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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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Databases for lncRNAs: a comparative evaluation of emerging tools.

Sabrina Fritah1, Simone P Niclou1, Francisco Azuaje2

  • 1NorLux Neuro-Oncology Laboratory, Department of Oncology, Centre de Recherche Public de la Santé (CRP-Santé), Luxembourg L-1526, Luxembourg.

RNA (New York, N.Y.)
|October 18, 2014
PubMed
Summary
This summary is machine-generated.

The human transcriptome contains many long noncoding RNAs (lncRNAs) with unknown functions. This review evaluates lncRNA databases, assessing their annotations and utility for understanding lncRNA roles in biology, particularly in cancer.

Keywords:
databaseslncRNAsnoncoding RNAs

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Area of Science:

  • Genomics and Molecular Biology
  • Bioinformatics and Computational Biology

Background:

  • The majority of the human transcriptome comprises non-protein-coding sequences, including a rapidly growing number of long noncoding RNAs (lncRNAs).
  • Despite the known importance of individual lncRNAs, a significant knowledge gap exists regarding the functions of most identified lncRNAs.
  • Dedicated databases are emerging to curate information on lncRNAs.

Purpose of the Study:

  • To review and evaluate existing general and specialized databases for long noncoding RNA (lncRNA) research.
  • To assess the quality of annotations, reported molecular associations, and integration capabilities of these lncRNA resources.
  • To discuss the utility of these databases in elucidating lncRNA functions, with a focus on cancer-related lncRNAs.

Main Methods:

  • Comprehensive literature review of lncRNA databases.
  • Systematic evaluation of database content, focusing on annotation quality and functional association reporting.
  • Analysis of database integration with other resources and computational tools.
  • Case studies using known and novel cancer-related lncRNAs to illustrate database utility.

Main Results:

  • Identification and categorization of various general and specialized lncRNA databases.
  • Assessment of the strengths and weaknesses of these databases concerning annotation accuracy and functional data.
  • Demonstration of how databases can be used to explore the roles of lncRNAs in cancer biology.

Conclusions:

  • Current lncRNA databases vary in quality and scope, presenting both opportunities and challenges for researchers.
  • Improved annotations and integration are crucial for advancing our understanding of lncRNA functions.
  • Future database development should focus on enhancing functional data and computational integration to bridge the knowledge gap in lncRNA research.