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How tool combinations in different pipeline versions affect the outcome in RNA-seq analysis
Louisa Wessels Perelo1, Gisela Gabernet1, Daniel Straub1
1Quantitative Biology Center (QBiC), University of Tübingen, Otfried-Müller-Str. 37, 72076 Tübingen, Baden-Württemberg, 72076, Germany.
NAR Genomics and Bioinformatics
|March 8, 2024
Summary
Comparing RNA-seq analysis tools is crucial for consistent results. Different nf-core/rnaseq pipeline settings showed high gene classification overlap, but biases in low-concentration genes and gene isoforms were observed.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Data analysis tools, including RNA sequencing (RNA-seq) pipelines, are frequently updated.
- Changes in these tools can impact the comparability of results over time.
- Ensuring consistency in long-term transcriptomic studies is essential for reliable data interpretation.
Purpose of the Study:
- To evaluate the impact of different workflow options within the nf-core/rnaseq pipeline on data comparability.
- To compare the performance of various bioinformatics tools and pipeline versions for RNA-seq analysis.
- To identify factors influencing differential gene expression analysis outcomes.
Main Methods:
- Five distinct nf-core/rnaseq pipeline configurations were tested: STAR+Salmon, STAR+RSEM, STAR+featureCounts, HISAT2+featureCounts, and pseudoaligner Salmon.
- These configurations were applied to three diverse biological datasets: human, Arabidopsis, and zebrafish, all including External RNA Control Consortium (ERCC) spike-ins.
- Comparative analyses focused on fold change ratios and differential expression of both endogenous genes and spike-ins.
Main Results:
- An 85% overlap in differential gene classification was observed across the tested pipeline settings.
- Genes identified with a bias were predominantly those with lower expression concentrations.
- The number of gene isoforms and exons significantly influenced analysis outcomes, with previous featureCounts versions showing higher sensitivity for single-isoform genes like ERCC spike-ins.
Conclusions:
- While high concordance exists, variations in pipeline settings can introduce biases, particularly for lowly expressed genes and genes with complex isoform structures.
- Maintaining consistency in pipeline versions is recommended for long-term RNA-seq studies to ensure data comparability.
- During transitions to new pipeline versions, running both old and new versions concurrently is advised to validate gene targeting consistency.
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