Characterization and prediction of mRNA polyadenylation sites in human genes

Tzu Hao Chang, Li Ching Wu, Yu Ting Chen, Hsien Da Huang, Baw Jhiune Liu, Kuang Fu Cheng, Jorng Tzong Horng

Research output: Contribution to journalArticle

11 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)463-472
Number of pages10
JournalMedical and Biological Engineering and Computing
Volume49
Issue number4
DOIs
Publication statusPublished - Apr 2011
Externally publishedYes

Fingerprint

Polyadenylation
RNA
Poly A
Genes
Messenger RNA
Support vector machines
Learning systems

Keywords

  • Bioinformatics
  • Data mining
  • Polyadenylation poly(A)
  • Support vector machines (SVMs)

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Science Applications
  • Medicine(all)

Cite this

Characterization and prediction of mRNA polyadenylation sites in human genes. / Chang, Tzu Hao; Wu, Li Ching; Chen, Yu Ting; Huang, Hsien Da; Liu, Baw Jhiune; Cheng, Kuang Fu; Horng, Jorng Tzong.

In: Medical and Biological Engineering and Computing, Vol. 49, No. 4, 04.2011, p. 463-472.

Research output: Contribution to journalArticle

Chang, Tzu Hao ; Wu, Li Ching ; Chen, Yu Ting ; Huang, Hsien Da ; Liu, Baw Jhiune ; Cheng, Kuang Fu ; Horng, Jorng Tzong. / Characterization and prediction of mRNA polyadenylation sites in human genes. In: Medical and Biological Engineering and Computing. 2011 ; Vol. 49, No. 4. pp. 463-472.
@article{e0fc2c080f514547a9913d7e31cb26ad,
title = "Characterization and prediction of mRNA polyadenylation sites in human genes",
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.",
keywords = "Bioinformatics, Data mining, Polyadenylation poly(A), Support vector machines (SVMs)",
author = "Chang, {Tzu Hao} and Wu, {Li Ching} and Chen, {Yu Ting} and Huang, {Hsien Da} and Liu, {Baw Jhiune} and Cheng, {Kuang Fu} and Horng, {Jorng Tzong}",
year = "2011",
month = "4",
doi = "10.1007/s11517-011-0732-4",
language = "English",
volume = "49",
pages = "463--472",
journal = "Medical and Biological Engineering and Computing",
issn = "0140-0118",
publisher = "Peter Peregrinus Ltd",
number = "4",

}

TY - JOUR

T1 - Characterization and prediction of mRNA polyadenylation sites in human genes

AU - Chang, Tzu Hao

AU - Wu, Li Ching

AU - Chen, Yu Ting

AU - Huang, Hsien Da

AU - Liu, Baw Jhiune

AU - Cheng, Kuang Fu

AU - Horng, Jorng Tzong

PY - 2011/4

Y1 - 2011/4

N2 - 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.

AB - 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.

KW - Bioinformatics

KW - Data mining

KW - Polyadenylation poly(A)

KW - Support vector machines (SVMs)

UR - http://www.scopus.com/inward/record.url?scp=79955622645&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79955622645&partnerID=8YFLogxK

U2 - 10.1007/s11517-011-0732-4

DO - 10.1007/s11517-011-0732-4

M3 - Article

C2 - 21286831

AN - SCOPUS:79955622645

VL - 49

SP - 463

EP - 472

JO - Medical and Biological Engineering and Computing

JF - Medical and Biological Engineering and Computing

SN - 0140-0118

IS - 4

ER -