MEME version 3.0 (Release date: 2001/03/03 13:05:22)
For further information on how to interpret these results or to get a copy of the MEME software please access http://meme.sdsc.edu.
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.sdsc.edu.
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.
DATAFILE= D:\seqs 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
This information can also be useful in the event you wish to report a problem with the MEME software. command: meme meme.6266.data -protein -mod zoops -nmotifs 3 -minw 6 -maxw 50 -evt 10000 -time 7200 -nostatus -maxiter 20 model: mod= zoops nmotifs= 3 evt= 10000 object function= E-value of product of p-values width: minw= 6 maxw= 50 minic= 0.00 width: wg= 11 ws= 1 endgaps= yes nsites: minsites= 2 maxsites= 5 wnsites= 0.8 theta: prob= 1 spmap= pam spfuzz= 120 em: prior= megap b= 4515 maxiter= 20 distance= 1e-05 data: n= 903 N= 5 sample: seed= 0 seqfrac= 1 Dirichlet mixture priors file: prior30.plib Letter frequencies in dataset: A 0.072 C 0.029 D 0.069 E 0.078 F 0.043 G 0.058 H 0.025 I 0.048 K 0.086 L 0.087 M 0.018 N 0.053 P 0.032 Q 0.029 R 0.031 S 0.059 T 0.048 V 0.070 W 0.017 Y 0.050 Background letter frequencies (from dataset with add-one prior applied): A 0.072 C 0.029 D 0.068 E 0.077 F 0.043 G 0.057 H 0.026 I 0.048 K 0.086 L 0.087 M 0.018 N 0.053 P 0.033 Q 0.029 R 0.031 S 0.059 T 0.048 V 0.069 W 0.017 Y 0.050
Time 1.97 secs.
Time 3.05 secs.
Time 4.09 secs.
CPU: nbcr2
MOTIFS
For each motif that it discovers in the training set, MEME prints the following information:
Multilevel TTATGTGAACGACGTCACACT consensus AA T A G A GA AA sequence T C TT T
You can convert these blocks to PSSMs (position-specific scoring matrices), LOGOS (color representations of the motifs), phylogeny trees and search them against a database of other blocks by pasting everything from the "BL" line to the "//" line (inclusive) into the Multiple Alignment Processor. If you include the -print_fasta switch on the command line, MEME prints the motif sites in FASTA format instead of BLOCKS format.