RT Journal Article
SR Electronic
T1 Accounting for fragments of unexpected origin improves transcript quantification in RNA-seq simulations focused on increased realism
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.01.17.426996
DO 10.1101/2021.01.17.426996
A1 Avi Srivastava
A1 Mohsen Zakeri
A1 Hirak Sarkar
A1 Charlotte Soneson
A1 Carl Kingsford
A1 Rob Patro
YR 2021
UL http://biorxiv.org/content/early/2021/01/19/2021.01.17.426996.abstract
AB Transcript and gene quantification is the first step in many RNA-seq analyses. While many factors and properties of experimental RNA-seq data likely contribute to differences in accuracy between various approaches to quantification, it has been demonstrated (1) that quantification accuracy generally benefits from considering, during alignment, potential genomic origins for sequenced fragments that reside outside of the annotated transcriptome.Recently, Varabyou et al. (2) demonstrated that the presence of transcriptional noise leads to systematic errors in the ability of tools — particularly annotation-based ones — to accurately estimate transcript expression. Here, we confirm the findings of Varabyou et al. (2) using the simulation framework they have provided. Using the same data, we also examine the methodology of Srivastava et al.(1) as implemented in recent versions of salmon (3), and show that it substantially enhances the accuracy of annotation-based transcript quantification in these data.Competing Interest StatementCK and RP are co-founders of Ocean Genomics Inc.