The cis-regulatory code is to quantify the gene expression level to represent the transcriptional activity from DNA sequence. The cis-regulatory sequence includes enhancers, promoters, silencers, insulators, et al. Enhancers can contact target gene promoter to regulate gene expression which involves DNA methylation, histone modifications (HMs), transcription factors (TFs), coactivators, mediators, et al. It’s important to decipher the cis-regulatory elements and the epigenomic features regulate gene expression.
Recently, many artificial intelligence algorithms have been developed to predict predicting genomic features which include transcription factor binding, chromatin accessibility, enhancers, 3D chromatin structures, gene expression, et al. The most important method is predicting these features from DNA sequence alone based on Convolutional/recurrent neural networks (CNN, RNN) or Transformer model. The large-scale published datasets provide the possibility to advance the prediction accuracy of these models. The main purpose of these models is to interpret the prediction results and experimental assays to understand the cis-regulatory code.
This Research Topic welcomes the submission of Original Research articles, Review, Mini Review, Perspective articles, Editorial, Brief Research Report, and Method articles. We aim to bring state-of-the-art research contributions in computational or experimental models to address new problems and improve existing methods to decipher the cis-regulatory mechanisms, but not limited to:
1. Create benchmark datasets or databases for cis-regulatory elements.
2. Interpreting and explaining the prediction models and experimental assays.
3. Develop new prediction models for cis-regulatory code with multi-omics datasets.
4. Large-scale validation of the prediction cis-regulatory elements with experimental assays.
5. Determine key factors in cis-regulation code and their relationships.
6. Apply published prediction models to discover cell-type specific cis-regulatory elements in development and diseases.
Keywords:
cis-regulatory elements, machine learning, massively parallel reporter assays (MPRA) and CRISPR-based perturbations
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
The cis-regulatory code is to quantify the gene expression level to represent the transcriptional activity from DNA sequence. The cis-regulatory sequence includes enhancers, promoters, silencers, insulators, et al. Enhancers can contact target gene promoter to regulate gene expression which involves DNA methylation, histone modifications (HMs), transcription factors (TFs), coactivators, mediators, et al. It’s important to decipher the cis-regulatory elements and the epigenomic features regulate gene expression.
Recently, many artificial intelligence algorithms have been developed to predict predicting genomic features which include transcription factor binding, chromatin accessibility, enhancers, 3D chromatin structures, gene expression, et al. The most important method is predicting these features from DNA sequence alone based on Convolutional/recurrent neural networks (CNN, RNN) or Transformer model. The large-scale published datasets provide the possibility to advance the prediction accuracy of these models. The main purpose of these models is to interpret the prediction results and experimental assays to understand the cis-regulatory code.
This Research Topic welcomes the submission of Original Research articles, Review, Mini Review, Perspective articles, Editorial, Brief Research Report, and Method articles. We aim to bring state-of-the-art research contributions in computational or experimental models to address new problems and improve existing methods to decipher the cis-regulatory mechanisms, but not limited to:
1. Create benchmark datasets or databases for cis-regulatory elements.
2. Interpreting and explaining the prediction models and experimental assays.
3. Develop new prediction models for cis-regulatory code with multi-omics datasets.
4. Large-scale validation of the prediction cis-regulatory elements with experimental assays.
5. Determine key factors in cis-regulation code and their relationships.
6. Apply published prediction models to discover cell-type specific cis-regulatory elements in development and diseases.
Keywords:
cis-regulatory elements, machine learning, massively parallel reporter assays (MPRA) and CRISPR-based perturbations
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.