Nucleic Acids Research Advance Access published online on May 30, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm362
© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Melina II: a web tool for comparisons among several predictive algorithms to find potential motifs from promoter regions
Toshiyuki Okumura1,
Hiroki Makiguchi1,
Yuko Makita2,
Riu Yamashita3 and
Kenta Nakai3,*
1Mitsui Knowledge Industry Co. Ltd, 2RIKEN Genomic Sciences Center and 3Human Genome Center, Institute of Medical Science, University of Tokyo, Japan
*To whom correspondence should be addressed: Tel: +81-3-5449-5131; Fax: +81-3-5449-5133; Email: knakai{at}ims.u-tokyo.ac.jp
Received January 30, 2007. Revised April 13, 2007. Accepted April 25, 2007.
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ABSTRACT
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We present the second version of Melina, a web-based tool for
promoter analysis. Melina II shows potential DNA motifs in promoter
regions with a combination of several available programs, Consensus,
MEME, Gibbs sampler, MDscan and Weeder, as well as several parameter
settings. It allows running a maximum of four programs simultaneously,
and comparing their results with graphical representations.
In addition, users can build a weight matrix from a predicted
motif and apply it to upstream sequences of several typical
genomes (human, mouse,
S. cerevisiae,
E. coli,
B. subtilis or
A. thaliana) or to public motif databases (JASPAR or DBTBS)
in order to find similar motifs. Melina II is a client/server
system developed by using Adobe (Macromedia) Flash and is accessible
over the web at
http://melina.hgc.jp.
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INTRODUCTION
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Transcription factor binding sites (TFBSs) play important roles
in the regulation of gene expression. Extraction of a common
TFBS from a set of DNA sequences is a practically important
problem. Although a number of algorithms have been released
so far to overcome this problem, none of them seem to be perfect
(
1–4). Thus, to avoid missing important motifs relying
on only one algorithm or to check the effect of changing parameter
values, it is useful to compare the prediction results obtained
from different algorithms/parameter values. To support this
function, we previously released a web tool named Melina (
5).
Recently, it was updated to its second version, Melina II. In
Melina II, some of the integrated algorithms are replaced with
more modern ones and the graphical representation is extensively
improved. Melina II enables users to compare the results of
promoter analysis more efficiently and easily.
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OVERVIEW
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Melina II allows running at most four out of five external algorithms
[Consensus (
6), MEME (
7), Gibbs sampler (
8), MDscan (
9) and
Weeder (
10)] with users’ specified parameter values to
avoid missing important motifs. MDscan and Weeder are newly
added in this release. MDscan is a hybrid of two motif search
strategies, word enumeration and position-specific weight matrix.
Weeder adopts an enumerative pattern discovery algorithm carrying
out an almost exhaustive search. The integration of algorithms
based on different principles should help detecting subtle motifs
and reducing the number of false positives. It may also be helpful
to narrow down motif candidates or to detect alternative motifs
by the combination of different algorithms and/or parameter
values. Results of these algorithms are comparatively displayed
with intuitive graphics (
Figure 1).
As shown in
Figure 1, three simple steps are sufficient to use
Melina II:
Step 1: Input query sequences (Figure 1a)
In the Query input panel, multiple input sequences are fed in the FASTA format.
Step 2: Select predictive algorithms and their parameters (Figure 1a and b)
Although defaults are provided, users can choose the prediction algorithms and their specific parameter values at this step. Default parameters are sometimes chosen originally to make the search conditions as similar as possible to each other. They are: (1) the motif length is around 10 bases (‘6–10’ for MEME and Weeder; otherwise, ‘10’); (2) both strands are searched and (3) multiple occurrences are allowed for each sequence. Selecting the same algorithm with different parameter values at the same time is allowed.
Step 3: Submit a query and get results (Figure 1c)
After submitting a query, a job ID is displayed on the screen while the job is running. Users can later access the results by using this job ID.
After Melina II finishes the motif detection, the results of each prediction are integrated and displayed graphically (Figure 1c). Detected motif candidates are illustrated with colored arrows in the summarized view (upper-right corner of the result view). If users click a motif candidate in the summarized view, more information is shown in the detailed view (lower-right corner) and the predicted motif is illustrated by Sequence Logo (11) [the script for its drawing was taken from WebLogo (12)] or a weight matrix. This integrated result helps finding motif candidates and figuring out the outline of cis-regulatory modules. With the ‘PDF’ button, the output can be saved as a pdf file, which is useful either for users’ further manipulation and inclusion in publication or for getting the entire view by adjusting the scale. The ‘FIT’ button is used for conveniently getting the entire view along its horizontal axis and for hiding the detailed information at its lower half.
Furthermore, users can build a weight matrix from a predicted motif and apply it to upstream sequences of several typical genomes (human, mouse, A. thaliana, S. cerevisiae, E. coli or B. subtilis) or to public motif databases [JASPAR (13) or DBTBS (14)] in order to find similar motifs. For the former search, we used the HMMER package by Sean Eddy (http://hmmer.janelia.org/). More details are available from the help document.
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EXAMPLES AND DISCUSSION
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To illustrate how Melina II works, we give two examples. The
first is a set of artificial DNA sequences containing several
known motifs. The second consists of upstream sequences of functionally
related genes.
Example 1: Embedded motifs in artificial sequences
In this example, the dataset consists of three 250-bp long DNA sequences (Figure 2a). Each DNA sequence was randomly generated by the Random Sequence Generator, which is a function of Melina II. Three known consensus motifs were inserted into each sequence (Figure 2b). Motifs were set in random order to check the influence of their location. In general, it is difficult for multiple alignment programs to detect all motifs from this kind of dataset.
In this case, we used four algorithms, Consensus, MEME, Gibbs
sampler and Weeder, with their original default parameters.
This result shows that there is no predictive algorithm which
can correctly detect all motifs. However, we can recover all
the inserted motifs if we take motifs detected by at least two
algorithms, as illustrated in
Figure 3.
For the same dataset, we show another result in
Figure 4. In
this case, we used Consensus with default parameters and Gibbs
sampler with three different sets of parameter values. This
result clearly shows that values of parameters such as motif
size and cut-off value can significantly influence motif detection.
Because Melina II enables fine specification of parameters,
expert users can analyze datasets multilaterally.
Example 2: Upstream sequences of functionally related genes
We present here an example of real promoters containing a common
motif. This dataset consists of 300 bp upstream sequences from
the translational start sites of five
Bacillus subtilis genes,
known to be regulated by a well-known global regulator, CcpA.
As shown in
Figure 5, a common motif is identified and, through
the search against DBTBS, it is confirmed that the motif found
corresponds to the CcpA motif. (
Figure 5b and c).
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FUTURE PROSPECTS
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One future direction is to endow Melina a function to ‘guide’
favorable parameter values to improve the detection accuracy.
It is not an easy task because optimal parameter values for
each algorithm could depend on, say, the length and the number
of input sequences as well as the nature of the pattern to be
sought. Nevertheless, it seems to be possible more or less to
categorize typical cases with suggested optimal parameter values
for each (
15).
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IMPLEMENTATION
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Melina II was developed as a web-based tool by using Adobe (Macromedia)
Flash. You may need to install the Flash Plug-in beforehand.
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ACKNOWLEDGEMENTS
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We would like to thank all groups and authors including Gary
Stormo, Bill Thompson, Charles E. Lawrence, Timothy L. Bailey,
Douglas L. Brutlag, Xiaole S. Liu, Giulio Pavesi, Graziano Pesole,
Boris Lenhard, Sean Eddy, Thomas D. Schneider and Steven E.
Brenner that made the following algorithms freely available:
Consensus, Gibbs sampler, MEME, MDscan, Weeder, RefSeq, JASPAR,
HMMER and Sequence Logo/WebLogo. We thank Nicolas Sierro also
for critically reading the manuscript. This work was partly
supported by Grant-in-Aid for Scientific Research on Priority
Areas ‘Comprehensive Genomics’ from the Ministry
of Education, Culture, Sports, Science and Technology of Japan.
Funding to pay the Open Access publication charges for this
article was provided by a budget from the Ministry of Education,
Culture, Sports, Science and Technology, Japan.
Conflict of interest statement. None declared.
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