ArticleScientific reports2020
Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction.
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 21 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
21 citing papers in PubMed, 44 citations in OpenAlex.
- YBX1 Expression Marks Proliferative Tumour States with Context-Dependent Genomic Instability: A Pan-Cancer Analysis.International journal of molecular sciences · 2026Article
- Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends.Biosensors · 2025Review
- Artificial intelligence-driven circRNA vaccine development: multimodal collaborative optimization and a new paradigm for biomedical applications.Briefings in bioinformatics · 2025Article
- Transcriptomic Profiling Provides Insight into the Molecular Basis of Heterosis in Philippine-RearedInsects · 2025Article
- Exploring RNA-Seq Data Analysis Through Visualization Techniques and Tools: A Systematic Review of Opportunities and Limitations for Clinical Applications.Bioengineering (Basel, Switzerland) · 2025Review
- Normalization and Selecting Non-Differentially Expressed Genes Improve Machine Learning Modelling of Cross-Platform Transcriptomic Data.Transactions on artificial intelligence · 2025Article
- Investigating the overlap of machine learning algorithms in the final results of RNA-seq analysis on gene expression estimation.Health information science and systems · 2024Article
- Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review.Computational and structural biotechnology journal · 2024Review
- Targeted DNA-seq and RNA-seq of Reference Samples with Short-read and Long-read Sequencing.Scientific data · 2024Article
- Genomic reproducibility in the bioinformatics era.Genome biology · 2024Review
- Potential role of short-chain fatty acids in the pathogenesis and management of acute lymphocytic leukemia.Annals of translational medicine · 2024Review
- A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials.Nature communications · 2024Article
- An analysis pipeline for understanding 6-thioguanine effects on a mouse tumour genome.Cancer immunology, immunotherapy : CII · 2024Article
- Ambiguous genes due to aligners and their impact on RNA-seq data analysis.Scientific reports · 2023Article
- Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq.Advanced genetics (Hoboken, N.J.) · 2023Review
- Druggable transcriptomic pathways revealed in Parkinson's patient-derived midbrain neurons.NPJ Parkinson's disease · 2022Article
- Emerging technologies and their impact on regulatory science.Experimental biology and medicine (Maywood, N.J.) · 2022Review
- RNA Sequencing Data from Human Intracranial Aneurysm Tissue Reveals a Complex Inflammatory Environment Associated with Rupture.Molecular diagnosis & therapy · 2021Article
- Article
- Comprehensive Assessments of RNA-seq by the SEQC Consortium: FDA-Led Efforts Advance Precision Medicine.Pharmaceutics · 2016Review
Corrections and comments
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Authors and funding
7 authors at 15 institutions in 6 countries.
Funding
Abstract
To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline's performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.
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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.