Evidence map›Paper›PMID 32831172›Full record

ArticleeLife2020

An improved zebrafish transcriptome annotation for sensitive and comprehensive detection of cell type-specific genes.

Nathan D Lawson, Rui Li, Masahiro Shin, Ann Grosse, Onur Yukselen, Oliver A Stone, Alper Kucukural, Lihua Zhu

Abstract read
In one paragraph

Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers.

0numbers the graph read from it
0cells of the map it votes in
90citing 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

90 citing papers in PubMed.

  1. Article
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  6. Review
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  10. Intracellular pH dynamics promotes zebrafish larval tail regeneration.bioRxiv : the preprint server for biology · 2026
    Article
  11. Article
  12. Article
  13. Hb-EGF directs systemic muscle repair.Development (Cambridge, England) · 2026
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

30 more citing papers are in PubMed but not listed here.

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

8 authors.

Nathan D LawsonDepartment of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, United States.ORCID 0000-0001-7788-9619
Rui LiDepartment of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, United States.
Masahiro ShinDepartment of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, United States.
Ann GrosseDepartment of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, United States.
Onur YukselenBioinformatics Core, University of Massachusetts Medical School, Worcester, United States.
Oliver A StoneDepartment of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.
Alper KucukuralProgram in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States.ORCID 0000-0001-9983-394X
Lihua ZhuDepartment of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, United States.

Funding

University of Massachusetts Center for Clinical Science and Translational SupplementUL1TR001453 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LUZURIAGA, KATHERINE F · 2015 to 2024
$39.0M
Rules of gene expression modeled on human dendritic cell response to pathogensU01HG007910 · NHGRI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI GARBER, MANUEL, LUBAN, JEREMY · 2015 to 2017
$6.1M
Embryonic origins of endothelial heterogeneityR35HL140017 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LAWSON, NATHAN D · 2018 to 2023
$6.0M
Identification of blood-brain-barrier pericyte-specific cis regulatory elementsR21NS105654 · NINDS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LAWSON, NATHAN D · 2018 to 2019
$461k
NCATS NIH HHS UL1 TR001453NHGRI NIH HHS U01 HG007910NHLBI NIH HHS R35 HL140017NINDS NIH HHS R21 NS105654
6 · The paper itself

Abstract

The zebrafish is ideal for studying embryogenesis and is increasingly applied to model human disease. In these contexts, RNA-sequencing (RNA-seq) provides mechanistic insights by identifying transcriptome changes between experimental conditions. Application of RNA-seq relies on accurate transcript annotation for a genome of interest. Here, we find discrepancies in analysis from RNA-seq datasets quantified using Ensembl and RefSeq zebrafish annotations. These issues were due, in part, to variably annotated 3' untranslated regions and thousands of gene models missing from each annotation. Since these discrepancies could compromise downstream analyses and biological reproducibility, we built a more comprehensive zebrafish transcriptome annotation that addresses these deficiencies. Our annotation improves detection of cell type-specific genes in both bulk and single cell RNA-seq datasets, where it also improves resolution of cell clustering. Thus, we demonstrate that our new transcriptome annotation can outperform existing annotations, providing an important resource for zebrafish researchers.

Indexed as

Transcriptome3' Untranslated RegionsAnimalsComputational BiologyGene Expression ProfilingGene OntologyGenomeMolecular Sequence AnnotationSequence Analysis, RNAZebrafish3' Untranslated Regionsannotationdevelopmental biologyRNA-seqtranscriptomezebrafish

Identifiers

PMID32831172
PMCPMC7486121

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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.