Skip to main content
Download PDF

70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features

BMC Systems Biology volume 12, Article number: 44 (2018) Cite this article

Abstract

Background

Promoter is an important sequence regulation element, which is in charge of gene transcription initiation. In prokaryotes, σ70 promoters regulate the transcription of most genes. The promoter recognition has been a crucial part of gene structure recognition. It’s also the core issue of constructing gene transcriptional regulation network. With the successfully completion of genome sequencing from an increasing number of microbe species, the accurate identification of σ70 promoter regions in DNA sequence is not easy.

Results

In order to improve the prediction accuracy of sigma70 promoters in prokaryote, a promoter recognition model 70ProPred was established. In this work, two sequence-based features, including position-specific trinucleotide propensity based on single-stranded characteristic (PSTNPss) and electron-ion potential values for trinucleotides (PseEIIP), were assessed to build the best prediction model. It was found that 79 features of PSTNPSS combined with 64 features of PseEIIP obtained the best performance for sigma70 promoter identification, with a promising accuracy and the Matthews correlation coefficient (MCC) at 95.56% and 0.90, respectively.

Conclusion

The jackknife tests showed that 70ProPred outperforms the existing sigma70 promoter prediction approaches in terms of accuracy and stability. Additionally, this approach can also be extended to predict promoters of other species. In order to facilitate experimental biologists, an online web server for the proposed method was established, which is freely available at http://server.malab.cn/70ProPred/.

Background

Transcription is strictly regulated and controlled by cis-regulatory DNA elements, which were known as promoters and enhancers. These elements control the level of gene expression and cell fate. Promoters are cis-acting DNA sequences that switch on or off the gene expression. They are generally located upstream of the transcription start sites of genes. In prokaryotes, promoters are identified by RNA polymerase and a related sigma factor [1]. Different σ factors interact with well-defined consensus promoter sequences. Each σ factor is marked according to its molecular weight. σ70 is a well-known factor that regulates the transcription of most housekeeping genes in normal circumstances [1]. For σ70 promoter, two short sequence elements approximately located at around -10 bp and -35 bp nucleotides upstream from the transcription start site (TSS) with consensus TATAAT and TTGACA respectively [2]. It is important to identify the promoters in a genome, because it can help clarify the regulatory mechanism in the genome and explain disease-causing variants within cis-regulatory elements [3, 4]. Meanwhile, it’s a crucial part of gene structure recognition and the core issue of building gene transcriptional regulation network. Man’s understanding of promoter is developing all the time. It’s an area of great concern as people place increasing attention on their importance not only in developmental gene expression but also in environmental response [5, 6].

Due to the rapid development of genome sequencing technology, large-scale data has been generated [7,8,9], the stable and accurate identification of promoter is an important problem. Because standard laboratory methods are time-consuming and performance overhead costing, bioinformatics technologies with perfect precision represent the ideal alternative for massive fast recognition of promoter.

The σ70 promoter recognition task is a binary classification task. Feature extraction and classifier design are the key problems in promoter identification technology. In the past 20 years, based on the feature of promoter sequences, a serious of approaches have been developed for detecting promoter region in prokaryotes [10,11,12,13,14,15,16,17,18,19,20,21,22,23]. In 2007, Zhang proposed an algorithm using increment of diversity with quadratic discriminant (IDQD) analysis [21]. Position weight matrix (PWM) is always regarded as a description of the sequence information, but sometimes it gives poor results [22]. After that, Wu proposed an improved Position Weight Matrix (IPWM) [23] in 2011. In the same year, Lin proposed a hybrid method (IPMD), which combines location-related scoring function and diversity increment with improved Mahalanobis Discriminant to predict promoters [16]. The next year, variable-window Z-curve was used for extracting basic features of prokaryotic promoter [14]. Recently, Lin developed an improved Z-curve called ‘multi-window Z-curve’ (PseZNC), which can express the frequency characteristics and three dimensionality characteristics of different length sequences [15], etc. They were mainly focused on the σ70 promoter recognition. Among these approaches, some typical machine learning algorithms have been used to develop prokaryotic promoter region prediction. Such as, SVM (Support Vector Machine), RF (Random Forest), NB (Naïve Bayes), PLS (Partial Least Square), etc. Although these approaches have contributed to the advancement of promoter recognition, their performance demonstrates that there is a long way to go to predict promoter accurately for the following reasons. (i) Most existing approaches overlooked the correlation of neighboring nucleotides in each position, especially their difference in positive and negative samples. (ii) The local biological and physical properties of DNA may have a certain relationship with the promoters, which plays an important role in identifying them but were utterly ignored. (iii) Few web-servers were provided as the predictors, and hence their usage is quite limited [24].

In current research, we exploited a new bioinformatics tool called 70ProPred, to predict σ70 promoter through a combination of position-specific trinucleotide propensity (PSTNP) and electron-ion interaction pseudopotentials (EIIPs) of nucleotides. Finally, based on the results analysis of jackknife test, 70ProPred significantly outperforms existing prediction models, and should be useful for identifying σ70promoter.

Methods

Briefly, 70ProPred is a prediction model based on support vector machine (SVM), which was built by PSTNPSS and PseEIIP sequence coding strategies. An outline of the computational framework of 70ProPred predictor is shown in Fig. 1.

Fig. 1
figure 1

Overview of the proposed 70ProPred predictor. The diagram mainly contains datasets, sequence descriptors and 70ProPred prediction system. The optimal encoding combination PSTNPSS and PseEIIP are used as the input to train a SVM classifier. After optimization of the SVM parameters, the best SVM model is constructed based on the jackknife performance

Full size image

Datasets

In the present study, we used the latest datasets in both [15] and [16]. A total of 741 σ70 promoter samples were selected from the E.coli K-12 genome, which have been verified by experiments and downloaded from the RegulonDB9.0 (http://regulondb.ccg.unam.mx/) [25]. The promoter region [TSS-60…TSS + 20] (the locus of TSS is 0) were prepared as positive samples with a length of 81 bp.

As there are not enough experimental confirmed negative sequences, negative samples are collected from both coding and non-coding regions. In simple terms, the benchmark dataset S used in this study can be expressed as:

$$ \left\{\begin{array}{c}S={S}^{+}\cup {S}^{-}\\ {}{S}^{-}={S}_{\mathrm{coding}}^{-}\cup {S}_{\mathrm{non}\hbox{-} \mathrm{coding}}^{-}\end{array}\right. $$
(1)

Where S+ contains 741 σ70 promoter samples, S− contains 1400 non-promoter samples, \( {S}_{\mathrm{coding}}^{-} \) contains 700 coding sequences, \( {S}_{\mathrm{non}\hbox{-} \mathrm{coding}}^{-} \) contains 700 convergent intergenic sequences. Each sample contains 81 nucleotides, which is selected by a sliding window. Additionally, symbol ∪ means union.

Analysis of σ 70 promoter samples for conserved motif composition

The MEME Suite is designed to screen common sequence motifs from a set of sequences (amino acid or nucleotide). A motif can be assumed to be a conservative sequence pattern that repeats itself over a set of related sequences [26]. MEME is a useful sequence analysis tool that can rapidly detect new, non-gapped motifs for biological sequence data (protein, DNA and RNA) [27]. Then, we applied this tool to analyze the main motifs of σ70 promoter samples and found that only a small part of these samples which have corresponding motifs in Fig. 2. The maximum number of motif was set to 3 and the remaining arguments were set to default.

Fig. 2
figure 2

Motif of σ70 promoter samples as found by the MEME system. The corresponding three-motif logos as visualized for σ70 promoter samples (details in Table 1)

Full size image

Although phylogenetic foot printing takes the advantage of relatively conservative of motifs between species [28], these motifs are short and not complete species [29, 30]. For example, in Table 1, the number of sites contributing to the construction of motif 1 only 47, which may result in a great deal of false positive results. Therefore, it would be practicable to turn to the machine learning-based methods and has been proved to be effective in many fields [26, 31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46].

Table 1 Conserved motifs of σ70 promoter samples identified by the MEME system
Full size table

Feature vector construction

In order to get more useful information from the sequence, we extracted two kinds of features. Position-specific tendencies of trinucleotide (PSTNPss or PSTNPDS) was adopted to reveal the differences in the distribution of all positive and negative samples between trinucleotide. While the electron-ion interaction pseudopotentials (PseEIIP) were adopted to represent the interaction of trinucleotides.

Position-specific trinucleotide propensity based on single-stranded or double-stranded characteristic of DNA (PSTNPSS/PSTNPDS)

The recently proposed position-specific theory has been successfully applied to numerous fields of bioinformatics, such as identifying protein S-nitrosylation sites [47], hydroxyproline and hydroxylysine sites [48], DNA replication origin sites [49], Enhancer [41], etc. Besides, a series of studies have shown that the trinucleotides composition was effective in recognition of promoter [28, 50,51,52]. Inspired by the above studies, we presented a novel feature extraction strategy, which combined position-specific trinucleotide property (PSTNP) with the K-mer nucleotide composition information to predict σ70 promoter. A brief account of feature extraction is as follows.

Let S represents a sample which was consisted of A, G, C, and T, i.e.

$$ S={N}_1{N}_2{N}_3\cdots {N}_L $$
(2)

In which L means the length of the sample and L = 81, and

$$ {N}_i\in \left\{\mathrm{A},\mathrm{C},\mathrm{G},\mathrm{T}\right\}\left(i=1,2,\dots, L\right) $$
(3)

represents the i-th position of corresponding nucleotide in the sequence.

PSTNPSS

Feature PSTNPSS using a statistical strategy based on single-stranded characteristics of DNA. There are 43 = 64trinucleotides: AAA, AAC, AAG, ..., TTT. So, for an 81 bp sample, its details of the trinucleotides position specificity can be expressed by the following 64 × 79 matrix [41]:

$$ Z=\left[\begin{array}{cccc}{z}_{1,1}& {z}_{1,2}& \cdots & {z}_{1,79}\\ {}{z}_{2,1}& {z}_{2,2}& \cdots & {z}_{2,79}\\ {}\vdots & \vdots & \cdots & \vdots \\ {}{z}_{64,1}& {z}_{64,2}& \cdots & {z}_{64,79}\end{array}\right] $$
(4)

where the variable

$$ {\displaystyle \begin{array}{l}{z}_{i,j}={F}^{+}\left(3{mer}_i|j\right)-{F}^{-}\left(3{mer}_i|j\right)\kern0.4em \\ {}\kern1.6em \left(i=1,2,\dots, 64;j=1,2,...\mathrm{79}\right)\end{array}} $$
(5)

F+(3mer i | j) and F−(3mer i | j) denote the frequency of the i-th trinucleotide (3mer i ) at the j-th position appear in the positive (S+) and negative (S−) data sets, respectively. In the formula, 3mer1 equals AAA,3mer2 equals AAC, …, 3mer64 equals TTT.

Therefore, the sample of Eq.2 can be expressed as:

$$ \mathrm{S}={\left[{\phi}_1,{\phi}_2,\dots, {\phi}_u,\dots, {\phi}_{79}\right]}^T $$
(6)

where T is the operator of transpose and Ï• u was defined as follows:

$$ {\phi}_u=\left\{\begin{array}{c}{z}_{1,u},\kern0.8000001em \mathrm{when}\kern0.2em {\mathrm{N}}_u{\mathrm{N}}_{u+1}{\mathrm{N}}_{u+2}=\mathrm{AAA}\\ {}{z}_{2,u},\kern0.8000001em \mathrm{when}\kern0.2em {\mathrm{N}}_u{\mathrm{N}}_{u+1}{\mathrm{N}}_{u+2}=\mathrm{AAC}\\ {}{z}_{3,u},\kern0.8000001em \mathrm{when}\kern0.2em {\mathrm{N}}_u{\mathrm{N}}_{u+1}{\mathrm{N}}_{u+2}=\mathrm{AAG}\\ {}\begin{array}{cc}\begin{array}{cc}\begin{array}{cc}\kern0.1em \begin{array}{cc}\vdots & \end{array}& \end{array}& \vdots \end{array}& \end{array}\kern1.00em \vdots \\ {}{z}_{64,u},\kern0.7em \mathrm{when}\kern0.2em {\mathrm{N}}_u{\mathrm{N}}_{u+1}{\mathrm{N}}_{u+2}=\mathrm{TTT}\end{array}\right.\kern1.1em \left(1\le u\le 79\right)\kern0.3em $$
(7)

PSTNPDS

Feature PSTNPDS using a statistical strategy based on double-stranded characteristics of DNA according to complementary base pairing, so they have more evident statistical features. At this point, we deem A and T as identical, the same to C and G. Thus, for every sample, it can be converted into a sequence contained A and T only. As shown in Fig. 3, promoter-1 converted into promoter-1 AC.

Fig. 3
figure 3

promoter-1 converted into promoter-1 AC

Full size image

So, there are 23 = 8 trinucleotides: AAA, AAC, ..., CCC. Therefore, for an 81 bp sample, its details of the trinucleotides position specificity can be expressed by the following 8 × 79 matrix:

$$ {Z}^{\hbox{'}}=\left[\begin{array}{cccc}{z^{\hbox{'}}}_{1,1}& {z^{\hbox{'}}}_{1,2}& \cdots & {z^{\hbox{'}}}_{1,79}\\ {}{z^{\hbox{'}}}_{2,1}& {z^{\hbox{'}}}_{2,2}& \cdots & {z^{\hbox{'}}}_{2,79}\\ {}\vdots & \vdots & \cdots & \vdots \\ {}{z^{\hbox{'}}}_{8,1}& {z^{\hbox{'}}}_{8,2}& \cdots & {z^{\hbox{'}}}_{8,79}\end{array}\right] $$
(8)

where the variable

$$ {\displaystyle \begin{array}{l}{z^{\hbox{'}}}_{i,j}={F}^{+}\left(3{mer}_i|j\right)-{F}^{-}\left(3{mer}_i|j\right)\kern0.4em \\ {}\kern1.6em \left(i=1,2,\dots, 8;j=1,2,...\mathrm{79}\right)\end{array}} $$
(9)

F+(3mer i | j) and F−(3mer i | j) denote the frequency of the i-th trinucleotide (3mer i ) at the j-th position appear in the positive (S+) and negative (S−) data sets, respectively. In the formula, 3mer1 equals AAA,3mer2 equals AAC, …, 3mer8 equals CCC.

Therefore, the sample of Eq.2 can be expressed as:

$$ {\mathrm{S}}^{\hbox{'}}={\left[{\phi}_1^{\hbox{'}},{\phi}_2^{\hbox{'}},\dots, {\phi}_u^{\hbox{'}},\dots, {\phi}_{79}^{\hbox{'}}\right]}^T $$
(10)

where T is the operator of transpose and Ï•' u was defined as follows:

$$ {\phi^{\hbox{'}}}_u=\left\{\begin{array}{c}{z^{\hbox{'}}}_{1,u},\kern0.8000001em \mathrm{when}\kern0.2em {\mathrm{N}}_u{\mathrm{N}}_{u+1}{\mathrm{N}}_{u+2}=\mathrm{AAA}\\ {}{z^{\hbox{'}}}_{2,u},\kern0.8000001em \mathrm{when}\kern0.2em {\mathrm{N}}_u{\mathrm{N}}_{u+1}{\mathrm{N}}_{u+2}=\mathrm{AAC}\\ {}{z^{\hbox{'}}}_{3,u},\kern0.8000001em \mathrm{when}\kern0.2em {\mathrm{N}}_u{\mathrm{N}}_{u+1}{\mathrm{N}}_{u+2}=\mathrm{ACA}\\ {}\begin{array}{cc}\begin{array}{cc}\begin{array}{cc}\begin{array}{cc}\kern0.1em \begin{array}{cc}\vdots & \end{array}& \end{array}& \vdots \end{array}& \end{array}& \end{array}\kern0.5em \vdots \\ {}{z^{\hbox{'}}}_{8,u},\kern0.7em \mathrm{when}\kern0.2em {\mathrm{N}}_u{\mathrm{N}}_{u+1}{\mathrm{N}}_{u+2}=\mathrm{CCC}\end{array}\right.\kern1.1em \left(1\le u\le 79\right)\kern0.3em $$
(11)

Electron-ion interaction pseudopotentials of trinucleotide (PseEIIP)

Nair [53] came up with electron-ion interaction pseudopotentials (EIIP) value of nucleotides A, G, C, T. The EIIP value based methods have been shown effective through previous studies, such as the recognition of gene F56F11.4, prediction of the cystic-fibrosis gene [54], recognition of enhancer [41], and so on [55, 56].

The electron-ion interaction pseudopotentials value for the nucleotides [53] are shown in Table 2. We let EIIP A , EIIP T , EIIP G , and EIIP C denote the EIIP values of nucleotides A, T, G and C, respectively. Then, we employed the mean EIIP value of trinucleotides in each sample to construct feature vector, which can be formulated as:

$$ V=\left[{EIIP}_{AAA}\cdot {f}_{AAA},{EIIP}_{AAC}\cdot {f}_{AAC},\dots, {EIIP}_{TTT}\cdot {f}_{TTT}\right] $$
(12)

where f xyz equal to the normalized frequency of the i-th trinucleotide (3mer i ), EIIP xyz  = EIIP x  + EIIP y  + EIIP z expresses the EIIP value of one trinucleotide and X, Y, Z∈ [A, C, G, T]. Obviously, the dimension of vector V is 64.

Table 2 EIIP values of nucleotides
Full size table

Model building and parameter selection

SVM classification algorithm plays a significant role in some areas of bioinformatics [18, 36, 40, 57]. In this work, SVM was implemented using the LIBSVM packet [58] to build models and execute predictions. The radial basis function (RBF) was selected as the kernel function. At the same time, penalty parameter C and kernel parameter γ were optimized using SVMcg in the LIBSVM package. The final parameters C = 22.6274 and γ = 2.8284 were selected for the prediction of σ70 promoters and non-promoters.

The jackknife test is regarded as a unique random test that can produce the unique result for a given dataset [59]. Therefore, all these parameters were optimized through jackknife test.

In order to evaluate the predictive performance of the model, four metrics are calculated: sensitivity (Sn), specificity (Sp), accuracy (Acc) and MCC:

$$ Sn=\frac{TP}{TP+ FN} $$
(13)
$$ SP=\frac{TN}{TN+ FP} $$
(14)
$$ Acc=\frac{TP+ TN}{TP+ TN+ FP+ FN} $$
(15)
$$ MCC=\frac{TP\times TN- FP\times FN}{\kern0em \sqrt{\left( TP+ FP\right)\left( TP+ FN\right)\left( TN+ FP\right)\left( TN+ FN\right)}} $$
(16)

In the formula, TP, TN, FP and FN represent the numbers of true positives (accurately predicted σ70 promoters), true negatives (accurately predicted non-promoters), false positives (falsely predicted σ70 promoters) and false negatives (falsely predicted non-promoters).

Results and discussion

Prediction of σ 70 promoter using only PSTNP

PSTNP was first proposed for predicting enhancer [41]. The obvious advantage of this approach is that the feature vectors are encoded in a way that contains information from all training samples. In this work, the ability of PSTNPSS and PSTNPDS to discriminate σ70 promoter and non-promoter were first declared by jackknife test (Table 3). For σ70 promoter, the PSTNPDS model obtained a good performance, reaching at 75.98% sensitivity, 88.57% specificity, 84.21% accuracy and the 0.6493 of MCC value, while the PSTNPSS model obtained a Sn of 90.82%, a Sp of 96.57%, an Acc of 94.58% and a MCC of 0.8797.

Table 3 Jackknife test performance of PSTNPSS and PSTNPDS
Full size table

A comparative figure (Fig. 4) with F-value of trinucleotides in different position also declared the difference in forecast results.

Fig. 4
figure 4

F-score value of trinucleotides in both PSTNPSS and PSTNPDS

Full size image

Furthermore, we used the Entropy (Additional file 1) [23, 60] to demonstrate the conservative sites of trinucleotides property in σ70 promoter. In order to comparing, the calculated entropy values for the trinucleotide of the σ70 promoter and non-promoter are shown in Fig. 5. Obviously, the lower entropy, the more conservative the position is. From this figure, we can see that in σ70 promoter most sites are obviously of lower entropy values compared with non-promoter. It may prove that PSTNPSS feature extraction agreed well with the previous prediction that in different positions the trinucleotide is conservative in σ70promoter.

Fig. 5
figure 5

Entropy of trinucleotide in the σ70 promoter and non-promoter

Full size image

From all above, the composition of trinucleotide which based on single-stranded characteristics of DNA contributes to the detection of σ70 promoter. This result indicated that the PSTNPSS model performs better than the PSTNPDS model in σ70 promoter prediction. Consequently, the training model optimized on the basis of the feature PSTNPSS.

Improving performance by incorporating PseEIIP

Because the physicochemical property indexes of nucleotides affect the recognition of promoter, incorporating the sample’s average energy of delocalized electrons (EIIP), especially, the EIIP value of trinucleotides with PSTNPSS might boost the performance of the training model, the prediction results are listed in Table 4.

Table 4 Performances of our model on the jackknife test
Full size table

Eventually, the prediction model was established using the PSTNPSS + PseEIIP feature extraction methods combined with the SVM classifier (cost parameter –c 22.6274, −g 2.8284) to predict σ70promoter.

In order to gauge the predictive performance of training model, the ROC curve and the area under the ROC curve (AUC) were adopted. The AUC value the 70ProPred model was 0.990 (Fig. 6).

Fig. 6
figure 6

The ROC curves to assess the predictive performance based on different sequences encoding schemes for σ70promoter

Full size image

Furthermore, we constructed a heat map to visually show the distribution of feature PseEIIP in positive dataset, as shown in Fig. 7. Each hotspot in the heat map corresponds to a unique trinucleotide; for instance, hotspot (1, 1) corresponds to triplet AAA. For more detailed information on the heat map, please see Additional file 2: Table S1. Red squares are positively associated with recognition ability.

Fig. 7
figure 7

A heat map for the F-score values of the 64 trinucleotides with different EIIP values. The blue boxes indicate the features with a lower effect for recognition of the σ70 promoter, while the red boxes indicate the features that are useful for the recognition of the σ70promoter

Full size image

Comparison of the performance between SVM and other classifiers

In order to select a better classifier to identify σ70 promoter, we analyzed the performances of KNN [61], NB [62], RF [63], EB [64], LibD3C [65], GBDT [66] and SVM, which are the most widely used classification algorithms in bioinformatics. Since it is known that the number of neighbors has an impact on the performance of KNN algorithm and the tree number has an impact on the performance of RF algorithm, the optimal parameters of KNN and RF are searched in the study, as shown in Additional file 3: Table S2 and Additional file 4: Table S3.

The performances for the above classifiers in the jackknife test are shown in Table 5. The results indicate that SVM performs better than the other classifiers with the highest MCC value 0.9018.

Table 5 Comparison of different classifiers for identifying σ70promoter
Full size table

Comparison of 70ProPred with other σ 70 promoter prediction methods

The 5-fold cross-validation results achieved by Z-curve [14], PseZNC [15], IPMD [16], PSTNPDS and 70ProPred on the benchmark dataset are listed in Table 6. Their marked difference is the feature extraction strategy. Therefore, the advantage of 70ProPred superior to other methods is mainly due to the combination of PSTNPSS and PseEIIP coding strategy. PSTNPSS employs primary sequence information of trinucleotides, and PseEIIP is closely related to the physicalchemical property of DNA sample. As shown in Table 3, based on the PSTNPSS feature only, the prediction result of 70ProPred was significantly higher than the other methods. The performance has been further improved by adding the feature PseEIIP, as shown in Table 4, especially in sensitivity (Sn). Taken together, the application of feature PSTNPSS and feature PseEIIP achieved a good performance in the prediction of σ70promoter.

Table 6 performances of our model, Z-curve, PSTNPDS, PseZNC and IPMD on 5-fold cross-validation
Full size table

The results in Table 6 also show that the PSTNPDS-based model performs better than the multi-window Z-curve-based method. It can also be a supplement to the present methods for predicting other DNA related predictions.

Conclusions

The 70ProPred is a new bioinformatics tool for predicting σ70 promoter. This tool uses the feature extraction methods of PSTNPSS and PseEIIP. The combination of features and SVM could achieve an overall MCC value of 0.90. Compared to other σ70 promoter prediction models, 70ProPred produced better results. Although this method shows good performance in σ70 promoter prediction, there is still room to improve prediction performance due to the following reasons. (i) Since structural information is a supplementary to sequence information, the future work may build a model combine with the two aspects. (ii) The feature selection algorithms can be used to delete the redundant features to improve the prediction model. (iii) More species of promoters should be adopted to estimate the performance of 70ProPred method. In conclusion, our future work is to extend this method to other species promoter region prediction. We suspect that our feature extraction methods is not only suitable for identifying promoter, but also for other bioinformatics sequence classification tasks.

Availability

The web-server for 70ProPred has been established. It is now freely available to all interested users at http://server.malab.cn/70ProPred/. All the data sets used in this study can also be download on the website.

Abbreviations

AUC:

Area Under the ROC Curve

EB:

Ensembles for Boosting

GBDT:

Gradient Boosting Decision Tree

KNN:

k-Nearest Neighbor

MCC:

Matthew’s Correlation Coefficient

NB:

Naïve Bayes

PseEIIP:

Electron-ion interaction pseudopotentials of nucleotides

PSTNPDS :

Position-specific trinuclotide propensity based on double-stranded characteristic of DNA

PSTNPss:

Position-specific trinuclotide propensity based on single-stranded characteristic of DNA

RF:

Random Forest

References

  1. Barrios H, Valderrama B, Morett E. Compilation and analysis of sigma(54)-dependent promoter sequences. Nucleic Acids Res. 1999;27(22):4305–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Hawley DK, McClure WR. Compilation and analysis of Escherichia coli promoter DNA sequences. Nucleic Acids Res. 1983;11(8):2237–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Li Y, Chen CY, Wasserman WW. Deep feature selection: theory and application to identify enhancers and promoters. J Comput Biol. 2016;23(5):322–36.

    Article  CAS  PubMed  Google Scholar 

  4. Geng Y, Zhao ZM, Zhang XP, Wang WK, Cui XJ, Ye K, et al. An improved burden-test pipeline for identifying associations from rare germline and somatic variants. BMC Genomics. 2017;18(7):55–62.

  5. Bernardo LM, Johansson LU, Skarfstad E, Shingler V. sigma54-promoter discrimination and regulation by ppGpp and DksA. J Biol Chem. 2009;284(2):828–38.

    Article  CAS  PubMed  Google Scholar 

  6. Lu C, Xie MC, Wendl MC, Wang JY, McLellan MD, Leiserson MDM, et al. Patterns and functional implications of rare germline variants across 12 cancer types. Nat Commun. 2015;6

  7. Mrozek D, Malysiak-Mrozek B, Klapcinski A. Cloud4Psi: cloud computing for 3D protein structure similarity searching. Bioinformatics. 2014;30(19):2822–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Mrozek D, Danilowicz P, Malysiak-Mrozek B. HDInsight4PSi: boosting performance of 3D protein structure similarity searching with HDInsight clusters in Microsoft azure cloud. Inform Sciences. 2016;349:77–101.

    Article  Google Scholar 

  9. Mrozek D, Gosk P, Malysiak-Mrozek B. Scaling ab initio predictions of 3D protein structures in Microsoft azure cloud. J Grid Comput. 2015;13(4):561–85.

    Article  Google Scholar 

  10. Zhou X, Li Z, Dai Z, Zou X. Predicting promoters by pseudo-trinucleotide compositions based on discrete wavelets transform. J Theor Biol. 2013;319:1–7.

    Article  CAS  PubMed  Google Scholar 

  11. Wang H, Benham CJ. Promoter prediction and annotation of microbial genomes based on DNA sequence and structural responses to superhelical stress. BMC Bioinformatics. 2006;7:248.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Towsey MW, Gordon JJ, Hogan JM. The prediction of bacterial transcription start sites using SVMs. Int J Neural Syst. 2006;16(5):363–70.

    Article  PubMed  Google Scholar 

  13. Towsey M, Hogan JM, Mathews S, Timms P. The in silico prediction of promoters in bacterial genomes. Genome Inform. 2007;19:178–89.

    CAS  PubMed  Google Scholar 

  14. Song K. Recognition of prokaryotic promoters based on a novel variable-window Z-curve method. Nucleic Acids Res. 2012;40(3):963–71.

    Article  CAS  PubMed  Google Scholar 

  15. Lin H, Liang ZY, Tang H, Chen W. Identifying sigma70 promoters with novel pseudo nucleotide composition. IEEE/ACM Trans Comput Biol Bioinform. 2017;PP(99):1–1.

  16. Lin H, Li QZ. Eukaryotic and prokaryotic promoter prediction using hybrid approach. Theory Biosci. 2011;130(2):91–100.

    Article  PubMed  Google Scholar 

  17. Kanhere A, Bansal M. A novel method for prokaryotic promoter prediction based on DNA stability. BMC Bioinformatics. 2005;6:1.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Jia CZ, He WY, Yao YH. OH-PRED: prediction of protein hydroxylation sites by incorporating adapted normal distribution bi-profile Bayes feature extraction and physicochemical properties of amino acids. J Biomol Struct Dyn. 2017;35(4):829–35.

    CAS  PubMed  Google Scholar 

  19. Huerta AM, Collado-Vides J. Sigma70 promoters in Escherichia coli: specific transcription in dense regions of overlapping promoter-like signals. J Mol Biol. 2003;333(2):261–78.

    Article  CAS  PubMed  Google Scholar 

  20. de Avila ESS, Gerhardt GJ, Echeverrigaray S. Rules extraction from neural networks applied to the prediction and recognition of prokaryotic promoters. Genet Mol Biol. 2011;34(2):353–60.

    Article  Google Scholar 

  21. ZHANG Y, JIA Y, Lü J. THE RECOGNITION OF σ~(70) PROMOTERS IN Escherichia coli K-12. Acta Biophysica Sinica. 2007;6:011.

    Google Scholar 

  22. Barrick D, Villanueba K, Childs J, Kalil R, Schneider TD, Lawrence CE, et al. Quantitative analysis of ribosome binding sites in E.Coli. Nucleic Acids Res. 1994;22(7):1287–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wu Q, Wang J, Yan H. An improved position weight matrix method based on an entropy measure for the recognition of prokaryotic promoters. Int J Data Min Bioinform. 2011;5(1):22–37.

    Article  PubMed  Google Scholar 

  24. Mrozek D, Malysiak-Mrozek B, Siaznik A. Search GenBank: interactive orchestration and ad-hoc choreography of web services in the exploration of the biomedical resources of the National Center for Biotechnology Information. Bmc Bioinformatics. 2013;14(1):1–19.

  25. Gama-Castro S, Salgado H, Santos-Zavaleta A, Ledezma-Tejeida D, Muniz-Rascado L, Garcia-Sotelo JS, et al. RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond. Nucleic Acids Res. 2016;44(D1):D133–43.

    Article  CAS  PubMed  Google Scholar 

  26. Liao Z, Wang X, Zeng Y, Zou Q. Identification of DEP domain-containing proteins by a machine learning method and experimental analysis of their expression in human HCC tissues. Sci Rep. 2016;6:39655.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Bailey TL, Johnson J, Grant CE, Noble WS. The MEME suite. Nucleic Acids Res. 2015;43(W1):W39–49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lin H, Deng EZ, Ding H, Chen W, Chou KC. iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res. 2014;42(21):12961–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Abeel T, Saeys Y, Bonnet E, Rouze P, Van de Peer Y. Generic eukaryotic core promoter prediction using structural features of DNA. Genome Res. 2008;18(2):310–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Abeel T, Saeys Y, Rouze P, Van de Peer Y. ProSOM: core promoter prediction based on unsupervised clustering of DNA physical profiles. Bioinformatics. 2008;24(13):i24–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Liao Z, Wang X. Chen X. Comb Chem High Throughput Screen: Zou Q. Prediction and Identification of Kruppel-like transcription factors by machine learning method; 2017.

    Google Scholar 

  32. Xiong H, Liu D, Li Q, Lei M, Xu L, Wu L, et al. RED-ML: a novel, effective RNA editing detection method based on machine learning. Gigascience. 2017;6(5):1–8.

    Article  Google Scholar 

  33. Chen W, Xing P, Zou Q. Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble support vector machines. Sci Rep. 2017;7:40242.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Chen W, Feng P, Yang H, Ding H, Lin H, Chou KC. iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences. Oncotarget. 2017;8(3):4208–17.

    PubMed  Google Scholar 

  35. Meher PK, Sahu TK, Saini V, Rao AR. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC. Sci Rep. 2017;7:42362.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Jia CZ, Liu T, Wang ZP. O-GlcNAcPRED: a sensitive predictor to capture protein O-GlcNAcylation sites. Mol BioSyst. 2013;9(11):2909–13.

    Article  CAS  PubMed  Google Scholar 

  37. Jia C, Lin X, Wang Z. Prediction of protein S-nitrosylation sites based on adapted normal distribution bi-profile Bayes and Chou's pseudo amino acid composition. Int J Mol Sci. 2014;15(6):10410–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Chen W, Feng PM, Lin H, Chou KC. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res. 2013;41(6):e68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Kabir M, Hayat M. iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples. Mol Gen Genomics. 2016;291(1):285–96.

    Article  CAS  Google Scholar 

  40. Jia C, He W. EnhancerPred: a predictor for discovering enhancers based on the combination and selection of multiple features. Sci Rep. 2016;6:38741.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. He W, Jia C. EnhancerPred2.0: predicting enhancers and their strength based on position-specific trinucleotide propensity and electron-ion interaction potential feature selection. Mol BioSyst. 2017;13(4):767–74.

    Article  CAS  PubMed  Google Scholar 

  42. Liu B, Fang L, Wang S, Wang X, Li H, Chou KC. Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy. J Theor Biol. 2015;385:153–9.

    Article  CAS  PubMed  Google Scholar 

  43. Liu B, Fang L, Long R, Lan X, Chou KC. iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. Bioinformatics. 2016;32(3):362–9.

    Article  CAS  PubMed  Google Scholar 

  44. Chen W, Feng PM, Deng EZ, Lin H, Chou KC. iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal Biochem. 2014;462:76–83.

    Article  CAS  PubMed  Google Scholar 

  45. Liu Z, Xiao X, Qiu WR, Chou KC. iDNA-methyl: identifying DNA methylation sites via pseudo trinucleotide composition. Anal Biochem. 2015;474:69–77.

    Article  CAS  PubMed  Google Scholar 

  46. Jia J, Zhang L, Liu Z, Xiao X, Chou KC. pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC. Bioinformatics. 2016;32(20):3133–41.

    Article  CAS  PubMed  Google Scholar 

  47. Xu Y, Shao XJ, Wu LY, Deng NY, Chou KC. iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ. 2013;1:e171.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Xu Y, Wen X, Shao XJ, Deng NY, Chou KC. iHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition. Int J Mol Sci. 2014;15(5):7594–610.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Xiao X, Ye HX, Liu Z, Jia JH, Chou KC. iROS-gPseKNC: predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition. Oncotarget. 2016;7(23):34180–9.

    PubMed  PubMed Central  Google Scholar 

  50. Chou K-C. Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol. 2011;273(1):236–47.

    Article  CAS  PubMed  Google Scholar 

  51. Chen W, Lin H, Chou K-C. Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. Mol BioSyst. 2015;11(10):2620–34.

    Article  CAS  PubMed  Google Scholar 

  52. Pei Li MG. Chunyu Wang, Xiaoyan Liu, Quan Zou. An overview of SNP interactions in genome-wide association studies. Briefings in Functional Genomics. 2015;14(2):143–55.

    Article  PubMed  Google Scholar 

  53. Nair AS, Sreenadhan SP. A coding measure scheme employing electron-ion interaction pseudopotential (EIIP). Bioinformation. 2006;1(6):197–202.

    PubMed  PubMed Central  Google Scholar 

  54. Rao KD, Swamy M. Analysis of genomics and proteomics using DSP techniques. IEEE Transactions on Circuits and Systems I: Regular Papers. 2008;55(1):370–8.

    Article  Google Scholar 

  55. Sahu SS, Panda G. Efficient localization of hot spots in proteins using a novel S-transform based filtering approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB). 2011;8(5):1235–46.

    Article  CAS  Google Scholar 

  56. Khan A. G-protein-coupled receptor prediction using pseudo-amino-acid composition and multiscale energy representation of different physiochemical properties. Anal Biochem. 2011;412(2):173–82.

    Article  PubMed  Google Scholar 

  57. Wei L, Liao M, Gao X, Zou Q. Enhanced protein fold prediction method through a novel feature extraction technique. IEEE Transactions on Nanobioscience. 2015;14(6):649–59.

    Article  PubMed  Google Scholar 

  58. Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST). 2011;2(3):27.

    Google Scholar 

  59. Chou K-C, Shen H-B. Cell-PLoc 2.0: An improved package of web-servers for predicting subcellular localization of proteins in various organisms. Natural Science. 2010;2(10):1090.

    Article  CAS  Google Scholar 

  60. Fickett JW. Quantitative discrimination of MEF2 sites. Mol Cell Biol. 1996;16(1):437–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inf Theory. 1967;13(1):21–7.

    Article  Google Scholar 

  62. Rish I. An empirical study of the naive Bayes classifier. Journal of Universal Computer Science. 2001;1(2):127.

  63. Ho TK. Random decision forests. International Conference on Document Analysis and Recognition. IEEE Computer Society. 1995;278.

  64. Opitz D, Maclin R. Popular ensemble methods: an empirical study. J Artif Intell Res. 1999;11:169–98.

    Google Scholar 

  65. Lin C, Chen W, Qiu C, Wu Y, Krishnan S, Zou Q. LibD3C: ensemble classifiers with a clustering and dynamic selection strategy. Neurocomputing. 2014;123:424–35.

    Article  Google Scholar 

  66. Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29(5):1189–232.

Download references

Acknowledgements

The work was supported by the Natural Science Foundation of China (No. 61771331).

Funding

Publication costs were funded by the Natural Science Foundation of China (No. 61771331).

Availability of data and materials

All data analyzed during this study are included in this published article.

About this supplement

This article has been published as part of BMC Systems Biology Volume 12 Supplement 4, 2018: Selected papers from the 11th International Conference on Systems Biology (ISB 2017). The full contents of the supplement are available online at https://bmcsystbiol.biomedcentral.com/articles/supplements/volume-12-supplement-4.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China

    Wenying He & Quan Zou

  2. Department of Mathematics, Dalian Maritime University, Dalian, 116026, China

    Cangzhi Jia

  3. College of Information and Technology, Hainan University, Haikou, 570228, China

    Yucong Duan

Authors
  1. Wenying He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cangzhi Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yucong Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Quan Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

QZ conceived the research. WH performed the study. CJ, QZ and YD provided valuable suggestions and improvements. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Quan Zou.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional files

Additional file 1:

Entropy. (DOC 94 kb)

Additional file 2:

Table S1. Rules of composition of heat maps. (DOC 41 kb)

Additional file 3:

Table S2. Comparison prediction results of different k neighbors. (DOC 47 kb)

Additional file 4:

Table S3. Comparison prediction results of different nTrees. (DOC 42 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, W., Jia, C., Duan, Y. et al. 70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features. BMC Syst Biol 12 (Suppl 4), 44 (2018). https://doi.org/10.1186/s12918-018-0570-1

Download citation

  • Published:

  • DOI: https://doi.org/10.1186/s12918-018-0570-1

Keywords

BMC Systems Biology

ISSN: 1752-0509

Contact us