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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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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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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Ribosome Profiling02:24

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Types of RNA01:20

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Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in regulating gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
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Overview
Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in the regulation of gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
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Ribosomal RNA Synthesis02:53

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Ribosome synthesis is a highly complex and coordinated process involving more than 200 assembly factors. The synthesis and processing of ribosomal components occurs not only in the nucleolus but also in the nucleoplasm and the cytoplasm of eukaryotic cells.
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Interpreting and integrating big data in non-coding RNA research.

Simona Cantarella1, Elena Di Nisio2, Davide Carnevali1

  • 1Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124 Parma, Italy.

Emerging Topics in Life Sciences
|February 1, 2021
PubMed
Summary
This summary is machine-generated.

Advances in non-coding RNA (ncRNA) research, driven by RNA sequencing, highlight the need for integrated data analysis. Computational tools and literature mining are key to understanding ncRNA roles in gene regulation and disease.

Keywords:
RNA sequencingbioinformaticscancercompeting endogenous RNAsdatabasenon-coding RNA

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • The last two decades have seen a surge in non-coding RNA (ncRNA) research, fueled by high-throughput RNA sequencing.
  • Growing awareness of ncRNA involvement in complex gene regulatory networks necessitates standardized data analysis.
  • Understanding ncRNA's role in gene regulation requires novel integrative approaches and computational tools.

Purpose of the Study:

  • To address the need for standardized criteria in ncRNA data collection and analysis.
  • To develop and utilize novel computational tools for interpreting large-scale ncRNA data.
  • To explore the regulatory interactions of ncRNAs with protein-coding messenger RNAs (mRNAs).

Main Methods:

  • High-throughput RNA sequencing strategies.
  • Development of computational tools for ncRNA big data integration.
  • Systematic literature mining to complement big data analysis.

Main Results:

  • New computational tools enable the interpretation of transcriptome changes influenced by ncRNAs.
  • Integration of ncRNA big data aids in understanding gene regulation in physiological and pathological conditions.
  • Combined approaches identify potential ncRNA players, biomarkers, and therapeutic targets.

Conclusions:

  • Integrating ncRNA big data with literature mining is crucial for advancing gene regulation studies.
  • Computational tools are essential for making biological sense of vast molecular information.
  • This integrated approach is invaluable for identifying novel biomarkers and therapeutic targets in diseases like cancer.