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.
All data generated in this study are available in accompanying source data files. Transcriptome annotation files described in this study are available for download at zf-transcriptome.umassmed.edu. Raw and processed RNA-seq data generated in this study are available at GEO (GSE152759).
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Animal experimentation: Zebrafish studies were performed in accordance with protocols #A2613 and #A2632 approved by the University of Massachusetts institutional animal care and use committee (IACUC).
© 2020, Lawson et al.
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