High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica.

Area of Science:

• Plant Molecular Biology
• Genomics
• Bioinformatics

Background:

• Transcriptome annotations are crucial for understanding gene expression but are often incomplete for crop plants like rice (Oryza sativa indica).
• Existing annotations, such as those in Ensembl, lack comprehensive alternative transcript isoforms and alternative splicing (AS) events, limiting precise analysis.
• Accurate reference transcriptomic datasets (RTDs) are essential for precise transcript quantification and differential expression studies.

Purpose of the Study:

• To generate a high-quality, comprehensive reference transcriptomic dataset (RTD) for Oryza sativa indica.
• To increase the diversity of identified transcripts and alternative splicing (AS) events in rice.
• To provide a resource for precise transcript quantification and gene expression analysis in rice.

Main Methods:

• Conducted RNA sequencing on rice leaf samples at various time points during Rhizoctonia solani infection.
• Utilized a recently developed computational analysis pipeline for transcript assembly.
• Performed stringent quality filtering on assembled transcripts.

Main Results:

• Assembled a novel transcriptome annotation for O. sativa indica, named IndicaRTD, comprising 122,968 non-redundant transcripts from 53,695 genes.
• Identified a significantly increased number of transcripts and AS events compared to existing Ensembl data.
• Discovered numerous novel transcripts potentially involved in regulating plant molecular and physiological processes.

Conclusions:

• The newly constructed IndicaRTD offers a vastly expanded and more accurate transcriptome annotation for O. sativa indica.
• This dataset significantly enhances the diversity of known transcripts and alternative splicing events in rice.
• IndicaRTD enables fast and highly precise quantification of transcript and gene expression in rice.