Sequencing Saturation Does Not Uniquely Determine Molecular Recovery in UMI Transcriptomics

Clicks: 5
ID: 324392
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #195 of 821 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 821 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Motivation Accurate sequencing depth planning for UMI-based transcriptomic experiments currently relies on heuristic metrics such as reads per cell and sequencing saturation. However, sequencing saturation cannot uniquely determine molecular recovery because the relationship between these quantities depends on amplification heterogeneity. Results Here we present NB-Lib, a modeling framework based on a zero-truncated negative binomial representation of reads per molecule that jointly estimates amplification heterogeneity and library complexity from transcriptomic libraries. Across 150 single-cell and spatial transcriptomic datasets, NB-Lib accurately reconstructs sequencing saturation curves and predicts sequencing depth requirements from shallow pilot sequencing experiments. We show that amplification heterogeneity and library complexity define a compact parameterization linking sequencing depth, saturation, and molecular recovery across transcriptomic platforms and explain substantial variation in sequencing cost and recovery between samples and technologies. Finally, we demonstrate that molecular recovery directly determines the reproducibility of low-abundance gene detection. Together, these results establish a unified framework for interpreting sequencing saturation, molecular recovery, and sequencing efficiency across UMI-based transcriptomic technologies. Availability The scdepth package is available at https://github.com/gwlab-ca/scdepth . Code needed to reproduce the analyses presented in this work is available at https://github.com/gwlab-ca/scdepth_manuscript . A selection of raw data and all the post-processed data is available at https://doi.org/10.5281/zenodo.15518941. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7201978035 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Gavin W. Wilson, Sangeetha N Kalimuthu, Jonathan Yeung
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag593
URL
https://doi.org/10.1093/bioinformatics/btag593
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.