Comprehensive mapping of the alternative polyadenylation site usage and its dynamics at single cell resolution

Alternative polyadenylation (APA) plays an important role in post-transcriptional gene regulation such as transcript stability and translation efficiency. However, our knowledge about APA dynamics at single cell level is largely unexplored. Here we developed single cell polyadenylation sequencing (scPolyA-seq), a strand-specific approach for sequencing 3’ end of transcripts, to investigate the landscape of APA at single cell level. By analyzing several cell lines, we found many genes using multiple polyA sites in bulk data are prone to use only one polyA site in each single cell. Interestingly, cell cycle was significantly enriched in genes showing high variation of polyA site usages. We further identified 414 genes showing polyA site usage switch after cell synchronization. Genes showing cell cycle associated polyA site usage switch were grouped into 6 clusters, with cell phase specific functional categories enriched in each cluster. Furthermore, scPolyA-seq could facilitate study of APA in various biological processes.

Activation and inhibition of nonsense-mediated mRNA decay controls the abundance of alternative polyadenylation products

SUMMARYAlternative polyadenylation (APA) produces transcript 3’ untranslated regions (3’UTRs) with distinct sequences, lengths, stability, and functions. We show here that APA products include a class of cryptic nonsense-mediated mRNA decay (NMD) substrates with extended 3’UTRs that gene- or transcript-level analyses of NMD often fail to detect. Transcriptome-wide, the core NMD factor UPF1 preferentially recognizes long 3’UTR products of APA, leading to their systematic downregulation. Further, we find that many APA events consistently observed in multiple tumor types are controlled by NMD. Additionally, PTBP1, previously implicated in direct modulation of co-transcriptional polyA site choice, regulates the balance of short and long 3’UTR isoforms by inhibiting NMD. Our data suggest that PTBP1 binding near polyA sites can drive production of long 3’UTR APA products in the nucleus and/or protect them from decay in the cytoplasm. Together, our findings reveal a widespread role for NMD in shaping the outcomes of APA.

DeepPASTA: deep neural network based polyadenylation site analysis

Motivation Alternative polyadenylation (polyA) sites near the 3′ end of a pre-mRNA create multiple mRNA transcripts with different 3′ untranslated regions (3′ UTRs). The sequence elements of a 3′ UTR are essential for many biological activities such as mRNA stability, sub-cellular localization, protein translation, protein binding and translation efficiency. Moreover, numerous studies in the literature have reported the correlation between diseases and the shortening (or lengthening) of 3′ UTRs. As alternative polyA sites are common in mammalian genes, several machine learning tools have been published for predicting polyA sites from sequence data. These tools either consider limited sequence features or use relatively old algorithms for polyA site prediction. Moreover, none of the previous tools consider RNA secondary structures as a feature to predict polyA sites. Results In this paper, we propose a new deep learning model, called DeepPASTA, for predicting polyA sites from both sequence and RNA secondary structure data. The model is then extended to predict tissue-specific polyA sites. Moreover, the tool can predict the most dominant (i.e. frequently used) polyA site of a gene in a specific tissue and relative dominance when two polyA sites of the same gene are given. Our extensive experiments demonstrate that DeepPASTA signisficantly outperforms the existing tools for polyA site prediction and tissue-specific relative and absolute dominant polyA site prediction. Availability and implementation https://github.com/arefeen/DeepPASTA Supplementary information Supplementary data are available at Bioinformatics online.