ArticleBioinformatics (Oxford, England)2011
Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers, 1 of them a synthesis that pooled it.
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Who cites it
83 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq.Advanced genetics (Hoboken, N.J.) · 2023Review
- 3' RNA-seq is superior to standard RNA-seq in cases of sparse data but inferior at identifying toxicity pathways in a model organism.Frontiers in bioinformatics · 2023Article
- Effect of chronic intermittent ethanol vapor exposure on RNA content of brain-derived extracellular vesicles.Alcohol (Fayetteville, N.Y.) · 2022Article
- Modeling and cleaning RNA-seq data significantly improve detection of differentially expressed genes.BMC bioinformatics · 2022Article
- Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data.Genome biology · 2022Article
- An integrated analysis of mRNAs, lncRNAs, and miRNAs based on weighted gene co-expression network analysis involved in bovine endometritis.Scientific reports · 2021Article
- Super-enhancer-associated long noncoding RNA RP11-569A11.1 inhibited cell progression and metastasis by regulating IFIT2 in colorectal cancer.Journal of clinical laboratory analysis · 2021Article
- Circular RNAs and Cardiovascular Regeneration.Frontiers in cardiovascular medicine · 2021Review
- Long Non-coding RNA Expression Profiling Using Arraystar LncRNA Microarrays.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Comparative Transcriptome Analysis of Milk Somatic Cells During Lactation Between Two Intensively Reared Dairy Sheep Breeds.Frontiers in genetics · 2021Article
- A computational pipeline to infer alternative poly-adenylation from 3' sequencing data.Methods in enzymology · 2021Article
- Network of clinically-relevant lncRNAs-mRNAs associated with prognosis of hepatocellular carcinoma patients.Scientific reports · 2020Article
- AP-TSS: A New Method for the Analysis of RNA Expression from Particular and Challenging Transcription Start Sites.Biomolecules · 2020Article
- Systematic benchmarking of omics computational tools.Nature communications · 2019Review
- Early Transcriptional Landscapes ofFrontiers in cellular and infection microbiology · 2019Article
- Gene co-expression network analysis reveals coordinated regulation of three characteristic secondary biosynthetic pathways in tea plant (Camellia sinensis).BMC genomics · 2018Article
- TDP-43 regulates the alternative splicing of hnRNP A1 to yield an aggregation-prone variant in amyotrophic lateral sclerosis.Brain : a journal of neurology · 2018Article
23 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
motivationMeasurement precision determines the power of any analysis to reliably identify significant signals, such as in screens for differential expression, independent of whether the experimental design incorporates replicates or not. With the compilation of large-scale RNA-Seq datasets with technical replicate samples, however, we can now, for the first time, perform a systematic analysis of the precision of expression level estimates from massively parallel sequencing technology. This then allows considerations for its improvement by computational or experimental means.
resultsWe report on a comprehensive study of target identification and measurement precision, including their dependence on transcript expression levels, read depth and other parameters. In particular, an impressive recall of 84% of the estimated true transcript population could be achieved with 331 million 50 bp reads, with diminishing returns from longer read lengths and even less gains from increased sequencing depths. Most of the measurement power (75%) is spent on only 7% of the known transcriptome, however, making less strongly expressed transcripts harder to measure. Consequently, <30% of all transcripts could be quantified reliably with a relative error<20%. Based on established tools, we then introduce a new approach for mapping and analysing sequencing reads that yields substantially improved performance in gene expression profiling, increasing the number of transcripts that can reliably be quantified to over 40%. Extrapolations to higher sequencing depths highlight the need for efficient complementary steps. In discussion we outline possible experimental and computational strategies for further improvements in quantification precision. CONTACT: rnaseq10@boku.ac.at
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