Alternative polyadenylation (APA) has been discovered to play regulatory roles in the development of many cancer cells through preferential addition of a poly(A) tail at specific sites of pre-mRNA. A recent study found that APA was involved in the mediation of acute myeloid leukaemia (AML). However, unlike gene expression heterogeneity, little attention has been directed toward variations in single-cell APA for different cell types during AML development. Here, we used single-cell RNA-seq data of a massive population of 16,843 bone marrow mononuclear cells (BMMCs) from healthy and AML patient samples to investigate dynamic APA usage in different cell types. Abnormalities of APA dynamics in the BMMCs from AML patient samples were uncovered compared to the stable APA dynamics in samples from healthy individuals, as well as lower APA diversity between eight cell types in AML patients. Genes with APA dynamics specific to the AML samples were significantly enriched in cellular signal transduction pathways that contribute to AML development. Moreover, many leukaemic cell marker genes such as NF-κB, GATA2 and IAP-Family genes exhibited APA dynamics that specifically affected abnormal proliferation and differentiation of leukemic BMMCs. Additionally, mature erythroid cells displayed greater APA dynamics and global 3′ UTR shortening compared with other cell types. Our results revealed extensive involvement of APA regulation in leukemia development and erythropoiesis at the single-cell level, providing a high-resolution atlas to navigate cellular mRNA processing landscapes of differentiated cells in AML.

Alternative cleavage and polyadenylation (APA) is increasingly recognized as an important regulatory mechanism in eukaryotic gene expression and is dynamically modulated in a developmental, tissue-specific, or environmentally responsive manner. Given the functional importance of APA and the rapid accumulation of APA sites in plants, a comprehensive and easily accessible APA site database is necessary for improved understanding of APA-mediated gene expression regulation. We present a database called PlantAPAdb that catalogs the most comprehensive APA site data derived from sequences from diverse 3′ sequencing protocols and biological samples in plants. Currently, PlantAPAdb contains APA sites in six species, Oryza sativa (japonica and indica), Arabidopsis (Arabidopsis thaliana), Medicago truncatula, Trifolium pratense, Phyllostachys edulis, and Chlamydomonas reinhardtii. APA sites in PlantAPAdb are available for bulk download and can be queried in a Google-like manner. PlantAPAdb provides rich information of the whole-genome APA sites, including genomic locations, heterogeneous cleavage sites, expression levels, and sample information. It also provides comprehensive poly(A) signals for APA sites in different genomic regions according to distinct profiles of cis-elements in plants. In addition, PlantAPAdb contains events of 3′ untranslated region shortening/lengthening resulting from APA, which helps to understand the mechanisms underlying systematic changes in 3′ untranslated region lengths. Additional information about conservation of APA sites in plants is also available, providing insights into the evolutionary polyadenylation configuration across species. As a user-friendly database, PlantAPAdb is a large and extendable resource for elucidating APA mechanisms, APA conservation, and gene expression regulation.

Alternative polyadenylation (APA) plays important roles in modulating mRNA stability, translation, and subcellular localization, and contributes extensively to shaping eukaryotic transcriptome complexity and proteome diversity. Identification of poly(A) sites (pAs) on a genome-wide scale is a critical step toward understanding the underlying mechanism of APA-mediated gene regulation. A number of established computational tools have been proposed to predict pAs from diverse genomic data. Here we provided an exhaustive overview of computational approaches for predicting pAs from DNA sequences, bulk RNA sequencing (RNA-seq) data, and single-cell RNA sequencing (scRNA-seq) data. Particularly, we examined several representative tools using bulk RNA-seq and scRNA-seq data from peripheral blood mononuclear cells and put forward operable suggestions on how to assess the reliability of pAs predicted by different tools. We also proposed practical guidelines on choosing appropriate methods applicable to diverse scenarios. Moreover, we discussed in depth the challenges in improving the performance of pA prediction and benchmarking different methods. Additionally, we highlighted outstanding challenges and opportunities using new machine learning and integrative multi-omics techniques, and provided our perspective on how computational methodologies might evolve in the future for non-3′ untranslated region, tissue-specific, cross-species, and single-cell pA prediction.

Alternative polyadenylation (APA) plays a key post-transcriptional regulatory role in mRNA stability and functions in eukaryotes. Single cell RNA-seq (scRNA-seq) is a powerful tool to discover cellular heterogeneity at gene expression level. Given 3′ enriched strategy in library construction, the most commonly used scRNA-seq protocol—10× Genomics enables us to improve the study resolution of APA to the single cell level. However, currently there is no computational tool available for investigating APA profiles from scRNA-seq data. Here, we present a package scDAPA for detecting and visualizing dynamic APA from scRNA-seq data. Taking bam/sam files and cell cluster labels as inputs, scDAPA detects APA dynamics using a histogram-based method and the Wilcoxon rank-sum test, and visualizes candidate genes with dynamic APA. Benchmarking results demonstrated that scDAPA can effectively identify genes with dynamic APA among different cell groups from scRNA-seq data. The scDAPA package is implemented in Shell and R, and is freely available at https://scdapa.sourceforge.io. Supplementary data are available at Bioinformatics online.