ArticleGenetics2010
Statistical design and analysis of RNA sequencing data.
Article in Genetics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 200 papers, 3 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
200 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Drought-responsive genes in tomato: meta-analysis of gene expression using machine learning.Scientific reports · 2023Pooled it
- Here We Are, But Where Do We Go? A Systematic Review of Crustacean Transcriptomic Studies from 2014-2015.Integrative and comparative biology · 2016Pooled it
- Meta-analysis and meta-regression of transcriptomic responses to water stress in Arabidopsis.The Plant journal : for cell and molecular biology · 2016Pooled it
- Tau Ablation Rewires Brain Cell Programs in Health and Restores Function in Disease.bioRxiv : the preprint server for biology · 2026Article
- Mild Warming Induces Divergent Plastic Responses in Gene Expression Among Populations of a Temperate Butterfly.Molecular ecology · 2026Article
- Microgravity effects on secondary metabolism of plant-affecting bacteria.Microbiology spectrum · 2026Article
- Integration of bulk RNA-seq pipeline metrics for assessing low-quality samples.BMC bioinformatics · 2026Article
- The Interplay of Ontogeny and Phylogeny at the Transcriptome Level of the Tetrapod Heart.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2025Article
- Identification and correction of time-series transcriptomic anomalies.Nucleic acids research · 2025Article
- Variation in bulk RNA-seq and estimated cell type proportion using deconvolution when comparing pancreatic cancer samples within the same individual.medRxiv : the preprint server for health sciences · 2025Article
- Quality assessment of long read data in multisample lrRNA-seq experiments using SQANTI-reads.Genome research · 2025Article
- Unveiling Tissue-Specific RNA Landscapes in Mouse Organs During Fasting and Feeding Using Nanopore Direct RNA Sequencing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Bulk RNA-seq deconvolution heterogeneity across paired pancreatic cancer human samples.Frontiers in genetics · 2025Article
- Telmisartan modulates exercise responses in peripheral artery disease: Analyses of skeletal muscle from the TELEX Trial.JVS-vascular science · 2025Article
- Comparison of direct cDNA and PCR-cDNA Nanopore sequencing of RNA fromMicrobial genomics · 2024Article
- SQANTI-reads: a tool for the quality assessment of long read data in multi-sample lrRNA-seq experiments.bioRxiv : the preprint server for biology · 2024Article
- Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasets.Genome biology · 2024Article
- The Clinical Utility of lncRNAs and Their Application as Molecular Biomarkers in Breast Cancer.International journal of molecular sciences · 2023Review
- CT Image-Based Biopsy to Aid Prediction of HOPX Expression Status and Prognosis for Non-Small Cell Lung Cancer Patients.Cancers · 2023Article
- Article
140 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Next-generation sequencing technologies are quickly becoming the preferred approach for characterizing and quantifying entire genomes. Even though data produced from these technologies are proving to be the most informative of any thus far, very little attention has been paid to fundamental design aspects of data collection and analysis, namely sampling, randomization, replication, and blocking. We discuss these concepts in an RNA sequencing framework. Using simulations we demonstrate the benefits of collecting replicated RNA sequencing data according to well known statistical designs that partition the sources of biological and technical variation. Examples of these designs and their corresponding models are presented with the goal of testing differential expression.
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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.