Sign in
BioinformaticsBMC Bioinformatics
Peer-reviewed journal article

Short paired-end reads trump long single-end reads for expression analysis

Adam H. FreedmaniD, John M. GaspariD, Timothy B. SacktoniD

Abstract

BackgroundTypical experimental design advice for expression analyses using RNA-seq generally assumes that single-end reads provide robust gene-level expression estimates in a cost-effective manner, and that the additional benefits obtained from paired-end sequencing are not worth the additional cost. However, in many cases (e.g., with Illumina NextSeq and NovaSeq instruments), shorter paired-end reads and longer single-end reads can be generated for the same cost, and it is not obvious which strategy should be preferred. Using publicly available data, we test whether short-paired end reads can achieve more robust expression estimates and differential expression results than single-end reads of approximately the same total number of sequenced bases. ResultsAt both the transcript and gene levels, 2x40 paired-end reads unequivocally provide expression estimates that are more highly correlated with 2x125 than 1x75 reads; in nearly all cases, those correlations are also greater than for 1x125, despite the greater total number of sequenced bases for the latter. Across an array of metrics, differential expression tests based upon 2x40 consistently outperform those using 1x75. ConclusionResearchers seeking a cost-effective approach for gene-level expression analysis should prefer short paired-end reads over a longer single-end strategy. Short paired-end reads will also give reasonably robust expression estimates and differential expression results at the isoform level.

02 /Specification & Classification

Click any tag to browse matching papers
Citations
21
FWCI
0.97
Field percentile
top 29%

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISShetty '19Fonseca '14Hsu '22Shi '20Križanović '17Han '24
67% semantic matchGenomics
Amol Carl Shetty, John Mattick, Matthew C. Chung+7 more·Microbial Genomics·Dec 18, 2019· 1 citations