Noncoding Rna Networks
Noncoding Rna Networks - Related Videos
07:35
Computational Analysis Tutorial for Chimeric Small Noncoding RNA: Target RNA Sequencing Libraries
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Cited by 2 •
2023
Here, we present a protocol demonstrating the installation and use of a bioinformatics pipeline to analyze chimeric RNA sequencing data used in the study of in vivo RNA:RNA interactions.
06:44
Isolation of Small Noncoding RNAs from Human Serum
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Cited by 13 •
2014
This protocol describes a method for extracting small RNAs from human serum. We have used this method to isolate microRNAs from cancer serum for use in DNA arrays and also singleplex quantitative PCR. The protocol utilizes phenol and guanidinium thiocyanate reagents with modifications to yield high quality RNA.
13:04
Overexpressing Long Noncoding RNAs Using Gene-activating CRISPR
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Cited by 6 •
2019
Traditional cDNA-based overexpression techniques have a limited applicability for the overexpression of long noncoding RNAs due to their multiple splice forms with potential functionality. This review reports a protocol using CRISPR technology to overexpress multiple splice variants of a long noncoding RNA.
02:26
Protein Networks
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2020
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions. These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
02:18
Network Covalent Solids
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2020
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds. To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
FAQs
Network effects arise because RNA molecules can interact with messenger RNAs and regulatory proteins rather than acting in isolation. These interactions can change whether a transcript remains stable, is translated, or influences transcription and chromatin. As a result, one regulatory RNA may affect several genes, creating coordinated changes in cellular programs instead of a single isolated effect.
Noncoding RNA networks have both broad and convergent organization. A single RNA can be connected to multiple gene targets, allowing it to influence a wider regulatory program. Conversely, distinct RNAs can affect different components of the same pathway, producing a combined effect. This architecture helps explain how cells coordinate complex processes such as identity, development, and environmental responses.
The effects can occur at several regulatory levels, including transcription, RNA stability, translation, and chromatin state. These levels influence different stages of gene expression, from controlling how genetic information is transcribed to determining whether RNA persists or is translated. Examining the affected level helps researchers interpret how a network changes cellular behavior.
Cellular identity depends on coordinated gene activity rather than regulation of isolated genes. Noncoding RNA relationships can connect multiple regulatory steps and pathways, helping maintain characteristic cellular states during development. The same network logic can also adjust gene activity when environmental signals change, providing a mechanism for coordinated cellular responses while preserving relationships among interacting genes and regulatory proteins.
Mapping organizes relationships among microRNAs, long noncoding RNAs, circular RNAs, messenger RNAs, and regulatory proteins, then connects those relationships to affected regulatory processes or pathways. This approach can reveal whether a change involves transcription, RNA stability, translation, or chromatin state. It also helps distinguish isolated molecular effects from broader network-level dysregulation.
Network mapping can identify RNA relationships or regulatory patterns associated with altered cellular states and disease. Such patterns may be more informative than examining one RNA alone because they capture interactions, pathway convergence, and coordinated regulation. Researchers can therefore use network-level findings to search for biomarkers that reflect underlying biological changes rather than merely a single molecular measurement.
Researchers may investigate these networks when normal gene regulation becomes dysregulated in disease or when they need to understand changes in development and cellular responses. Network analysis can indicate which regulatory relationships or pathways are altered. Those findings may support RNA-based therapies designed to restore or modify gene regulation, although the useful target depends on the specific network disturbance.