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lncRNA - Long Non-coding RNAs02:39

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Unveiling Long Non-coding RNA Networks from Single-Cell Omics Data Through Artificial Intelligence.

Guangshuo Cao1, Dijun Chen2

  • 1State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China.

Methods in Molecular Biology (Clifton, N.J.)
|December 20, 2024
PubMed
Summary
This summary is machine-generated.

Single-cell omics and artificial intelligence (AI) reveal long non-coding RNA (lncRNA) roles in cellular diversity and disease. These advanced methods help construct single-cell gene regulatory networks (scGRNs) for deeper biological insights.

Keywords:
Artificial intelligence (AI)Gene regulatory networks (GRNs)Long non-coding RNAs (lncRNAs)Multi-omicsSingle-cell sequencing

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell omics technologies provide high-resolution insights into gene expression.
  • Long non-coding RNAs (lncRNAs) play crucial roles in cellular processes and disease.
  • Artificial intelligence (AI) enhances the analysis of complex biological data.

Purpose of the Study:

  • To review advancements in single-cell omics data analysis for lncRNA research.
  • To highlight the role of AI in understanding lncRNA function and disease implications.
  • To summarize resources and models for constructing single-cell gene regulatory networks (scGRNs).

Main Methods:

  • Analysis of single-cell omics data to study lncRNA expression dynamics.
  • Integration of AI methodologies for functional genomics and disease association.
  • Development and application of computational models for scGRN construction.

Main Results:

  • Single-cell omics enables detailed characterization of lncRNA expression and cell-type specificity.
  • AI facilitates the discovery of lncRNA-driven regulatory networks.
  • Established resources and AI models aid in building scGRNs.

Conclusions:

  • The integration of single-cell omics and AI is pivotal for deciphering lncRNA biology.
  • Understanding lncRNA-mediated scGRNs offers insights into cellular heterogeneity and disease mechanisms.
  • Future research directions focus on the challenges and prospects of scGRN exploration in lncRNA studies.