Methods and Tools for achieving accurate and fast characterization of transcriptomes at your fingertips

Understanding the current limitations of RNA-seq is crucial for reliable analysis. We have developed several computational resources, methods and tools to address the challenges of RNA-seq data analysis, with the emphasis on plant species. We have also taken away all the unnecessary barriers as much as possible for doing the analyis by designing extremely user-friendly interfaces and workflows, automating the process and making tools available at your fingertips. In this talk, I will focus on two pieces of work:

1)    A BBSRC funded project to construct an automated pipeline with multiple assemblers to capture the diversity of transcripts from different sources and technologies and stringent filters to construct a comprehensive Reference Transcript Dataset for plants (RTDBox). Extensive experimental validation showed that RTDs constructed using our method outperform other available transcriptomes in RNA-seq analysis in quantification accuracy [1][2]. RTDBox will be made available and can be applied to all plant species and beyond.

2)    A cutting-edge pipeline (3D RNA-seq) [3] for differential gene expression, alternative splicing, and transcript usage as well as transcript isoform switches. 3D RNA-seq incorporates the state-of-the-art methodologies while remaining simple and rapid. It allows (lab) biologists with no programming skills to perform a complete differential expression analysis of RNA-seq data in 3 days.  

These tools/methods enabled the discovery of massive and rapid expression and AS responses to cold in Arabidopsis and identification of hundreds of genes with very early changes in expression/AS, including numerous novel cold-responsive transcription factors and splicing factors/RNA binding protein genes [4]. We are exploring gene and splicing networks constructed with the cold time-series data.

[1] Zhang et al. (2017) Nucleic Acid Research, 45 (9): 5061-5073; [2] Flores et al (2019) BioRxiv https://doi.org/10.1101/638106; [3] Guo at al. (2019) BioRxiv. https://doi.org/10.1101/656686; [4] Calixto et al. (2018) Plant Cell, 30(7):1424-1444, 2018

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