University of Massachusetts Medical School Faculty Publications

UMMS Affiliation

Department of Molecular, Cell, and Cancer Biology; Bioinformatics Core; Program in Molecular Medicine; Program in Bioinformatics and Integrative Biology

Publication Date

2020-08-24

Document Type

Article

Disciplines

Bioinformatics | Developmental Biology

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.

Keywords

RNA-seq, annotation, developmental biology, transcriptome, zebrafish

Rights and Permissions

Copyright Lawson et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

DOI of Published Version

10.7554/eLife.55792

Source

Lawson ND, Li R, Shin M, Grosse A, Yukselen O, Stone OA, Kucukural A, Zhu L. An improved zebrafish transcriptome annotation for sensitive and comprehensive detection of cell type-specific genes. Elife. 2020 Aug 24;9:e55792. doi: 10.7554/eLife.55792. Epub ahead of print. PMID: 32831172. Link to article on publisher's site

Related Resources

Link to Article in PubMed

Journal/Book/Conference Title

eLife

PubMed ID

32831172

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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