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Towards biological characters of interactions between transcription factors and their DNA targets in mammals.

Zheng G, Liu Q, Ding G, Wei C, Li Y - BMC Genomics (2012)

Bottom Line: Results indicate that TFs and TFBSs are reciprocal in sequence, structure, and evolution level.Our work demonstrates that, to some extent, TFs and TFBSs have developed a coevolutionary relationship in order to keep their physical binding and maintain their regulatory functions.In summary, our work will help understand transcriptional regulation and interpret binding mechanism between proteins and DNAs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. zhenggy@sibs.ac.cn

ABSTRACT

Background: In post-genomic era, the study of transcriptional regulation is pivotal to decode genetic information. Transcription factors (TFs) are central proteins for transcriptional regulation, and interactions between TFs and their DNA targets (TFBSs) are important for downstream genes' expression. However, the lack of knowledge about interactions between TFs and TFBSs is still baffling people to investigate the mechanism of transcription.

Results: To expand the knowledge about interactions between TFs and TFBSs, three biological features (sequence feature, structure feature, and evolution feature) were utilized to build TFBS identification models for studying binding preference between TFs and their DNA targets in mammals. Results show that each feature does have fairly well performance to capture TFBSs, and the hybrid model combined all three features is more robust for TFBS identification. Subsequently, correspondence between TFs and their TFBSs was investigated to explore interactions among them in mammals. Results indicate that TFs and TFBSs are reciprocal in sequence, structure, and evolution level.

Conclusions: Our work demonstrates that, to some extent, TFs and TFBSs have developed a coevolutionary relationship in order to keep their physical binding and maintain their regulatory functions. In summary, our work will help understand transcriptional regulation and interpret binding mechanism between proteins and DNAs.

Show MeSH
Performance comparison of different models for 309 TF-TFBSs (with PWM information). Panel(a)-(d): boxplots of 5 models (the control model, the sequence model, the structure model, the evolution model, and the hybrid model) for sensitivity, specificity, accuracy and AUC measurement. For a boxplot, the 5 whiskers from bottom to top denote the 5th, 25th, 50th, 75th, and 95th percentile respectively.
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Figure 1: Performance comparison of different models for 309 TF-TFBSs (with PWM information). Panel(a)-(d): boxplots of 5 models (the control model, the sequence model, the structure model, the evolution model, and the hybrid model) for sensitivity, specificity, accuracy and AUC measurement. For a boxplot, the 5 whiskers from bottom to top denote the 5th, 25th, 50th, 75th, and 95th percentile respectively.

Mentions: 10-fold cross validation tests were executed for each TF-TFBS model in dataset 1(with PWM) and dataset 2 (without PWM). Detailed results of the 10-fold cross validation test were included in the Additional file 6. Since the control model and the sequence model required PWM information, performance of these two models on dataset 2 was not presented. Detailed results of AUC measurement were listed in Additional file 7. Figure 1 showed different models’ sensitivity, specificity, accuracy, and AUC distribution in dataset 1. While Figure 2 showed those distributions in dataset 2. Table 2 and 3 summarized the mean and standard deviation of model performance for dataset 1 and 2 respectively.


Towards biological characters of interactions between transcription factors and their DNA targets in mammals.

Zheng G, Liu Q, Ding G, Wei C, Li Y - BMC Genomics (2012)

Performance comparison of different models for 309 TF-TFBSs (with PWM information). Panel(a)-(d): boxplots of 5 models (the control model, the sequence model, the structure model, the evolution model, and the hybrid model) for sensitivity, specificity, accuracy and AUC measurement. For a boxplot, the 5 whiskers from bottom to top denote the 5th, 25th, 50th, 75th, and 95th percentile respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472306&req=5

Figure 1: Performance comparison of different models for 309 TF-TFBSs (with PWM information). Panel(a)-(d): boxplots of 5 models (the control model, the sequence model, the structure model, the evolution model, and the hybrid model) for sensitivity, specificity, accuracy and AUC measurement. For a boxplot, the 5 whiskers from bottom to top denote the 5th, 25th, 50th, 75th, and 95th percentile respectively.
Mentions: 10-fold cross validation tests were executed for each TF-TFBS model in dataset 1(with PWM) and dataset 2 (without PWM). Detailed results of the 10-fold cross validation test were included in the Additional file 6. Since the control model and the sequence model required PWM information, performance of these two models on dataset 2 was not presented. Detailed results of AUC measurement were listed in Additional file 7. Figure 1 showed different models’ sensitivity, specificity, accuracy, and AUC distribution in dataset 1. While Figure 2 showed those distributions in dataset 2. Table 2 and 3 summarized the mean and standard deviation of model performance for dataset 1 and 2 respectively.

Bottom Line: Results indicate that TFs and TFBSs are reciprocal in sequence, structure, and evolution level.Our work demonstrates that, to some extent, TFs and TFBSs have developed a coevolutionary relationship in order to keep their physical binding and maintain their regulatory functions.In summary, our work will help understand transcriptional regulation and interpret binding mechanism between proteins and DNAs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. zhenggy@sibs.ac.cn

ABSTRACT

Background: In post-genomic era, the study of transcriptional regulation is pivotal to decode genetic information. Transcription factors (TFs) are central proteins for transcriptional regulation, and interactions between TFs and their DNA targets (TFBSs) are important for downstream genes' expression. However, the lack of knowledge about interactions between TFs and TFBSs is still baffling people to investigate the mechanism of transcription.

Results: To expand the knowledge about interactions between TFs and TFBSs, three biological features (sequence feature, structure feature, and evolution feature) were utilized to build TFBS identification models for studying binding preference between TFs and their DNA targets in mammals. Results show that each feature does have fairly well performance to capture TFBSs, and the hybrid model combined all three features is more robust for TFBS identification. Subsequently, correspondence between TFs and their TFBSs was investigated to explore interactions among them in mammals. Results indicate that TFs and TFBSs are reciprocal in sequence, structure, and evolution level.

Conclusions: Our work demonstrates that, to some extent, TFs and TFBSs have developed a coevolutionary relationship in order to keep their physical binding and maintain their regulatory functions. In summary, our work will help understand transcriptional regulation and interpret binding mechanism between proteins and DNAs.

Show MeSH