Shortening of 3’ UTRs in most cell types composing tumor tissues implicates alternative polyadenylation in protein metabolism

Burri, Dominik; Zavolan, Mihaela

doi:10.1101/2021.06.30.450496

Cited by 2 publications

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…and roar [118], directly discover APA site switching by detecting sudden change of read density at terminal exons without identifying APA sites. Recently, several tools were developed for scRNA-seq, such as SCUREL [119], scMAPA [120], and scDAPA [121].…”

Section: Methods For Apa Analysis Rather Than Pa Predictionmentioning

confidence: 99%

“…Some approaches for RNA-seq, such as PHMM [115], ChangePoint [116], MISO [117], and roar [118], directly discover APA site switching by detecting sudden change of read density at terminal exons without identifying APA sites. Recently, several tools were developed for scRNA-seq, such as SCUREL [119], scMAPA [120], and scDAPA [121]. For example, our group developed scDAPA [121] for characterizing differential usages of APA in different cell types using 10x Chromium data, and found APA plays important role in acute myeloid leukemia [114].…”

Section: Computational Approaches For Pa Predictionmentioning

confidence: 99%

See 1 more Smart Citation

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

Wenbin

Lian

et al. 2022

Preprint

View full text Add to dashboard Cite

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-seq data, and single-cell RNA-seq (scRNA-seq) data. Particularly, we examined several representative tools using 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’ UTR, tissue-specific, cross-species, and single-cell pA prediction.

show abstract

Section: Methods For Apa Analysis Rather Than Pa Predictionmentioning

confidence: 99%

Section: Computational Approaches For Pa Predictionmentioning

confidence: 99%

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

Wenbin

Lian

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Comprehensive annotation of 3′UTRs from primary cells and their quantification from scRNA-seq data

Fansler

Zhao²,

Mayr

2021

Preprint

View full text Add to dashboard Cite

Although half of human genes use alternative polyadenylation (APA) to generate mRNA isoforms that encode the same protein but differ in their 3′UTRs, most single cell RNA-sequencing (scRNA-seq) pipelines only measure gene expression. Here, we describe an open-access pipeline, called scUTRquant (https://github.com/Mayrlab/scUTRquant), that measures gene and 3′UTR isoform expression from scRNA-seq data obtained from known cell types in any species. scUTRquant-derived gene and 3′UTR transcript counts were validated against standard methods which demonstrated their accuracy. 3′UTR isoform quantification was substantially more reproducible than previous methods. scUTRquant provides an atlas of high-confidence 3′ end cleavage sites at single-nucleotide resolution to allow APA comparison across mouse datasets. Analysis of 120 mouse cell types revealed that during differentiation genes either change their expression or they change their 3′UTR isoform usage. Therefore, we identified thousands of genes with 3′UTR isoform changes that have previously not been implicated in specific biological processes.

show abstract

Shortening of 3’ UTRs in most cell types composing tumor tissues implicates alternative polyadenylation in protein metabolism

Cited by 2 publications

References 44 publications

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

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

Comprehensive annotation of 3′UTRs from primary cells and their quantification from scRNA-seq data

Contact Info

Product

Resources

About