As an inhibitor of microRNA (miRNA) activity, competing endogenous RNA (ceRNA), also called miRNA sponge or miRNA decoy, plays an important role in many biological processes, including the development of human complex diseases. Currently, ceRNAs are mainly classified into four types: long non-coding RNAs (lncRNAs), pseudogenes, circular RNAs (circRNAs), and messenger RNAs (mRNAs). To uncover ceRNA regulation, computational or dry-lab methods have been proved to profoundly reduce the time and cost of wet-lab experiments. Novel computational methods or tools are being developed to shortlist high-confidence ceRNAs for subsequent wet-lab experiments. The development of computational methods or tools will consequently speed up the research on ceRNA.
This Research Topic will focus on the computational or in silico identification of ceRNA regulation, including ceRNA discovery, ceRNA network inference, and ceRNA module identification. The goals of this Research Topic are to propose novel computational methods or software tools for identifying ceRNA regulation, and develop online databases or web servers for facilitating the study of ceRNA regulation, and apply existing methods of ceRNA regulation to new datasets, and conduct comparison studies to select appropriate methods of ceRNA regulation for assisting in subsequent experimental design.
This Research Topic will cover all kinds of studies aiming at inferring ceRNA regulation. We welcome Original Research, Methods, Technology and Code, Brief Research Report, Opinion, Perspective, Review, and Mini Review including (but not limited to) the following topics:
• Overview, perspective and critical review on ceRNA regulation
• Computational methods or software tools to identify ceRNA regulation
• Online databases or web servers to aid researchers to study ceRNA regulation
• Apply existing methods of ceRNA regulation to new bulk-cell or single-cell sequencing data
• Comparison studies of a variety of computational methods or tools
• Clinical applications of ceRNAs as biomarkers
As an inhibitor of microRNA (miRNA) activity, competing endogenous RNA (ceRNA), also called miRNA sponge or miRNA decoy, plays an important role in many biological processes, including the development of human complex diseases. Currently, ceRNAs are mainly classified into four types: long non-coding RNAs (lncRNAs), pseudogenes, circular RNAs (circRNAs), and messenger RNAs (mRNAs). To uncover ceRNA regulation, computational or dry-lab methods have been proved to profoundly reduce the time and cost of wet-lab experiments. Novel computational methods or tools are being developed to shortlist high-confidence ceRNAs for subsequent wet-lab experiments. The development of computational methods or tools will consequently speed up the research on ceRNA.
This Research Topic will focus on the computational or in silico identification of ceRNA regulation, including ceRNA discovery, ceRNA network inference, and ceRNA module identification. The goals of this Research Topic are to propose novel computational methods or software tools for identifying ceRNA regulation, and develop online databases or web servers for facilitating the study of ceRNA regulation, and apply existing methods of ceRNA regulation to new datasets, and conduct comparison studies to select appropriate methods of ceRNA regulation for assisting in subsequent experimental design.
This Research Topic will cover all kinds of studies aiming at inferring ceRNA regulation. We welcome Original Research, Methods, Technology and Code, Brief Research Report, Opinion, Perspective, Review, and Mini Review including (but not limited to) the following topics:
• Overview, perspective and critical review on ceRNA regulation
• Computational methods or software tools to identify ceRNA regulation
• Online databases or web servers to aid researchers to study ceRNA regulation
• Apply existing methods of ceRNA regulation to new bulk-cell or single-cell sequencing data
• Comparison studies of a variety of computational methods or tools
• Clinical applications of ceRNAs as biomarkers
