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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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The Landscape of long noncoding RNA classification.

Georges St Laurent1, Claes Wahlestedt2, Philipp Kapranov3

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Summary
This summary is machine-generated.

The study reviews long noncoding RNA (lncRNA) classifications, highlighting the need for a clear framework. It proposes guidelines for lncRNA classification and functional annotation using systems biology data.

Keywords:
annotation of long non-coding RNAsclassification of long non-coding RNAsfunction of long non-coding RNAslincRNAlncRNAlong non-coding RNAsystems biologytranscriptomevlincRNA

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Transcriptome sequencing advances reveal numerous long noncoding RNAs (lncRNAs).
  • Current lncRNA classification systems are complex and lack standardization.
  • Ambiguous terminology hinders understanding of lncRNA function and annotation.

Purpose of the Study:

  • To review existing long noncoding RNA (lncRNA) classification systems, nomenclature, and terminology.
  • To address the challenges posed by the lack of a clear conceptual framework for lncRNA annotation and interpretation.
  • To propose conceptual guidelines for lncRNA classification and functional annotation.

Main Methods:

  • Comprehensive review of existing literature on lncRNA classification and terminology.
  • Analysis of current challenges in noncoding transcriptome data annotation and interpretation.
  • Development of proposed guidelines based on systems biology datasets.

Main Results:

  • Identified a proliferation of lncRNA classes and associated terminology.
  • Highlighted the ambiguity and lack of clarity in the field due to inconsistent nomenclature.
  • Recognized the need for a unified conceptual framework for lncRNA classification.

Conclusions:

  • A standardized and unambiguous classification framework is crucial for advancing lncRNA research.
  • Proposed guidelines aim to improve the annotation and interpretation of noncoding transcriptome data.
  • Integration of systems biology approaches is key to unraveling lncRNA functions.