A promoter is a DNA sequence fragment located upstream of a structural gene. It can activate RNA polymerase to precisely bind to template DNA and has the specificity of transcription initiation. In prokaryotes, promoters are considered the key elements of sigma factor recognition in the transcription process and the initial step of gene expression. It has been found that prokaryotic cells can adjust their transcription program to adapt to environmental changes through various mechanisms, one of which is changing the promoters' settings. With the accumulation of promoter data, studying prokaryotic promoters will provide more useful information for understanding microbial gene transcription.
As promoters play the most critical role in the regulation of gene transcription, this Research Topic aims to provide an important platform for scientific exchange on the use of artificial intelligence and big data to analyse prokaryotic promoters, including the development and application of computational methods and techniques for the analysis and study of promoters in prokaryotic genomes. This Research Topic will not only focus on the construction of algorithms and models related to prokaryotic promoter research but also on using these methods and models to understand the biological functions related to prokaryotic promoters and discover new biological mechanisms phenomena and explain the biological problems dominated by promoters.
We welcome the following subtopic but are not limited to:
• Application of machine learning methods in prokaryotic promoter identification
• Developing new computational methods to study prokaryotic promoters
• Knowledge graph-based network analysis on the prokaryotic promoters
• The database construction of prokaryotic promoters
• Designing computational models for promoter strength prediction
• Phylogenetic analysis of prokaryotic promoters in different groups
• Construction of physical models of promoter-mediated gene transcription
A promoter is a DNA sequence fragment located upstream of a structural gene. It can activate RNA polymerase to precisely bind to template DNA and has the specificity of transcription initiation. In prokaryotes, promoters are considered the key elements of sigma factor recognition in the transcription process and the initial step of gene expression. It has been found that prokaryotic cells can adjust their transcription program to adapt to environmental changes through various mechanisms, one of which is changing the promoters' settings. With the accumulation of promoter data, studying prokaryotic promoters will provide more useful information for understanding microbial gene transcription.
As promoters play the most critical role in the regulation of gene transcription, this Research Topic aims to provide an important platform for scientific exchange on the use of artificial intelligence and big data to analyse prokaryotic promoters, including the development and application of computational methods and techniques for the analysis and study of promoters in prokaryotic genomes. This Research Topic will not only focus on the construction of algorithms and models related to prokaryotic promoter research but also on using these methods and models to understand the biological functions related to prokaryotic promoters and discover new biological mechanisms phenomena and explain the biological problems dominated by promoters.
We welcome the following subtopic but are not limited to:
• Application of machine learning methods in prokaryotic promoter identification
• Developing new computational methods to study prokaryotic promoters
• Knowledge graph-based network analysis on the prokaryotic promoters
• The database construction of prokaryotic promoters
• Designing computational models for promoter strength prediction
• Phylogenetic analysis of prokaryotic promoters in different groups
• Construction of physical models of promoter-mediated gene transcription
