Using equivalence class counts for fast and accurate testing of differential transcript usage.

Background: RNA sequencing has enabled high-throughput and fine-grained quantitative analyses of the transcriptome. While differential gene expression is the most widely used application of this technology, RNA-seq data also has the resolution to infer differential transcript usage (DTU), which can elucidate the role of different transcript isoforms between experimental conditions, cell types or tissues. DTU has typically been inferred from exon-count data, which has issues with assigning reads unambiguously to counting bins, and requires alignment of reads to the genome. Recently, approaches have emerged that use transcript quantifications estimates directly for DTU. Transcript counts can be inferred from 'pseudo' or lightweight aligners, which are significantly faster than traditional genome alignment. However, recent evaluations show lower sensitivity in DTU analysis. Transcript abundances are estimated from equivalence classes (ECs), which determine the transcripts that any given read is compatible with. Recent work has proposed performing differential expression testing directly on equivalence class read counts (ECs). Methods: Here we demonstrate that ECs can be used effectively with existing count-based methods for detecting DTU. We evaluate this approach on simulated human and drosophila data, as well as on a real dataset through subset testing. Results: We find that ECs counts have similar sensitivity and false discovery rates as exon-level counts but can be generated in a fraction of the time through the use of pseudo-aligners. Conclusions: We posit that equivalence class read counts are a natural unit on which to perform many types of analysis.

Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification.

Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.

Robust identification of Ptbp1-dependent splicing events by a junction-centric approach in Xenopus laevis.

Regulation of alternative splicing is an important process for cell differentiation and development. Down-regulation of Ptbp1, a regulatory RNA-binding protein, leads to developmental skin defects in Xenopus laevis. To identify Ptbp1-dependent splicing events potentially related to the phenotype, we conducted RNAseq experiments following Ptbp1 depletion. We systematically compared exon-centric and junction-centric approaches to detect differential splicing events. We showed that the junction-centric approach performs far better than the exon-centric approach in Xenopus laevis. We carried out the same comparisons using simulated data in human, which led us to propose that the better performances of the junction-centric approach in Xenopus laevis essentially relies on an incomplete exonic annotation associated with a correct transcription unit annotation. We assessed the capacity of the exon-centric and junction-centric approaches to retrieve known and to discover new Ptbp1-dependent splicing events. Notably, the junction-centric approach identified Ptbp1-controlled exons in agfg1, itga6, actn4, and tpm4 mRNAs, which were independently confirmed. We conclude that the junction-centric approach allows for a more complete and informative description of splicing events, and we propose that this finding might hold true for other species with incomplete annotations.