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Systematic Identification of Non-coding RNAs.

Yun Xiao1, Jing Hu2, Wenkang Yin2

  • 1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China. xiaoyun@ems.hrbmu.edu.cn.

Advances in Experimental Medicine and Biology
|September 8, 2018
PubMed
Summary
This summary is machine-generated.

This study presents pipelines for identifying microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) using next-generation sequencing. The research also details lncRNA re-annotation from microarray data and transcriptome construction for heart failure studies.

Keywords:
AnnotationExpression quantificationIdentification pipelineNon-coding RNATranscriptome

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Non-coding RNAs (ncRNAs) regulate gene expression at transcriptional and post-transcriptional levels.
  • MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are key ncRNA classes involved in biological processes and diseases.
  • Accurate identification of ncRNAs is crucial for understanding their functions and mechanisms.

Purpose of the Study:

  • To overview pipelines for identifying miRNAs and lncRNAs using next-generation sequencing (NGS) technologies.
  • To present methods for miRNA expression quantification and lncRNA re-annotation from microarray data.
  • To construct a comprehensive transcriptome for heart failure research.

Main Methods:

  • Application of NGS-based pipelines for miRNA identification and expression profiling (mature, precursor, primary).
  • Development of an alternative method for re-annotating lncRNAs from existing microarray data.
  • Utilization of RNA-seq and miRNA-seq data to build a integrated transcriptome.

Main Results:

  • Successful identification and expression quantification of miRNAs across multiple cell lines.
  • Established a comprehensive resource for lncRNA annotation, including comparison of databases and parameters.
  • Constructed a detailed transcriptome encompassing miRNAs, lncRNAs, and protein-coding genes in heart failure.

Conclusions:

  • The developed pipelines facilitate robust identification and analysis of miRNAs and lncRNAs.
  • The study provides valuable insights into ncRNA roles in biological processes and disease contexts like heart failure.
  • Integrated transcriptome data aids in dissecting complex gene regulatory networks in disease states.