ArticleBMC genomics2011
RNA-seq: technical variability and sampling.
Article in BMC genomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 163 papers, 1 of them a synthesis that pooled it.
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Who cites it
163 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Temporal dynamics of chicken host's molecular response against Fowl adenovirus serotype 8b infection via RNA-sequencing.BMC genomics · 2025Article
- An Investigation of TDA1 Deficiency in Saccharomyces cerevisiae During Diauxic Growth.Yeast (Chichester, England) · 2025Article
- Effects of Chronic Alcohol Intake on the Composition of the Ensemble of Drug-Metabolizing Enzymes and Transporters in the Human Liver.Journal of xenobiotics · 2025Article
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- RanBALL: An Ensemble Random Projection Model for Identifying Subtypes of B-Cell Acute Lymphoblastic Leukemia.bioRxiv : the preprint server for biology · 2025Article
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- Relationship of quantitative reverse transcription polymerase chain reaction (RT-PCR) to RNA Sequencing (RNAseq) transcriptome identifies mouse preimplantation embryo reference genes†.Biology of reproduction · 2023Article
- Spatial transcriptomics reveals novel genes during the remodelling of the embryonic human arterial valves.PLoS genetics · 2023Article
- Shifts in isoform usage underlie transcriptional differences in regulatory T cells in type 1 diabetes.Communications biology · 2023Article
- MyBrain-Seq: A Pipeline for MiRNA-Seq Data Analysis in Neuropsychiatric Disorders.Biomedicines · 2023Article
- Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
103 more citing papers are in PubMed but not listed here.
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7 authors.
Funding
Abstract
backgroundRNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript.
resultsIn this study three independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage.
conclusionsTechnical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.
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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.