Gene expression is regulated transcriptionally and post-transcriptionally at the RNA level. Alternative splicing (AS) is a major post-transcriptional mechanism that allows a single gene to code for multiple transcript isoforms. Transcription and AS are functionally coupled processes that involve complex synergistic effects of multiple molecules, mainly transcription factors and DNA, splicing factors and RNA, and protein-protein interactions. In Systems Biology, the interaction among these multiple molecules can be studied by constructing network models, which are used to represent the regulation among genes, transcripts, proteins and other biomolecules. RNA-sequencing datasets are often used to construct regulatory networks through the top-down approach. Yet, much of these regulatory networks have focused on total gene expression data, ignoring transcript-specific regulation. Therefore, the development and use of new methods that take into account the widespread AS regulation becomes a high priority, providing an unprecedented opportunity to investigate the co-regulation between transcription and AS mechanisms.
The data generation capabilities of high-throughput sequencing technologies, through short read RNA-sequencing, and more recently, full-length transcript sequencing (namely Pacific Biosciences and Oxford Nanopore Technologies), can be used to accurately quantify genome-wide gene and transcript expression levels. Computational and mathematical methods can be developed and applied to infer transcriptional as well as alternative splicing regulatory networks from expression data in conjunction with functional information, such as regulatory roles of transcription factors and splicing factors, non-coding RNAs, miRNAs and RNA-binding proteins. Despite the advances in Systems Biology methods, we are still far from capturing the dynamics and complexity of biological networks. The diversity, abundance, timing and spatial location of functional molecules can change in response to internal and external cues, which provides the flexibility for adaption to environmental changes. New network approaches are required to reveal the complexity and dynamic reprogramming of regulations and interactions among biomolecules.
This Research Topic focuses on the development and use of novel network inference methods to study transcription and alternative splicing regulations. Possible topics of interest include, but are not limited to:
• Novel insights of using transcript and gene level quantifications to infer regulatory networks
• Construction of alternative splicing regulatory networks from experimental data
• Cross-talk between transcription and alternative splicing networks
• Approaches to integrate multiple biological data types to reveal the transcriptional and alternative splicing regulatory mechanisms
• Inference of dynamic regulatory networks from time course experimental data, such as time-series and developmental-series
• Characterization of the dynamic reprogramming of transcription and alternative splicing mechanisms in response to endogenous or exogenous cues, such as differential gene expression and alternative splicing upon a treatment or genetic modification and change of rhythmicity
Gene expression is regulated transcriptionally and post-transcriptionally at the RNA level. Alternative splicing (AS) is a major post-transcriptional mechanism that allows a single gene to code for multiple transcript isoforms. Transcription and AS are functionally coupled processes that involve complex synergistic effects of multiple molecules, mainly transcription factors and DNA, splicing factors and RNA, and protein-protein interactions. In Systems Biology, the interaction among these multiple molecules can be studied by constructing network models, which are used to represent the regulation among genes, transcripts, proteins and other biomolecules. RNA-sequencing datasets are often used to construct regulatory networks through the top-down approach. Yet, much of these regulatory networks have focused on total gene expression data, ignoring transcript-specific regulation. Therefore, the development and use of new methods that take into account the widespread AS regulation becomes a high priority, providing an unprecedented opportunity to investigate the co-regulation between transcription and AS mechanisms.
The data generation capabilities of high-throughput sequencing technologies, through short read RNA-sequencing, and more recently, full-length transcript sequencing (namely Pacific Biosciences and Oxford Nanopore Technologies), can be used to accurately quantify genome-wide gene and transcript expression levels. Computational and mathematical methods can be developed and applied to infer transcriptional as well as alternative splicing regulatory networks from expression data in conjunction with functional information, such as regulatory roles of transcription factors and splicing factors, non-coding RNAs, miRNAs and RNA-binding proteins. Despite the advances in Systems Biology methods, we are still far from capturing the dynamics and complexity of biological networks. The diversity, abundance, timing and spatial location of functional molecules can change in response to internal and external cues, which provides the flexibility for adaption to environmental changes. New network approaches are required to reveal the complexity and dynamic reprogramming of regulations and interactions among biomolecules.
This Research Topic focuses on the development and use of novel network inference methods to study transcription and alternative splicing regulations. Possible topics of interest include, but are not limited to:
• Novel insights of using transcript and gene level quantifications to infer regulatory networks
• Construction of alternative splicing regulatory networks from experimental data
• Cross-talk between transcription and alternative splicing networks
• Approaches to integrate multiple biological data types to reveal the transcriptional and alternative splicing regulatory mechanisms
• Inference of dynamic regulatory networks from time course experimental data, such as time-series and developmental-series
• Characterization of the dynamic reprogramming of transcription and alternative splicing mechanisms in response to endogenous or exogenous cues, such as differential gene expression and alternative splicing upon a treatment or genetic modification and change of rhythmicity
