- Research article
- Open Access
A comparison study on feature selection of DNA structural properties for promoter prediction
© Gan et al; licensee BioMed Central Ltd. 2012
- Received: 27 July 2011
- Accepted: 7 January 2012
- Published: 7 January 2012
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.
- Feature Selection
- Feature Subset
- Feature Selection Method
- Feature Selection Technique
- Promoter Prediction
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 [2, 3]. Promoters refer to crucial control regions surrounding transcription start sites (TSSs), which are the basis of transcription initiation and are responsible for steering the binding of RNA polymerase [4, 5]. 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 (CGIs) , TATA boxes , CAAT boxes , some specific transcription factor binding sites (TFBSs) , 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 [6, 11]. 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 [13, 14]. 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 [17–20], 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 [18, 20]. 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
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 (peak) locates at the position -30 bp upstream of TSS, where the TATA-binding protein (TBP) binds. The second trough (peak) is around the TSS. These two troughs (peaks) 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 -30 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 2, 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 (SVM) 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 (length 251 bp) 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 0. Second, we combine these feature vectors into an integrated vector whose dimension is 13*251, 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 (IG), Chi Square (CHI), ReliefF and Correlation-based Feature Selection (CFS), are used to select the most discriminative dimension subsets (see Methods). 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 100, 200, 300, 400 and 500. Furthermore, we use the SVM classifier to evaluate the predictive ability of each selected subset.
The feature selection results of the filter methods
Duplex free energy
Duplex disrupt energy
The performance evaluation of the feature subsets selected by different filter feature selection methods
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 evaluation of the wrapper feature selection methods
Classification performance comparison with other existing methods
In this section, we evaluate the effectiveness of the selected structural feature subsets in promoter prediction (termed as FSPP in this paper), and compare them with nine existing promoter prediction methods. We select five classical methods that perform well in previous comparative studies [12, 35], 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. EP3 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 .
Performance comparison of promoter prediction methods on the human genome
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 (Additional File 1, Table S1). 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.
Core promoter datasets
To extensively analyze the structural features of promoter sequences, we retrieve a collection of promoter sequences from DBTSS (version 7.0) . DBTSS is built on experimentally validated TSSs, which is mainly based on full-length cDNA transcripts, and the 5'-ends of oligo-cap selected cDNAs are experimentally determined . This dataset provides the best training data to recent computational studies in genomic annotation and promoter analysis. DBTSS includes the promoter sequences of several species. Here, our analysis is based on one of the largest dataset, the human dataset. We extract the sequences from 200 bp upstream to 50 bp downstream flanking TSSs as the promoter sequences. Through filtering, we obtain 11,682 experimentally validated human TSSs. Correspondingly, we retrieve the same number of non-promoter sequences. In order to avoid the position bias in extracting sequences from the human genome, we build a randomized non-promoter dataset by shuffling the real promoter sequences. From the promoter dataset, we obtain a first-order Markov model that preserves the dinucleotide frequencies. Furthermore, based on the Markov model, the randomized non-promoter sequences are generated.
To unbiasedly compare the performance of different promoter prediction methods, the comparative evaluation is conducted on another dataset - the CAGE dataset, which is different from our training dataset and has a wider coverage of human genome. The CAGE dataset is based on the cap analysis gene expression (CAGE) technique and is retrieved from the Riken institute website . As previous study, only tag clusters identified by two or more tags are included in our analysis . Mapping these tags to human genome allows us to obtain 181,046 unique human TSSs. The whole genome (hg19) is retrieved via the UCSC Genome Browser .
Calculating structural profile
Because of important roles in different key biological processes, many studies have focused on biophysical understanding of the intrinsic structural properties of DNA sequences. Recent experimental analyses have shown that, in some manner, eukaryotic core promoters are indeed marked by specific structures comparing with coding or non-regulatory sequences. For instance, the duplex free energy content at TSS positions is much higher than that at the other positions. As a region with high free energy is more active than a region with low thermodynamic energy content , the stable regions may provide a contrasting background for the highly unstable peaks, and make the peaks prominent for guiding transcription apparatus to locate the appropriate transcription sites . Also, the propeller twist capacity measures the rigidity of a sequence . A region with high negative propeller twist is rigid, which makes this region hard to wrap around a nucleosome and facilitates RNA polymerase to bind in this area . Besides these two features, we also investigate other eleven structural properties, including stacking energy , DNA denaturation , duplex disrupt energy , protein deformation , Z-DNA , bending stiffness , A-philicity , nucleosome position , protein-DNA twist , B-DNA twist , and bendability .
In order to quantitatively analyze structural properties of promoters, we convert the retrieved promoter and non-promoter sequences into numerical structural profiles corresponding to different structural features. For each feature, the calculation of structural profile is divided into two steps, as illustrated in Figure 1. We first transform each DNA sequence into a numerical vector. Each dinucleotide (or trinucleotide) is replaced by its corresponding structural value [18, 20]. The transformation models for different structural features are obtained from various bio-chemical experiments, and are summarized as the validated conversion schemas by Florquin et al. . Specifically, since genome-wide nucleosome maps for different species have been recently published, we use the new in vivo human nucleosome positioning data from Schones et al.'s study to calculate the structural profiles related to nucleosome positioning . We take all the nucleosome-bound sequences from their collection, and then align these sequences with regard to their centers. By using the same method as in the previous study , we obtain the new nucleosome parameters. Accordingly, the new structural profiles related to nucleosome positioning are calculated. Second, we use a sliding window approach to smooth the raw profiles, with a window-size of 3 nt and a step-size of 1 bp. When the window slides along a sequence, a vector of structural values is output. We plot the average value on each position to get the structural profile of the sequence with regard to each feature. At last, every sequence has thirteen structural profiles corresponding to all these structural features.
Feature selection framework
Feature selection has been an important issue in machine learning, data mining and statistics fields, and has drawn much attention in diverse bioinformatics applications . Here, to gain a deeper insight into the structural properties of promoters, to avoid the overfitting problem and to improve the performance of promoter prediction models, we adopt different feature selection techniques to analyze these structural features we studied. We identify features that are highly correlated with promoter sequences and effective in promoter recognition. According to the combination mode between feature selection and classification process, feature selection techniques generally fall into the following two categories: wrapper and filter methods .
A wrapper method depends on a target classifier to identify the optimal feature subset that provides the target classifier with the best classification performance. The feature selection process consists of a search in the feature space guided by the performance of the target classifier. While bringing good accuracy for the final classifier, wrapper methods are computationally expensive. Filter methods are independent of any target classifier. These methods select features by looking only at the intrinsic properties of the data, such as class separability or correlation between features and classes. First, a feature relevance score is calculated, and the low-scoring features are filtered. Afterwards, selected features can be presented as input to a classification algorithm. Hence, the filter feature selection methods are computationally efficient and can easily scale to high-dimensional datasets.
TP, FP and FN represent the numbers of true positives, false positives and false negatives respectively. Generally, sensitivity is the proportion of correct predictions of TSSs over all experimental TSSs. Specificity is the proportion of correct prediction of TSSs out of all counted positive predictions. The higher the value of Se is, the more false positives may be reported, and the lower the value of Sp is. It is a trade-off to balance sensitivity and specificity. F-measure is a single measure that can compare prediction methods with different sensitivity and specificity. Meanwhile, the quality of a classifier can be evaluated by ROC score, which computes the area under the ROC curve . The value of the ROC score ranges from zero to one, with a score of 0.5 corresponding to random guess and a score of 1.0 indicating perfect separation. The ROC score indicates the accuracy with which a classifier separates promoters from non-promoters.
Discussions with W. Zhang are gratefully acknowledged. This work was supported in part by the National Basic Research Program of China under grant No. 2010CB126604 and the National Natural Science Foundation of China (NSFC) under grant No. 60873040 and No. 61173118. YLG was also supported by a grant from the China Scholarship Council. JHG was also supported by the Fundamental Research Funds for the Central Universities and the Shuguang Schalor Program of Shanghai Education Development Foundation.
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