ArticleCell systems2016
Modeling Enzyme Processivity Reveals that RNA-Seq Libraries Are Biased in Characteristic and Correctable Ways.
Article in Cell systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets.BMC genomics · 2024Review
- Quantifying transcriptome diversity: a review.Briefings in functional genomics · 2024Review
- An optimized workflow of full-length transcriptome sequencing for accurate fusion transcript identification.RNA biology · 2024Article
- Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision.Briefings in bioinformatics · 2021Article
- A Paradox in Bacterial Pathogenesis: Activation of the Local Macrophage Inflammasome Is Required for Virulence ofPathogens (Basel, Switzerland) · 2020Article
- Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing.Cells · 2020Review
- Millefy: visualizing cell-to-cell heterogeneity in read coverage of single-cell RNA sequencing datasets.BMC genomics · 2020Article
- Cellular, transcriptomic and isoform heterogeneity of breast cancer cell line revealed by full-length single-cell RNA sequencing.Computational and structural biotechnology journal · 2020Article
- Article
- Elimination of PCR duplicates in RNA-seq and small RNA-seq using unique molecular identifiers.BMC genomics · 2018Article
- Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs.Nature communications · 2018Article
- An ultraprocessive, accurate reverse transcriptase encoded by a metazoan group II intron.RNA (New York, N.Y.) · 2018Article
- Fingerprints of Modified RNA Bases from Deep Sequencing Profiles.Journal of the American Chemical Society · 2017Article
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4 authors.
Funding
Abstract
Experimental procedures for preparing RNA-seq and single-cell (sc) RNA-seq libraries are based on assumptions regarding their underlying enzymatic reactions. Here, we show that the fairness of these assumptions varies within libraries: coverage by sequencing reads along and between transcripts exhibits characteristic, protocol-dependent biases. To understand the mechanistic basis of this bias, we present an integrated modeling framework that infers the relationship between enzyme reactions during library preparation and the characteristic coverage patterns observed for different protocols. Analysis of new and existing (sc)RNA-seq data from six different library preparation protocols reveals that polymerase processivity is the mechanistic origin of coverage biases. We apply our framework to demonstrate that lowering incubation temperature increases processivity, yield, and (sc)RNA-seq sensitivity in all protocols. We also provide correction factors based on our model for increasing accuracy of transcript quantification in existing samples prepared at standard temperatures. In total, our findings improve our ability to accurately reflect in vivo transcript abundances in (sc)RNA-seq libraries.
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