PolyA-miner: accurate assessment of differential alternative poly-adenylation from 3′Seq data using vector projections and non-negative matrix factorization

Abstract

<jats:title>Abstract</jats:title> <jats:p>Almost 70% of human genes undergo alternative polyadenylation (APA) and generate mRNA transcripts with varying lengths, typically of the 3′ untranslated regions (UTR). APA plays an important role in development and cellular differentiation, and its dysregulation can cause neuropsychiatric diseases and increase cancer severity. Increasing awareness of APA’s role in human health and disease has propelled the development of several 3′ sequencing (3′Seq) techniques that allow for precise identification of APA sites. However, despite the recent data explosion, there are no robust computational tools that are precisely designed to analyze 3′Seq data. Analytical approaches that have been used to analyze these data predominantly use proximal to distal usage. With about 50% of human genes having more than two APA isoforms, current methods fail to capture the entirety of APA changes and do not account for non-proximal to non-distal changes. Addressing these key challenges, this study demonstrates PolyA-miner, an algorithm to accurately detect and assess differential alternative polyadenylation specifically from 3′Seq data. Genes are abstracted as APA matrices, and differential APA usage is inferred using iterative consensus non-negative matrix factorization (NMF) based clustering. PolyA-miner accounts for all non-proximal to non-distal APA switches using vector projections and reflects precise gene-level 3′UTR changes. It can also effectively identify novel APA sites that are otherwise undetected when using reference-based approaches. Evaluation on multiple datasets—first-generation MicroArray Quality Control (MAQC) brain and Universal Human Reference (UHR) PolyA-seq data, recent glioblastoma cell line NUDT21 knockdown Poly(A)-ClickSeq (PAC-seq) data, and our own mouse hippocampal and human stem cell-derived neuron PAC-seq data—strongly supports the value and protocol-independent applicability of PolyA-miner. Strikingly, in the glioblastoma cell line data, PolyA-miner identified more than twice the number of genes with APA changes than initially reported. With the emerging importance of APA in human development and disease, PolyA-miner can significantly improve data analysis and help decode the underlying APA dynamics.</jats:p>

Alternative splicing and alternative polyadenylation define tumor immune microenvironment and pharmacogenomic landscape in clear cell renal carcinoma

“…Growing evidence suggests that an APA event has a strong efficacy in predicting clinical outcomes of human cancers. 10 Herein, the distal polyA site usage index (PDUI) value was used to indicate the frequency of APA events. By applying the NMF algorithm, ccRCC patients were classified into two APA RNA processing patterns based on the top 1,000 APA events with the most variance ( Figures S6 A–S6C).…”

“… 9 In almost 70% of human genes, cleavage and polyadenylation occur in multiple locations through APA, which is a major post-transcriptional mechanism for gene regulation. 10 Mounting evidence highlights the critical roles of AS and APA events in human cancers, such as driving oncogenic gene expression, 11 chemotherapy resistance, 12 and tumor microenvironment (TME). 13 Furthermore, global AS and APA events possess predictive potential as prognostic biomarkers of human cancers.…”

The Detection and Bioinformatic Analysis of Alternative 3′ UTR Isoforms as Potential Cancer Biomarkers

“…PolyA-miner [ 8 ] creates a matrix of poly(A) sites (as rows) and samples (as columns) from 3 focused sequencing data to apply non-negative matrix factorization which captures gene expression patterns. It first extracts all potential sample-wise poly(A) sites and pools them to construct a poly(A) library and then extensively filters out false poly(A) sites and maps the rest to their respective genes.…”

“…This occurs due to the presence of alternative adenylation sites in the genome and leads to significant transcriptome diversity. Nearly 70% of mammalian genes harbour multiple cleavage and polyadenylation sites i.e., poly(A) sites [ 7 , 8 , 9 , 10 ]. These sites can cause differential expression of mRNA transcripts by influencing their nuclear export, stability, subcellular localization, interaction with microRNAs, RNA binding proteins (RBPs), long non-coding RNAs (lncRNAs) and translation efficiency [ 11 , 12 , 13 , 14 , 15 ].…”

Emerging roles of alternative cleavage and polyadenylation (APA) in human disease

“…APAlyzer helps in identification of APAs in 3′‐UTR and intronic regions (R. Wang & Tian, 2020). PolyA‐miner accurately analyzes 3′Seq data and also identified novel APA sites (Yalamanchili et al, 2020). A detailed description of the recent advances in 3′ sequencing and computational tools for APA analysis are well documented (Jensen et al, 2021; Yalamanchili et al, 2021).…”