ArticleScientific reports2020
Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 papers, 3 of them syntheses that pooled it.
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Who cites it
132 citing papers in PubMed, 3 syntheses or guidelines pooled it.
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- Non-Thermal Effects of Terahertz Radiation on Gene Expression: Systematic Review and Meta-Analysis.Genes · 2024Pooled it
- Meta-analysis towards FSHD reveals misregulation of neuromuscular junction, nuclear envelope, and spliceosome.Communications biology · 2024Pooled it
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- Deciphering Cell-Type-Specific Transcriptional Regulation in Tomato Leaves Through Ensemble Machine Learning and Single-Cell Transcriptomics.Plants (Basel, Switzerland) · 2026Article
- Article
- USADAE: a deep learning approach to disentangle hidden covariates in RNA-seq data.Briefings in bioinformatics · 2026Article
- Confidence: a web app for cross-platform differential gene expression analysis, gene scoring, and enrichment analysis.Scientific reports · 2026Article
- The ubiquitin receptor StDA1 negatively regulates aurantio-obtusin biosynthesis by mediating the degradation of the StHDR1 protein in Senna tora seeds.Communications biology · 2026Article
- A Simple and Easy Method for RNA Extraction from the CyanobacteriumBio-protocol · 2026Article
- Microarray-Based Genomic Profiling in Low-Dose Radiation Research: Evidence, Limitations, and Translational Perspectives.International journal of molecular sciences · 2026Review
- Deep learning-based non-invasive profiling of tumor transcriptomes from cell-free DNA for precision oncology.bioRxiv : the preprint server for biology · 2026Article
- Identification of differentially expressed genes in RNA-seq data via semi-rigid orthogonal sparse KL-NMTF.BMC bioinformatics · 2026Article
- Optimizing bioinformatic workflows to extract clinically usable gene expression data from targeted tumor RNA sequencing panels: comparison with total RNA-seq in cancer samples.Bioinformatics advances · 2026Article
- A dual approach to evaluate the performance of RNA-Seq data analysis pipelines with weak signals.Bioinformatics advances · 2026Article
- Transcriptomics in Space: A Basic R Pipeline for Analyzing Bulk RNA-Sequencing Data.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Revisiting differential expression analysis: An updated six-dimensional comparative study.PloS one · 2026Article
72 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA-seq is currently considered the most powerful, robust and adaptable technique for measuring gene expression and transcription activation at genome-wide level. As the analysis of RNA-seq data is complex, it has prompted a large amount of research on algorithms and methods. This has resulted in a substantial increase in the number of options available at each step of the analysis. Consequently, there is no clear consensus about the most appropriate algorithms and pipelines that should be used to analyse RNA-seq data. In the present study, 192 pipelines using alternative methods were applied to 18 samples from two human cell lines and the performance of the results was evaluated. Raw gene expression signal was quantified by non-parametric statistics to measure precision and accuracy. Differential gene expression performance was estimated by testing 17 differential expression methods. The procedures were validated by qRT-PCR in the same samples. This study weighs up the advantages and disadvantages of the tested algorithms and pipelines providing a comprehensive guide to the different methods and procedures applied to the analysis of RNA-seq data, both for the quantification of the raw expression signal and for the differential gene expression.
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