ArticleNAR genomics and bioinformatics2024
How tool combinations in different pipeline versions affect the outcome in RNA-seq analysis.
Article in NAR genomics and bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- metaFun: An analysis pipeline for metagenomic big data with fast and unified functional searches.Gut microbes · 2026Article
- RNA-SeqEZPZ: a point-and-click pipeline for comprehensive transcriptomics analysis with interactive visualizations.GigaScience · 2026Article
- How far are we from the era of big data in transcriptomics? Lessons from the bacterial data in GEO.Briefings in bioinformatics · 2025Review
- Identification and validation of hub genes for kidney renal clear cell carcinoma treated with metformin and everolimus combination therapy.Translational cancer research · 2025Article
- Treatment-Induced Gene Expression Changes in Metastatic Renal Cell Carcinoma: Insights from a Syngeneic Mouse Model.Current oncology (Toronto, Ont.) · 2025Article
- Transcriptomic insights into the synergistic effects of darkness and mechanical stimulation on peanut pod development.BMC plant biology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data analysis tools are continuously changed and improved over time. In order to test how these changes influence the comparability between analyses, the output of different workflow options of the nf-core/rnaseq pipeline were compared. Five different pipeline settings (STAR+Salmon, STAR+RSEM, STAR+featureCounts, HISAT2+featureCounts, pseudoaligner Salmon) were run on three datasets (human, Arabidopsis, zebrafish) containing spike-ins of the External RNA Control Consortium (ERCC). Fold change ratios and differential expression of genes and spike-ins were used for comparative analyses of the different tools and versions settings of the pipeline. An overlap of 85% for differential gene classification between pipelines could be shown. Genes interpreted with a bias were mostly those present at lower concentration. Also, the number of isoforms and exons per gene were determinants. Previous pipeline versions using featureCounts showed a higher sensitivity to detect one-isoform genes like ERCC. To ensure data comparability in long-term analysis series it would be recommendable to either stay with the pipeline version the series was initialized with or to run both versions during a transition time in order to ensure that the target genes are addressed the same way.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.