The impact of RNA-seq aligners on gene expression estimation

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• RNA-sequencing (RNA-seq) is crucial for gene expression analysis.
• Accurate gene expression estimation relies heavily on precise sequence alignment.
• The influence of different aligners on RNA-seq quantification accuracy is not well understood.

Purpose of the Study:

• To investigate the impact of various spliced aligners on gene expression estimation accuracy in RNA-seq data.
• To establish correlations between alignment performance metrics and gene expression estimation outcomes.
• To identify reliable metrics for assessing RNA-seq analysis pipeline performance.

Main Methods:

• Constructed nine RNA-seq analysis pipelines using nine spliced aligners and one quantifier.
• Utilized simulated RNA-seq data for controlled experimental conditions.
• Evaluated alignment using percentage of reads aligned, ZeroMismatchPercentage, and ZeroOneMismatchPercentage.
• Assessed gene expression estimation using gene detection accuracy, FalseExpNum, and FalseFcNum.

Main Results:

• A significant correlation was observed between the number of falsely quantified genes (FalseExpNum) and falsely estimated fold changes (FalseFcNum).
• FalseExpNum demonstrated a linear correlation with the percentage of reads aligned and ZeroMismatchPercentage.
• FalseFcNum showed a linear correlation with ZeroMismatchPercentage, indicating its utility in predicting estimation errors.

Conclusions:

• The percentage of reads aligned and ZeroMismatchPercentage are valuable metrics for predicting gene expression estimation performance in RNA-seq.
• These alignment metrics can serve as indicators to assess the reliability of RNA-seq analysis pipelines.
• Understanding aligner impact is critical for improving the accuracy and reproducibility of RNA-seq studies.