DOI: 10.5061/dryad.0zpc8678g
title: Comprehensive analysis of mRNA 3′ ends during early zebrafish development reveals dynamics of 3′UTR changes on a genome-wide scale

description: Eggs accumulate more than ten thousand mRNAs, from which proteins are
 synthesized constitutively or in temporally controlled manners. Although
 changes in the translation state of these mRNAs are crucial for early
 development, how embryos orchestrate them remains unclear. Here, we
 investigated changes in mRNA 3′ untranslated regions (UTRs) using
 3′end-RNA sequencing of zebrafish embryos and computational methods.
 Consistent with our previous finding that pou5f3 mRNA shortens the 3′UTR
 during early development, thousands of mRNAs were found to shorten their
 3′UTRs. Moreover, we found that most mRNAs in embryos contained several
 different 3′ ends, and their proportion was dynamically changed. These
 changes were coupled with protein synthesis. Our results reveal
 genome-wide 3′-end dynamics in the direction of biological
 processes. The datasets consist of RNA sequencing data from
 3'end-RNA sequencing of zebrafish embryos at 0, 2, and 4 hours
 post-fertilization (hpf).
description: # Comprehensive analysis of mRNA 3′ ends during early zebrafish
 development reveals dynamics of 3′UTR changes on a genome-wide scale
 Dataset DOI: [10.5061/dryad.0zpc8678g](10.5061/dryad.0zpc8678g) ##
 Description of the data and file structure 3’ end-RNA sequencing libraries
 were constructed using the Lexogen QuantSeq 3’ mRNA-seq Library Prep Kit
 for Illumina platforms following the manufacturer’s instructions. RNA
 samples were collected at 3 separate time-points (0, 2, and 4 hours post
 fertilisation (hpf)) from 50 embryos each time. The experiment was
 repeated thrice to ensure reproducibility with different fish pairs. The
 quality before sequencing was ensured using Tape Station (high D1000) to
 confirm the DNA fragments size and purity. Sequencing was carried out by
 Macrogen Inc. using the Illumina HiSeqX Ten platform. The sequencing depth
 for each sample was set to a target of 30 million reads to ensure
 sufficient coverage for downstream analyses. ### Files and variables ####
 File: 0hpf-3_1.fastq **Description:** Basecalled raw file from the 0 hpf
 dataset (sample n°3), forward #### File: 0hpf-3_2.fastq
 **Description:** Basecalled raw file from the 0 hpf dataset (sample n°3),
 reverse #### File: 2hpf-1_1.fastq **Description:** Basecalled raw file
 from the 2 hpf dataset (sample n°1), forward #### File: 2hpf-1_2.fastq
 **Description:** Basecalled raw file from the 2 hpf dataset (sample n°1),
 reverse #### File: 4hpf-3_1.fastq **Description:** Basecalled raw file
 from the 4 hpf dataset (sample n°3), forward #### File: 4hpf-3_2.fastq
 **Description:** Basecalled raw file from the 4 hpf dataset (sample n°3),
 reverse #### File: 0hpf-2_1.fastq **Description:** Basecalled raw file
 from the 0 hpf dataset (sample n°2), forward #### File: 0hpf-2_2.fastq
 **Description:** Basecalled raw file from the 0 hpf dataset (sample n°2),
 reverse #### File: 2hpf-2_1.fastq **Description:** Basecalled raw file
 from the 2 hpf dataset (sample n°2), forward #### File: 2hpf-2_2.fastq
 **Description:** Basecalled raw file from the 2 hpf dataset (sample n°2),
 reverse #### File: 4hpf-2_1.fastq **Description:** Basecalled raw file
 from the 4 hpf dataset (sample n°2), forward #### File: 4hpf-2_2.fastq
 **Description:** Basecalled raw file from the 4 hpf dataset (sample n°2),
 reverse #### File: 2hpf-3_1.fastq **Description:** Basecalled raw file
 from the 2 hpf dataset (sample n°3), forward #### File: 2hpf-3_2.fastq
 **Description:** Basecalled raw file from the 2 hpf dataset (sample n°3),
 reverse ## Code/software Raw sequencing reads were initially processed
 with the fastp tool (version 0.23.4) to perform quality trimming and
 filtering of low-quality bases, adapters, and short reads. The resulting
 high-quality reads were subjected to a quality control check using FASTQC
 (version 0.11.8), and any problematic reads identified through the FASTQC
 analysis were further trimmed or excluded. Reads were aligned to the
 zebrafish genome (GRCz11) using STAR (version 2.7.9a) with default
 parameters. Subsequent bam files were then analysed in pairs (ex: 0hpf-3
 vs 2hpf-1 or 2hpf-1 vs 4hpf-3) using 3 software: a change point model
 ([http://utr.sourceforge.net/](http://utr.sourceforge.net/)) with Java,
 APAlyzer
 ([https://github.com/RJWANGbioinfo/APAlyzer](https://github.com/RJWANGbioinfo/APAlyzer)) with R, and Peaks2UTR ([https://github.com/haessar/peaks2utr](https://github.com/haessar/peaks2utr)) with Python. For these codes to run, the same annotation from Ensembl was used (GRCz11.110). The basic provided code, as per the manual of each of aforementioned tool, was run plus or minus below modifications: For change point (version 0.1.1): the shortening mode was used, and a default of 20 reads was necessary for each region to be analysed.  For APAlyzer (version 1.0): 3’end files were created using the ThreeMostPair function, and reference files were created using the Ensembl annotation file (GRCz11.110) with PAS2GEF. For Peaks2UTR (version 1.1.2): the override-utr function was used, and the maximum distance was set to 3000 bases, as zebrafish utrs tend to be long. ## Access information Libraries were created for the purpose of this study hence, this is the first upload of the raw data.