Cancer-associated dynamics and potential regulators of intronic polyadenylation revealed by IPAFinder using standard RNA-seq data

Abstract

Intronic polyadenylation (IpA) usually leads to changes in coding region of an mRNA, and its implication in diseases has been recognized, though at its very beginning status. Conveniently and accurately identifying IpA is of great importance for further evaluating its biological significance. Here, we developed IPAFinder, a bioinformatic method for the de novo identification of intronic poly(A) sites and their dynamic changes from standard RNA-seq data. Applying IPAFinder to 256 pan-cancer tumor/normal pairs across six tumor types, we discovered 490 recurrent dynamically changed IpA events, some of which are novel and derived from cancer-associated genes such as TSC1, SPERD2, and CCND2. Furthermore, IPAFinder revealed that IpA could be regulated by factors related to splicing and m6A modification. In summary, IPAFinder enables the global discovery and characterization of biologically regulated IpA with standard RNA-seq data and should reveal the biological significance of IpA in various processes.

Intron mutations and early transcription termination in Duchenne and Becker muscular dystrophy

“…Splice site strength predictions utilized the following tools: MaxEntScan (http://hollywood.mit.edu/burgelab/maxent/Xmaxentscan_scoreseq.html), Human Splicing Finder (HSF3.1, http://www.umd.be/HSF3/HSF.shtml), ESEfinder3.0 (http://krainer01.cshl.edu/cgi-bin/tools/ESE3/esefinder.cgi?process=home), and the SpliceAI deep learning algorithm (Jaganathan et al, 2019). Bioinformatic prediction of intronic polyadenylation sites utilized the IPAFinder_PS_FET.py program from IPAFinder and the POLYAR program with parameters for PAS‐strong poly(A) sites (Akhtar et al, 2010; Zhao et al, 2021). Genome‐wide, precomputed SpliceAI Δ scores were downloaded from https://basespace.illumina.com/s/5u6ThOblecrh and variants from chrX:31140097‐33229348 with splice acceptor or donor Δ scores greater than 0.1 were extracted.…”

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

“…EBChangePoint [93], APAtrap [40], TAPAS [41], moutainClimber [94], and IPAFinder [95]. According to our previous benchmark on 11 tools for RNA-seq [50], TAPAS generally .…”

“…Different from most pA prediction tools focusing mainly on 3′ UTR, IPAFinder was specifically proposed for identifying intronic pAs from RNA-seq [95]. Zhao et al applied IPAFinder to pan-cancer datasets across six tumor types and discovered 490 recurrent dynamically changed intronic pAs [95].…”

Transcriptome sequencing suggests that pre-mRNA splicing counteracts widespread intronic cleavage and polyadenylation

“…A combination of these protocols yielded a consolidated set of more than 500,000 human PASs (25)(26)(27)27), however many more PASs may be active in tissue-and disease-specific conditions. A number of computational methods also attempt to identify PASs from the standard polyA + RNA-seq data as genomic loci that exhibit an abrupt decrease in read coverage (13,28,29,(29)(30)(31)(32). However, since the density of RNA-seq reads is highly non-uniform along the gene length, many of these methods are limited to PASs that are located in the last exon or 3'-UTR, thus focusing on quantifying relative usage of PASs with known genomic positions rather than identifying novel PASs.…”

“…Remarkably, the truncated oncosuppressor proteins that are generated by IPA often lack the tumor-suppressive functions and contribute significantly to tumor onset and progression ( 11 ). Thousands of recurrent and dynamically changing IPA events have been identified in transcriptomic studies, indicating that current knowledge on IPA is largely incomplete ( 13 ).…”