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Introduction

MEMERIS is an extension of the MEME (Bailey and Elkan (1994)) motif finder.
MEMERIS integrates information about RNA secondary structures into the motif search to guide the search towards single-stranded regions.

Reference:
Hiller M, Pudimat R, Busch A, and Backofen R. Using RNA secondary structures to guide sequence motif finding towards single-stranded regions. Nucleic Acids Res. 2006

Download

The source code of MEMERIS is available here.

A list of all modifications in the MEME 3.5.1. source code is here.


Install

MEMERIS is compiled like MEME. For details how to install MEME refer to the README file.
In general it should be sufficient to do:
configure
make
make install

The result is a binary file 'memeris'.


Usage
 
Using MEMERIS requires two steps:

  1. Compute the secondary structure values, i.e. either EF or PU. This job is done by the GetSecondaryStructureValues.perl script.

    It produces a file that contains the EF or PU value for each possible motif starting point.
    Details how to use GetSecondaryStructureValues.perl can be found here.

  2. Use MEMERIS with the file containing the EF or PU values to find motifs.

    MEMERIS has two new parameters:

    -secstruct<filename>
    -pi<double>

    The 'secstruct' parameter is followed by the filename containing EF/PU values. This file should be computed in step 1.
    The 'pi' parameter specifies the pseudocount that is used to flatten the prior probability distribution of the motif starts.
    In general: The lower the pseudocount, the more important the secondary structures become.
    More information about the pseudocount is given below.

    All other parameters of MEME can be used with the following restrictions:
    1. You have to specify a fixed word length (parameter -w) that was used when computing the EF/PU values (parameter -l in GetSecondaryStructureValues.perl).
      This is necessary since the EF/PU values are computed for a fixed motif length. If you want to test several motif lengths, repeat step 1 and 2.
    2. You cannot search motifs in the reverse complementary orientation since this makes no sense for RNA's.
    3. Of course, you cannot set a protein alphabet.

    3. Except for the offset due to the computation of the secondary structure values, MEMERIS has the same runtime as MEME.
    In practice, MEMERIS is reasonable fast for normal data sets (for example searching two motifs of length 6 nt in a set of 120 sequences of length 50 nt takes only a few seconds on today´s standard workstations).
    There is no restriction in the number of sequences or the maximal sequence length, but the computation time increases with the number of sequences and their length.
    Since the input parameters as well as the output format in MEMERIS is retained from MEME, we refer for a detailed description to the MEME website.
                

General hints

  1. As for MEME, the input sequences must consist of the letters A,C,G, or T. No U's are allowed. You can use the following perl script to automatically replace U's by T's:

    perl -w ReplaceUbyT.perl <inputfile> <outputfile>

  2. You should vary the pseudocount -pi. It makes sense to start with a very low pseudocount (e.g. -pi 0.01 or even -pi 0) and to increase it gradually. At the beginning, MEMERIS may find a motif with a rather low information content but with a high average single-strandedness. As the pseudocount increases, the single-strandedness will drop while the information content will increase.

  3. One MEMERIS run requires a fixed motif length (-w). In cases where the correct motif length is not known, it is best to vary the motif length by repeating step 1 and 2.

  4. If the length of the sequences is small (say < 100 nt), it might be beneficial not to estimate the background distribution of the characters from the dataset.
    For example, a AAAAAA motif in sequences of length 50 will result in a higher estimated background frequency of A's which reduces the chance to find the A-rich motif. Therefore, in case of short sequences we propose to set a uniform background frequency distribution. This can be done with the parameter '-bfile Uniform_bfile' where the file 'Uniform_bfile' contains the background frequency. If you want to use another distribution, you have to adjust this file (or create another file).
  5. MEME's speed and accuracy benefits from finding a good start point. MEMERIS, in contrast to MEME, uses a non-uniformly distributed prior which directs the motif search.
    We fnd MEMERIS to perform better if we relax the starting point a bit. This can be done with the parameter 'spfuzz x' where 'x' is a number (e.g. 2).
    For example, if the best starting point is the sequence ACGT then without this parameter MEMERIS will start with a theta matrix
            [ 0.500000  0.166667  0.166667  0.166667,
              0.166667  0.500000  0.166667  0.166667,
              0.166667  0.166667  0.500000  0.166667,
              0.166667  0.166667  0.166667  0.500000]

    and with 'spfuzz 2'
            [ 0.333333  0.222222  0.222222  0.222222,
              0.222222  0.333333  0.222222  0.222222,
              0.222222  0.222222  0.333333  0.222222,
                  0.222222  0.222222  0.222222  0.333333].
  6. We intended to use secondary structures to direct the motif search to single-stranded regions but not to decide whether a motif that is rather double-stranded should be considered as a motif occurrence. Thus, MEMERIS may output all occurrences of a motif in the ZOOPS and TCM model, rather double-stranded hits too. The decision up to which single-strandedness a motif hit is considered, is left to the user. If you want to exclude double-stranded occurrences, you have to restrict the number of motif hits.

    For ZOOPS and TCM, you can set '-maxsites x -minsites x' where x is the number of occurrences. Then, MEMERIS will find the most single-stranded motif hits. To determine a number 'x', we propose to use the procedure described in the paper, although this involves a bit of manual analysis.

    1. Run MEMERIS with a very high pseudocount (e.g. -pi 10 or -pi 100).
      This results in a behaviour like MEME and a good sequence motif is found.
    2. Analyse the output in section "Motif 1 sites sorted by position p-value" and decide according to the sequence and single-strandedness of the occurrences how many motif hits you want to consider.
    3. Run MEMERIS with a small pseudocount (e.g. -pi 0.1) and restrict the number of motif hits (e.g. -maxsites 10 -minsites 10).

    Again, you should vary the number of motif hits and compare MEMERIS' results.