Decoding Cis-regulatory Code with Machine Learning and Experimental Assay

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Background

The field of genomics has made significant strides in understanding the cis-regulatory code, which is crucial for quantifying gene expression levels and representing transcriptional activity from DNA sequences. Cis-regulatory sequences, including enhancers, promoters, silencers, and insulators, play a pivotal role in gene regulation. Enhancers, for instance, can interact with target gene promoters to modulate gene expression through mechanisms such as DNA methylation, histone modifications, and the involvement of transcription factors and coactivators. Despite the progress, there remain substantial gaps in fully deciphering how these cis-regulatory elements and epigenomic features regulate gene expression. Recent advancements in artificial intelligence, particularly convolutional/recurrent neural networks (CNN, RNN) and Transformer models, have shown promise in predicting genomic features from DNA sequences alone. However, the challenge lies in improving the accuracy of these predictions and interpreting the results in a biologically meaningful way. Large-scale published datasets offer a valuable resource to enhance these models, yet a comprehensive understanding of the cis-regulatory code remains elusive.

This research topic aims to advance the understanding of cis-regulatory mechanisms by leveraging both computational and experimental approaches. The primary objectives include creating benchmark datasets for cis-regulatory elements, developing new prediction models that integrate multi-omics datasets, and validating these predictions through large-scale experimental assays. Additionally, the research seeks to interpret and explain the results of these models and assays to uncover the key factors in cis-regulation and their interrelationships. By addressing these aims, the research hopes to provide deeper insights into the cis-regulatory code and its role in gene expression, particularly in the context of development and disease.

To gather further insights into the boundaries of cis-regulatory code research, we welcome articles addressing, but not limited to, the following themes:
- Creation of benchmark datasets or databases for cis-regulatory elements.
- Interpretation and explanation of prediction models and experimental assays.
- Development of new prediction models for the cis-regulatory code using multi-omics datasets.
- Large-scale validation of predicted cis-regulatory elements through experimental assays.
- Identification of key factors in the cis-regulatory code and their relationships.
- Application of published prediction models to discover cell-type-specific cis-regulatory elements in development and diseases.

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This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

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  • Editorial
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  • Hypothesis and Theory
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Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

  • Brief Research Report
  • Editorial
  • General Commentary
  • Hypothesis and Theory
  • Methods
  • Mini Review
  • Opinion
  • Original Research
  • Perspective
  • Review
  • Technology and Code

Keywords: cis-regulatory elements, machine learning, massively parallel reporter assays (MPRA) and CRISPR-based perturbations

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