ArticleNucleic acids research2017
Modeling bias and variation in the stochastic processes of small RNA sequencing.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Article
- A heteroskedastic model of Park Grass spring hay yields in response to weather suggests continuing yield decline with climate change in future decades.Journal of the Royal Society, Interface · 2022Article
- Article
- Role of MicroRNAs in Renal Parenchymal Diseases-A New Dimension.International journal of molecular sciences · 2018Review
- Study protocol: rationale and design of the community-based prospective cohort study of kidney function and diabetes in rural New Mexico, the COMPASS study.BMC nephrology · 2018Article
- Fingerprints of Modified RNA Bases from Deep Sequencing Profiles.Journal of the American Chemical Society · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The use of RNA-seq as the preferred method for the discovery and validation of small RNA biomarkers has been hindered by high quantitative variability and biased sequence counts. In this paper we develop a statistical model for sequence counts that accounts for ligase bias and stochastic variation in sequence counts. This model implies a linear quadratic relation between the mean and variance of sequence counts. Using a large number of sequencing datasets, we demonstrate how one can use the generalized additive models for location, scale and shape (GAMLSS) distributional regression framework to calculate and apply empirical correction factors for ligase bias. Bias correction could remove more than 40% of the bias for miRNAs. Empirical bias correction factors appear to be nearly constant over at least one and up to four orders of magnitude of total RNA input and independent of sample composition. Using synthetic mixes of known composition, we show that the GAMLSS approach can analyze differential expression with greater accuracy, higher sensitivity and specificity than six existing algorithms (DESeq2, edgeR, EBSeq, limma, DSS, voom) for the analysis of small RNA-seq data.
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Registered trials
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