RNA-Seq gene profiling--a systematic empirical comparison.

Area of Science:

• Genomics
• Bioinformatics
• Transcriptomics

Background:

• Accurate gene expression quantification is crucial for RNA-sequencing (RNA-seq) experiments.
• RNA-seq analysis involves aligning short reads to a reference genome or transcriptome and quantifying expression levels.
• Variations in alignment and quantification tools can substantially alter expression results and biological interpretations.

Purpose of the Study:

• To investigate whether different RNA-seq analysis pipelines affect inferred gene expression levels.
• To assess the accuracy of inferred expression levels compared to true quantification values.
• To identify factors contributing to discrepancies in gene expression quantification.

Main Methods:

• Evaluation of fifty gene profiling pipelines using both experimental and simulated RNA-sequencing datasets.
• Utilized simulated data to compare inferred expression levels against known 'ground truth' values.
• Analyzed datasets with varying characteristics, including read length and sequencing depth.

Main Results:

• High correlation observed between expression levels from optimal pipelines and true quantification values.
• Significant variability in the error of estimated gene expression across different genes.
• Identification of a subset of genes with consistently high expression estimation errors across datasets and methods.
• Quantification methods demonstrated a greater impact on results than mapping software.

Conclusions:

• RNA-sequencing analysis pipeline choice critically influences gene expression quantification accuracy.
• Quantification methods are more impactful than mapping tools in determining expression level accuracy.
• Understanding pipeline-specific biases is essential for reliable biological interpretation of RNA-seq data.