At the beginning of May 2023 published a paper in the journal Nature Reviews in molecular cell biology entitled "The contribution of databases to the understanding of long, non-coding RNA elements of lncRNAs", in which he presented the following:
At the beginning of May 2023, a paper was published in Nature Reviews in molecular cell biology, entitled "The Contribution of Databases to the Understanding of Long Noncoding RNA Elements of LncRNAs", in which two genomic researchers presented a review of the types and properties of lncRNAs, and a discussion of their contribution to addressing challenges and opportunities in the field of long noncoding RNA elements research.
Long noncoding RNAs are a class of RNA-identifying RNA molecules composed of more than 200 nucleotides that do not have the ability to make proteins or have limited coding capabilities for proteins. Intensive studies have identified and characterized lncRNAs in a group of noa, demonstrating that lncRNAs exceed the number of protein-coding genes and have significant regulatory roles in disease, balance, and stress responses.
Valuable efforts have been made recently to build databases of diverse lncRNAs, which work together as an essential infrastructure for the scientific community. In their paper, the researchers provide a review of those databases, identify the features of many of them, and discuss challenges and opportunities for a better understanding of the lncRNAs universe.
From the databases the paper referred to its features:
- TANRIC, Mitransscriptome, and ReflNC: rules that focus on dynamic expression profiles of lncRNAs in cancer samples.
ExorBase: focuses on identifying extracellular vesicles for various human body fluids.
- lncatLAs and RnaLocate: in them is the concentration of subcellular components of lncRNAs.
Dynamic-BM: It focuses on the developmental processes of lncRNAs.
lncExpDB: This database evaluates the expression capabilities of lncRNAs in diverse biological contexts and provides comprehensive expression profiles and co-expression networks for human lncRNAs.
Each of these databases has its own unique features and focuses on different aspects of lncRNAs research.
The paper concludes by calling for worldwide collaboration to unify efforts and standards to identify, characterize, and correlate lncRNAs, with the aim of building a world full of data on lncRNAs.
Ma, L., Zhang, Z. The contribution of databases towards understanding the universe of long non-coding RNAs. Nat Rev Mol Cell Biol (2023). https://doi.org/10.1038/s41580-023-00612-z
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