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Related Concept Videos

RNA-seq03:21

RNA-seq

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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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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Automated identification of reference genes based on RNA-seq data.

Rosario Carmona1, Macarena Arroyo2, María José Jiménez-Quesada1

  • 1Plant Reproductive Biology Laboratory, Department of Biochemistry, Cell and Molecular Biology of Plants, Estación Experimental del Zaidín, CSIC, Granada, Spain.

Biomedical Engineering Online
|August 24, 2017
PubMed
Summary

This study introduces an automated workflow for identifying reliable reference genes (RGs) from RNA-sequencing data. The method ensures accurate gene expression normalization across various species and experimental conditions.

Keywords:
CancerNormalizationOlive (Olea europaea L.)Quantitative PCRReal-time PCRReference genes

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Accurate gene expression analysis relies on appropriate reference genes (RGs) for normalization.
  • Traditional housekeeping genes may not maintain invariant expression across all conditions.
  • RNA-sequencing (RNA-seq) offers a rich source for identifying novel candidate RGs.

Purpose of the Study:

  • To develop and validate an automated workflow for discovering highly and invariantly expressed reference genes.
  • To leverage RNA-seq data for RG identification across diverse species and experimental settings.

Main Methods:

  • An automated bioinformatics workflow was designed using mapped next-generation sequencing (NGS) reads.
  • Normalization was based on reads per mapped million, and gene expression stability was assessed using the coefficient of variation.
  • The workflow was tested on RNA-seq data from olive tree, Arabidopsis thaliana, and human cancer samples.

Main Results:

  • The workflow successfully identified candidate RGs for each tested species and condition.
  • Many proposed RGs were previously validated in scientific literature.
  • Experimental validation confirmed the suitability of selected RGs in olive tree.

Conclusions:

  • The developed workflow effectively extracts suitable RGs for PCR validation from RNA-seq data, irrespective of sequencing technology or experimental design.
  • Different subsets of experimental conditions can yield distinct sets of optimal RGs, highlighting the need for condition-specific validation.