Long-read RNA sequencing reveals widespread sex-specific alternative splicing in threespine stickleback fish

Abstract

Alternate isoforms are important contributors to phenotypic diversity across eukaryotes. Although short-read RNA-sequencing has increased our understanding of isoform diversity, it is challenging to accurately detect full-length transcripts, preventing the identification of many alternate isoforms. Long-read sequencing technologies have made it possible to sequence full-length alternative transcripts, accurately characterizing alternative splicing events, alternate transcription start and end sites, and differences in UTR regions. Here, we use Pacific Biosciences (PacBio) long-read RNA-sequencing (Iso-Seq) to examine the transcriptomes of five organs in threespine stickleback fish (Gasterosteus aculeatus), a widely used genetic model species. The threespine stickleback fish has a refined genome assembly in which gene annotations are based on short-read RNA sequencing and predictions from coding sequence of other species. This suggests some of the existing annotations may be inaccurate or alternative transcripts may not be fully characterized. Using Iso-Seq we detected thousands of novel isoforms, indicating many isoforms are absent in the current Ensembl gene annotations. In addition, we refined many of the existing annotations within the genome. We noted many improperly positioned transcription start sites that were refined with long-read sequencing. The Iso-Seq-predicted transcription start sites were more accurate and verified through ATAC-seq. We also detected many alternative splicing events between sexes and across organs. We found a substantial number of genes in both somatic and gonadal samples that had sex-specific isoforms. Our study highlights the power of long-read sequencing to study the complexity of transcriptomes, greatly improving genomic resources for the threespine stickleback fish.

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

“…Recently, Naftaly et al (2021) published a large bulk analysis of adult stickleback Iso-Seq data from 16 PacBio SMRT cells and 4 tissue types (gonad, brain, pronephros, and liver). We reanalyzed this dataset to test whether we would see comparable improvements in our scRNAseq analysis (Supplementary File 3).…”

“…We downloaded the raw data from Naftaly et al (2021) from NCBI. This adult Iso-Seq dataset contains gonad, pronephros, brain, and liver reads from both sexes, a total of 16 SMRT cells.…”

“…This adult Iso-Seq dataset contains gonad, pronephros, brain, and liver reads from both sexes, a total of 16 SMRT cells. We processed reads from this dataset with the following steps: we generated circular consensus reads with (–min-rq=.9; v6.0.0), clipped barcodes listed in Naftaly et al (2021) with (–isoseq –dump-clips; v2.2.0), removed polyA tails with (–require-polyA; v3.4.0-0), and clustered reads with (v3.4.0-0) from the PacBio SMRT Analysis software (Supplementary File 6). For the analysis with solely these data, we aligned reads to the stickleback genome (BROAD S1, 104.1 database version), collapsed transcripts with (v28.0.0), and filtered and classified with (v4.2) as was done with the ScISOr-Seq data.…”

“…Again, we used the from (Supplementary File 3) to run as described below. Due to reduced novel and increased annotated genes relative to Naftaly et al (2021) , we additionally tried using with -ax splice -uf -C5 -secondary=no to match their parameters; however, we observed negligible changes. We propose that differences in our analyses arise from different earlier processing steps, different versions of stickleback gene models, and an updated version of .…”

“…Beck and J. Postlethwait for perceptive remarks on the manuscript. They also thank J. Searcy for assisting us at downloading Naftaly et al (2021) ’s raw data from google cloud server.…”