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 

作者机构：Pasteurien CollegeSuzhou Medical College of Soochow UniversitySoochow UniversitySuzhou 215000China Department of AutomationXiamen UniversityXiamen 361005China Key Laboratory of the Coastal and Wetland EcosystemsMinistry of EducationCollege of the Environment and EcologyXiamen UniversityXiamen 361005China 

出 版 物：《Genomics, Proteomics & Bioinformatics》 (基因组蛋白质组与生物信息学报（英文版）)

年 卷 期：2023年第21卷第1期

页      面：67-83页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0711[理学-系统科学] 07[理学] 08[工学] 

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) 

主　　题：Polyadenylation Predictive modeling RNA-seq scRNA-seq Machine learning 

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 *** 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 *** 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)***,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 *** also proposed practical guidelines on choosing appropriate methods applicable to diverse ***,we discussed in depth the challenges in improving the performance of pA prediction and benchmarking different ***,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.