BACKGROUND
transcription factors  function by binding to the recognition sites in gene regulatory regions.
tf binding to dna is mediated through base-dependent hydrogen bonds and other structural propensities that are the result of dinucleotide stacking: salt bridges to the phosphate backbone, hydrogen bonds and van der waal's interactions  <cit> .
tfs are also often members of multimolecular complexes to which the dna binds through further sequence and structural features.
where tfbs sequences are known, one can try to search for similar sequences computationally.
these binding sites are often represented by a consensus, which is just a pattern of bases that occur at specific positions in a site.
because the sites are often degenerate, mismatches to the consensus are often admissible.
but consensus presentation has limited use for even moderately variable bss, because it preserves too little or no information about nucleotide variability.
tfbs prediction is usually attempted using position weight matrices   <cit> .
this method implies that there are some contributions from each base at each position and that the sum of all these contributions is above a certain threshold.
however, this is inadequate for three reasons.
the core sequence-specific positions are so few that the matrices have a high false positive rate.
many tfbss have too few functionally characterised sequences to populate, to a statistically meaningful extent, a dinucleotide pwm of sufficient length to capture the long-range structural propensities.
finally, they are severely limited by the assumption that positions in a site contribute additively to the total score.
experimental evidence suggests that this assumption of independence is not always true  <cit> .
this assumption may be just a good and useful approximation, which however does not fit data perfectly and is therefore not quite correct  <cit> .
the high false-positive rates in tfbss prediction using pwms of the core motifs have led to various attempts to draw in extra information to improve performance  <cit> .
one is to look for conservation of predicted bss between homologous genes of different species taking into account the evolutionary distances  <cit> , though this will result in the potential elimination of species-specific tfbss.
given that tfs usually act through multi-protein complexes, another has been to search for pairs or higher multiples of tfbss  <cit> .
this approach is restricted either to known or presumed tfbs pairs.
the simplest way to increase performance of conventional pwm is the calculation of dependencies between adjacent positions.
this model is represented by a dinucleotide pwms   <cit> .
besides the obvious advantage of involving higher-order statistics, they certainly may capture the longest lengths of motifs.
another tool for tfbss prediction used pwms constructed on the basis of degenerate oligonucleotide motifs  <cit> .
this approach may represent more than two non-adjacent positions together, but still the motif structure is preliminary restricted.
additional statistical features in the flanking regions of sites may also support tfbs recognition  <cit> .
this analysis uses many types of features, for instance oligonucleotide content, structural and chemical context-dependent parameters like helical twist or melting temperature.
a similar approach, based on discriminant function of retrieved features to e2f bss, appears to be very promising  <cit> .
namely, the false positive rate of pwm may be substantially decreased and this especially refers to revealing high scoring sites.
another successful approach for pwm improvement incorporated position-dependent information content and pairwise correlations  <cit> .
in this work the notion of scope delimited the correlating nucleotides .
recently, other approaches have been reported for pwm improvement by the consideration of dependencies between distant positions  <cit> .
the interaction of distant site positions can be important for the formation of dna secondary structure that aids tf recruitment, its interaction with dna duplex and stable tf-dna complex formation.
therefore correlations between arbitrary bs positions are expected to be important.
these correlations provide long-range structural rather than sequence-specific interactions.
some tfs function through association with nucleosomes  <cit> , which do not bind to any sequence-specific motif but rather through quaternary structures of the duplex dna  <cit> .
in this work we have further developed a new method, entitled sitega  <cit> , using a genetic algorithm  involving a discriminant function of locally positioned dinucleotides  and applied it to clinically important tfbss.
it is derived from the analysis of local dinucleotide context  <cit> , and provides the subtlety of discriminant analysis  <cit>  with the speed of a ga in detecting significant features.
to evaluate the performance of our approach, we compared it with optimized pwms, whose lengths were adjusted until they performed at their best and also compared mono- and dinucleotide matrices.
duplex-dna quaternary structures result from the dna bending and flexibility, which arise from the stacking interactions of successive dinucleotides  <cit> .
such structural approaches could be defined by a dinucleotide pwm, but this would require hundreds of sites to develop a statistically meaningful  <dig> ×  matrix .
discriminant analysis provides an approach to determining which dinucleotides and positions appear to be significant.
the pattern beyond the canonical footprint reflects the genome nucleotide context of neighbour regulatory regions around bss.
consideration of these regions helps to increase the recognition accuracy.
subtle context features besides the site footprint may be related to the presence of other still unknown features within the overall regulatory element .
finally we combined the sitega and optimized pwm models together and applied them to the human promoters from epd  <cit> .
this reveals the most reliable potential bs targets.
RESULTS
pwms: window lengths and performance
the average ranks calculated on the basis of nine tfbs types
the average ranks calculated on the basis of nine tfbs types
detailed values for the optimized pwm models are in table  <dig>  with figure  <dig> showing fp rates for the optimized models according to model length.
we found that motif lengths should be greater than  <dig> bases long for lower fp rates, and generally that dinucleotide slightly outperformed mononucleotide pwms, for all except srebp.
one might have expected that the structural information implicit in dinucleotide stacks would have resulted in a generally much better performance.
we attribute this meagre improvement to the shortage of sequences available to populate a  <dig> ×  matrix needed for dinucleotides .
<dig> – di – dinucleotide pwm, mono – mononucleotide pwm.
pwms and sitega: performance comparison
sitega represents a radically different approach to pwms in that the genetic algorithm has discovered the dinucleotide interactions that are most significant, with probabilities assigned to their ability to discriminate between genuine and false sites.
the significant dinucleotide pairs may be very far apart, and the intrinsic information not detectable using pwms.
its parameters for the nine tfbss are also shown in table  <dig>  and figure  <dig> shows the roc plots for sitega and pwm models.
the most striking feature of these results is that the sitega approach often performed close to and sometimes better than the optimized pwms.
for the latter, the jackknife versus the bootstrap techniques gave closely similar plots, whereas for the former, there was a wider disparity between the two.
it might be an artefact of the procedure, but the jackknife technique tended to give a step-wise series of steeper curves.
the plots have been ordered according to sequence length and it is clear that the shorter sites  performed the least well by either method, i.e.
at 50% tp rates, the fp rate was > <dig> e- <dig>  of the remainder, sitega did not perform as well as the optimized pwms for irf <dig> and isgf <dig>  but this can be accounted for by the comparatively low number of sites in the set  leading to the low number of discriminatory lpds .
larger datasets could improve this performance.
by contrast, sitega outperforms pwms at most tp rates for e2f, which has the longest sequence length and the third highest number of representative sequences.
sitega: patterns beyond the canonical core sequences
one of the most interesting questions in our study was to clarify the nature of context patterns both within and outside of the well-known site core region.
for this we looked in detail at sf- <dig>  for which we have the largest dataset .
we found that the most significant context features were inherent to the consensus  and footprint  regions.
locations of dinucleotides were defined with respect to dinucleotide positions.
for example, the second bottom pair of lpds in figure 4c show a 'positive correlation between  gt and  tc'.
it means mutual occurrence of dinucleotides gt and tc at  <dig> and  <dig> positions correspondingly.
this positive correlation means that there is a frequent occurrence of trinucleotide gtc spanning nucleotides16– <dig>  indeed, it belongs to the consensus sequences gtcaaggtca  <cit> .
the first bottom pair of lpds is the negative correlation between  ga and  ca.
this means that when ca is found commencing at position  <dig>  ga is never found at position  <dig> and rarely if ever found at position  <dig>  furthermore, the second bottom line in figure 4d presents the negative correlation between dinucleotide gc and gt resided to the coincident location .
this corresponds to  <dig> out of  <dig> sites having c or t and position  <dig>
note that the locations of regions corresponding to significant correlations are not restricted to the core consensus region.
in general, the majority of the most significant correlations  located within consensus region , less significant correlations  covered both consensus and flanking regions .
there are also more positive rather than negative correlations, perhaps indicative of co-operative structural binding propensities.
pwms, sitega and their combination applied for epd promoter analysis
without exception, for a given tf and search method, the number of sites predicted increased with higher tp rates.
this is as expected, since a greater number of tps will add an increasing proportion of fps, and, as a crude guide, higher numbers correspond to greater numbers of fps being found.
as expected from observations above, hnf <dig>  srebp and stat <dig> predicted many sites, which almost certainly reflect the weak consensus leading to a higher proportion of fps.
in these cases, sitega consistently found fewer sites, perhaps indicative of better performance.
irf <dig>  isgf <dig> and to a lesser extent ppar had smaller training sets, which resulted in fewer sequences retrieved from epd, though sitega found more than pwms probably because the former models were still poorly defined.
for the remainder , pwms returned fewer sequences at the highest stringency  but more at the lower stringencies .
the combined search approach returned still fewer sequences, indicative of this being the most stringent approach and any given stringency.
in order to confirm that this refinement was a consequence of discounting fps, rather than just a proportional reduction of tps and fps, we computed the ratios tp/fp for all tfbss for sitega, pwm and the combined approach .
tp rates were estimated on the basis actual recognition rates for the training data and fp rates were evaluated as actual frequencies of predictions for background shuffled training sequences.
these calculations finally confirmed that:
sitega models generally have higher ratios tp/fp than pwms, indicating that the former may discriminate better between true and false sites;
for any tfbss tp/fp ratios are considerably larger for combined approach than for any separate model, thus the combination indeed is superior with respect to any single models.
the epd genes retrieved by the combined approach are presented in table  <dig>  nf-κb was excluded from this study because the detected genes had a broad range functional roles that appeared contradictory  <cit> , while srebp was discounted for similar reasons and because it might still have a high number of fps.
predicted sites for the remaining seven tfbss were sorted by several criteria.
apart from the genes known and strongly suspected of being regulated by the tfs, the "possibly regulated" column includes genes that might be part of the physiological response mediated by the tf.
the unrelated and unknown genes might also, but we have no evidence to support their involvement.
some tfs belong to gene families whose binding sites are similar, these genes might be regulated by the tf in question, or their identification might be the accidental result of sequence similarity.
prediction for potential targets of e2f, irf <dig>  isgf <dig>  hnf <dig>  ppar, sf- <dig> and stat <dig> were done by combined pwm & sitega approach.
for each tfs stringencies for sitega and pwm are in terms of the same fixed tp rate .
known targets – experimentally confirmed targets; very possible or possible targets – a lot of or some indirect evidence on the target functionality; bss of homologous tfs – potential targets of homologous tfs approved or may be supposed; possibly unrelated – presumably false positives, unknown – no information on the target functionality.
<dig> – gene have two predicted sites;  <dig> – gene have three predicted sites; † – potential targets of members of irf family; # – potential targets of lrh- <dig>  that is homolog of sf-1; ‡ – potential targets of members of stat family.
analysis of whole human genome
finally to estimate a fp rate on a real genome sequence we evaluate potential sf <dig> site density for whole human chromosomes .
the stringency was the same as for epd analysis, i.e.
tp rate fixed at 60% for sitega and optimized pwm).
obviously, whole genome sequences have a very small portion of functional sites, even if some sequences may bind tf in vitro, the did not bind tf in vivo.
there are several alternative explanations for this .
sitega web tool
the sitega web tool  <cit>  has undergone major revision and rationalisation since it was first reported  <cit> .
it now allows the user to select a subset of recognition methods, so that the output provides results for every tfbs in turn.
the input data for sitega  should be in fasta format.
the web interface allows the user to search for  <dig> tfbss.
the tool starts when the tf, strand and thresholds options are specified.
all threshold settings are supplied with corresponding fn and fp rates.
the interface also provides tf full names and links to the swissprot data, the training-set sites and all sites in trrd  <cit> .
an example output data is given in figure  <dig>  first of all, the table denotes threshold settings for all predicted sites, and then sites are listed for which no hits were found.
the results for each sequence and predicted site in turn are printed, specifying sequence name and total length, then tf type, total hit count and the list of predicted sites sorted by position order.
for each predicted site, the output includes its score, strand and short sequence containing the most conservative region  in bold uppercase.
adding new tfbss as sufficient functional site sequences become available is constantly refining the web tool.
discussion
we propose a supplementary approach for tfbs prediction, entitled sitega.
it is based on the detection of locally positioned dinucleotides, identified from known sites using a ga with discriminant analysis.
we have previously applied this combination for computer analysis of nucleosome formation potentials, recon  <cit> , which has been extensively validated and used to investigate a variety of genomic locations  <cit> .
the approach has also proved for the drosophila melanogaster promoter recognition  <cit> .
the techniques for modelling dependencies between distant positions has been successfully used for the recognition of splicing sites  <cit> , recombination sites  <cit>  and genes  <cit> .
recently  <cit> , we introduced the sitega method as one among other alternatives to traditional pwm approach.
now we propose a far more comprehensive study of tfbs recognition, which includes a range of modifications/improvements to the sitega method and web tool itself:  one window system for training instead three-window system was used ; then, a set of locally positioned dinucleotides was sought for each of the regions separately, finally three recognition function combined);  three new types of recombination in genetic algorithm were applied and described , in  <cit>  only one operator was used and described.
moreover, in comparison with the previous paper  <cit> , the method is more carefully presented here, with more details of the implementation and algorithm.
the careful comparison included jackknife  and bootstrap tests for accuracy estimation.
these tests were done for all the sitega and optimized pwms recognition models .
among other conclusion from these tests, we demonstrated that pwms may be significantly improved by:
a) dinucleotide statistics  exhaustive search among different length and location of pwm window.
chen et al.
<cit>  have studied selecting a window size for the acceptor and donor splice site sequence.
they suggested an optimal length for the donor and acceptor splice site, i.e.
a window from  <dig> bases upstream to  <dig> bases downstream for the donor splice site, and a window from  <dig> bases upstream to  <dig> bases downstream of acceptor splice site.
thus, a proper window size is among the most important factors for performance improvement.
a similar effect of window size  on the accuracy was investigated in the comparison of five motif discovery algorithms by hu et al.
<cit> .
from this comparison we may conclude that very short motif width showed the worst results.
finally, they suggested running algorithms multiple times with different motif widths to get the best result.
thus, we followed this advice and performed exhaustive searches of window size for all  <dig> tfbss.
though, we should notice that motif discovery is not the same as site recognition, but intrinsically the approaches have many similar features.
we applied the algorithms of berg and von hippel's  <cit> , match  <cit>  and log-odds  <cit> , as well as our own variant of the latter and compared these.
for many types of tfbss, we have shown that our matrices perform better
nine tfbss have been investigated in this study.
e2f is a key regulator of cell cycle.
the good recognition performance achieved for this tf  may be considered as a consequence of it participation in composite elements and a number of additional context and structural features in the flanking regions  <cit> .
tfs isgf <dig> and stat <dig> are strongly inducible tfs  <cit> .
isgf <dig> is activated by interferons type i. stat <dig> may be activated by all interferons and some other cytokines.
the isgf <dig> and stat <dig> enhance transcription of many interferon-inducible genes at early stage of induction , whereas irf <dig> ensures enhanced transcription of many interferon-inducible genes for a long time in infected cells  <cit> .
sf- <dig> is a key regulator of the steroidogenic genes expression in gonads and adrenals  <cit> .
moreover, sf- <dig> is required for development and differentiation at all the levels of the hypothalamic-pituitary-gonadal and adrenal axis  <cit> .
there is experimental evidence for the presence of the sf- <dig> bss in the regulatory regions of genes functioning within this axis  <cit> .
nf-κb is a factor involved in regulation of many types of genes, being induced by cytokines, growth factors, and some other stimuli.
nf-κb is involved not only in regulation of the immune response, but also of many other processes  <cit> .
nevertheless, the significance of interactions between distant positions and their competence for recognition improvement was already confirmed for nf-κb bss  <cit> .
the bss of ppar were already found earlier as good examples of interactions between distant positions  <cit> .
markov model application for large hnf4α set  revealed many dependent non-adjacent positions  <cit> .
also this discovery confirmed the importance of a large dataset for performance improvement.
there is ample evidence to suggest that the duplex-dna quaternary structures in the tf-dna complex and site flanking regions are the main factors that explain the observed differences between accuracy estimates of traditional pwms and other models which consider distant interactions.
taking into account the relations between tfbss recognition accuracy, potential involvement of tfs in specific regulation or in processes in wide range of systems and tissues we may suppose that accuracy estimates reflected the hidden context pattern of tfbss.
most probably duplex-dna quaternary structures, which we have here interpreted as a set of mutually adjusted locally positioned dinucleotides, may be more strictly predefined if we find a stronger pattern of these context features.
in the current work, the sophisticated method sitega was developed for tfbs recognition.
in order to evaluate the efficiency of our approach, we developed special techniques for traditional pwms optimization.
namely, the best of mononucleotide or dinucleotide pwms and optimal window lengths chosen using jackknife tests were implemented for  <dig> types of tfbss.
we revealed that for six tfbss  performances are better and optimal lengths are longer  than for hnf <dig>  srebp and stat <dig>  maybe each of those six tfbss has a stronger context pattern or they have a more stable set of general co-factors.
the latter case may be for example if a quantity of genes, subjected to tf-specific regulation may be roughly functionally restricted.
in comparison with other well-known approaches for weights calculation  <cit> , the new formula developed here , section method, pwms) performed on a par if not better than the best of the others.
we further optimized our pwms through the adjustment of lengths by jackknife tests, which were mainly based on dinucleotide statistics .
thus we have captured the longest lengths  than traditional widely used pwms   <cit>  and our pwms have shown very substantial performance improvement .
performance estimates for all pwm models did not notably depend on the exact type of resampling tests .
the same was observed in almost all cases for sitega models.
the possible exclusions are sitega models for irf <dig> and ppar bss .
at least for the former, this may be related with small dataset size .
for the latter, this effect is not so notable.
in all other cases the differences between jackknife or bootstrap tests for sitega models were not observed.
additional sites for training cause the differences between jackknife and bootstrap tests, i.e.
this may be interpreted not only as a result of substantially small, but rather not sufficient data.
sitega algorithm in contrast to pwm's is essentially stochastic, since sitega as all other gas do not guarantee the best solution.
since sitega accuracies did not notably depend on the type of resampling tests we may conclude that we achieved sufficient stability for sitega algorithm convergence.
generally, based on  <dig> types of tfbss, optimized pwm and sitega have shown similar performances .
by taking into account fuzzy local positioning of the dinucleotide context, one can possibly achieve considerable increase in the recognition accuracy when compared to that for pwms.
recall that pwm cannot be quite correct since it based on the assumption of additive contribution of site positions to the total score  <cit> .
thereby account of dependant contributions of different positions may help to improve the recognition accuracy.
for e2f, ppar, nf-κb, hnf <dig> bss we found other evidence for distant structural interactions  <cit> .
for first three of them sitega successfully competed with performance of pwms .
the worse results for hnf <dig>  may be related to a small dataset size , so even with pwms we were able to catch the shortest length .
also we found sf- <dig> as a good example in sitega comparison with pwms .
worse results achieved for other tfbss can probably be attributed to two reasons.
the first is a small and heterogenous dataset .
we may suspect that in terms of distant interactions these sets were not representative.
the solution space of any complicated model  is incommensurably greater than that for simple  models.
hence only a statistically large enough dataset size may allow a complicated model to realise its potential advantages.
whatever the case, the representative data are the result of a compromise between their heterogeneity and their total amount.
the isgf <dig> and irf <dig> are very divergent among other training sets: firstly they are the smallest sets, secondly the total count of dinucleotides exploited by their sitega models are very small in comparison with all other sitega models .
the second reason for worse accuracy of sitega in comparison with pwms is a weak and "short" context pattern , since these tfbss are found among the worst among all pwm models, and among the others they have nearly shortest lengths .
one may expect that for some tfbss, distant interactions may be substantially less important than close ones, so that additivity assumption  <cit>  supported by observed context pattern.
comparisons between the performance of pwm and sitega models estimated by resampling tests  lead us to conclude that pwms perform better when only smaller datasets are available .
sitega models need more representative training sets  to achieve better results.
the comparison of performances for datasets of different size suggests that a subtle context pattern, which may increase the performance, might be successfully retrieved only from larger training data  <cit> .
simulated data analyses have shown that performances of mono- and dinucleotide pwms, as well as of optimized markov model  are increased when dataset size grows from  <dig> to  <dig> sequences.
moreover, the most substantial growth of performance for omim was observed when the dataset size was increased from  <dig> up to  <dig> sites.
this observation allows us to suppose that nearly all our datasets  still far from the optimal size.
we realise, that as a quite sophisticated model, sitega in comparison with pwms may be prone to overfitting.
in the case of overfitting, the accuracy achieved by the training data should be quite good, but the independent control data do not confirm good performance.
this effect should be noticeable for a relatively small dataset and careful resampling tests allow us to appreciate possible overfitting effects.
in our paper, we carried out two types of resampling tests , thus, the crucial interference of overfitting is excluded.
to conclude, we may note that increasing the number of training sequences makes a sophisticated method like sitega less prone to overfitting, thus in the case of larger datasets sitega may successfully compete with optimized pwms.
we may emphasize at least three possible ways for further improvement of sitega:
extension of all current tfbss datasets up to at least ~40– <dig> sequences;
careful adjustment of sitega-specific parameters ;
analysis of different lengths.
that was extremely important for pwms , but still sitega models have been implemented with predefined lengths.
but there is no guarantee that pwms and sitega have exactly the same setting for optimal window length.
next our task was to study the hidden context patterns that allow better recognition performance.
for example, the sf- <dig> bs has been described by the motifs ccaaggtca  <cit> , caaggta  <cit>  and gtcaaggtca, that was derived from our data set  <cit> .
according to the data extracted from trrd  <cit> , the region protected from nuclease digestion in the footprinting experiment was not longer than  <dig> bp.
it followed that the core region of the sf- <dig> bs was not longer than that.
we took local dinucleotides of the sitega sf- <dig> model and studied the distribution of their locations within and outside the core region.
we found that most significant sf- <dig> context features were inherent to the consensus  <cit>  and footprint  regions .
the revealed dependencies between locally positioned dinucleotides for all sitega models were split into close and distant ones , which revealed that:
the most significant correlations are mainly between pairs of close dinucleotides, mostly resided to the core region ;
larger portions of less significant correlations are mainly between distant dinucleotides;
total numbers of distant dependencies are substantially higher than numbers of close ones.
since the significance of the distant dependencies is generally lower than for the close ones, the larger dataset is favourable for detailed clarification.
we may note that the domination of close dependencies among most significant correlations  is indeed the basis for assumption of independence  <cit> , which was accepted in our case by pwms.
finally, a large portion of the current research was devoted to analysis of epd data, i.e.
nearly all human promoters ~10% of genome .
this is a substantial development that is worth bringing to the attention of a wider audience.
firstly we separately used optimized pwm and sitega models on human epd promoters  and simulated data analysis , and applied stringent thresholds  to reduce the number of fps.
a small number of predicted sites may be considered as indirect evidence of better recognition performance, since we can expect obvious enrichment of total epd dataset with functional tfbss.
indeed for both pwms and sitega, the ratios of predicted site frequencies to those for background  varied from ~ <dig> to ~ <dig> at the most permissive stringency  depending on tf type.
the comparison of pwm and sitega model predictions applied to epd promoter data  as well as for random sets  suggests the following:
for e2f, hnf <dig>  nf-κb, sf- <dig> and stat <dig>  sitega appeared to be better than the respective pwms.
some deviation from this general rule is not very important.
for instance, a small advantage of e2f pwm at tp 50%  was suppressed by considerable dominance of sitega at two other thresholds, tp 60% and 70%.
ambiguous situations are found for ppar and srebp: the ratios of pwm and sitega predictions  may be more or less than unity depending on threshold, but generally it is hard to choose the better model.
for irf <dig> and isgf <dig>  pwms achieved better results than sitega, which may be attributed to their having only small datasets.
another possible explanation is the small total count of lpds captured by sitega models .
this epd analysis suggests that sitega models are able to outperform the optimized dinucleotide pwms.
analysis of whole human genome sequences  confirmed that sitega and optimized pwms have similar performances and the combination of two different approaches has a substantially reduced fp rate.
obviously, the density of predicted sites for y chromosome is an outlier among others, most probably explained by differences in base content and gene density  <cit> .
the average predicted site density for other chromosomes  are  <dig> e- <dig>   <dig> e- <dig> and  <dig> e- <dig> for optimized pwm, sitega and their combination, respectively .
these figures are slightly lower than the random sequence scores for pwms and sitega of  <dig> e- <dig> , which is probably accounted for by minor differences in base composition.
whereas for epd promoters, there were notably higher predicted-site densities .
this may be just a consequence of closer similarity of nucleotide contents between training data and epd promoters , than between training data and full-length chromosomes.
finally, we consider the high number of predicted sf- <dig> bss on human chromosomes .
the first and obvious explanation is the presence of bss that have similar consensus sequences.
for instance, sf- <dig> bss closely resemble those of lrh- <dig>  <cit> , both tfs function as monomers.
in general, closely related tfs share the same consensus .
futhermore, the specific location of an predicted site may be non-functional in vivo for a range of reasons:  the position may lie in tightly packaged heterochromatin far from gene regulatory machinery  although in putatively regulatory dna, it also may be not exposed enough for tf binding, and finally  in a given tissue or development stage where the tf is present, only a subset of the potential binding sites are available.
hence in genomic dna, we may separate 'in vitro' and 'in vivo' false positives.
that means that the latter can bind tf in vitro, but in vivo this interaction is not observed.
we have previously confirmed that our computer tools are able to predict functional sites at least in vitro quite well  <cit> .
however, the majority of predicted sites on human chromosomes are probably 'in vivo' false positives, which can only filtered out with extra knowledge.
CONCLUSIONS
we have refined the sitega approach for tfbs prediction.
the approach uses a genetic algorithm with a discriminant function of locally positioned dinucleotides  to find out the most important positions and dinucleotides.
this technique provides a mechanism to infer and apply long-range structural tf-dna interactions.
sitega and optimized pwms have been applied to  <dig> tfbss .
we performed jackknife and bootstrap resampling tests to compare performances of sitega and pwms, than we applied both methods separately to epd promoter data.
these comparisons allow us to conclude that for the sitega models and optimized pwms have similar performances.
the analysis of dependencies between frequencies of lpds found by sitega models revealed a number of significant correlations between close and distant lpds.
analysis has shown that the majority among the most significant of these correlations are close and mainly located in the core  region.
among the less significant correlations, the distant were dominating and resided in both core and flanking regions.
finally we applied combined sitega & pwm approach to epd promoter data.
we have demonstrated that the combined approach effectively reduced the false positive rate, which is especially important for higher eukaryotes, whose regulatory regions are long and poorly annotated.
this combination looks very promising for future genome-wide searches, since the two different models together ensured a substantial reduction in the number of false positives.
thereby only the most reliable potential tf targets may be found.
the sitega web tool interface  <cit>  for  <dig> tfbss types has been implemented.

 transcription factors function by binding to the recognition sites in gene regulatory regions. tf binding to dna is mediated through base-dependent hydrogen bonds and other structural propensities that are the result of dinucleotide stacking: salt bridges to the phosphate backbone, hydrogen bonds and van der waal's interactions. tfs are also often members of multimolecular complexes to which the dna binds through further sequence and structural features. where tfbs sequences are known, one can try to search for similar sequences computationally. these binding sites are often represented by a consensus, which is just a pattern of bases that occur at specific positions in a site. because the sites are often degenerate, mismatches to the consensus are often admissible. but consensus presentation has limited use for even moderately variable bss, because it preserves too little or no information about nucleotide variability. tfbs prediction is usually attempted using position weight matrices. this method implies that there are some contributions from each base at each position and that the sum of all these contributions is above a certain threshold. however, this is inadequate for three reasons. the core sequence-specific positions are so few that the matrices have a high false positive rate. many tfbss have too few functionally characterised sequences to populate, to a statistically meaningful extent, a dinucleotide pwm of sufficient length to capture the long-range structural propensities. finally, they are severely limited by the assumption that positions in a site contribute additively to the total score. experimental evidence suggests that this assumption of independence is not always true. this assumption may be just a good and useful approximation, which however does not fit data perfectly and is therefore not quite correct. the high false-positive rates in tfbss prediction using pwms of the core motifs have led to various attempts to draw in extra information to improve performance. one is to look for conservation of predicted bss between homologous genes of different species taking into account the evolutionary distances, though this will result in the potential elimination of species-specific tfbss. given that tfs usually act through multi-protein complexes, another has been to search for pairs or higher multiples of tfbss. this approach is restricted either to known or presumed tfbs pairs. the simplest way to increase performance of conventional pwm is the calculation of dependencies between adjacent positions. this model is represented by a dinucleotide pwms. besides the obvious advantage of involving higher-order statistics, they certainly may capture the longest lengths of motifs. another tool for tfbss prediction used pwms constructed on the basis of degenerate oligonucleotide motifs. this approach may represent more than two non-adjacent positions together, but still the motif structure is preliminary restricted. additional statistical features in the flanking regions of sites may also support tfbs recognition. this analysis uses many types of features, for instance oligonucleotide content, structural and chemical context-dependent parameters like helical twist or melting temperature. a similar approach, based on discriminant function of retrieved features to e2f bss, appears to be very promising. namely, the false positive rate of pwm may be substantially decreased and this especially refers to revealing high scoring sites. another successful approach for pwm improvement incorporated position-dependent information content and pairwise correlations. in this work the notion of scope delimited the correlating nucleotides. recently, other approaches have been reported for pwm improvement by the consideration of dependencies between distant positions. the interaction of distant site positions can be important for the formation of dna secondary structure that aids tf recruitment, its interaction with dna duplex and stable tf-dna complex formation. therefore correlations between arbitrary bs positions are expected to be important. these correlations provide long-range structural rather than sequence-specific interactions. some tfs function through association with nucleosomes, which do not bind to any sequence-specific motif but rather through quaternary structures of the duplex dna. in this work we have further developed a new method, entitled sitega, using a genetic algorithm involving a discriminant function of locally positioned dinucleotides and applied it to clinically important tfbss. it is derived from the analysis of local dinucleotide context, and provides the subtlety of discriminant analysis with the speed of a ga in detecting significant features. to evaluate the performance of our approach, we compared it with optimized pwms, whose lengths were adjusted until they performed at their best and also compared mono- and dinucleotide matrices. duplex-dna quaternary structures result from the dna bending and flexibility, which arise from the stacking interactions of successive dinucleotides. such structural approaches could be defined by a dinucleotide pwm, but this would require hundreds of sites to develop a statistically meaningful × matrix. discriminant analysis provides an approach to determining which dinucleotides and positions appear to be significant. the pattern beyond the canonical footprint reflects the genome nucleotide context of neighbour regulatory regions around bss. consideration of these regions helps to increase the recognition accuracy. subtle context features besides the site footprint may be related to the presence of other still unknown features within the overall regulatory element. finally we combined the sitega and optimized pwm models together and applied them to the human promoters from epd. this reveals the most reliable potential bs targets.  pwms: window lengths and performance the average ranks calculated on the basis of nine tfbs types the average ranks calculated on the basis of nine tfbs types detailed values for the optimized pwm models are in table with figure showing fp rates for the optimized models according to model length. we found that motif lengths should be greater than bases long for lower fp rates, and generally that dinucleotide slightly outperformed mononucleotide pwms, for all except srebp. one might have expected that the structural information implicit in dinucleotide stacks would have resulted in a generally much better performance. we attribute this meagre improvement to the shortage of sequences available to populate a × matrix needed for dinucleotides. – di – dinucleotide pwm, mono – mononucleotide pwm. pwms and sitega: performance comparison sitega represents a radically different approach to pwms in that the genetic algorithm has discovered the dinucleotide interactions that are most significant, with probabilities assigned to their ability to discriminate between genuine and false sites. the significant dinucleotide pairs may be very far apart, and the intrinsic information not detectable using pwms. its parameters for the nine tfbss are also shown in table and figure shows the roc plots for sitega and pwm models. the most striking feature of these results is that the sitega approach often performed close to and sometimes better than the optimized pwms. for the latter, the jackknife versus the bootstrap techniques gave closely similar plots, whereas for the former, there was a wider disparity between the two. it might be an artefact of the procedure, but the jackknife technique tended to give a step-wise series of steeper curves. the plots have been ordered according to sequence length and it is clear that the shorter sites performed the least well by either method, i.e. at 50% tp rates, the fp rate was > e- of the remainder, sitega did not perform as well as the optimized pwms for irf and isgf but this can be accounted for by the comparatively low number of sites in the set leading to the low number of discriminatory lpds. larger datasets could improve this performance. by contrast, sitega outperforms pwms at most tp rates for e2f, which has the longest sequence length and the third highest number of representative sequences. sitega: patterns beyond the canonical core sequences one of the most interesting questions in our study was to clarify the nature of context patterns both within and outside of the well-known site core region. for this we looked in detail at sf- for which we have the largest dataset. we found that the most significant context features were inherent to the consensus and footprint regions. locations of dinucleotides were defined with respect to dinucleotide positions. for example, the second bottom pair of lpds in figure 4c show a'positive correlation between gt and tc'. it means mutual occurrence of dinucleotides gt and tc at and positions correspondingly. this positive correlation means that there is a frequent occurrence of trinucleotide gtc spanning nucleotides16– indeed, it belongs to the consensus sequences gtcaaggtca. the first bottom pair of lpds is the negative correlation between ga and ca. this means that when ca is found commencing at position ga is never found at position and rarely if ever found at position furthermore, the second bottom line in figure 4d presents the negative correlation between dinucleotide gc and gt resided to the coincident location. this corresponds to out of sites having c or t and position note that the locations of regions corresponding to significant correlations are not restricted to the core consensus region. in general, the majority of the most significant correlations located within consensus region, less significant correlations covered both consensus and flanking regions. there are also more positive rather than negative correlations, perhaps indicative of co-operative structural binding propensities. pwms, sitega and their combination applied for epd promoter analysis without exception, for a given tf and search method, the number of sites predicted increased with higher tp rates. this is as expected, since a greater number of tps will add an increasing proportion of fps, and, as a crude guide, higher numbers correspond to greater numbers of fps being found. as expected from observations above, hnf srebp and stat predicted many sites, which almost certainly reflect the weak consensus leading to a higher proportion of fps. in these cases, sitega consistently found fewer sites, perhaps indicative of better performance. irf isgf and to a lesser extent ppar had smaller training sets, which resulted in fewer sequences retrieved from epd, though sitega found more than pwms probably because the former models were still poorly defined. for the remainder, pwms returned fewer sequences at the highest stringency but more at the lower stringencies. the combined search approach returned still fewer sequences, indicative of this being the most stringent approach and any given stringency. in order to confirm that this refinement was a consequence of discounting fps, rather than just a proportional reduction of tps and fps, we computed the ratios tp/fp for all tfbss for sitega, pwm and the combined approach. tp rates were estimated on the basis actual recognition rates for the training data and fp rates were evaluated as actual frequencies of predictions for background shuffled training sequences. these calculations finally confirmed that: sitega models generally have higher ratios tp/fp than pwms, indicating that the former may discriminate better between true and false sites; for any tfbss tp/fp ratios are considerably larger for combined approach than for any separate model, thus the combination indeed is superior with respect to any single models. the epd genes retrieved by the combined approach are presented in table nf-κb was excluded from this study because the detected genes had a broad range functional roles that appeared contradictory, while srebp was discounted for similar reasons and because it might still have a high number of fps. predicted sites for the remaining seven tfbss were sorted by several criteria. apart from the genes known and strongly suspected of being regulated by the tfs, the "possibly regulated" column includes genes that might be part of the physiological response mediated by the tf. the unrelated and unknown genes might also, but we have no evidence to support their involvement. some tfs belong to gene families whose binding sites are similar, these genes might be regulated by the tf in question, or their identification might be the accidental result of sequence similarity. prediction for potential targets of e2f, irf isgf hnf ppar, sf- and stat were done by combined pwm & sitega approach. for each tfs stringencies for sitega and pwm are in terms of the same fixed tp rate. known targets – experimentally confirmed targets; very possible or possible targets – a lot of or some indirect evidence on the target functionality; bss of homologous tfs – potential targets of homologous tfs approved or may be supposed; possibly unrelated – presumably false positives, unknown – no information on the target functionality. – gene have two predicted sites; – gene have three predicted sites; † – potential targets of members of irf family; # – potential targets of lrh- that is homolog of sf-1; ‡ – potential targets of members of stat family. analysis of whole human genome finally to estimate a fp rate on a real genome sequence we evaluate potential sf site density for whole human chromosomes. the stringency was the same as for epd analysis, i.e. tp rate fixed at 60% for sitega and optimized pwm). obviously, whole genome sequences have a very small portion of functional sites, even if some sequences may bind tf in vitro, the did not bind tf in vivo. there are several alternative explanations for this. sitega web tool the sitega web tool has undergone major revision and rationalisation since it was first reported. it now allows the user to select a subset of recognition methods, so that the output provides results for every tfbs in turn. the input data for sitega should be in fasta format. the web interface allows the user to search for tfbss. the tool starts when the tf, strand and thresholds options are specified. all threshold settings are supplied with corresponding fn and fp rates. the interface also provides tf full names and links to the swissprot data, the training-set sites and all sites in trrd. an example output data is given in figure first of all, the table denotes threshold settings for all predicted sites, and then sites are listed for which no hits were found. the results for each sequence and predicted site in turn are printed, specifying sequence name and total length, then tf type, total hit count and the list of predicted sites sorted by position order. for each predicted site, the output includes its score, strand and short sequence containing the most conservative region in bold uppercase. adding new tfbss as sufficient functional site sequences become available is constantly refining the web tool. discussion we propose a supplementary approach for tfbs prediction, entitled sitega. it is based on the detection of locally positioned dinucleotides, identified from known sites using a ga with discriminant analysis. we have previously applied this combination for computer analysis of nucleosome formation potentials, recon, which has been extensively validated and used to investigate a variety of genomic locations. the approach has also proved for the drosophila melanogaster promoter recognition. the techniques for modelling dependencies between distant positions has been successfully used for the recognition of splicing sites, recombination sites and genes. recently, we introduced the sitega method as one among other alternatives to traditional pwm approach. now we propose a far more comprehensive study of tfbs recognition, which includes a range of modifications/improvements to the sitega method and web tool itself: one window system for training instead three-window system was used; then, a set of locally positioned dinucleotides was sought for each of the regions separately, finally three recognition function combined); three new types of recombination in genetic algorithm were applied and described, in only one operator was used and described. moreover, in comparison with the previous paper, the method is more carefully presented here, with more details of the implementation and algorithm. the careful comparison included jackknife and bootstrap tests for accuracy estimation. these tests were done for all the sitega and optimized pwms recognition models. among other conclusion from these tests, we demonstrated that pwms may be significantly improved by: a) dinucleotide statistics exhaustive search among different length and location of pwm window. chen et al. have studied selecting a window size for the acceptor and donor splice site sequence. they suggested an optimal length for the donor and acceptor splice site, i.e. a window from bases upstream to bases downstream for the donor splice site, and a window from bases upstream to bases downstream of acceptor splice site. thus, a proper window size is among the most important factors for performance improvement. a similar effect of window size on the accuracy was investigated in the comparison of five motif discovery algorithms by hu et al.. from this comparison we may conclude that very short motif width showed the worst results. finally, they suggested running algorithms multiple times with different motif widths to get the best result. thus, we followed this advice and performed exhaustive searches of window size for all tfbss. though, we should notice that motif discovery is not the same as site recognition, but intrinsically the approaches have many similar features. we applied the algorithms of berg and von hippel's, match and log-odds, as well as our own variant of the latter and compared these. for many types of tfbss, we have shown that our matrices perform better nine tfbss have been investigated in this study. e2f is a key regulator of cell cycle. the good recognition performance achieved for this tf may be considered as a consequence of it participation in composite elements and a number of additional context and structural features in the flanking regions. tfs isgf and stat are strongly inducible tfs. isgf is activated by interferons type i. stat may be activated by all interferons and some other cytokines. the isgf and stat enhance transcription of many interferon-inducible genes at early stage of induction, whereas irf ensures enhanced transcription of many interferon-inducible genes for a long time in infected cells. sf- is a key regulator of the steroidogenic genes expression in gonads and adrenals. moreover, sf- is required for development and differentiation at all the levels of the hypothalamic-pituitary-gonadal and adrenal axis. there is experimental evidence for the presence of the sf- bss in the regulatory regions of genes functioning within this axis. nf-κb is a factor involved in regulation of many types of genes, being induced by cytokines, growth factors, and some other stimuli. nf-κb is involved not only in regulation of the immune response, but also of many other processes. nevertheless, the significance of interactions between distant positions and their competence for recognition improvement was already confirmed for nf-κb bss. the bss of ppar were already found earlier as good examples of interactions between distant positions. markov model application for large hnf4α set revealed many dependent non-adjacent positions. also this discovery confirmed the importance of a large dataset for performance improvement. there is ample evidence to suggest that the duplex-dna quaternary structures in the tf-dna complex and site flanking regions are the main factors that explain the observed differences between accuracy estimates of traditional pwms and other models which consider distant interactions. taking into account the relations between tfbss recognition accuracy, potential involvement of tfs in specific regulation or in processes in wide range of systems and tissues we may suppose that accuracy estimates reflected the hidden context pattern of tfbss. most probably duplex-dna quaternary structures, which we have here interpreted as a set of mutually adjusted locally positioned dinucleotides, may be more strictly predefined if we find a stronger pattern of these context features. in the current work, the sophisticated method sitega was developed for tfbs recognition. in order to evaluate the efficiency of our approach, we developed special techniques for traditional pwms optimization. namely, the best of mononucleotide or dinucleotide pwms and optimal window lengths chosen using jackknife tests were implemented for types of tfbss. we revealed that for six tfbss performances are better and optimal lengths are longer than for hnf srebp and stat maybe each of those six tfbss has a stronger context pattern or they have a more stable set of general co-factors. the latter case may be for example if a quantity of genes, subjected to tf-specific regulation may be roughly functionally restricted. in comparison with other well-known approaches for weights calculation, the new formula developed here, section method, pwms) performed on a par if not better than the best of the others. we further optimized our pwms through the adjustment of lengths by jackknife tests, which were mainly based on dinucleotide statistics. thus we have captured the longest lengths than traditional widely used pwms and our pwms have shown very substantial performance improvement. performance estimates for all pwm models did not notably depend on the exact type of resampling tests. the same was observed in almost all cases for sitega models. the possible exclusions are sitega models for irf and ppar bss. at least for the former, this may be related with small dataset size. for the latter, this effect is not so notable. in all other cases the differences between jackknife or bootstrap tests for sitega models were not observed. additional sites for training cause the differences between jackknife and bootstrap tests, i.e. this may be interpreted not only as a result of substantially small, but rather not sufficient data. sitega algorithm in contrast to pwm's is essentially stochastic, since sitega as all other gas do not guarantee the best solution. since sitega accuracies did not notably depend on the type of resampling tests we may conclude that we achieved sufficient stability for sitega algorithm convergence. generally, based on types of tfbss, optimized pwm and sitega have shown similar performances. by taking into account fuzzy local positioning of the dinucleotide context, one can possibly achieve considerable increase in the recognition accuracy when compared to that for pwms. recall that pwm cannot be quite correct since it based on the assumption of additive contribution of site positions to the total score. thereby account of dependant contributions of different positions may help to improve the recognition accuracy. for e2f, ppar, nf-κb, hnf bss we found other evidence for distant structural interactions. for first three of them sitega successfully competed with performance of pwms. the worse results for hnf may be related to a small dataset size, so even with pwms we were able to catch the shortest length. also we found sf- as a good example in sitega comparison with pwms. worse results achieved for other tfbss can probably be attributed to two reasons. the first is a small and heterogenous dataset. we may suspect that in terms of distant interactions these sets were not representative. the solution space of any complicated model is incommensurably greater than that for simple models. hence only a statistically large enough dataset size may allow a complicated model to realise its potential advantages. whatever the case, the representative data are the result of a compromise between their heterogeneity and their total amount. the isgf and irf are very divergent among other training sets: firstly they are the smallest sets, secondly the total count of dinucleotides exploited by their sitega models are very small in comparison with all other sitega models. the second reason for worse accuracy of sitega in comparison with pwms is a weak and "short" context pattern, since these tfbss are found among the worst among all pwm models, and among the others they have nearly shortest lengths. one may expect that for some tfbss, distant interactions may be substantially less important than close ones, so that additivity assumption supported by observed context pattern. comparisons between the performance of pwm and sitega models estimated by resampling tests lead us to conclude that pwms perform better when only smaller datasets are available. sitega models need more representative training sets to achieve better results. the comparison of performances for datasets of different size suggests that a subtle context pattern, which may increase the performance, might be successfully retrieved only from larger training data. simulated data analyses have shown that performances of mono- and dinucleotide pwms, as well as of optimized markov model are increased when dataset size grows from to sequences. moreover, the most substantial growth of performance for omim was observed when the dataset size was increased from up to sites. this observation allows us to suppose that nearly all our datasets still far from the optimal size. we realise, that as a quite sophisticated model, sitega in comparison with pwms may be prone to overfitting. in the case of overfitting, the accuracy achieved by the training data should be quite good, but the independent control data do not confirm good performance. this effect should be noticeable for a relatively small dataset and careful resampling tests allow us to appreciate possible overfitting effects. in our paper, we carried out two types of resampling tests, thus, the crucial interference of overfitting is excluded. to conclude, we may note that increasing the number of training sequences makes a sophisticated method like sitega less prone to overfitting, thus in the case of larger datasets sitega may successfully compete with optimized pwms. we may emphasize at least three possible ways for further improvement of sitega: extension of all current tfbss datasets up to at least ~40– sequences; careful adjustment of sitega-specific parameters; analysis of different lengths. that was extremely important for pwms, but still sitega models have been implemented with predefined lengths. but there is no guarantee that pwms and sitega have exactly the same setting for optimal window length. next our task was to study the hidden context patterns that allow better recognition performance. for example, the sf- bs has been described by the motifs ccaaggtca, caaggta and gtcaaggtca, that was derived from our data set. according to the data extracted from trrd, the region protected from nuclease digestion in the footprinting experiment was not longer than bp. it followed that the core region of the sf- bs was not longer than that. we took local dinucleotides of the sitega sf- model and studied the distribution of their locations within and outside the core region. we found that most significant sf- context features were inherent to the consensus and footprint regions. the revealed dependencies between locally positioned dinucleotides for all sitega models were split into close and distant ones, which revealed that: the most significant correlations are mainly between pairs of close dinucleotides, mostly resided to the core region; larger portions of less significant correlations are mainly between distant dinucleotides; total numbers of distant dependencies are substantially higher than numbers of close ones. since the significance of the distant dependencies is generally lower than for the close ones, the larger dataset is favourable for detailed clarification. we may note that the domination of close dependencies among most significant correlations is indeed the basis for assumption of independence, which was accepted in our case by pwms. finally, a large portion of the current research was devoted to analysis of epd data, i.e. nearly all human promoters ~10% of genome. this is a substantial development that is worth bringing to the attention of a wider audience. firstly we separately used optimized pwm and sitega models on human epd promoters and simulated data analysis, and applied stringent thresholds to reduce the number of fps. a small number of predicted sites may be considered as indirect evidence of better recognition performance, since we can expect obvious enrichment of total epd dataset with functional tfbss. indeed for both pwms and sitega, the ratios of predicted site frequencies to those for background varied from ~ to ~ at the most permissive stringency depending on tf type. the comparison of pwm and sitega model predictions applied to epd promoter data as well as for random sets suggests the following: for e2f, hnf nf-κb, sf- and stat sitega appeared to be better than the respective pwms. some deviation from this general rule is not very important. for instance, a small advantage of e2f pwm at tp 50% was suppressed by considerable dominance of sitega at two other thresholds, tp 60% and 70%. ambiguous situations are found for ppar and srebp: the ratios of pwm and sitega predictions may be more or less than unity depending on threshold, but generally it is hard to choose the better model. for irf and isgf pwms achieved better results than sitega, which may be attributed to their having only small datasets. another possible explanation is the small total count of lpds captured by sitega models. this epd analysis suggests that sitega models are able to outperform the optimized dinucleotide pwms. analysis of whole human genome sequences confirmed that sitega and optimized pwms have similar performances and the combination of two different approaches has a substantially reduced fp rate. obviously, the density of predicted sites for y chromosome is an outlier among others, most probably explained by differences in base content and gene density. the average predicted site density for other chromosomes are e- e- and e- for optimized pwm, sitega and their combination, respectively. these figures are slightly lower than the random sequence scores for pwms and sitega of e-, which is probably accounted for by minor differences in base composition. whereas for epd promoters, there were notably higher predicted-site densities. this may be just a consequence of closer similarity of nucleotide contents between training data and epd promoters, than between training data and full-length chromosomes. finally, we consider the high number of predicted sf- bss on human chromosomes. the first and obvious explanation is the presence of bss that have similar consensus sequences. for instance, sf- bss closely resemble those of lrh-, both tfs function as monomers. in general, closely related tfs share the same consensus. futhermore, the specific location of an predicted site may be non-functional in vivo for a range of reasons: the position may lie in tightly packaged heterochromatin far from gene regulatory machinery although in putatively regulatory dna, it also may be not exposed enough for tf binding, and finally in a given tissue or development stage where the tf is present, only a subset of the potential binding sites are available. hence in genomic dna, we may separate'in vitro' and'in vivo' false positives. that means that the latter can bind tf in vitro, but in vivo this interaction is not observed. we have previously confirmed that our computer tools are able to predict functional sites at least in vitro quite well. however, the majority of predicted sites on human chromosomes are probably'in vivo' false positives, which can only filtered out with extra knowledge.  we have refined the sitega approach for tfbs prediction. the approach uses a genetic algorithm with a discriminant function of locally positioned dinucleotides to find out the most important positions and dinucleotides. this technique provides a mechanism to infer and apply long-range structural tf-dna interactions. sitega and optimized pwms have been applied to tfbss. we performed jackknife and bootstrap resampling tests to compare performances of sitega and pwms, than we applied both methods separately to epd promoter data. these comparisons allow us to conclude that for the sitega models and optimized pwms have similar performances. the analysis of dependencies between frequencies of lpds found by sitega models revealed a number of significant correlations between close and distant lpds. analysis has shown that the majority among the most significant of these correlations are close and mainly located in the core region. among the less significant correlations, the distant were dominating and resided in both core and flanking regions. finally we applied combined sitega & pwm approach to epd promoter data. we have demonstrated that the combined approach effectively reduced the false positive rate, which is especially important for higher eukaryotes, whose regulatory regions are long and poorly annotated. this combination looks very promising for future genome-wide searches, since the two different models together ensured a substantial reduction in the number of false positives. thereby only the most reliable potential tf targets may be found. the sitega web tool interface for tfbss types has been implemented. 
to improve this situation, we have quantified the performance of several position weight matrix algorithms, using exhaustive approaches to find their optimal length and position. when sitega and optimized pwm models were applied together, this substantially reduced false positives at least at higher stringencies. we have also gained extra specificity using a method, entitled sitega, which takes into account structural interactions within tfbs core and flanking regions, using a genetic algorithm with a discriminant function of locally positioned dinucleotide frequencies. it adds to the range of techniques available for tfbs prediction, and epd analysis has led to a list of genes which appear to be regulated by the above tfs. analysis of dependencies between close and distant lpds revealed by sitega models has shown that the most significant correlations are between close lpds, and are generally located in the core region. the resulting sitega recognition models can now be used to search sequences for bss using the web tool, sitega. we applied these approaches to bio-medically important tfbss involved in the regulation of cell growth and proliferation as well as in inflammatory, immune, and antiviral responses, obesity and lipid metabolism, regulation of the steroidogenic and cell cycle genes expression. to ensure a higher confidence in our approach, we applied resampling-jackknife and bootstrap tests for the comparison, it appears that, optimized pwm and sitega have shown similar recognition performances. a greater number of less significant correlations are mainly between distant lpds, which spanned both core and flanking regions. however, current methods to predict tfbss are hampered by the high false-positive rates that occur when only sequence conservation at the core binding-sites is considered.
 reliable transcription factor binding site prediction methods are essential for computer annotation of large amount of genome sequence data. however, current methods to predict tfbss are hampered by the high false-positive rates that occur when only sequence conservation at the core binding-sites is considered.  to improve this situation, we have quantified the performance of several position weight matrix algorithms, using exhaustive approaches to find their optimal length and position. we applied these approaches to bio-medically important tfbss involved in the regulation of cell growth and proliferation as well as in inflammatory, immune, and antiviral responses, obesity and lipid metabolism, regulation of the steroidogenic and cell cycle genes expression. we have also gained extra specificity using a method, entitled sitega, which takes into account structural interactions within tfbs core and flanking regions, using a genetic algorithm with a discriminant function of locally positioned dinucleotide frequencies. to ensure a higher confidence in our approach, we applied resampling-jackknife and bootstrap tests for the comparison, it appears that, optimized pwm and sitega have shown similar recognition performances. then we applied sitega and optimized pwms to sequences in the eukaryotic promoter database. the resulting sitega recognition models can now be used to search sequences for bss using the web tool, sitega. analysis of dependencies between close and distant lpds revealed by sitega models has shown that the most significant correlations are between close lpds, and are generally located in the core region. a greater number of less significant correlations are mainly between distant lpds, which spanned both core and flanking regions. when sitega and optimized pwm models were applied together, this substantially reduced false positives at least at higher stringencies.  based on this analysis, sitega adds substantial specificity even to optimized pwms and may be considered for large-scale genome analysis. it adds to the range of techniques available for tfbs prediction, and epd analysis has led to a list of genes which appear to be regulated by the above tfs. 