Characterization and prediction of mRNA polyadenylation sites in human genes.
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| Title: | Characterization and prediction of mRNA polyadenylation sites in human genes. |
|---|---|
| Authors: | Chang, Tzu-Hao1, Wu, Li-Ching2, Chen, Yu-Ting3, Huang, Hsien-Da, Liu, Baw-Jhiune4, Cheng, Kuang-Fu, Horng, Jorng-Tzong horng@db.csie.ncu.edu.tw |
| Source: | Medical & Biological Engineering & Computing. Apr2011, Vol. 49 Issue 4, p463-472. 10p. |
| Subjects: | Messenger RNA, Machine learning, Genomics, Bioinformatics, Data mining, Support vector machines |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical & Biological Engineering & Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 59671589 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Characterization and prediction of mRNA polyadenylation sites in human genes. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chang%2C+Tzu-Hao%22">Chang, Tzu-Hao</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Wu%2C+Li-Ching%22">Wu, Li-Ching</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Chen%2C+Yu-Ting%22">Chen, Yu-Ting</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Huang%2C+Hsien-Da%22">Huang, Hsien-Da</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Baw-Jhiune%22">Liu, Baw-Jhiune</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Cheng%2C+Kuang-Fu%22">Cheng, Kuang-Fu</searchLink><br /><searchLink fieldCode="AR" term="%22Horng%2C+Jorng-Tzong%22">Horng, Jorng-Tzong</searchLink><i> horng@db.csie.ncu.edu.tw</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+%26+Biological+Engineering+%26+Computing%22">Medical & Biological Engineering & Computing</searchLink>. Apr2011, Vol. 49 Issue 4, p463-472. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Messenger+RNA%22">Messenger RNA</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Genomics%22">Genomics</searchLink><br /><searchLink fieldCode="DE" term="%22Bioinformatics%22">Bioinformatics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical & Biological Engineering & Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11517-011-0732-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 463 Subjects: – SubjectFull: Messenger RNA Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Genomics Type: general – SubjectFull: Bioinformatics Type: general – SubjectFull: Data mining Type: general – SubjectFull: Support vector machines Type: general Titles: – TitleFull: Characterization and prediction of mRNA polyadenylation sites in human genes. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chang, Tzu-Hao – PersonEntity: Name: NameFull: Wu, Li-Ching – PersonEntity: Name: NameFull: Chen, Yu-Ting – PersonEntity: Name: NameFull: Huang, Hsien-Da – PersonEntity: Name: NameFull: Liu, Baw-Jhiune – PersonEntity: Name: NameFull: Cheng, Kuang-Fu – PersonEntity: Name: NameFull: Horng, Jorng-Tzong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2011 Type: published Y: 2011 Identifiers: – Type: issn-print Value: 01400118 Numbering: – Type: volume Value: 49 – Type: issue Value: 4 Titles: – TitleFull: Medical & Biological Engineering & Computing Type: main |
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