Benchmarking sequencing methods and tools that facilitate the study of alternative polyadenylation

Abstract

Background Alternative cleavage and polyadenylation (APA), an RNA processing event, occurs in over 70% of human protein-coding genes. APA results in mRNA transcripts with distinct 3′ ends. Most APA occurs within 3′ UTRs, which harbor regulatory elements that can impact mRNA stability, translation, and localization. Results APA can be profiled using a number of established computational tools that infer polyadenylation sites from standard, short-read RNA-seq datasets. Here, we benchmarked a number of such tools—TAPAS, QAPA, DaPars2, GETUTR, and APATrap— against 3′-Seq, a specialized RNA-seq protocol that enriches for reads at the 3′ ends of genes, and Iso-Seq, a Pacific Biosciences (PacBio) single-molecule full-length RNA-seq method in their ability to identify polyadenylation sites and quantify polyadenylation site usage. We demonstrate that 3′-Seq and Iso-Seq are able to identify and quantify the usage of polyadenylation sites more reliably than computational tools that take short-read RNA-seq as input. However, we find that running one such tool, QAPA, with a set of polyadenylation site annotations derived from small quantities of 3′-Seq or Iso-Seq can reliably quantify variation in APA across conditions, such asacross genotypes, as demonstrated by the successful mapping of alternative polyadenylation quantitative trait loci (apaQTL). Conclusions We envisage that our analyses will shed light on the advantages of studying APA with more specialized sequencing protocols, such as 3′-Seq or Iso-Seq, and the limitations of studying APA with short-read RNA-seq. We provide a computational pipeline to aid in the identification of polyadenylation sites and quantification of polyadenylation site usages using Iso-Seq data as input.

A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences, Bulk RNA-seq, and Single-cell RNA-seq

“…APAtrap [40] is the top performer for Arabidopsis data, while TAPAS [41] performs the best on human or mouse data. Recently, Shah et al [51] benchmarked five tools for RNA-seq against 3’ seq, Iso-Seq, and a full-length RNA-seq method and found that pAs from 3’ seq and Iso-Seq are more reliable than pAs predicted from RNA-seq. They suggested that incorporating the RNA-seq prediction tool QAPA [38] with pA annotations derived from 3’ seq or Iso-Seq can reliably quantify APA dynamics across conditions.…”

“…Previously, our group benchmarked 11 representative tools for predicting pAs and/or dynamic APA events from RNA-seq [50]. Lately, Shah et al [51] evaluated five tools for RNA-seq against 3′ seq, Iso-Seq, and a full-length RNA-seq method in identifying pAs and quantifying pA usage. However, there is no study to provide an exhaustive evaluation of existing tools for pA prediction from different kinds of data, particularly those tools for scRNA-seq.…”

“…The copyright holder for this preprint (which this version posted July 18, 2022. ; https://doi.org/10.1101/2022.07.17.500329 doi: bioRxiv preprint from RNA-seq data. Another benchmark study [51] benchmarked five tools for RNA-seq and compared their performance with 3′ seq, Iso-Seq, and PacBio single-molecule fulllength RNA-seq method. Ye et al [52] briefly summarized three computational methods for detecting APA dynamics from diverse single cell types.…”

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

“…Previous research has demonstrated that detection of alternative polyadenylation events can be significantly improved by incorporating experimental annotations such as data from 3’ RNA-seq experiments (Ha et al, 2018; Shah et al, 2021). Here, we augment txrevise promoter annotations using experimental CAGE (Shiraki et al, 2003) data from the FANTOM5 project (FANTOM Consortium and the RIKEN PMI and CLST et al, 2014).…”

stAPAminer: Mining Spatial Patterns of Alternative Polyadenylation for Spatially Resolved Transcriptomic Studies

“…APA is dynamically regulated in various cellular processes and diseases, such as cell activation, proliferation differentiation, neurodegenerative disorders, and cancer [3][4][5][6][7][8][9][10][11][12]. Studies using bulk 3′-end sequencing (reviewed in [2,13,14]) and/or RNA-seq (reviewed in [14,15]) have revealed extensive 3′ UTR lengthening/shortening events in various processes. For example, 3′ UTRs generally shorten in proliferating cells, while 3′ UTRs lengthen during embryonic differentiation [16] and animal neurogenesis [17,18].…”