Bioinformatics Seminar
Time: 11AM
Venue: Davis Auditorium and Online
21 November 2023
Towards transcript-level analyses of RNA-seq data with edgeR
Pedro BaldoniWEHI Bioinformatics
Differential expression analysis of short read RNA-seq data is arguably one of the most commonly performed bioinformatics analyses. Yet, transcript-level quantifications are inherently more uncertain than gene-level read counts due to the ambiguous assignment of sequence reads to transcripts. While sequence reads can usually be assigned unambiguously to a gene, reads are very often compatible with multiple transcripts for that gene, particularly for genes with many transcripts. In the first part of this talk, I will discuss recent advancements in edgeR/limma that unlock differential expression analyses of RNA-seq data at the transcript-level. Specifically, I will present approaches to account for the read-to-transcript ambiguity in RNA-seq analyses to detect differential transcript expression (DTE) and differential transcript usage (DTU) with edgeR/limma. I will compare the presented pipelines with state-of-the-art methods developed for DTE and DTU analyses using both simulated and real experiments. In the second part of this talk, I will present a discussion on the use of transcript-level quantifications to draw gene-level inferences with RNA-seq data. Using a series of real data sets, I explore the effects of an incomplete annotation on RNA-seq quantification, the extent to which DTU impacts gene-level read counting, and the accuracy of alignment- and pseudoalignment-based pipelines on measuring gene-level differential expression.