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Paper text:
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 ( 14 ) .
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
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 .
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 is a hybrid of two motif search strategies , word enumeration and position - specific weight matrix .
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 ) . 

basic usage of melina ii . 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 (  610 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
step 3 : submit a query and get results ( figure 1c ) after submitting a query , a job i d is displayed on the screen while the job is running .
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 .
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/ ) .
the second consists of upstream sequences of functionally related genes . 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 .
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 . 

figure 2.embedded motifs in artificial sequences . embedded motifs in artificial sequences . 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 . 

, 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 . 

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 .
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 .
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 . 

figure 2.embedded motifs in artificial sequences . embedded motifs in artificial sequences . 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 . 

, 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 . 

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 ) . 
 figure 5.real promoters and motif database search . real promoters and motif database search .
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 ) .
melina ii was developed as a web - based tool by using adobe ( macromedia ) flash .
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 .