Characterization and Prediction of MRNA Polyadenylation Sites in Human Genes

Overview

Journal Med Biol Eng Comput

Publisher Springer

Specialties Biomedical Engineering
Medical Informatics

Date 2011 Feb 3

PMID 21286831

Citations 10

Authors

Tzu-Hao Chang

Li-Ching Wu

Yu-Ting Chen

Hsien-Da Huang

Baw-Jhiune Liu

Kuang-Fu Cheng

Jorng-Tzong Horng

Affiliations

Soon will be listed here.

Abstract

The accurate identification of potential poly(A) sites has contributed to all many studies with regard to alternative polyadenylation. The aim of this study was the development of a machine-learning methodology that will help to discriminate real polyadenylation signals from randomly occurring signals in genomic sequence. Since previous studies have revealed that RNA secondary structure in certain genes has significant impact, the authors tried to computationally pinpoint common structural patterns around the poly(A) sites and to investigate how RNA secondary structure may influence polyadenylation. This involved an initial study on the impact of RNA structure and it was found using motif search tools that hairpin structures might be important. Thus, it was propose that, in addition to the sequence pattern around poly(A) sites, there exists a widespread structural pattern that is also employed during human mRNA polyadenylation. In this study, the authors present a computational model that uses support vector machines to predict human poly(A) sites. The results show that this predictive model has a comparable performance to the current prediction tool. In addition, it was identified common structural patterns associated with polyadenylation using several motif finding programs and this provides new insight into the role of RNA secondary structure plays in polyadenylation.

Citing Articles

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

Ye W, Lian Q, Ye C, Wu X Genomics Proteomics Bioinformatics. 2022; 21(1):67-83.

PMID: 36167284 PMC: 10372920. DOI: 10.1016/j.gpb.2022.09.005.

From shallow to deep: some lessons learned from application of machine learning for recognition of functional genomic elements in human genome.

Jankovic B, Gojobori T Hum Genomics. 2022; 16(1):7.

PMID: 35180894 PMC: 8855580. DOI: 10.1186/s40246-022-00376-1.

Biomarker Identification through Multiomics Data Analysis of Prostate Cancer Prognostication Using a Deep Learning Model and Similarity Network Fusion.

Wang T, Lee C, Lee T, Huang H, Hsu J, Chang T Cancers (Basel). 2021; 13(11).

PMID: 34064004 PMC: 8196729. DOI: 10.3390/cancers13112528.

Sequence determinants of polyadenylation-mediated regulation.

Vainberg Slutskin I, Weinberger A, Segal E Genome Res. 2019; 29(10):1635-1647.

PMID: 31530582 PMC: 6771402. DOI: 10.1101/gr.247312.118.

Inference of the human polyadenylation code.

Leung M, Delong A, Frey B Bioinformatics. 2018; 34(17):2889-2898.

PMID: 29648582 PMC: 6129302. DOI: 10.1093/bioinformatics/bty211.

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