Back

Improved detection of genetic effects on promoter usage with augmented transcript annotations

Vija, A.; Alasoo, K.

2022-07-14 bioinformatics
10.1101/2022.07.12.499800 bioRxiv
Show abstract

Disease-associated non-coding variants can modulate their target genes by disrupting multiple mechanisms, including regulating total gene expression level, splicing, alternative polyadenylation or promoter usage. Quantifying promoter usage from standard RNA sequencing data is challenging due to incomplete reference transcriptome annotations and low read coverage observed at the ends of transcripts. We previously developed the txrevise tool (https://github.com/kauralasoo/txrevise) to quantify promoter usage events from RNA-seq data using reference transcriptome annotations. Here, we augment the txrevise promoter event annotations with experimentally identified Cap Analysis of Gene Expression (CAGE) promoters from the FANTOM5 project. Applying the new annotations to RNA-seq data from 358 individuals, we found that augmented promoter event annotations increased the power to detect promoter usage quantitative trait loci (puQTLs) by ~30%. However, concordance between puQTLs inferred from RNA-seq data and those directly measured using CAGE remained low, suggesting that additional experimental and computational improvements are needed to capture the full range of regulatory effects of non-coding variants.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.