glu-RNA: aliGn highLy strUctured ncRNAs using only sequence similarity

Prapaporn Techa-Angkoon; Yanni Sun

doi:10.1145/2506583.2506617

glu-RNA: aliGn highLy strUctured ncRNAs using only sequence similarity

Prapaporn Techa-Angkoon, Yanni Sun^*

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

Generating reliable alignments for ncRNAs is an important step in ncRNA secondary structure prediction and ncRNA gene finding. Existing sequence alignment programs can generate reliable alignments for ncRNAs with high sequence conservation. For highly structured ncRNAs that may lack strong sequence similarity, structural alignment programs are required. However, conducting reliable structural alignment is much more expensive than sequence alignment and is not ideal for large-scale input such as whole genomes or next-generation sequencing data.

In this paper, we propose an accurate ncRNA alignment approach to align highly structured ncRNAs using only sequence similarity. By incorporating posterior probability and a machine learning approach, we can generate accurate alignments of highly structured ncRNAs without using structural information. We tested our approach on over three hundreds of pairs of highly structured ncRNAs from BRAliBase 2.1. The experimental results show that our approach can achieve more accurate alignments than commonly used sequence alignment programs and a popular structural alignment tool.

Original language	English
Title of host publication	BCB'13 Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Publisher	Association for Computing Machinery
Pages	508-517
ISBN (Print)	978-1-4503-2434-2
DOIs	https://doi.org/10.1145/2506583.2506617
Publication status	Published - Sept 2013
Externally published	Yes
Event	4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM-BCB 2013) - Wshington, United States Duration: 22 Sept 2013 → 25 Sept 2013

Publication series

Name	2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013

Conference

Conference	4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM-BCB 2013)
Place	United States
City	Wshington
Period	22/09/13 → 25/09/13

Research Keywords

Machine learning methods
NcRNAs
Sequence alignment

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10.1145/2506583.2506617

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Techa-Angkoon, P., & Sun, Y. (2013). glu-RNA: aliGn highLy strUctured ncRNAs using only sequence similarity. In BCB'13 Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics (pp. 508-517). (2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013). Association for Computing Machinery. https://doi.org/10.1145/2506583.2506617

Techa-Angkoon, Prapaporn ; Sun, Yanni. / glu-RNA : aliGn highLy strUctured ncRNAs using only sequence similarity. BCB'13 Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics. Association for Computing Machinery, 2013. pp. 508-517 (2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013).

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title = "glu-RNA: aliGn highLy strUctured ncRNAs using only sequence similarity",

abstract = "Generating reliable alignments for ncRNAs is an important step in ncRNA secondary structure prediction and ncRNA gene finding. Existing sequence alignment programs can generate reliable alignments for ncRNAs with high sequence conservation. For highly structured ncRNAs that may lack strong sequence similarity, structural alignment programs are required. However, conducting reliable structural alignment is much more expensive than sequence alignment and is not ideal for large-scale input such as whole genomes or next-generation sequencing data. In this paper, we propose an accurate ncRNA alignment approach to align highly structured ncRNAs using only sequence similarity. By incorporating posterior probability and a machine learning approach, we can generate accurate alignments of highly structured ncRNAs without using structural information. We tested our approach on over three hundreds of pairs of highly structured ncRNAs from BRAliBase 2.1. The experimental results show that our approach can achieve more accurate alignments than commonly used sequence alignment programs and a popular structural alignment tool. ",

keywords = "Machine learning methods, NcRNAs, Sequence alignment",

author = "Prapaporn Techa-Angkoon and Yanni Sun",

year = "2013",

month = sep,

doi = "10.1145/2506583.2506617",

language = "English",

isbn = "978-1-4503-2434-2 ",

series = "2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013",

publisher = "Association for Computing Machinery",

pages = "508--517",

booktitle = "BCB'13 Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics",

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note = "4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM-BCB 2013) ; Conference date: 22-09-2013 Through 25-09-2013",

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Techa-Angkoon, P & Sun, Y 2013, glu-RNA: aliGn highLy strUctured ncRNAs using only sequence similarity. in BCB'13 Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics. 2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013, Association for Computing Machinery, pp. 508-517, 4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM-BCB 2013), Wshington, District of Columbia, United States, 22/09/13. https://doi.org/10.1145/2506583.2506617

glu-RNA: aliGn highLy strUctured ncRNAs using only sequence similarity. / Techa-Angkoon, Prapaporn; Sun, Yanni.
BCB'13 Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics. Association for Computing Machinery, 2013. p. 508-517 (2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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T1 - glu-RNA

T2 - 4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM-BCB 2013)

AU - Techa-Angkoon, Prapaporn

AU - Sun, Yanni

PY - 2013/9

Y1 - 2013/9

N2 - Generating reliable alignments for ncRNAs is an important step in ncRNA secondary structure prediction and ncRNA gene finding. Existing sequence alignment programs can generate reliable alignments for ncRNAs with high sequence conservation. For highly structured ncRNAs that may lack strong sequence similarity, structural alignment programs are required. However, conducting reliable structural alignment is much more expensive than sequence alignment and is not ideal for large-scale input such as whole genomes or next-generation sequencing data. In this paper, we propose an accurate ncRNA alignment approach to align highly structured ncRNAs using only sequence similarity. By incorporating posterior probability and a machine learning approach, we can generate accurate alignments of highly structured ncRNAs without using structural information. We tested our approach on over three hundreds of pairs of highly structured ncRNAs from BRAliBase 2.1. The experimental results show that our approach can achieve more accurate alignments than commonly used sequence alignment programs and a popular structural alignment tool.

AB - Generating reliable alignments for ncRNAs is an important step in ncRNA secondary structure prediction and ncRNA gene finding. Existing sequence alignment programs can generate reliable alignments for ncRNAs with high sequence conservation. For highly structured ncRNAs that may lack strong sequence similarity, structural alignment programs are required. However, conducting reliable structural alignment is much more expensive than sequence alignment and is not ideal for large-scale input such as whole genomes or next-generation sequencing data. In this paper, we propose an accurate ncRNA alignment approach to align highly structured ncRNAs using only sequence similarity. By incorporating posterior probability and a machine learning approach, we can generate accurate alignments of highly structured ncRNAs without using structural information. We tested our approach on over three hundreds of pairs of highly structured ncRNAs from BRAliBase 2.1. The experimental results show that our approach can achieve more accurate alignments than commonly used sequence alignment programs and a popular structural alignment tool.

KW - Machine learning methods

KW - NcRNAs

KW - Sequence alignment

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DO - 10.1145/2506583.2506617

M3 - RGC 32 - Refereed conference paper (with host publication)

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T3 - 2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013

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ER -

Techa-Angkoon P, Sun Y. glu-RNA: aliGn highLy strUctured ncRNAs using only sequence similarity. In BCB'13 Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics. Association for Computing Machinery. 2013. p. 508-517. (2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013). doi: 10.1145/2506583.2506617

glu-RNA: aliGn highLy strUctured ncRNAs using only sequence similarity

Abstract

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