******************************************************************************** MEME - Motif discovery tool ******************************************************************************** MEME version 2.2 (Release date: 1998/05/05 20:35:42) For further information on how to interpret these results or to get a copy of the MEME software please access http://www.sdsc.edu/MEME. 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://www.sdsc.edu/MEME. ******************************************************************************** ******************************************************************************** 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.s (deleted by web version of MEME) 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 ******************************************************************************** ******************************************************************************** EXPLANATION OF RESULTS ******************************************************************************** For each motif that it discovers in the training set, MEME prints the following information: Summary Line This line gives the width (`width') and expected number of occurrences in the training set (`sites') of the motif. MEME numbers the motifs consecutively from one as it finds them. MEME usually finds the most statistically significant motifs first. Each motif describes a pattern of a fixed width--no gaps are allowed in MEME motifs. MEME estimates the number of places the motif occurs in the training set. This need not be an integer value. Simplified Motif Letter-probability Matrix MEME motifs are represented by letter-probability matrices that specify the probability of each possible letter appearing at each possible position in an occurrence of the motif. In order to make it easier to see which letters are most likely in each of the columns of the motif, the simplified motif shows the letter probabilities multiplied by 10 rounded to the nearest integer. Zeros are replaced by ":" (the colon) for readability. Information Content Diagram The information content diagram provides an idea of which positions in the motif are most highly conserved. Each column (position) in a motif can be characterized by the amount of information it contains (measured in bits). Highly conserved positions in the motif have high information; positions where all letters are equally likely have low information. The diagram is printed so that each column lines up with the same column in the simplified motif letter-probability matrix above it. Summing the information content for each position in the motif gives the total information content of the motif (shown in parentheses to the left of the diagram). This gives a measure of the usefulness of the motif for database searches. For a motif to be useful for database searches, it must as a rule contain at least log_2(N) bits of information where N is the number of sequences in the database being searched. For example, to effectively search a database containing 100,000 sequences for occurrences of a single motif, the motif should have an IC of at least 16.6 bits. Motifs with lower information content are still useful when a family of sequences shares more than one motif since they can be combined in multiple motif searches (using MAST). Multilevel Consensus Sequence The multilevel consensus sequence corresponding to the motif is an aid in remembering and understanding the motif. It is calculated from the motif letter-probability matrix as follows. Separately for each column of the motif, the letters in the alphabet are sorted in decreasing order by the probability with which they are expected to occur in that position of motif occurrences. The sorted letters are then printed vertically with the most probable letter on top. Only letters with probabilities of 0.2 or higher at that position in the motif are printed. As an example, the multilevel consensus sequence of motif 2 in the sample output is: Multilevel LITGAASGIG consensus V GS sequence G This multilevel consensus sequence says several things about the motif. First, the most likely form of the motif can be read from the top line as LITGAASGIG. Second, that only letter L has probability more than 0.2 in position 1 of the motif, both I and V have probability greater than 0.2 in position 2, etc. Third, a rough approximation of the motif can be made by converting the multilevel consensus sequence into the Prosite signature L-[IV]-T-G-[AG]-[ASG]-S-G-I-G. The multilevel consensus sequence is printed so that each column lines up with the same column in the simplified motif and information content diagrams above it. Motif in BLOCKS or FASTA format For use with the BLOCKS (http://www.blocks.fhcrc.org/blocks) tools, MEME prints the sites in the sequences which were used to construct the motif in BLOCKS format. The sites reported are, for the different model types: OOPS position with highest z_i in each sequence, ZOOPS position with highest z_i > 0.5 in each sequence, TCM all positions with z_i > 0.5, where z_i is the probability that an occurrence of the motif starts at position i in the sequence given the sequence and the motif model. If you inlcude the -print_fasta switch on the command line, MEME prints the motif sites in FASTA format instead of BLOCKS format. Possible Examples of the Motif As a further aid in understanding the motif, MEME displays a list of possible occurrences of the motif in the training set. This list is made by converting the motif letter-probability matrix into a position-dependent scoring matrix (log-odds matrix) and using that to compute a match score between each position in the training set and the motif. All positions which score above a threshold score are listed. (The threshold score is chosen by MEME such that the expected number of non-motif positions listed in error will equal the number of actual motif positions not listed.) The format of the list is sequence name, starting position of the (putative) occurrence, match score of the position, and the actual sequence including the ten positions before and after the motif occurrence (`site'). Position-dependent Scoring Matrix The position-dependent scoring matrix corresponding to the motif is printed for use by database search programs such as MAST. This matrix is a log-odds matrix calculated by taking the log (base 2) of the ratio p/f at each position in the motif where p is the probability of a particular letter at that position in the motif, and f is the average frequency of that letter in the non-redundant database as of 9/22/96. The scoring matrix is printed "sideways"--columns correspond to the letters in the alphabet (in the same order as shown in the simplified motif) and rows corresponding to the positions of the motif, position one first. The scoring matrix is preceded by a line starting with "log-odds matrix:" and containing the length of the alphabet, width of the motif, number of characters in the training set and the scoring threshold used in the list of possible motif examples. Motif Letter-probability Matrix The motif itself is a position-dependent letter-probability matrix giving, for each position in the pattern, the probabilities of each possible letter occurring there. The letter-probability matrix is printed "sideways"--columns correspond to the letters in the alphabet (in the same order as shown in the simplified motif) and rows corresponding to the positions of the motif, position one first. The motif is preceded by a line starting with "letter-probability matrix:" and containing the length of the alphabet, width of the motif and number of characters in the training set. ******************************************************************************** ******************************************************************************** MOTIF 1 width = 12 sites = 25.2 ******************************************************************************** Simplified A :::13:33:3:: motif letter- C :::1:::::::: probability D :::1:::::::: matrix E :::::::::::: F 2:31:1:::::1 G :::11:11:1:: H ::1::::::::: I :::::1::::72 K :::::::::::: L ::11:4::8:15 M :::::1:::::1 N :::::::::::: P :::1:::::::: Q :::::::::::: R :::::::::::: S :1:12:22:2:: T :7:11:11:1:: V ::::1111:111 W ::11:::::::: Y 5:41:::::::: bits 6.2 5.6 5.0 4.4 Information 3.7 content 3.1 (16.0 bits) 2.5 1.9 * * * 1.2 *** * * ** 0.6 *** ******** 0.0 ------------ Multilevel YTYxALAALAIL consensus F sequence -------------------------------------------------------------------------------- Motif 1 in BLOCKS format -------------------------------------------------------------------------------- BL MOTIF 1 width=12 seqs=5 RAM1_YEAST ( 159) STYAAINALSLC 0.990937 RAM1_YEAST ( 209) DTRGIYCALSIA 0.528279 RAM1_YEAST ( 261) YTFCATASLAIL 0.999999 RAM1_YEAST ( 310) YSFWVGGSAAIL 0.984912 RAM1_YEAST ( 363) HTNYCLLGLAVA 0.937898 PFTB_RAT ( 152) PTYAAVNALCII 0.999758 PFTB_RAT ( 205) YCAASVASLTNI 0.711521 PFTB_RAT ( 251) YTFCGLAALVIL 0.999999 PFTB_RAT ( 300) YSFWQAGLLPLL 0.777049 PFTB_RAT ( 362) HTCYCLSGLSIA 0.99506 BET2_YEAST ( 38) GIYWGLTALCVL 0.990596 BET2_YEAST ( 84) HLLTTLSAVQIL 0.659322 BET2_YEAST ( 138) FVYTALSALSIL 0.999985 BET2_YEAST ( 186) QAFTCLGALAIA 0.986997 BET2_YEAST ( 237) YSWWVLSSLAII 0.999986 BET2_YEAST ( 286) HTVFGVAGLSLM 0.996301 RATRABGERB ( 97) YTLSAVQILTLY 0.792918 RATRABGERB ( 145) FSFCAVATLALL 0.999862 RATRABGERB ( 241) YSWWVLASLKII 0.999853 RATRABGERB ( 290) HTLFGIAGLSLL 0.997351 CAL1_YEAST ( 41) IIFYSISGLSIF 0.995787 CAL1_YEAST ( 105) NTLFALLSMIML 0.620376 CAL1_YEAST ( 167) FCYIAVAILYIC 0.697111 CAL1_YEAST ( 219) YTSCALSTLALL 0.999914 CAL1_YEAST ( 289) YAFWCLNSLHLL 0.982553 CAL1_YEAST ( 341) HSCLGSAALALI 0.531026 // ------------------------------------------------------------------------- Possible examples of motif 1 in the training set ------------------------------------------------------------------------- Sequence name Start Score Site ------------- ----- ----- ------------ RAM1_YEAST 159 13.17 GGPGQLSHLA STYAAINALSLC DNIDGCWDRI RAM1_YEAST 209 6.94 FKTCLEVGEV DTRGIYCALSIA TLLNILTEEL RAM1_YEAST 261 26.80 CPHVDEAHGG YTFCATASLAIL RSMDQINVEK RAM1_YEAST 310 12.93 GRSNKLVDGC YSFWVGGSAAIL EAFGYGQCFN RAM1_YEAST 363 11.41 DKPGAHSDFY HTNYCLLGLAVA ESSYSCTPND PFTB_RAT 103 6.50 YECLDASRPW LCYWILHSLELL DEPIPQIVAT PFTB_RAT 152 17.51 GGPGQYPHLA PTYAAVNALCII GTEEAYNVIN PFTB_RAT 205 7.22 VGGEVDVRSA YCAASVASLTNI ITPDLFEGTA PFTB_RAT 251 27.65 GVPGMEAHGG YTFCGLAALVIL KKERSLNLKS PFTB_RAT 300 9.62 GRCNKLVDGC YSFWQAGLLPLL HRALHAQGDP PFTB_RAT 362 16.66 DKPGKSRDFY HTCYCLSGLSIA QHFGSGAMLH BET2_YEAST 38 14.98 YWLTEHLRLN GIYWGLTALCVL DSPETFVKEE BET2_YEAST 84 7.44 AFAPFPRHDA HLLTTLSAVQIL ATYDALDVLG BET2_YEAST 138 22.41 GDRFGEVDTR FVYTALSALSIL GELTSEVVDP BET2_YEAST 186 12.60 LCPNAESHAA QAFTCLGALAIA NKLDMLSDDQ BET2_YEAST 237 23.65 GRPSKLPDVC YSWWVLSSLAII GRLDWINYEK BET2_YEAST 286 14.46 DRPENEVDVF HTVFGVAGLSLM GYDNLVPIDP RATRABGERB 94 8.87 GVSASIGHDP HLLYTLSAVQIL TLYDSIHVIN RATRABGERB 97 8.28 ASIGHDPHLL YTLSAVQILTLY DSIHVINVDK RATRABGERB 142 7.06 SFAGDIWGEI DTRFSFCAVATL ALLGKLDAIN RATRABGERB 145 19.78 GDIWGEIDTR FSFCAVATLALL GKLDAINVEK RATRABGERB 193 7.10 CRPGSESHAG QIYCCTGFLAIT SQLHQVNSDL RATRABGERB 241 19.99 GRPEKLPDVC YSWWVLASLKII GRLHWIDREK RATRABGERB 290 16.13 DRPGDMVDPF HTLFGIAGLSLL GEEQIKPVSP CAL1_YEAST 41 14.80 HQGHDVNRMA IIFYSISGLSIF DVNVSAKYGD CAL1_YEAST 105 6.48 IPHATTINLP NTLFALLSMIML RDYEYFETIL CAL1_YEAST 167 9.48 GSSVDSDDLR FCYIAVAILYIC GCRSKEDFDE CAL1_YEAST 219 20.93 FGAHNEPHSG YTSCALSTLALL SSLEKLSDKF CAL1_YEAST 289 14.92 GRENKFADTC YAFWCLNSLHLL TKDWKMLCQT CAL1_YEAST 341 8.03 KNDEEDADLY HSCLGSAALALI EGKFNGELCI ------------------------------------------------------------------------- log-odds matrix: alength= 20 w= 12 n= 1845 bayes= 6.17646 -1.278 -1.158 -2.137 -1.994 2.125 -2.550 1.014 -1.413 -1.915 -1.190 -0.786 -1.677 -2.332 -1.670 -1.700 -1.450 -1.800 -1.367 0.618 3.841 -1.559 -1.318 -2.616 -3.034 -2.749 -3.241 -2.191 -1.683 -2.173 -2.861 -1.202 -1.002 -2.944 -1.888 -2.214 0.549 3.538 -1.365 -2.785 -3.062 -2.291 -0.978 -2.733 -2.858 2.667 -2.665 1.161 -1.285 -2.296 -0.682 -0.633 -1.254 -2.581 -1.305 -1.648 -1.877 -1.995 -1.337 1.913 3.477 -0.247 1.670 -0.016 -1.127 0.856 0.643 0.682 -1.246 -0.827 -0.652 -1.207 -0.419 0.261 -0.433 -0.188 -0.536 -0.039 -1.342 2.340 1.396 2.112 1.164 -2.176 -2.245 -1.882 0.656 -1.593 -1.839 -2.418 -2.011 -1.012 -1.037 -0.758 -1.271 -1.755 1.322 0.669 -0.208 -1.948 -2.195 -1.657 -1.129 -4.416 -3.726 0.977 -3.592 -2.308 1.409 -3.480 2.287 2.100 -3.517 -3.072 -2.102 -2.891 -3.228 -1.600 0.530 -0.801 -1.038 2.110 1.071 -2.168 -2.245 -1.881 0.642 -1.589 -1.884 -2.418 -1.950 -1.012 -0.912 -0.754 -1.264 -1.754 1.343 0.673 -0.258 -1.937 -2.195 2.110 1.059 -2.176 -2.245 -1.848 0.658 -1.599 -1.782 -2.418 -1.980 -1.012 -1.036 -0.755 -1.314 -1.754 1.346 0.682 -0.252 -1.948 -2.195 -2.671 -2.305 -4.128 -3.388 -1.085 -4.181 -2.730 -0.487 -3.171 3.107 0.457 -3.553 -3.341 -2.436 -2.750 -3.302 -2.550 -1.042 -2.461 -2.491 2.110 1.107 -2.151 -2.239 -1.882 0.616 -1.480 -1.848 -2.323 -2.004 -1.012 -1.035 -0.733 -1.265 -1.755 1.346 0.677 -0.242 -1.948 -2.106 -2.740 -2.702 -3.683 -3.740 -2.174 -4.211 -3.611 3.586 -3.311 -0.203 -0.277 -3.299 -4.207 -3.440 -3.632 -3.256 -2.418 1.141 -3.210 -2.703 -1.800 -1.115 -4.738 -4.056 1.062 -4.033 -2.490 1.439 -3.833 2.317 2.188 -3.907 -3.222 -2.188 -3.102 -3.806 -1.856 0.404 -0.779 -1.060 letter-probability matrix: alength= 20 w= 12 n= 1845 0.030170 0.008141 0.011762 0.015655 0.175732 0.011842 0.045301 0.021136 0.015510 0.040190 0.013378 0.014413 0.010067 0.012806 0.015970 0.027018 0.017067 0.024954 0.020481 0.468407 0.024827 0.007283 0.008442 0.007611 0.005991 0.007331 0.004913 0.017522 0.012969 0.012628 0.010029 0.023002 0.006584 0.011013 0.011184 0.107952 0.689883 0.024984 0.001936 0.003914 0.014949 0.009223 0.007784 0.008601 0.255863 0.010929 0.050144 0.023092 0.011908 0.057171 0.014872 0.019318 0.008468 0.016491 0.016564 0.020098 0.014903 0.025486 0.050242 0.363891 0.061665 0.057787 0.051175 0.028537 0.072895 0.108291 0.035973 0.023736 0.032983 0.058372 0.009989 0.034467 0.060709 0.030187 0.045551 0.050901 0.057817 0.025384 0.067559 0.086022 0.316258 0.040704 0.011451 0.013148 0.010931 0.109255 0.007435 0.015727 0.010947 0.022749 0.011437 0.022462 0.029973 0.016892 0.015378 0.184502 0.094435 0.055723 0.003458 0.007138 0.023194 0.008305 0.002423 0.004711 0.079275 0.005750 0.004528 0.149434 0.005242 0.447711 0.098918 0.004024 0.006025 0.009496 0.006995 0.007877 0.019600 0.092917 0.007659 0.015917 0.315878 0.038172 0.011511 0.013150 0.010937 0.108201 0.007457 0.015249 0.010948 0.023744 0.011437 0.024488 0.030051 0.016972 0.015382 0.187247 0.094738 0.053814 0.003484 0.007140 0.315752 0.037847 0.011449 0.013149 0.011188 0.109370 0.007402 0.016369 0.010947 0.023248 0.011438 0.022466 0.030020 0.016394 0.015383 0.187657 0.095285 0.054036 0.003458 0.007140 0.011492 0.003677 0.002960 0.005957 0.018988 0.003822 0.003381 0.040165 0.006496 0.790095 0.031664 0.003925 0.005001 0.007530 0.007714 0.007484 0.010148 0.031265 0.002422 0.005815 0.315834 0.039115 0.011647 0.013205 0.010933 0.106219 0.008038 0.015639 0.011689 0.022864 0.011436 0.022481 0.030484 0.016953 0.015380 0.187639 0.094969 0.054426 0.003458 0.007590 0.010955 0.002791 0.004027 0.004666 0.008927 0.003744 0.001835 0.675827 0.005895 0.079701 0.019036 0.004681 0.002743 0.003754 0.004187 0.007724 0.011114 0.141930 0.001441 0.005019 0.021008 0.008385 0.001939 0.003747 0.084125 0.004234 0.003992 0.152559 0.004105 0.457010 0.105081 0.003071 0.005430 0.008941 0.006043 0.005278 0.016407 0.085189 0.007776 0.015679 Time 100.00 secs. ******************************************************************************** MOTIF 2 width = 8 sites = 13.4 ******************************************************************************** Simplified A 1:::1::: motif letter- C :::::::: probability D 1:::1::: matrix E 5::::::: F :::9:::: G :99:49:: H :::::::: I :::::::: K 1::::::: L :::::::: M :::::::: N ::::1::: P :::::::8 Q 1::::::: R ::::::9: S ::::1::: T :::::::: V :::::::: W :::::::: Y :::::::: bits 6.2 5.6 5.0 4.4 Information 3.7 content 3.1 * * (19.8 bits) 2.5 *** *** 1.9 *** *** 1.2 *** *** 0.6 ******** 0.0 -------- Multilevel EGGFGGRP consensus sequence -------------------------------------------------------------------------------- Motif 2 in BLOCKS format -------------------------------------------------------------------------------- BL MOTIF 2 width=8 seqs=5 RAM1_YEAST ( 144) GGPFGGGP 0.788086 RAM1_YEAST ( 245) EGGFGSCP 0.996593 RAM1_YEAST ( 295) ERGFCGRS 0.988684 PFTB_RAT ( 137) DGGFGGGP 0.999725 PFTB_RAT ( 236) EGGIGGVP 0.991497 PFTB_RAT ( 285) EGGFQGRC 0.999553 BET2_YEAST ( 171) DGGFGLCP 0.937204 BET2_YEAST ( 222) EGGLNGRP 0.999962 BET2_YEAST ( 271) KGGISDRP 0.952635 RATRABGERB ( 178) DGGFGCRP 0.999802 RATRABGERB ( 226) SGGLNGRP 0.999392 RATRABGERB ( 275) TGGFADRP 0.998884 CAL1_YEAST ( 274) DGGFQGRE 0.999515 // --------------------------------------------------------------------- Possible examples of motif 2 in the training set --------------------------------------------------------------------- Sequence name Start Score Site ------------- ----- ----- -------- RAM1_YEAST 144 8.96 VVKLFTISPS GGPFGGGP GQLSHLASTY RAM1_YEAST 245 16.41 LNYLKNCQNY EGGFGSCP HVDEAHGGYT RAM1_YEAST 295 14.11 EWSSARQLQE ERGFCGRS NKLVDGCYSF PFTB_RAT 137 19.48 CQFLELCQSP DGGFGGGP GQYPHLAPTY PFTB_RAT 236 13.82 AEWIARCQNW EGGIGGVP GMEAHGGYTF PFTB_RAT 285 19.39 QWVTSRQMRF EGGFQGRC NKLVDGCYSF BET2_YEAST 171 12.71 VDFVLKCYNF DGGFGLCP NAESHAAQAF BET2_YEAST 222 21.36 GWWLCERQLP EGGLNGRP SKLPDVCYSW BET2_YEAST 271 10.94 EFILKCQDEK KGGISDRP ENEVDVFHTV RATRABGERB 178 20.86 IEFVLSCMNF DGGFGCRP GSESHAGQIY RATRABGERB 226 17.31 GWWLCERQLP SGGLNGRP EKLPDVCYSW RATRABGERB 275 16.15 SFILACQDEE TGGFADRP GDMVDPFHTL CAL1_YEAST 274 18.28 ELNASYDQSD DGGFQGRE NKFADTCYAF --------------------------------------------------------------------- log-odds matrix: alength= 20 w= 8 n= 1865 bayes= 7.10824 -0.421 -4.546 1.484 2.879 -4.003 -1.821 -1.066 -2.588 -0.054 -2.495 -1.690 -0.877 -1.517 0.863 -1.381 -1.170 -1.189 -1.799 -3.930 -2.920 -1.642 -2.942 -2.196 -2.942 -4.022 3.647 -2.699 -4.037 -2.767 -4.485 -3.269 -1.901 -3.583 -3.212 -2.701 -2.161 -3.192 -3.548 -3.247 -3.567 -1.642 -2.942 -2.196 -2.942 -4.022 3.647 -2.699 -4.037 -2.765 -4.485 -3.269 -1.901 -3.575 -3.212 -2.707 -2.160 -3.192 -3.548 -3.247 -3.567 -3.123 -2.084 -4.117 -4.122 4.401 -4.396 -3.510 -2.106 -4.191 -1.381 -1.661 -4.004 -3.821 -4.220 -4.293 -3.146 -3.827 -2.415 -1.573 -0.361 -0.170 -0.476 0.467 -0.824 -1.950 2.599 -0.122 -2.766 -0.991 -2.389 -2.165 0.924 -1.098 -0.615 -1.166 -0.004 -0.682 -2.067 -1.386 -1.473 -1.641 -2.909 -2.182 -2.939 -4.009 3.646 -2.689 -4.032 -2.763 -4.461 -3.266 -1.899 -3.567 -3.205 -2.701 -2.156 -3.186 -3.544 -3.233 -3.557 -3.244 -2.507 -4.085 -4.157 -4.347 -3.839 -1.613 -3.770 -1.012 -3.756 -3.366 -2.959 -3.399 -2.041 4.094 -3.468 -3.509 -4.393 -2.684 -3.790 -0.809 -2.310 -2.186 -2.028 -2.836 -2.487 -1.887 -2.624 -1.826 -2.454 -2.235 -2.332 3.898 -1.635 -2.044 -1.379 -1.649 -2.202 -3.378 -3.210 letter-probability matrix: alength= 20 w= 8 n= 1865 0.054646 0.000778 0.144691 0.458526 0.002513 0.019617 0.010714 0.009361 0.056339 0.016267 0.007151 0.025083 0.017704 0.074137 0.019925 0.032795 0.026061 0.018500 0.000875 0.004319 0.023435 0.002364 0.011291 0.008111 0.002480 0.868741 0.003455 0.003428 0.008593 0.004095 0.002392 0.012337 0.004230 0.004399 0.007980 0.016503 0.006500 0.005503 0.001405 0.002757 0.023437 0.002364 0.011291 0.008111 0.002480 0.868729 0.003455 0.003428 0.008604 0.004095 0.002392 0.012337 0.004253 0.004399 0.007950 0.016508 0.006500 0.005503 0.001405 0.002757 0.008397 0.004283 0.002981 0.003581 0.850902 0.003293 0.001969 0.013075 0.003203 0.035209 0.007296 0.002873 0.003584 0.002187 0.002647 0.008337 0.004187 0.012067 0.004484 0.025446 0.065011 0.013063 0.071518 0.035219 0.010429 0.420073 0.020608 0.008275 0.029439 0.017507 0.005143 0.087437 0.023679 0.026620 0.023135 0.073577 0.037031 0.015356 0.005106 0.011775 0.023457 0.002418 0.011399 0.008129 0.002502 0.868158 0.003478 0.003440 0.008616 0.004165 0.002398 0.012357 0.004276 0.004420 0.007981 0.016564 0.006527 0.005518 0.001419 0.002777 0.007721 0.003195 0.003048 0.003495 0.001980 0.004844 0.007332 0.004125 0.029013 0.006789 0.002237 0.005928 0.004804 0.009905 0.886191 0.006671 0.005220 0.003063 0.002075 0.002362 0.041763 0.003664 0.011373 0.015288 0.005643 0.012369 0.006064 0.009133 0.016498 0.016736 0.004901 0.009150 0.755579 0.013126 0.012582 0.028379 0.018946 0.013991 0.001284 0.003532 Time 180.99 secs. Stopped because nmotifs = 2 reached. CPU: ghidorah ******************************************************************************** DEBUG INFORMATION ******************************************************************************** This information can also be useful in the event you wish to report a problem with the MEME software. model: mod= tcm nmotifs= 2 chi= 1 width: minw= 8 maxw= 57 shorten= yes lambda: minsites= 0 maxsites= 0 theta: prob= 1 spmap= pam spfuzz= 120 em: prior= mega b= 19000 maxiter= 50 distance= 0.001 data: n= 1900 N= 5 strands: w53 sample: seed= 0 seqfrac= 1 LRT: adj= root Dirichlet mixture priors file: prior30.plib Letter frequencies: A 0.061 C 0.037 D 0.062 E 0.061 F 0.044 G 0.075 H 0.030 I 0.053 K 0.051 L 0.114 M 0.021 N 0.034 P 0.041 Q 0.038 R 0.041 S 0.078 T 0.046 V 0.057 W 0.018 Y 0.041 Non-redundant database letter frequencies: A 0.073 C 0.018 D 0.052 E 0.062 F 0.040 G 0.069 H 0.022 I 0.056 K 0.058 L 0.092 M 0.023 N 0.046 P 0.051 Q 0.041 R 0.052 S 0.074 T 0.059 V 0.064 W 0.013 Y 0.033 Effective length of alphabet = 20 Entropy of dataset (bits) = -4.2 meme farntrans5.s -mod tcm -protein -nostatus -nmotifs 2 -gcg ********************************************************************************