Study : Oryza sativa cultivar:Nipponbare Transcriptome or Gene expression

Identification

Name

Oryza sativa cultivar:Nipponbare Transcriptome or Gene expression

Identifier

dXJuOkVWQS9zdHVkeS9QUkpOQTQ4MjIxNw==

Source

Data link

Link to this study on EBI European Nucleotide Archive

Description

Rice is an important staple food and model monocot consumed by half population worldwide. However, inaccurate genome annotation hampers the progress of cultivar breeding and functional studies. To this end, we applied single-molecule long-read RNA sequencing (lrRNA_seq)-based proteogenomics to reveal the complexity of the full-length rice transcriptome and its coding abilities. Surprisingly, approximately 60% of loci identified by lrRNA_seq were associated with natural antisense transcripts (NATs). The high-density genomic arrangement of NAT genes suggests their potential roles in the multifaceted control of gene expression. In addition, a large number of fusion and intergenic transcripts have been observed, enriching our understanding of the genome complexity. Furthermore, a total of 906,456 transcript isoforms were identified, and 72.9% of the genes can generate splicing isoforms. In total, 706,075 post-transcriptional events were subsequently categorized into ten subtypes, demonstrating the interdependence of the post-transcriptional mechanisms in contributing to transcriptome diversity. Parallel short-read RNA sequencing (srRNA_seq) was also carried out for comparison. The results indicate that lrRNA_seq has a superior capacity for the identification of longer full-length transcripts. In addition, 237,808 proteins were subsequently identified using spectral data generated by qualitative proteomics and online datasets. Among these, 1,630 novel coding loci and 130,245 proteins translated using alternative translation sites were identified, expanding the diversity of the rice proteome. Our findings indicate that the genome organization, transcriptome diversity and coding potential of the rice transcriptome are far more complex than previously anticipated.

Data files

Genotype

Accession number	Name	Taxon