Current technologies to measure gene and transcript expression are called RNA sequencing (RNA-seq) which by sequencing millions of transcripts allows RNA levels to be measured on a genome-wide scale. Currently, transcript annotations in many plant species are poor and are further exacerbated by thousands of novel but misassembled transcripts from poorly controlled RNA-seq transcript assemblies.

A high-quality RTD for a plant/crop species of interest allows RNA sequencing data to be analysed rapidly and accurately with state-of-art alignment-free programs (Salmon; Kallisto) giving improved, robust analysis of differential gene expression and differential alternative splicing analysis with reduced false discovery rates. Accurate gene/transcript expression analysis of, for example, biotic/abiotic stress responses, development pathways, genotype-phenotype relationships, mutants etc using RNA-seq data can identify key genes of interest to biotechnologists and plant breeders.

The research team are developing a fully automated pipeline (RTDBox) that can be used by scientists with basic bioinformatics skills or bioinformaticians with little experience in transcriptomics. Such a program would also be designed to allow the incremental improvement of the RTD with the automatic incorporation of any new RNA-seq data (Illumina, PacBio, Nanopore). Within the pipeline, the team are developing a transcript evaluation suite that will provide evaluation metrics to help biologists to identify and remove mis-constructed transcripts from assembly programs as well as understand the quality and completeness of the RTD generated. All their experience and expertise has been brought together to make user-friendly software for plant scientists to measure gene expressions more accurately and thereby improving the exploration of biological processes across the globe.

Project Leader: Dr Runxuan Zhang, James Hutton Institute

Funding: BBSRC Biological and Bioinformatics Resources BB/S020160/1