MEME version 3.5.1 (Release date: 2006/02/01 02:08:55)
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.
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= A0028.fa ALPHABET= ACGT Sequence name Weight Length Sequence name Weight Length ------------- ------ ------ ------------- ------ ------ M13483 1.0000 500 M26773 1.0000 501 galGal2_refGene_NM_00103 1.0000 500 danRer3_refGene_NM_21478 1.0000 500
This information can also be useful in the event you wish to report a problem with the MEME software. command: meme /home/meme/meme351/LOGS/meme.26952.data -dna -mod zoops -nmotifs 20 -maxsites 4 -minw 6 -maxw 15 -evt 1e100 -revcomp -time 7200 -maxsize 60000 -nostatus -maxiter 20 model: mod= zoops nmotifs= 20 evt= 1e+100 object function= E-value of product of p-values width: minw= 6 maxw= 15 minic= 0.00 width: wg= 11 ws= 1 endgaps= yes nsites: minsites= 2 maxsites= 4 wnsites= 0.8 theta: prob= 1 spmap= uni spfuzz= 0.5 em: prior= dirichlet b= 0.01 maxiter= 20 distance= 1e-05 data: n= 2001 N= 4 strands: + - sample: seed= 0 seqfrac= 1 Letter frequencies in dataset: A 0.238 C 0.262 G 0.262 T 0.238 Background letter frequencies (from dataset with add-one prior applied): A 0.238 C 0.262 G 0.262 T 0.238
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MOTIFS
For each motif that it discovers in the training set, MEME prints the following information:
J. Kyte and R. Doolittle, 1982. "A Simple Method for Displaying the Hydropathic Character of a Protein", J. Mol Biol. 157, 105-132.
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). The total information content is approximately equal to the log likelihood ratio divided by the number of occurrences times ln(2). The total information content 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 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.
Note: The probability p used to compute the PSSM is not exactly the same as the corresponding value in the Position Specific Probability Matrix (PSPM). The values of p used to compute the PSSM take into account the motif prior, whereas the values in the PSPM are just the observed frequencies of letters in the motif sites.
Note: Earlier versions of MEME gave the posterior probabilities--the probability after applying a prior on letter frequencies--rather than the observed frequencies. These versions of MEME also gave the number of possible positions for the motif rather than the actual number of occurrences. The output from these earlier versions of MEME can be distinguished by "n=" rather than "nsites=" in the line preceding the matrix.