Evidence map›Paper›PMID 35143654›Full record

ArticleGenetics2022

Single-cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis.

Hope M Healey, Susan Bassham, William A Cresko

Abstract read
In one paragraph

Article in Genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Advances in single-cell long-read sequencing technologies.NAR genomics and bioinformatics · 2024
    Article
  10. A machine-readable specification for genomics assays.Bioinformatics (Oxford, England) · 2024
    Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hope M HealeyInstitute of Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA.ORCID 0000-0001-9978-1553
Susan BasshamInstitute of Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA.ORCID 0000-0002-7309-2095
William A CreskoInstitute of Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA.ORCID 0000-0002-3496-8074

Funding

Graduate Training in Genetics Administrative Supplement - Career Curriculum DevelopmentT32GM007413 · NIGMS · UNIVERSITY OF OREGON · PI GUILLEMIN, KAREN J · 1985 to 2022
$7.8M
NIGMS NIH HHS T32 GM007413
6 · The paper itself

Abstract

Single-cell RNA sequencing is a powerful technique that continues to expand across various biological applications. However, incomplete 3'-UTR annotations can impede single-cell analysis resulting in genes that are partially or completely uncounted. Performing single-cell RNA sequencing with incomplete 3'-UTR annotations can hinder the identification of cell identities and gene expression patterns and lead to erroneous biological inferences. We demonstrate that performing single-cell isoform sequencing in tandem with single-cell RNA sequencing can rapidly improve 3'-UTR annotations. Using threespine stickleback fish (Gasterosteus aculeatus), we show that gene models resulting from a minimal embryonic single-cell isoform sequencing dataset retained 26.1% greater single-cell RNA sequencing reads than gene models from Ensembl alone. Furthermore, pooling our single-cell sequencing isoforms with a previously published adult bulk Iso-Seq dataset from stickleback, and merging the annotation with the Ensembl gene models, resulted in a marginal improvement (+0.8%) over the single-cell isoform sequencing only dataset. In addition, isoforms identified by single-cell isoform sequencing included thousands of new splicing variants. The improved gene models obtained using single-cell isoform sequencing led to successful identification of cell types and increased the reads identified of many genes in our single-cell RNA sequencing stickleback dataset. Our work illuminates single-cell isoform sequencing as a cost-effective and efficient mechanism to rapidly annotate genomes for single-cell RNA sequencing.

Indexed as

High-Throughput Nucleotide SequencingSmegmamorpha3' Untranslated RegionsAnimalsMolecular Sequence AnnotationSequence Analysis, RNASingle-Cell Analysis3' Untranslated Regions10X Genomicsemerging model organismsPacBio

Identifiers

PMID35143654
PMCPMC8893252

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.