» Articles » PMID: 39975309

Sources of Non-uniform Coverage in Short-read RNA-Seq Data

Overview
Journal bioRxiv
Date 2025 Feb 20
PMID 39975309
Authors
Affiliations
Soon will be listed here.
Abstract

The origin of several normal cellular functions and related abnormalities can be traced back to RNA splicing. As such, RNA splicing is currently the focus of a vast array of studies. To quantify the transcriptome, short-read RNA-Seq remains the standard assay. The primary technical artifact of RNASeq library prep, which severely interferes with analysis, is extreme non-uniformity in coverage across transcripts. This non-uniformity is present in both bulk and single-cell RNA-Seq and is observed even when the sample contains only full-length transcripts. This issue dramatically affects the accuracy of isoform-level quantification of multi-isoform genes. Understanding the sources of this non-uniformity is critical to developing improved protocols and analysis methods. Here, we explore eight potential sources of non-uniformity. We demonstrate that it cannot be explained by one factor alone. We performed targeted experiments to investigate the effect of fragment length, PCR ramp rate, and ribosomal depletion. We assessed existing data sets with varying sample quality, PCR cycle number, reverse transcriptase, and technical or biological replicates. We found evidence that interference of reverse transcription by secondary structure is unlikely to be the major contributing factor, that rRNA pull-down methods do not cause non-uniformity, that PCR ramp rate does not substantially impact non-uniformity, and that shorter fragments do not reduce non-uniformity. All these findings contradict prior publications or recommendations.

References
1.
Kivioja T, Vaharautio A, Karlsson K, Bonke M, Enge M, Linnarsson S . Counting absolute numbers of molecules using unique molecular identifiers. Nat Methods. 2011; 9(1):72-4. DOI: 10.1038/nmeth.1778. View

2.
Lorenz R, Bernhart S, Honer Zu Siederdissen C, Tafer H, Flamm C, Stadler P . ViennaRNA Package 2.0. Algorithms Mol Biol. 2011; 6:26. PMC: 3319429. DOI: 10.1186/1748-7188-6-26. View

3.
Xu H, Yao J, Wu D, Lambowitz A . Improved TGIRT-seq methods for comprehensive transcriptome profiling with decreased adapter dimer formation and bias correction. Sci Rep. 2019; 9(1):7953. PMC: 6538698. DOI: 10.1038/s41598-019-44457-z. View

4.
Hu Y, Liu Y, Mao X, Jia C, Ferguson J, Xue C . PennSeq: accurate isoform-specific gene expression quantification in RNA-Seq by modeling non-uniform read distribution. Nucleic Acids Res. 2013; 42(3):e20. PMC: 3919567. DOI: 10.1093/nar/gkt1304. View

5.
Love M, Hogenesch J, Irizarry R . Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation. Nat Biotechnol. 2016; 34(12):1287-1291. PMC: 5143225. DOI: 10.1038/nbt.3682. View