PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 19, 2020BMC Bioinformatics24 citationsOpen Access

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

AFAdam H. FreedmanJGJohn M. GasparTSTimothy B. Sackton

Key Points

Key points are not available for this paper at this time.

Abstract

BACKGROUND: Typical 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. RESULTS: At both the transcript and gene levels, 2 × 40 paired-end reads unequivocally provide expression estimates that are more highly correlated with 2 × 125 than 1 × 75 reads; in nearly all cases, those correlations are also greater than for 1 × 125, despite the greater total number of sequenced bases for the latter. Across an array of metrics, differential expression tests based upon 2 × 40 consistently outperform those using 1 × 75. CONCLUSION: Researchers 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.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Freedman et al. (2020) studied this question.

synapsesocial.com/papers/6a86b5cacbc6304817beea70https://doi.org/10.1186/s12859-020-3484-z
Ask AI
Helpful
Bookmark
Share
View Full Paper