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Related Concept Videos

RNA-seq03:21

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A comparison of transcriptome analysis methods with reference genome.

Xu Liu1,2, Jialu Zhao1,2,3,4, Liting Xue1,2

  • 1Department of Medical Genetics and Developmental Biology, School of Basic Medical Sciences, Capital Medical University, Beijing, China.

BMC Genomics
|March 26, 2022
PubMed
Summary

Selecting RNA-seq analysis workflows depends on computational resources and research focus. Gene expression results are highly correlated, especially for medium abundance genes, but differ for high/low expression levels.

Keywords:
BallgownCuffdiffDESeq2Differentially expressed analysisDifferentially expressed genes (DEGs)RNA-seqSleuthTranscriptome data analysis

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Area of Science:

  • Genomics
  • Bioinformatics
  • Transcriptomics

Background:

  • RNA sequencing (RNA-seq) is widely used, with numerous analysis workflows available.
  • Choosing the optimal RNA-seq analysis pipeline is crucial for researchers.

Purpose of the Study:

  • To compare the performance of six popular RNA-seq analysis pipelines.
  • To evaluate similarities and differences in gene expression quantification and differential expression analysis.

Main Methods:

  • Six RNA-seq analysis pipelines were tested on four datasets (mouse, human, rat, macaque).
  • Gene expression values, fold changes, and statistical significance were assessed.
  • Quantitative real-time PCR (qRT-PCR) was used for validation of differentially expressed genes (DEGs).

Main Results:

  • HTseq-based pipelines showed high correlation in results.
  • Differences in expression values were most pronounced for high and low abundance genes.
  • HISAT2-StringTie-Ballgown was sensitive to low expression genes; Kallisto-Sleuth suited medium-to-high abundance genes.
  • StringTie-Ballgown yielded fewer DEGs, while HTseq-based methods yielded more DEGs.
  • Biological validation rates for medium abundance DEGs were similar across pipelines.

Conclusions:

  • RNA-seq analysis procedure selection can be guided by computational resources and interest in specific gene expression levels.
  • Using multiple pipelines can enhance reliability or provide comprehensive DE profiles.
  • HTseq-based quantification offers consistent results for RNA-seq data analysis.