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

作　　者：Wenbin Ye Qiwei Lian Congting Ye Xiaohui Wu 

机构地区：[1]Pasteurien College,Suzhou Medical College of Soochow University,Soochow University,Suzhou 215000,China [2]Department of Automation,Xiamen University,Xiamen 361005,China [3]Key Laboratory of the Coastal and Wetland Ecosystems,Ministry of Education,College of the Environment and Ecology,Xiamen University,Xiamen 361005,China

出　　处：《Genomics, Proteomics & Bioinformatics》2023年第1期67-83,共17页基因组蛋白质组与生物信息学报（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China(Grant No.61871463 to XW);the Natural Science Foundation of Fujian Province of China(Grant No.2020J01047 to CY).

摘　　要：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 genomewide 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-30 untranslated region,tissuespecific,cross-species,and single-cell pA prediction.

关 键 词：POLYADENYLATION Predictive modeling RNA-SEQ scRNA-seq Machine learning 

分 类 号：Q811.4[生物学—生物工程]