-09)]_6 PFTB_RAT 3.08e-19 49_[3(1.45e-06)]_11_[3(3.82e-08)]_19_ [1(4.06e-08)]_22_[2(1.38e-10)]_3_[1(9.07e-10)]_7_[3(5.77e-11)]_5_[2(8.29e-08)]_3 _[1(9.97e-08)]_21_[2(1.99e-09)]_3_[1(8.60e-09)]_4_[3(8.26e-07)]_6_[2(5.90e-11)]_ 32_[3(1.82e-06)]_6_[2(4.62e-08)]_3_[1(1.31e-07)]_28_[3(4.11e-06)]_23 BET2_YEAST 9.82e-18 6_[3(7.95e-09)]_20_[1(7.52e-09)]_4_[3 (3.82e-08)]_6_[2(4.17e-08)]_39_[2(5.11e-08)]_3_[1(1.27e-09)]_4_[3(2.11e-06)]_5_[ 2(5.63e-10)]_3_[1(2.32e-08)]_24_[2(1.99e-09)]_3_[1(6.42e-10)]_4_[3(7.88e-10)]_6_ [2(8.71e-10)]_3_[1(6.15e-08)]_27 RATRABGERB 2.86e-20 17_[3(4.04e-07)]_20_[1(1.20e-07)]_4_[ 3(2.99e-08)]_5_[2(1.09e-07)]_19_[3(1.57e-08)]_5_[2(1.31e-07)]_3_[1(2.05e-09)]_4_ [3(6.10e-12)]_5_[2(4.94e-11)]_3_[1(7.50e-08)]_21_[2(2.25e-10)]_3_[1(2.39e-09)]_4 _[3(5.99e-09)]_6_[2(8.34e-11)]_3_[1(1.99e-07)]_29 CAL1_YEAST 2.39e-16 10_[3(4.04e-07)]_19_[1(2.94e-07)]_79_ [2(6.23e-06)]_23_[1(7.50e-08)]_10_[3(7.88e-10)]_5_[2(4.15e-07)]_1_[1(5.56e-08)]_ 43_[2(7.55e-10)]_3_[1(8.60e-09)]_6_[3(3.19e-06)]_7_[2(1.56e-07)]_38 -------------------------------------------------------------------------------- ******************************************************************************** ******************************************************************************** Stopped because nmotifs = 3 reached. ******************************************************************************** CPU: peterlenovo ******************************************************************************** Output files for usage example 8 File: adh.fasta >2BHD_STREX 20-BETA-HYDROXYSTEROID DEHYDROGENASE (EC 1.1.1.53) MNDLSGKTVIITGGARGLGAEAARQAVAAGARVVLADVLDEEGAATARELGDAARYQHLD VTIEEDWQRVVAYAREEFGSVDGLVNNAGISTGMFLETESVERFRKVVDINLTGVFIGMK TVIPAMKDAGGGSIVNISSAAGLMGLALTSSYGASKWGVRGLSKLAAVELGTDRIRVNSV HPGMTYTPMTAETGIRQGEGNYPNTPMGRVGNEPGEIAGAVVKLLSDTSSYVTGAELAVD GGWTTGPTVKYVMGQ >3BHD_COMTE 3-BETA-HYDROXYSTEROID DEHYDROGENASE (EC 1.1.1.51) TNRLQGKVALVTGGASGVGLEVVKLLLGEGAKVAFSDINEAAGQQLAAELGERSMFVRHD VSSEADWTLVMAAVQRRLGTLNVLVNNAGILLPGDMETGRLEDFSRLLKINTESVFIGCQ QGIAAMKETGGSIINMASVSSWLPIEQYAGYSASKAAVSALTRAAALSCRKQGYAIRVNS IHPDGIYTPMMQASLPKGVSKEMVLHDPKLNRAGRAYMPERIAQLVLFLASDESSVMSGG ELHADNSILGMGL >ADH_DROME ALCOHOL DEHYDROGENASE (EC 1.1.1.1) SFTLTNKNVIFVAGLGGIGLDTSKELLKRDLKNLVILDRIENPAAIAELKAINPKVTVTF YPYDVTVPIAETTKLLKTIFAQLKTVDVLINGAGILDDHQIERTIAVNYTGLVNTTTAIL DFWDKRKGGPGGIICNIGSVTGFNAIYQVPVYSGTKAAVVNFTSSLAKLAPITGVTAYTV NPGITRTTLVHKFNSWLDVEPQVAEKLLAHPTQPSLACAENFVKAIELNQNGAIWKLDLG TLEAIQWTKHWDSGI >AP27_MOUSE ADIPOCYTE P27 PROTEIN (AP27) MKLNFSGLRALVTGAGKGIGRDTVKALHASGAKVVAVTRTNSDLVSLAKECPGIEPVCVD LGDWDATEKALGGIGPVDLLVNNAALVIMQPFLEVTKEAFDRSFSVNLRSVFQVSQMVAR DMINRGVPGSIVNVSSMVAHVTFPNLITYSSTKGAMTMLTKAMAMELGPHKIRVNSVNPT VVLTDMGKKVSADPEFARKLKERHPLRKFAEVEDVVNSILFLLSDRSASTSGGGILVDAG YLAS >BA72_EUBSP 7-ALPHA-HYDROXYSTEROID DEHYDROGENASE (EC 1.1.1.159) (BILE ACID 7-DEH YDROXYLASE) (BILE ACID-INDUCIBLE PROTEIN) MNLVQDKVTIITGGTRGIGFAAAKIFIDNGAKVSIFGETQEEVDTALAQLKELYPEEEVL GFAPDLTSRDAVMAAVGQVAQKYGRLDVMINNAGITSNNVFSRVSEEEFKHIMDINVTGV FNGAWCAYQCMKDAKKGVIINTASVTGIFGSLSGVGYPASKASVIGLTHGLGREIIRKNI RVVGVAPGVVNTDMTNGNPPEIMEGYLKALPMKRMLEPEEIANVYLFLASDLASGITATT VSVDGAYRP >BDH_HUMAN D-BETA-HYDROXYBUTYRATE DEHYDROGENASE PRECURSOR (EC 1.1.1.30) (BDH) (3 -HYDROXYBUTYRATE DEHYDROGENASE) (FRAGMENT) GLRPPPPGRFSRLPGKTLSACDRENGARRPLLLGSTSFIPIGRRTYASAAEPVGSKAVLV TGCDSGFGFSLAKHLHSKGFLVFAGCLMKDKGHDGVKELDSLNSDRLRTVQLNVFRSEEV EKVVGDCPFEPEGPEKGMWGLVNNAGISTFGEVEFTSLETYKQVAEVNLWGTVRMTKSFL PLIRRAKGRVVNISSMLGRMANPARSPYCITKFGVEAFSDCLRYEMYPLGVKVSVVEPGN FIAATSLYNPESIQAIAKKMWEELPEVVRKDYGKKYFDEKIAKMETYCSSGSTDTSPVID AVTHALTATTPYTRYHPMDYYWWLRMQIMTHLPGAISDMIYIR >BPHB_PSEPS BIPHENYL-CIS-DIOL DEHYDROGENASE (EC 1.3.1.-) MKLKGEAVLITGGASGLGRALVDRFVAEAKVAVLDKSAERLAELETDLGDNVLGIVGDVR SLEDQKQAASRCVARFGKIDTLIPNAGIWDYSTALVDLPEESLDAAFDEVFHINVKGYIH AVKALPALVASRGNVIFTISNAGFYPNGGGPLYTAAKQAIVGLVRELAFELAPYVRVNGV GPGGMNSDMRGPSSLGMGSKAISTVPLADMLKSVLPIGRMPEVEEYTGAYVFFATRGDAA PASGALVNYDGGLGVRGFFSGAGGNDLLEQLNIHP >BUDC_KLETE ACETOIN(DIACETYL) REDUCTASE (EC 1.1.1.5) (ACETOIN DEHYDROGENASE) MQKVALVTGAGQGIGKAIALRLVKDGFAVAIADYNDATATAVAAEINQAGGRAVAIKVDV SRRDQVFAAVEQARKALGGFNVIVNNAGIAPSTPIESITEEIVDRVYNINVKGVIWGMQA AVEAFKKEGHGGKIVNACSQAGHVGNPELAVYSSSKFAVRGLTQTAARDLAPLGITVNGF CPGIVKTPMWAEIDRQCRKRRANRWATARLNLPNASPLAACRSLKTSPPACRSSPARIPT I >DHES_HUMAN ESTRADIOL 17 BETA-DEHYDROGENASE (EC 1.1.1.62) (20 ALPHA-HYDROXYSTERO ID DEHYDROGENASE) (E2DH) (17-BETA-HSD) (PLACENTAL 17-BETA-HYDROXYSTEROID DEHYDRO GENASE) [Part of this file has been deleted for brevity] GVHQKEGWPSSAYGVTKIGVTVLSRIHARKLSEQRKGDKILLNACCPGWVRTDMAGPKAT KSPEEGAETPVYLALLPPDAEGPHGQFVSEKRVEQW >FABI_ECOLI no comment MGFLSGKRILVTGVASKLSIAYGIAQAMHREGAELAFTYQNDKLKGRVEEFAAQLGSDIV LQCDVAEDASIDTMFAELGKVWPKFDGFVHSIGFAPGDQLDGDYVNAVTREGFKIAHDIS SYSFVAMAKACRSMLNPGSALLTLSYLGAERAIPNYNVMGLAKASLEANVRYMANAMGPE GVRVNAISAGPIRTLAASGIKDFRKMLAHCEAVTPIRRTVTIEDVGNSAAFLCSDLSAGI SGEVVHVDGGFSIAAMNELELK >FVT1_HUMAN no comment MLLLAAAFLVAFVLLLYMVSPLISPKPLALPGAHVVVTGGSSGIGKCIAIECYKQGAFIT LVARNEDKLLQAKKEIEMHSINDKQVVLCISVDVSQDYNQVENVIKQAQEKLGPVDMLVN CAGMAVSGKFEDLEVSTFERLMSINYLGSVYPSRAVITTMKERRVGRIVFVSSQAGQLGL FGFTAYSASKFAIRGLAEALQMEVKPYNVYITVAYPPDTDTPGFAEENRTKPLETRLISE TTSVCKPEQVAKQIVKDAIQGNFNSSLGSDGYMLSALTCGMAPVTSITEGLQQVVTMGLF RTIALFYLGSFDSIVRRCMMQREKSENADKTA >HMTR_LEIMA no comment MTAPTVPVALVTGAAKRLGRSIAEGLHAEGYAVCLHYHRSAAEANALSATLNARRPNSAI TVQADLSNVATAPVSGADGSAPVTLFTRCAELVAACYTHWGRCDVLVNNASSFYPTPLLR NDEDGHEPCVGDREAMETATADLFGSNAIAPYFLIKAFAHRSRHPSQASRTNYSIINMVD AMTNQPLLGYTIYTMAKGALEGLTRSAALELAPLQIRVNGVGPGLSVLVDDMPPAVWEGH RSKVPLYQRDSSAAEVSDVVIFLCSSKAKYITGTCVKVDGGYSLTRA >MAS1_AGRRA no comment MHQLWAYDVGTLGCVSYHALPDIKRHSPKSGHLYLNKPSLRSFILQCPSLARTLVLPSHQ PVSRSSTSSAMVQPISTRKKCTCKVKNIGVCRAPARTSVSMELANAKRFSPATFSANFLS XSVVCSPLLRAIQTALIANIGFLCFDIDEDLKERDFGKHEGGYGPLKMFEDNYPDCEDTE MFSLRVAKALTHAKNENTLFVSHGGVLRVIAALLGVDLTKEHTNNGRVLHFRRGFSHWTV EIHQSPVILVSGSNRGVGKAIAEDLIAHGYRLSLGARKVKDLEVAFGPQDEWLHYARFDA EDHGTMAAWVTAAVEKFGRIDGLVNNAGYGEPVNLDKHVDYQRFHLQWYINCVAPLRMTE LCLPHLYETGSGRIVNINSMSGQRVLNPLVGYNMTKHALGGLTKTTQHVGWDRRCAAIDI CLGFVATDMSAWTDLIASKDMIQPEDIAKLVREAIERPNRAYVPRSEVMCIKEATR >PCR_PEA no comment MALQTASMLPASFSIPKEGKIGASLKDSTLFGVSSLSDSLKGDFTSSALRCKELRQKVGA VRAETAAPATPAVNKSSSEGKKTLRKGNVVITGASSGLGLATAKALAESGKWHVIMACRD YLKAARAAKSAGLAKENYTIMHLDLASLDSVRQFVDNFRRSEMPLDVLINNAAVYFPTAK EPSFTADGFEISVGTNHLGHFLLSRLLLEDLKKSDYPSKRLIIVGSITGNTNTLAGNVPP KANLGDLRGLAGGLTGLNSSAMIDGGDFDGAKAYKDSKVCNMLTMQEFHRRYHEETGITF ASLYPGCIATTGLFREHIPLFRTLFPPFQKYITKGYVSEEESGKRLAQVVSDPSLTKSGV YWSWNNASASFENQLSQEASDAEKARKVWEVSEKLVGLA >RFBB_NEIGO no comment MQTEGKKNILVTGGAGFIGSAVVRHIIQNTRDSVVNLDKLTYAGNLESLTDIADNPRYAF EQVDICDRAELDRVFAQYRPDAVMHLAAESHVDRAIGSAGEFIRTNIVGTFDLLEAARAY WQQMPSEKREAFRFHHISTDEVYGDLHGTDDLFTETTPYAPSSPYSASKAAADHLVRAWQ RTYRLPSIVSNCSNNYGPRQFPEKLIPLMILNALSGKPLPVYGDGAQIRDWLFVEDHARA LYQVVTEGVVGETYNIGGHNEKTNLEVVKTICALLEELAPEKPAGVARYEDLITFVQDRP GHDARYAVDAAKIRRDLGWLPLETFESGLRKTVQWYLDNKTRRQNA >YURA_MYXXA no comment RQHTGGLHGGDELPDGVGDGCLQRPGTRAGAVARQAGVRVFAAGRRLPQLQAADEAPGGR RHRGARGVDVTKADATLERIRALDAEAGGLDLVVANAGVGGTTNAKRLPWERVRGIIDTN VTGAAATLSAVLPQMVERKRGHLVGVSSLAGFRGLPATRYSASKAFLSTFMESLRVDLRG TGVRVTCIYPGFVKSELTATNNFPMPFLMETHDAVELMGKGIVRGDAEVSFPWQLAVPTR MAKVLPNPLFDAAARRLR File: ex8.text ******************************************************************************** MEME - Motif discovery tool ******************************************************************************** MEME version 4.7.0 (Release date: Wed Sep 28 17:30:10 EST 2011) For further information on how to interpret these results or to get a copy of the MEME software please access http://meme.nbcr.net. This file may be used as input to the MAST algorithm for searching sequence databases for matches to groups of motifs. MAST is available for interactive use and downloading at http://meme.nbcr.net. ******************************************************************************** ******************************************************************************** REFERENCE ******************************************************************************** If you use this program in your research, please cite: Timothy L. Bailey and Charles Elkan, "Fitting a mixture model by expectation maximization to discover motifs in biopolymers", Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, pp. 28-36, AAAI Press, Menlo Park, California, 1994. ******************************************************************************** ******************************************************************************** TRAINING SET ******************************************************************************** DATAFILE= adh.fasta ALPHABET= ACDEFGHIKLMNPQRSTVWY Sequence name Weight Length Sequence name Weight Length ------------- ------ ------ ------------- ------ ------ 2BHD_STREX 1.0000 255 3BHD_COMTE 1.0000 253 ADH_DROME 1.0000 255 AP27_MOUSE 1.0000 244 BA72_EUBSP 1.0000 249 BDH_HUMAN 1.0000 343 BPHB_PSEPS 1.0000 275 BUDC_KLETE 1.0000 241 DHES_HUMAN 1.0000 327 DHGB_BACME 1.0000 262 DHII_HUMAN 1.0000 292 DHMA_FLAS1 1.0000 270 ENTA_ECOLI 1.0000 248 FIXR_BRAJA 1.0000 278 GUTD_ECOLI 1.0000 259 HDE_CANTR 1.0000 906 HDHA_ECOLI 1.0000 255 LIGD_PSEPA 1.0000 305 NODG_RHIME 1.0000 245 RIDH_KLEAE 1.0000 249 YINL_LISMO 1.0000 248 YRTP_BACSU 1.0000 238 CSGA_MYXXA 1.0000 166 DHB2_HUMAN 1.0000 387 DHB3_HUMAN 1.0000 310 DHCA_HUMAN 1.0000 276 FABI_ECOLI 1.0000 262 FVT1_HUMAN 1.0000 332 HMTR_LEIMA 1.0000 287 MAS1_AGRRA 1.0000 476 PCR_PEA 1.0000 399 RFBB_NEIGO 1.0000 346 [Part of this file has been deleted for brevity] -------------------------------------------------------------------------------- Combined block diagrams: non-overlapping sites with p-value < 0.0001 -------------------------------------------------------------------------------- SEQUENCE NAME COMBINED P-VALUE MOTIF DIAGRAM ------------- ---------------- ------------- 2BHD_STREX 3.00e-81 5_[2(6.76e-13)]_2_[8(2.79e-13)]_24_[3 (3.26e-12)]_12_[4(1.64e-13)]_2_[6(1.48e-15)]_5_[1(8.10e-19)]_[7(4.84e-10)]_24_[5 (1.29e-21)]_13 3BHD_COMTE 4.50e-74 5_[2(6.53e-15)]_2_[8(6.48e-16)]_24_[3 (4.42e-12)]_12_[4(1.98e-11)]_1_[6(3.58e-11)]_5_[1(1.62e-15)]_2_[7(1.89e-08)]_28_ [5(5.31e-21)]_6 ADH_DROME 2.38e-37 5_[2(3.69e-11)]_56_[3(1.89e-10)]_4_[4 (2.17e-11)]_5_[6(1.44e-11)]_5_[1(4.20e-13)]_[7(2.82e-07)]_66 AP27_MOUSE 6.69e-75 6_[2(1.73e-14)]_2_[8(5.45e-13)]_19_[3 (4.79e-10)]_12_[4(7.74e-13)]_3_[6(1.19e-11)]_5_[1(3.16e-22)]_[7(9.85e-08)]_25_[5 (3.17e-19)]_4 BA72_EUBSP 1.68e-81 5_[2(3.44e-14)]_2_[8(8.85e-13)]_29_[3 (1.25e-13)]_12_[4(2.96e-14)]_2_[6(2.51e-14)]_5_[1(1.55e-16)]_[7(3.30e-09)]_23_[5 (3.54e-23)]_3 BDH_HUMAN 1.27e-45 54_[2(9.49e-15)]_59_[3(1.36e-10)]_12_ [4(4.70e-13)]_1_[6(3.80e-14)]_5_[1(6.62e-18)]_107 BPHB_PSEPS 3.73e-42 4_[2(5.94e-14)]_1_[8(3.23e-06)]_24_[3 (9.73e-11)]_17_[4(1.11e-11)]_[6(1.24e-10)]_5_[1(4.44e-14)]_94 BUDC_KLETE 3.15e-66 1_[2(1.49e-17)]_2_[8(5.08e-13)]_27_[3 (1.52e-10)]_12_[4(1.59e-12)]_3_[6(1.82e-13)]_5_[1(2.03e-21)]_[7(5.92e-10)]_52 DHES_HUMAN 2.57e-42 1_[2(5.94e-14)]_58_[3(2.01e-11)]_12_[ 4(8.18e-12)]_2_[6(4.83e-13)]_5_[1(2.45e-17)]_144 DHGB_BACME 3.04e-66 6_[2(8.39e-15)]_56_[3(1.76e-12)]_12_[ 4(2.54e-14)]_3_[6(6.03e-10)]_6_[1(9.72e-20)]_[7(3.36e-07)]_24_[5(2.28e-20)]_12 DHII_HUMAN 1.93e-53 33_[2(4.63e-17)]_2_[8(1.21e-15)]_28_[ 3(1.70e-08)]_12_[4(6.26e-11)]_1_[6(1.10e-13)]_5_[1(7.62e-16)]_81 DHMA_FLAS1 8.76e-61 13_[2(8.39e-15)]_49_[3(5.34e-08)]_13_ [4(3.17e-15)]_8_[6(3.28e-11)]_5_[1(6.62e-18)]_34_[5(1.77e-22)]_14 ENTA_ECOLI 3.09e-68 4_[2(1.11e-16)]_44_[3(5.83e-10)]_12_[ 4(6.04e-13)]_2_[6(2.09e-11)]_5_[1(1.55e-16)]_[7(4.26e-08)]_33_[5(2.99e-25)]_5 FIXR_BRAJA 9.12e-69 35_[2(3.91e-15)]_52_[3(2.72e-09)]_18_ [4(2.86e-11)]_1_[6(9.83e-12)]_6_[1(3.46e-21)]_[7(5.02e-09)]_20_[5(5.45e-24)]_3 GUTD_ECOLI 1.30e-71 1_[2(4.40e-11)]_2_[8(6.15e-15)]_29_[3 (3.92e-10)]_12_[4(3.17e-15)]_3_[6(6.62e-12)]_5_[1(5.21e-19)]_44_[5(1.77e-22)]_4 HDE_CANTR 1.58e-58 7_[2(1.53e-11)]_60_[3(4.28e-11)]_12_[ 4(3.59e-08)]_2_[6(1.14e-07)]_5_[1(1.97e-12)]_21_[5(5.78e-05)]_80_[2(5.54e-17)]_5 0_[3(9.64e-14)]_12_[4(6.17e-14)]_2_[6(3.31e-14)]_5_[1(5.78e-18)]_57_[8(3.01e-13) ]_329 HDHA_ECOLI 5.20e-81 10_[2(2.96e-16)]_2_[8(3.51e-15)]_27_[ 3(9.10e-12)]_11_[4(1.78e-12)]_2_[6(4.26e-11)]_5_[1(6.04e-19)]_[7(4.32e-07)]_24_[ 5(7.10e-25)]_6 LIGD_PSEPA 3.19e-45 5_[2(1.34e-12)]_2_[8(8.35e-16)]_53_[4 (2.15e-13)]_3_[6(3.81e-13)]_5_[1(1.18e-15)]_120 NODG_RHIME 2.04e-87 5_[2(1.72e-12)]_2_[8(9.46e-16)]_24_[3 (1.76e-12)]_12_[4(2.54e-14)]_2_[6(1.18e-16)]_5_[1(4.63e-22)]_[7(4.68e-07)]_23_[5 (2.47e-23)]_4 RIDH_KLEAE 2.13e-56 13_[2(1.14e-15)]_2_[8(5.42e-20)]_24_[ 3(4.46e-09)]_12_[4(4.70e-13)]_2_[6(1.34e-10)]_5_[1(4.60e-17)]_61 YINL_LISMO 1.43e-58 4_[2(2.66e-17)]_2_[8(7.36e-16)]_27_[3 (1.24e-09)]_12_[4(9.87e-13)]_2_[6(2.06e-13)]_5_[1(5.04e-15)]_2_[7(5.94e-07)]_55 YRTP_BACSU 3.25e-69 5_[2(2.15e-16)]_2_[8(5.11e-14)]_27_[3 (2.07e-12)]_12_[4(5.23e-15)]_2_[6(5.95e-15)]_5_[1(5.59e-22)]_[7(1.07e-06)]_46 CSGA_MYXXA 2.43e-28 9_[3(1.51e-12)]_13_[4(3.03e-10)]_31_[ 1(1.25e-13)]_[7(1.33e-11)]_41 DHB2_HUMAN 1.75e-51 81_[2(2.62e-15)]_55_[3(5.65e-09)]_13_ [4(9.87e-13)]_1_[6(6.62e-12)]_5_[1(8.10e-19)]_1_[8(2.58e-13)]_101 DHB3_HUMAN 1.82e-48 47_[2(3.44e-14)]_2_[8(5.51e-15)]_26_[ 3(6.73e-08)]_14_[4(3.14e-12)]_2_[6(5.41e-12)]_5_[1(4.56e-15)]_84 DHCA_HUMAN 3.85e-44 3_[2(1.54e-14)]_3_[8(1.21e-05)]_27_[3 (1.10e-14)]_12_[4(4.78e-05)]_[6(2.51e-11)]_46_[1(7.01e-12)]_4_[7(1.11e-12)]_42 FABI_ECOLI 3.60e-30 5_[2(8.23e-11)]_132_[1(1.74e-13)]_34_ [5(2.46e-22)]_12 FVT1_HUMAN 2.52e-62 31_[2(1.36e-14)]_2_[8(1.50e-16)]_32_[ 3(6.81e-12)]_12_[4(3.91e-12)]_2_[6(7.76e-16)]_5_[1(1.13e-17)]_[7(5.08e-07)]_63_[ 4(2.64e-05)]_25 HMTR_LEIMA 2.44e-44 5_[2(1.23e-12)]_73_[3(8.68e-11)]_80_[ 1(1.14e-19)]_31_[5(1.29e-21)]_6 MAS1_AGRRA 2.00e-27 172_[7(1.01e-05)]_63_[2(4.05e-12)]_51 _[3(3.78e-12)]_19_[1(6.98e-11)]_43_[7(2.41e-08)]_47 PCR_PEA 6.40e-31 25_[1(2.02e-10)]_31_[2(1.54e-14)]_55_ [3(2.10e-10)]_13_[4(5.76e-11)]_95_[7(8.04e-08)]_87 RFBB_NEIGO 7.66e-16 5_[2(1.72e-12)]_138_[1(5.57e-15)]_153 YURA_MYXXA 5.59e-32 35_[8(6.92e-05)]_26_[3(6.11e-09)]_12_ [4(7.46e-06)]_2_[6(2.64e-13)]_4_[1(2.11e-19)]_[7(2.35e-07)]_61 -------------------------------------------------------------------------------- ******************************************************************************** ******************************************************************************** Stopped because motif E-value > 1.00e-02. ******************************************************************************** CPU: peterlenovo ******************************************************************************** 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 None. Notes 1. Command-line arguments The following original MEME options are not supported: -h : Use -help to get help information. -dna : EMBOSS will specify whether sequences use a DNA alphabet automatically. -protein : EMBOSS will specify whether sequences use a protein alphabet automatically. The following additional options are provided: outfile : Application output that was normally written to stdout. Note: ememe makes a temporary local copy of its input sequence data. You must ensure there is sufficient disk space for this in the directory that ememe is run. 2. Installing EMBASSY MEMENEW The EMBASSY MEMENEW package contains "wrapper" applications providing an EMBOSS-style interface to the applications in the original MEME package version 4.4.0 developed by Timothy L. Bailey. Please read the file README in the EMBASSY MEME package distribution for installation instructions. 3. Installing original MEME To use EMBASSY MEMENEW, you will first need to download and install the original MEME package: WWW home: http://meme.sdsc.edu/meme/ Distribution: http://meme.nbcr.net/downloads/old_versions/ Please read the file README in the the original MEME package distribution for installation instructions. 4. Setting up MEME For the EMBASSY MEMENEW package to work, the directory containing the original MEME executables *must* be in your path. For example if you executables were installed to "/usr/local/meme/bin", then type: set path=(/usr/local/meme/bin/ $path) rehash 5. Getting help Once you have installed the original MEME, type meme > meme.txt mast > mast.txt to retrieve the meme and mast documentation into text files. The same documentation is given here and in the ememe documentation. Please read the 'Notes' section below for a description of the differences between the original and EMBASSY MEMENEW, 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 Note: ememe makes a temporary local copy of its input sequence data. You must ensure there is sufficient disk space for this in the directory that ememe is run. 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 None. Exit status It always exits with status 0. Known bugs None. See also Program name Description antigenic Find antigenic sites in proteins eiprscan Motif detection elipop Predict lipoproteins emast Motif detection ememe Multiple EM for motif elicitation epestfind Find PEST motifs as potential proteolytic cleavage sites fuzzpro Search for patterns in protein sequences fuzztran Search for patterns in protein sequences (translated) omeme Motif detection patmatdb Search protein sequences with a sequence motif patmatmotifs Scan a protein sequence with motifs from the PROSITE database preg Regular expression search of protein sequence(s) pscan Scan protein sequence(s) with fingerprints from the PRINTS database sigcleave Report on signal cleavage sites in a protein sequence Author(s) Jon Ison European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK Please report all bugs to the EMBOSS bug team (emboss-bug (c) emboss.open-bio.org) not to the original author. This program is an EMBASSY wrapper to a program written by Timothy L. Bailey as part of his meme package. Please report any bugs to the EMBOSS bug team in the first instance, not to Timothy L. Bailey. History Target users This program is intended to be used by everyone and everything, from naive users to embedded scripts. Comments None