scholarly journals A Mutation in the Drosophila melanogaster eve Stripe 2 Minimal Enhancer Is Buffered by Flanking Sequences

2020 ◽  
Vol 10 (12) ◽  
pp. 4473-4482
Author(s):  
Francheska López-Rivera ◽  
Olivia K. Foster Rhoades ◽  
Ben J. Vincent ◽  
Edward C. G. Pym ◽  
Meghan D. J. Bragdon ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Drosophila Melanogaster ◽  
Binding Site ◽  
Expression Pattern ◽  
Dna Sequences ◽  
Expression Patterns ◽  
Transcription Factor Binding ◽  
Genomic Context ◽  
Site Mutation ◽  
Factor Binding

Enhancers are DNA sequences composed of transcription factor binding sites that drive complex patterns of gene expression in space and time. Until recently, studying enhancers in their genomic context was technically challenging. Therefore, minimal enhancers, the shortest pieces of DNA that can drive an expression pattern that resembles a gene’s endogenous pattern, are often used to study features of enhancer function. However, evidence suggests that some enhancers require sequences outside the minimal enhancer to maintain function under environmental perturbations. We hypothesized that these additional sequences also prevent misexpression caused by a transcription factor binding site mutation within a minimal enhancer. Using the Drosophila melanogaster even-skipped stripe 2 enhancer as a case study, we tested the effect of a Giant binding site mutation (gt-2) on the expression patterns driven by minimal and extended enhancer reporter constructs. We found that, in contrast to the misexpression caused by the gt-2 binding site deletion in the minimal enhancer, the same gt-2 binding site deletion in the extended enhancer did not have an effect on expression. The buffering of expression levels, but not expression pattern, is partially explained by an additional Giant binding site outside the minimal enhancer. Deleting the gt-2 binding site in the endogenous locus had no significant effect on stripe 2 expression. Our results indicate that rules derived from mutating enhancer reporter constructs may not represent what occurs in the endogenous context.

scholarly journals A mutation in the Drosophila melanogaster eve stripe 2 minimal enhancer is buffered by flanking sequences

2020 ◽  
Author(s):  
Francheska Lopez-Rivera ◽  
Olivia K. Foster ◽  
Ben J. Vincent ◽  
Edward C. G. Pym ◽  
Meghan D. J. Bragdon ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Drosophila Melanogaster ◽  
Binding Site ◽  
Expression Pattern ◽  
Dna Sequences ◽  
Expression Patterns ◽  
Transcription Factor Binding ◽  
Genomic Context ◽  
Site Mutation ◽  
Factor Binding

AbstractEnhancers are DNA sequences composed of transcription factor binding sites that drive complex patterns of gene expression in space and time. Until recently, studying enhancers in their genomic context was technically challenging. Therefore, minimal enhancers, the shortest pieces of DNA that can drive an expression pattern that resembles a gene’s endogenous pattern, are often used to study features of enhancer function. However, evidence suggests that some enhancers require sequences outside the minimal enhancer to maintain function under environmental perturbations. We hypothesized that these additional sequences also prevent misexpression caused by a transcription factor binding site mutation within a minimal enhancer. Using the Drosophila melanogaster even-skipped stripe 2 enhancer as a case study, we tested the effect of a Giant binding site mutation (gt-2) on the expression patterns driven by minimal and extended enhancer reporter constructs. We found that, in contrast to the misexpression caused by the gt-2 binding site mutation in the minimal enhancer, the same gt-2 binding site mutation in the extended enhancer did not have an effect on expression. The buffering of expression levels, but not expression pattern, is partially explained by an additional Giant binding site outside the minimal enhancer. Mutating the gt-2 binding site in the endogenous locus had no significant effect on stripe 2 expression. Our results indicate that rules derived from mutating enhancer reporter constructs may not represent what occurs in the endogenous context.

scholarly journals DeepGRN: Prediction of transcription factor binding site across cell-types using attention-based deep neural networks 

2021 ◽  
Author(s):  
Chen Chen ◽  
Jie Hou ◽  
Xiaowen Shi ◽  
Hua Yang ◽  
James A. Birchler ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Site ◽  
Transcription Factor Binding Site ◽  
Dna Sequences ◽  
Binding Sites ◽  
Transcription Factor Binding ◽  
Parallel Sequencing ◽  
Factor Binding Site ◽  
Factor Binding

Abstract BackgroundDue to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors.ResultsIn this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN.ConclusionsDeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

scholarly journals DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chen Chen ◽  
Jie Hou ◽  
Xiaowen Shi ◽  
Hua Yang ◽  
James A. Birchler ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Site ◽  
Transcription Factor Binding Site ◽  
Dna Sequences ◽  
Binding Sites ◽  
Transcription Factor Binding ◽  
Parallel Sequencing ◽  
Factor Binding Site ◽  
Factor Binding

Abstract Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

scholarly journals Prediction of transcription factor binding site across cell-types using attention-based deep neural networks

2020 ◽  
Author(s):  
Chen Chen ◽  
Jie Hou ◽  
Xiaowen Shi ◽  
Hua Yang ◽  
James A. Birchler ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Site ◽  
Transcription Factor Binding Site ◽  
Dna Sequences ◽  
Binding Sites ◽  
Transcription Factor Binding ◽  
Parallel Sequencing ◽  
Factor Binding Site ◽  
Factor Binding

Abstract Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

scholarly journals DeepGRN: Prediction of transcription factor binding site across cell-types using attention-based deep neural networks 

2020 ◽  
Author(s):  
Chen Chen ◽  
Jie Hou ◽  
Xiaowen Shi ◽  
Hua Yang ◽  
James A. Birchler ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Site ◽  
Transcription Factor Binding Site ◽  
Dna Sequences ◽  
Binding Sites ◽  
Transcription Factor Binding ◽  
Parallel Sequencing ◽  
Factor Binding Site ◽  
Factor Binding

Abstract BackgroundDue to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors.ResultsIn this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN.ConclusionsDeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

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