BACKGROUND
the advent of the second generation sequencing greatly speeds up the accumulation of genome data.
one of the most important tasks to understand such tremendous data is to functionally annotate genomes and analyze gene regulatory networks  <cit> , for which, one pre-requisite step is to identify promoters from genomic sequences  <cit> .
promoters refer to crucial control regions surrounding transcription start sites , which are the basis of transcription initiation and are responsible for steering the binding of rna polymerase  <cit> .
a thorough understanding of promoter regions can provide valuable insights into how, where and when transcription takes place.
concerning how rna polymerase exactly locates a promoter region at the initial stage of transcription, the presumption is that promoter sequences possess some special properties distinctive from the properties of the surrounding non-promoter regions.
previous studies mainly focus on dna sequence compositional features.
these studies have found that some local sequence compositional signals are specific to core promoter regions  <cit> , such as cpg islands   <cit> , tata boxes  <cit> , caat boxes  <cit> , some specific transcription factor binding sites   <cit> , pentamer matrix  <cit>  and oligonucleotides  <cit> .
based on these features, a variety of computational methods have been proposed for promoter prediction.
although much progress has been achieved, recent studies strikingly reveal that existing promoter prediction methods have several common limitations  <cit> .
in fact, due to the complexity and heterogeneity of promoter architectures, there are only a limited number of promoters that exactly match the consensus motifs.
the sequence motifs are not adequate to identify promoters, which results in a high number of false-positives when handling a whole genome.
the local sequence compositional signals alone can not accurately discriminate promoters from non-promoters  <cit> .
the selection of appropriate biological signals to predict promoters remains a field of intense investigation.
recently, the studies of dna crystal structures have brought to light the fact that structural properties of dna sequences play important roles in different genome functions  <cit> .
the second-order structural information encoded in promoter regions could be recognized by rna polymerase  <cit> .
specifically, the curvature and bendability of dna sequences may condition a favorable or inhibitory chromatin environment for the binding of rna polymerase, and may further contribute to the transcription process  <cit> .
previous studies have also shown that the eukaryotic core promoters indeed have distinct structural properties when compared with coding or non-regulatory sequences  <cit> , which presents a feasibility to explore promoter sequences from a structural perspective.
up to now, researchers have analyzed different structural features, including duplex free energy  <cit> , stacking energy  <cit> , dna denaturation  <cit> , duplex disrupt energy  <cit> , protein deformation  <cit> , z-dna  <cit> , dna-bending stiffness  <cit> , a-philicity  <cit> , nuclesome position  <cit> , propeller twist  <cit> , protein-dna twist  <cit> , b-dna twist and bendability  <cit> .
each of these features has been respectively used to predict promoters  <cit> .
as the high dimensionality of feature space will deteriorate the accuracy and time complexity of prediction models, it is costly to combine all these features together for promoter analysis.
meanwhile, biologists are practically inclined to know which features are more correlated with promoters.
however, this issue has seldom been addressed.
thus, in this paper, we systematically compare thirteen structural features of promoters and non-promoters, and propose a feature selection framework to explore which structural features are more related to promoter sequences.
by taking advantage of various feature selection techniques, a small subset of highly discriminative features are selected and further utilized to predict promoters.
from the comparative analysis, we observe that promoter sequences possess some specific structural features, like dna-bending stiffness, duplex free energy and duplex disrupt energy.
the results of different filter and wrapper feature selection methods indicate that energy-related features and dna-bending stiffness appear more frequently in the selected feature subsets, indicating that these structural features have a closer connection to promoter sequences and the features differentiate in capability for promoter prediction.
this finding is in line with the classification accuracy based on each individual feature.
furthermore, the experimental results demonstrate that the selected feature subsets can significantly improve the sensitivity and accuracy of promoter prediction.
RESULTS
comparison of structural patterns of different features
different from traditional promoter prediction methods that are mostly based on sequence compositional features, in this paper we first carry out an extensive comparison between promoter and non-promoter sequences in order to explore whether promoters possess specific structures.
here, our investigation focuses on thirteen different kinds of structural features, including duplex free energy, stacking energy, dna denaturation, duplex disrupt energy, protein deformation, z-dna, dna-bending stiffness, a-philicity, nucleosome position, propeller twist, protein-dna twist, b-dna twist and bendability.
for these features, different structure models have been derived from various biochemical experiments.
according to these models, dna sequences can be transformed into distinct structural profiles, as illustrated in figure  <dig>  although these structural models are based on dinucleotides or trinucleotides, several studies have proven that these structural features are different in terms of the nucleotide information, and offer additional thermo-physical information  <cit> .
based on the conversion schemas, we transform the promoter and non-promoter sequences into numerical vectors and examine their similarities and differences with regard to these structural features on the dbtss human tss dataset .
for all these thirteen kinds of structural features, we plot and compare the structural profiles of promoters and non-promoters .
through a comprehensive analysis, we obtain some interesting observations.
first, for each feature, the structural values along promoter sequences are different from those along non-promoter sequences.
second, promoters have specific patterns of structural profiles, with overall stable values and two sharp troughs or peaks.
the first clear trough  locates at the position - <dig> bp upstream of tss, where the tata-binding protein  binds.
the second trough  is around the tss.
these two troughs  may guide transcription apparatus to locate the appropriate initiation site, which is in agreement with the fact that promoters bear a special structure that is essential to the assembly of the transcription machinery.
on the contrary, the structural profiles of non-promoters do not exhibit such patterns.
the different patterns may originate from the strongly conserved sequences, such as the tata box around the - <dig> bp position and the inr element at the tss position.
however, recent studies indicate that the presence of these elements is not enough to explain the specific structural patterns of promoters.
promoter sequences without the tata element still have a clear trough in the structural profile of dna denaturation, although the trough is not as deep as that with tata box  <cit> .
this finding suggests that promoter sequences can denature more easily than non-promoter sequences, no matter whether they contain those elements or not.
in our previous study  <cit> , the analysis on cpg-related and non-cpg related promoters shows that the two different kinds of promoter sequences exhibit similar structural patterns.
as a result, we may conclude that the structural features are not only affected by the specific elements such as tata box, inr element and cpg island, but also by long-range combinations of nucleotides.
third, as shown in figure  <dig>  there are two types of structural features according to the profile patterns: either a large trough stretching over the tss region is visible, such as the plots in the first two rows, or a peak is visible, such as the features in the last three rows.
these distinctive peak or trough regions are possibly useful for identifying tsss.
furthermore, compared with the non-promoter sequences, the structural patterns of promoters with regard to some features are distinctive and clear, such as energy-related features, dna-bending stiffness and dna denaturation.
by analyzing the structural models, we observe that these features are closely related with the stability of promoter sequences.
in contrast, the profiles of b-dna twist, protein deformation and protein-dna twist are ambiguous and noisy.
these structural features have different capabilities of differentiating promoters and non-promoters.
these findings suggest that a variety of possible mechanisms may lead to promoter recognition by rna polymerase.
the binding of rna polymerase could result from the dna sequence preferences.
alternatively, promoter recognition may also be guided by the boundaries of the specific structural peaks or troughs around the tsss.
comparison of classification performance of different structural features
the above analysis implies that the structures of dna sequences are important signals in promoter recognition of rna polymerase, and integrating structural features may be helpful for promoter prediction.
in order to identify the most promising features, we move forward to test how well each individual feature can differentiate promoters and non-promoters.
the experiments are conducted on the dbtss human promoter dataset.
for each feature, we respectively convert the positive promoter and negative non-promoter sequences into corresponding numerical feature vectors.
because of the outstanding classification performance of the support vector machine  classifier, we apply it to discriminate promoters from non-promoters.
we train thirteen svm classifiers corresponding to all these different structural features and test their classification performance, which is evaluated by three measures, including sensitivity, specificity and f-measure.
ranking structural features by filter methods
to identify the discriminative structural features for promoter prediction, we adopt different filter feature selection methods to select feature subsets from the thirteen kinds of structural features.
first, each sequence  is converted to thirteen numerical vectors corresponding to the thirteen different structural features.
since the values of these features are at different levels, we respectively normalize these vectors into the range  <dig> <cit> .
second, we combine these feature vectors into an integrated vector whose dimension is 13* <dig>  and add a boolean dimension as class label with 'true' for promoters and 'false' for non-promoters.
third, various filter selection methods based on four evaluation metrics, including information gain , chi square , relieff and correlation-based feature selection , are used to select the most discriminative dimension subsets .
then, all these dimensions are ranked based on the scores that are assigned by the feature selection methods.
for the selected dimension subset, we calculate the ratio of each feature in the subset, that is, the ratio of the number of dimensions related to this feature over the total number of dimensions in the selected dimension subset.
only the top-ranked features are used for further classification purpose.
in order to obtain a general trend, for each evaluation criterion, we select several dimension subsets with growing dimensionality, including  <dig>   <dig>   <dig>   <dig> and  <dig>  furthermore, we use the svm classifier to evaluate the predictive ability of each selected subset.
the distribution of each feature in the dimension subset selected by different filter methods.
the percentage of each feature in a selected subset is calculated as follows: *100%.
as filter selection methods rank features totally based on the inherent characteristics of datasets, we need to further evaluate whether the selected features are effective for classification.
therefore, the selected feature subsets are further used to build svm classifiers.
table  <dig> shows the performance of the svm classifiers of each selected feature subset.
each evaluation criterion includes two subsets with dimensionality  <dig> and  <dig>  in the case of information gain, it gains the best performance when the top  <dig> dimensions are selected.
while for relieff and chi, the subset with  <dig> dimensions is preferable.
as for cfs criterion, subsets with dimensionality  <dig> or  <dig> are selected, and the subset with  <dig> dimensions is better for classification.
based on the feature subsets selected by four filter feature selection methods, we respectively build svm classifiers and compare the performance of these classifiers.
ranking structural features by wrapper methods
wrapper methods provide alternatives to perform multivariate feature selection, which are dependent on the target classifiers whose accuracies are utilized to evaluate the candidate feature subsets.
thus, the selected feature subsets are closely related to specific classifiers, and the classification performance is usually better than that of filter methods.
as it is prohibitively costly to evaluate all possible subsets, we adopt the genetic search strategy to search for the optimal subsets, in combination with svm and knn classifiers respectively.
the performance comparison of two classifiers  for promoter classification: with the wrapper feature selection  vs. without wrapper feature selection .
classification performance comparison with other existing methods
in this section, we evaluate the effectiveness of the selected structural feature subsets in promoter prediction , and compare them with nine existing promoter prediction methods.
we select five classical methods that perform well in previous comparative studies  <cit> , including firstef  <cit> , eponine  <cit> , dragongsf  <cit> , mcpromoter  <cit>  and arts  <cit> .
these methods are all traditional, in the sense that they were mainly built on sequence compositional features, such as cpg island, tata box and a number of motifs.
firstef is based on quadratic discriminant analysis of promoters, the first exon and the first donor site.
eponine constructs a relevance vector machine based on tata box in a g+c rich domain.
dragongsf utilizes artificial neural network.
mcpromoter also adopts neural network to predict promoters, but uses combined features from different segments.
by using support vector machine, arts integrates several compositional features and two structural features.
meanwhile, we include four newly proposed methods that depend on structural features to predict promoters.
ep <dig> directly uses a deviation from the average structural value to locate the promoter regions  <cit> .
observing the distinct structural patterns of promoter regions, pnnp develops a pattern-based promoter prediction method  <cit> .
based on dna melting temperature, profisi predicts promoters using simple thresholding  <cit> .
prosom applies self-organizing maps to distinguish promoters from non-pomoters  <cit> .
to achieve an unbiased evaluation of different promoter prediction methods, we conduct the performance comparison on the cage dataset, which is different from our training dataset.
all the predictions are subject to the same evaluation criterion.
if a prediction is within  <dig> bp of an annotated tss, we call it a true positive hit.
all existing prediction methods are tested with their default settings, which are provided as the optimal parameters by their developers.
the empirical performance results of different promoter prediction methods are shown in table  <dig>  though mcpromoter has a considerable specificity, its sensitivity is low, which leads to a bad f-measure score.
on the contrary, firstef achieves a balanced sensitivity and specificity.
from table  <dig>  we observe that the promoter prediction methods based on structural features perform better than those methods utilizing sequence compositional features.
however, as these methods use only one structural feature to predict promoters, the improvement of performance is not significant.
by adopting the feature selection methods to select the most discriminative feature subsets from all these structural features we studied, the sensitivity and specificity can be greatly improved.
these methods include nine existing promoter prediction methods and our prediction methods based on the filter and wrapper feature selection.
CONCLUSIONS
since most current promoter prediction methods rely on local sequence compositional signals, they suffer from low accuracy in identifying promoters.
this paper systematically analyzes promoter sequences from various structural perspectives.
we investigate thirteen different structural features to examine whether promoter sequences exhibit some specific structures for promoter recognition by rna polymerase.
by converting the promoter and non-promoter sequences into numerical vectors, the structural profiles of different features are explicitly presented.
these structural profiles manifest some useful findings.
on one hand, compared with non-promoter regions, promoter regions indeed show different structural values and patterns.
we infer that structural properties may be effective to differentiate promoters from non-promoters.
on the other hand, the profile patterns of all structural features are not equally distinct for promoters and non-promoters.
the profiles of some features such as duplex free energy, duplex disrupt energy and dna-bending stiffness are much more distinct than those of the other features.
comparative classification analysis based on individual feature further suggests that the predictive power of these structural features are quite different.
in order to quantify the difference, we turn to various feature selection methods including four typical filter methods and two wrapper methods based on svm and knn.
as filter methods are independent on the classifiers, they are more general for use.
for the wrapper methods, the feature selection results depend on the classifiers and the prediction performance may vary.
overall, b-dna twist and protein-dna twist seldom appear in the resulted feature subsets.
whereas the ranks of dna-bending stiffness and energy-related features based on these feature selection methods are higher than those of the other features, indicating a strong correlation with promoters.
this result is consistent with our further correlation analysis among all these structural features.
the pairwise pearson correlation coefficients indicate that the energy related features, including duplex free energy, duplex disrupt energy and dna denaturation, are highly correlated with each other .
also, the dna-bending stiffness and bendability are closely related to the energy-related features.
as a result, these features show similar presence in the selected feature subsets.
by analyzing these thirteen kinds of features in a uniform feature selection framework, the results show that the energy-related features and dna-bending stiffness are highly correlated with promoter sequences.
these specific structures of promoters can be taken as clues for promoter prediction.
furthermore, rather than use a single structural feature, we use the selected feature subsets to predict promoters.
the performance of promoter prediction confirms that the selected feature subsets are informative in promoter prediction.
the findings perhaps not only have biological meaning but also facilitate the applications of more powerful promoter prediction models on various genomes.

 the advent of the second generation sequencing greatly speeds up the accumulation of genome data. one of the most important tasks to understand such tremendous data is to functionally annotate genomes and analyze gene regulatory networks, for which, one pre-requisite step is to identify promoters from genomic sequences. promoters refer to crucial control regions surrounding transcription start sites, which are the basis of transcription initiation and are responsible for steering the binding of rna polymerase. a thorough understanding of promoter regions can provide valuable insights into how, where and when transcription takes place. concerning how rna polymerase exactly locates a promoter region at the initial stage of transcription, the presumption is that promoter sequences possess some special properties distinctive from the properties of the surrounding non-promoter regions. previous studies mainly focus on dna sequence compositional features. these studies have found that some local sequence compositional signals are specific to core promoter regions, such as cpg islands, tata boxes, caat boxes, some specific transcription factor binding sites, pentamer matrix and oligonucleotides. based on these features, a variety of computational methods have been proposed for promoter prediction. although much progress has been achieved, recent studies strikingly reveal that existing promoter prediction methods have several common limitations. in fact, due to the complexity and heterogeneity of promoter architectures, there are only a limited number of promoters that exactly match the consensus motifs. the sequence motifs are not adequate to identify promoters, which results in a high number of false-positives when handling a whole genome. the local sequence compositional signals alone can not accurately discriminate promoters from non-promoters. the selection of appropriate biological signals to predict promoters remains a field of intense investigation. recently, the studies of dna crystal structures have brought to light the fact that structural properties of dna sequences play important roles in different genome functions. the second-order structural information encoded in promoter regions could be recognized by rna polymerase. specifically, the curvature and bendability of dna sequences may condition a favorable or inhibitory chromatin environment for the binding of rna polymerase, and may further contribute to the transcription process. previous studies have also shown that the eukaryotic core promoters indeed have distinct structural properties when compared with coding or non-regulatory sequences, which presents a feasibility to explore promoter sequences from a structural perspective. up to now, researchers have analyzed different structural features, including duplex free energy, stacking energy, dna denaturation, duplex disrupt energy, protein deformation, z-dna, dna-bending stiffness, a-philicity, nuclesome position, propeller twist, protein-dna twist, b-dna twist and bendability. each of these features has been respectively used to predict promoters. as the high dimensionality of feature space will deteriorate the accuracy and time complexity of prediction models, it is costly to combine all these features together for promoter analysis. meanwhile, biologists are practically inclined to know which features are more correlated with promoters. however, this issue has seldom been addressed. thus, in this paper, we systematically compare thirteen structural features of promoters and non-promoters, and propose a feature selection framework to explore which structural features are more related to promoter sequences. by taking advantage of various feature selection techniques, a small subset of highly discriminative features are selected and further utilized to predict promoters. from the comparative analysis, we observe that promoter sequences possess some specific structural features, like dna-bending stiffness, duplex free energy and duplex disrupt energy. the results of different filter and wrapper feature selection methods indicate that energy-related features and dna-bending stiffness appear more frequently in the selected feature subsets, indicating that these structural features have a closer connection to promoter sequences and the features differentiate in capability for promoter prediction. this finding is in line with the classification accuracy based on each individual feature. furthermore, the experimental results demonstrate that the selected feature subsets can significantly improve the sensitivity and accuracy of promoter prediction.  comparison of structural patterns of different features different from traditional promoter prediction methods that are mostly based on sequence compositional features, in this paper we first carry out an extensive comparison between promoter and non-promoter sequences in order to explore whether promoters possess specific structures. here, our investigation focuses on thirteen different kinds of structural features, including duplex free energy, stacking energy, dna denaturation, duplex disrupt energy, protein deformation, z-dna, dna-bending stiffness, a-philicity, nucleosome position, propeller twist, protein-dna twist, b-dna twist and bendability. for these features, different structure models have been derived from various biochemical experiments. according to these models, dna sequences can be transformed into distinct structural profiles, as illustrated in figure although these structural models are based on dinucleotides or trinucleotides, several studies have proven that these structural features are different in terms of the nucleotide information, and offer additional thermo-physical information. based on the conversion schemas, we transform the promoter and non-promoter sequences into numerical vectors and examine their similarities and differences with regard to these structural features on the dbtss human tss dataset. for all these thirteen kinds of structural features, we plot and compare the structural profiles of promoters and non-promoters. through a comprehensive analysis, we obtain some interesting observations. first, for each feature, the structural values along promoter sequences are different from those along non-promoter sequences. second, promoters have specific patterns of structural profiles, with overall stable values and two sharp troughs or peaks. the first clear trough locates at the position - bp upstream of tss, where the tata-binding protein binds. the second trough is around the tss. these two troughs may guide transcription apparatus to locate the appropriate initiation site, which is in agreement with the fact that promoters bear a special structure that is essential to the assembly of the transcription machinery. on the contrary, the structural profiles of non-promoters do not exhibit such patterns. the different patterns may originate from the strongly conserved sequences, such as the tata box around the - bp position and the inr element at the tss position. however, recent studies indicate that the presence of these elements is not enough to explain the specific structural patterns of promoters. promoter sequences without the tata element still have a clear trough in the structural profile of dna denaturation, although the trough is not as deep as that with tata box. this finding suggests that promoter sequences can denature more easily than non-promoter sequences, no matter whether they contain those elements or not. in our previous study, the analysis on cpg-related and non-cpg related promoters shows that the two different kinds of promoter sequences exhibit similar structural patterns. as a result, we may conclude that the structural features are not only affected by the specific elements such as tata box, inr element and cpg island, but also by long-range combinations of nucleotides. third, as shown in figure there are two types of structural features according to the profile patterns: either a large trough stretching over the tss region is visible, such as the plots in the first two rows, or a peak is visible, such as the features in the last three rows. these distinctive peak or trough regions are possibly useful for identifying tsss. furthermore, compared with the non-promoter sequences, the structural patterns of promoters with regard to some features are distinctive and clear, such as energy-related features, dna-bending stiffness and dna denaturation. by analyzing the structural models, we observe that these features are closely related with the stability of promoter sequences. in contrast, the profiles of b-dna twist, protein deformation and protein-dna twist are ambiguous and noisy. these structural features have different capabilities of differentiating promoters and non-promoters. these findings suggest that a variety of possible mechanisms may lead to promoter recognition by rna polymerase. the binding of rna polymerase could result from the dna sequence preferences. alternatively, promoter recognition may also be guided by the boundaries of the specific structural peaks or troughs around the tsss. comparison of classification performance of different structural features the above analysis implies that the structures of dna sequences are important signals in promoter recognition of rna polymerase, and integrating structural features may be helpful for promoter prediction. in order to identify the most promising features, we move forward to test how well each individual feature can differentiate promoters and non-promoters. the experiments are conducted on the dbtss human promoter dataset. for each feature, we respectively convert the positive promoter and negative non-promoter sequences into corresponding numerical feature vectors. because of the outstanding classification performance of the support vector machine classifier, we apply it to discriminate promoters from non-promoters. we train thirteen svm classifiers corresponding to all these different structural features and test their classification performance, which is evaluated by three measures, including sensitivity, specificity and f-measure. ranking structural features by filter methods to identify the discriminative structural features for promoter prediction, we adopt different filter feature selection methods to select feature subsets from the thirteen kinds of structural features. first, each sequence is converted to thirteen numerical vectors corresponding to the thirteen different structural features. since the values of these features are at different levels, we respectively normalize these vectors into the range. second, we combine these feature vectors into an integrated vector whose dimension is 13* and add a boolean dimension as class label with'true' for promoters and'false' for non-promoters. third, various filter selection methods based on four evaluation metrics, including information gain, chi square, relieff and correlation-based feature selection, are used to select the most discriminative dimension subsets. then, all these dimensions are ranked based on the scores that are assigned by the feature selection methods. for the selected dimension subset, we calculate the ratio of each feature in the subset, that is, the ratio of the number of dimensions related to this feature over the total number of dimensions in the selected dimension subset. only the top-ranked features are used for further classification purpose. in order to obtain a general trend, for each evaluation criterion, we select several dimension subsets with growing dimensionality, including and furthermore, we use the svm classifier to evaluate the predictive ability of each selected subset. the distribution of each feature in the dimension subset selected by different filter methods. the percentage of each feature in a selected subset is calculated as follows: *100%. as filter selection methods rank features totally based on the inherent characteristics of datasets, we need to further evaluate whether the selected features are effective for classification. therefore, the selected feature subsets are further used to build svm classifiers. table shows the performance of the svm classifiers of each selected feature subset. each evaluation criterion includes two subsets with dimensionality and in the case of information gain, it gains the best performance when the top dimensions are selected. while for relieff and chi, the subset with dimensions is preferable. as for cfs criterion, subsets with dimensionality or are selected, and the subset with dimensions is better for classification. based on the feature subsets selected by four filter feature selection methods, we respectively build svm classifiers and compare the performance of these classifiers. ranking structural features by wrapper methods wrapper methods provide alternatives to perform multivariate feature selection, which are dependent on the target classifiers whose accuracies are utilized to evaluate the candidate feature subsets. thus, the selected feature subsets are closely related to specific classifiers, and the classification performance is usually better than that of filter methods. as it is prohibitively costly to evaluate all possible subsets, we adopt the genetic search strategy to search for the optimal subsets, in combination with svm and knn classifiers respectively. the performance comparison of two classifiers for promoter classification: with the wrapper feature selection vs. without wrapper feature selection. classification performance comparison with other existing methods in this section, we evaluate the effectiveness of the selected structural feature subsets in promoter prediction, and compare them with nine existing promoter prediction methods. we select five classical methods that perform well in previous comparative studies, including firstef, eponine, dragongsf, mcpromoter and arts. these methods are all traditional, in the sense that they were mainly built on sequence compositional features, such as cpg island, tata box and a number of motifs. firstef is based on quadratic discriminant analysis of promoters, the first exon and the first donor site. eponine constructs a relevance vector machine based on tata box in a g+c rich domain. dragongsf utilizes artificial neural network. mcpromoter also adopts neural network to predict promoters, but uses combined features from different segments. by using support vector machine, arts integrates several compositional features and two structural features. meanwhile, we include four newly proposed methods that depend on structural features to predict promoters. ep directly uses a deviation from the average structural value to locate the promoter regions. observing the distinct structural patterns of promoter regions, pnnp develops a pattern-based promoter prediction method. based on dna melting temperature, profisi predicts promoters using simple thresholding. prosom applies self-organizing maps to distinguish promoters from non-pomoters. to achieve an unbiased evaluation of different promoter prediction methods, we conduct the performance comparison on the cage dataset, which is different from our training dataset. all the predictions are subject to the same evaluation criterion. if a prediction is within bp of an annotated tss, we call it a true positive hit. all existing prediction methods are tested with their default settings, which are provided as the optimal parameters by their developers. the empirical performance results of different promoter prediction methods are shown in table though mcpromoter has a considerable specificity, its sensitivity is low, which leads to a bad f-measure score. on the contrary, firstef achieves a balanced sensitivity and specificity. from table we observe that the promoter prediction methods based on structural features perform better than those methods utilizing sequence compositional features. however, as these methods use only one structural feature to predict promoters, the improvement of performance is not significant. by adopting the feature selection methods to select the most discriminative feature subsets from all these structural features we studied, the sensitivity and specificity can be greatly improved. these methods include nine existing promoter prediction methods and our prediction methods based on the filter and wrapper feature selection.  since most current promoter prediction methods rely on local sequence compositional signals, they suffer from low accuracy in identifying promoters. this paper systematically analyzes promoter sequences from various structural perspectives. we investigate thirteen different structural features to examine whether promoter sequences exhibit some specific structures for promoter recognition by rna polymerase. by converting the promoter and non-promoter sequences into numerical vectors, the structural profiles of different features are explicitly presented. these structural profiles manifest some useful findings. on one hand, compared with non-promoter regions, promoter regions indeed show different structural values and patterns. we infer that structural properties may be effective to differentiate promoters from non-promoters. on the other hand, the profile patterns of all structural features are not equally distinct for promoters and non-promoters. the profiles of some features such as duplex free energy, duplex disrupt energy and dna-bending stiffness are much more distinct than those of the other features. comparative classification analysis based on individual feature further suggests that the predictive power of these structural features are quite different. in order to quantify the difference, we turn to various feature selection methods including four typical filter methods and two wrapper methods based on svm and knn. as filter methods are independent on the classifiers, they are more general for use. for the wrapper methods, the feature selection results depend on the classifiers and the prediction performance may vary. overall, b-dna twist and protein-dna twist seldom appear in the resulted feature subsets. whereas the ranks of dna-bending stiffness and energy-related features based on these feature selection methods are higher than those of the other features, indicating a strong correlation with promoters. this result is consistent with our further correlation analysis among all these structural features. the pairwise pearson correlation coefficients indicate that the energy related features, including duplex free energy, duplex disrupt energy and dna denaturation, are highly correlated with each other. also, the dna-bending stiffness and bendability are closely related to the energy-related features. as a result, these features show similar presence in the selected feature subsets. by analyzing these thirteen kinds of features in a uniform feature selection framework, the results show that the energy-related features and dna-bending stiffness are highly correlated with promoter sequences. these specific structures of promoters can be taken as clues for promoter prediction. furthermore, rather than use a single structural feature, we use the selected feature subsets to predict promoters. the performance of promoter prediction confirms that the selected feature subsets are informative in promoter prediction. the findings perhaps not only have biological meaning but also facilitate the applications of more powerful promoter prediction models on various genomes. 
traditional promoter analysis is mainly based on sequence compositional features. firstly, to examine whether promoters possess some special structures, we carry out a systematical comparison among the profiles of thirteen structural features on promoter and non-promoter sequences. thirdly, both filter and wrapper methods are utilized to select appropriate feature subsets from thirteen different kinds of structural features for promoter prediction, and the predictive power of the selected feature subsets is evaluated. secondly, we investigate the correlations between these structural features and promoter sequences. the predictive power for promoter sequences differentiates greatly among different structural features. promoter prediction is an integrant step for understanding gene regulation and annotating genomes. recently, many kinds of structural features have been employed in promoter prediction. however, considering the high-dimensionality and overfitting problems, it is unfeasible to utilize all available features for promoter prediction. this paper conducts an extensive comparison study on feature selection of dna structural properties for promoter prediction. finally, we compare the prediction performance of the feature subsets selected in this paper with nine existing promoter prediction approaches. selecting the relevant features can significantly improve the accuracy of promoter prediction. experimental results show that the structural features are differentially correlated to promoters. specifically, dna-bending stiffness, dna denaturation and energy-related features are highly correlated with promoters.
 promoter prediction is an integrant step for understanding gene regulation and annotating genomes. traditional promoter analysis is mainly based on sequence compositional features. recently, many kinds of structural features have been employed in promoter prediction. however, considering the high-dimensionality and overfitting problems, it is unfeasible to utilize all available features for promoter prediction. thus it is necessary to choose some appropriate features for the prediction task.  this paper conducts an extensive comparison study on feature selection of dna structural properties for promoter prediction. firstly, to examine whether promoters possess some special structures, we carry out a systematical comparison among the profiles of thirteen structural features on promoter and non-promoter sequences. secondly, we investigate the correlations between these structural features and promoter sequences. thirdly, both filter and wrapper methods are utilized to select appropriate feature subsets from thirteen different kinds of structural features for promoter prediction, and the predictive power of the selected feature subsets is evaluated. finally, we compare the prediction performance of the feature subsets selected in this paper with nine existing promoter prediction approaches.  experimental results show that the structural features are differentially correlated to promoters. specifically, dna-bending stiffness, dna denaturation and energy-related features are highly correlated with promoters. the predictive power for promoter sequences differentiates greatly among different structural features. selecting the relevant features can significantly improve the accuracy of promoter prediction. 