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Related Experiment Video

Updated: Feb 10, 2026

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
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Functional enrichment analysis based on long noncoding RNA associations.

Kuo-Sheng Hung1, Chung-Chi Hsiao1, Tun-Wen Pai2

  • 1Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan.

BMC Systems Biology
|May 11, 2018
PubMed
Summary

Integrating differentially expressed long noncoding RNAs (DE lncRNAs) with gene expression data improves biological interpretation. This approach reveals more gene ontology terms and pathways, offering a comprehensive understanding of gene regulation mechanisms.

Keywords:
Differential expressionGene ontology (GO)KEGGNeuron developmentlncRNA

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Differential gene expression (DE) analysis via RNA-seq is key to understanding gene regulation.
  • Traditional enrichment analyses (GO, KEGG) using only DE genes often miss crucial regulatory mechanisms.
  • A comprehensive understanding of gene regulation requires exploring broader gene networks beyond just DE genes.

Purpose of the Study:

  • To enhance functional enrichment analysis by incorporating long noncoding RNA (lncRNA) associated genes.
  • To identify additional gene ontology (GO) terms and KEGG pathways missed by traditional DE gene-only methods.
  • To improve the interpretation of gene regulation mechanisms, particularly those involving survivin (birc5).

Main Methods:

  • RNA-sequencing was performed on zebrafish embryos with survivin (birc5) gene knock-down and wild-type controls.
  • Differential expression (DE) analysis identified DE genes.
  • Genes associated with differentially expressed long noncoding RNAs (DE lncRNAs) were identified and combined with DE genes for comprehensive overrepresentation analysis.

Main Results:

  • A combined list of 638 DE genes and 616 DE lncRNA-associated genes (lncGenes) was analyzed.
  • The integrated approach identified 60% more GO terms and significant KEGG pathways compared to traditional methods.
  • Key pathways like FoxO and MAPK signaling, relevant to apoptosis and neuron development, were identified.

Conclusions:

  • Incorporating DE lncRNA-associated genes into DE gene lists significantly improves biological functional interpretation.
  • This method uncovers hidden interactions between lncRNAs and target genes for more comprehensive analyses.
  • The findings provide a more complete picture of gene regulation networks, especially concerning survivin's role.