ArticleNature biotechnology2014
Detecting and correcting systematic variation in large-scale RNA sequencing data.
Article in Nature biotechnology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
111 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide.Frontiers in bioengineering and biotechnology · 2023Pooled it
- Mining the Archives: A Cross-Platform Analysis of Gene Expression Profiles in Archival Formalin-Fixed Paraffin-Embedded Tissues.Toxicological sciences : an official journal of the Society of Toxicology · 2015Pooled it
- The Vacc-SeqQC project: Benchmarking RNA-Seq for clinical vaccine studies.Frontiers in immunology · 2022Trial
- Benchmarking Q40 sequencing for sensitive and efficient detection of rare genomic variants.Genome biology · 2026Article
- Integration of bulk RNA-seq pipeline metrics for assessing low-quality samples.BMC bioinformatics · 2026Article
- Integrative RNA-Seq and TCGA-BRCA Analyses Highlight the Role of LINC01133 in Triple-Negative Breast Cancer.Biomedicines · 2026Article
- Designing RNA sequencing experiments: A practical guide to reproducible gene expression analysis.Computational and structural biotechnology journal · 2026Review
- Gene expression signatures from whole blood predict amyotrophic lateral sclerosis case status and survival.Nature communications · 2025Article
- Bottlenecks in advancing and applying multiomic data integration-common data resources as rate-limiting drivers-the high-impact use case of atherosclerotic cardiovascular disease.Briefings in bioinformatics · 2025Review
- Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq.Nature communications · 2025Article
- CorrAdjust unveils biologically relevant transcriptomic correlations by efficiently eliminating hidden confounders.Nucleic acids research · 2025Article
- Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq.bioRxiv : the preprint server for biology · 2025Article
- Assessing and mitigating batch effects in large-scale omics studies.Genome biology · 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
- A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials.Nature communications · 2024Article
- Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling.Nature biotechnology · 2024Article
- Transcriptomics : Approaches to Quantifying Gene Expression and Their Application to Studying the Human Brain.Current topics in behavioral neurosciences · 2024Review
- Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method.Genome biology · 2023Article
- RNA-seq data science: From raw data to effective interpretation.Frontiers in genetics · 2023Review
51 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
High-throughput RNA sequencing (RNA-seq) enables comprehensive scans of entire transcriptomes, but best practices for analyzing RNA-seq data have not been fully defined, particularly for data collected with multiple sequencing platforms or at multiple sites. Here we used standardized RNA samples with built-in controls to examine sources of error in large-scale RNA-seq studies and their impact on the detection of differentially expressed genes (DEGs). Analysis of variations in guanine-cytosine content, gene coverage, sequencing error rate and insert size allowed identification of decreased reproducibility across sites. Moreover, commonly used methods for normalization (cqn, EDASeq, RUV2, sva, PEER) varied in their ability to remove these systematic biases, depending on sample complexity and initial data quality. Normalization methods that combine data from genes across sites are strongly recommended to identify and remove site-specific effects and can substantially improve RNA-seq studies.
Indexed as
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.