Local structural alignment of RNA with affine gap model

Thomas K.F. Wong; Brenda W.Y. Cheung; T. W. Lam; S. M. Yiu

doi:10.1007/978-3-642-13078-6_22

Local structural alignment of RNA with affine gap model

Thomas K.F. Wong, Brenda W.Y. Cheung, T. W. Lam, S. M. Yiu

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › peer-review

Abstract

Predicting new non-coding RNAs (ncRNAs) of a family can be done by aligning the potential candidate with a member of the family with known sequence and secondary structure. Existing tools either only consider the sequence similarity or cannot handle local alignment with gaps. In this paper, we consider the problem of finding the optimal local structural alignment between a query RNA sequence (with known secondary structure) and a target sequence (with unknown secondary structure) with the affine gap penalty model. We provide the algorithm to solve the problem. Based on a preliminary experiment, we show that there are ncRNA families in which considering local structural alignment with gap penalty model can identify real hits more effectively than using global alignment or local alignment without gap penalty model.

Original language	English
Title of host publication	Bioinformatics Research and Applications - 6th International Symposium, ISBRA 2010, Proceedings
Pages	191-202
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-642-13078-6_22
Publication status	Published - 2010
Externally published	Yes
Event	6th International Symposium on Bioinformatics Research and Applications, ISBRA 2010 - Storrs, CT, United States Duration: 23 May 2010 → 26 May 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6053 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Symposium on Bioinformatics Research and Applications, ISBRA 2010
Country/Territory	United States
City	Storrs, CT
Period	23/05/10 → 26/05/10

Access to Document

10.1007/978-3-642-13078-6_22

Cite this

Wong, T. K. F., Cheung, B. W. Y., Lam, T. W., & Yiu, S. M. (2010). Local structural alignment of RNA with affine gap model. In Bioinformatics Research and Applications - 6th International Symposium, ISBRA 2010, Proceedings (pp. 191-202). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6053 LNBI). https://doi.org/10.1007/978-3-642-13078-6_22

@inproceedings{67c57cfd7c2c4972a2ed9624bf6f1370,

title = "Local structural alignment of RNA with affine gap model",

abstract = "Predicting new non-coding RNAs (ncRNAs) of a family can be done by aligning the potential candidate with a member of the family with known sequence and secondary structure. Existing tools either only consider the sequence similarity or cannot handle local alignment with gaps. In this paper, we consider the problem of finding the optimal local structural alignment between a query RNA sequence (with known secondary structure) and a target sequence (with unknown secondary structure) with the affine gap penalty model. We provide the algorithm to solve the problem. Based on a preliminary experiment, we show that there are ncRNA families in which considering local structural alignment with gap penalty model can identify real hits more effectively than using global alignment or local alignment without gap penalty model.",

keywords = "Affine gap, Local structural alignment, Non-coding RNA",

author = "Wong, \{Thomas K.F.\} and Cheung, \{Brenda W.Y.\} and Lam, \{T. W.\} and Yiu, \{S. M.\}",

year = "2010",

doi = "10.1007/978-3-642-13078-6\_22",

language = "English",

isbn = "3642130771",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "191--202",

booktitle = "Bioinformatics Research and Applications - 6th International Symposium, ISBRA 2010, Proceedings",

note = "6th International Symposium on Bioinformatics Research and Applications, ISBRA 2010 ; Conference date: 23-05-2010 Through 26-05-2010",

}

Wong, TKF, Cheung, BWY, Lam, TW & Yiu, SM 2010, Local structural alignment of RNA with affine gap model. in Bioinformatics Research and Applications - 6th International Symposium, ISBRA 2010, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6053 LNBI, pp. 191-202, 6th International Symposium on Bioinformatics Research and Applications, ISBRA 2010, Storrs, CT, United States, 23/05/10. https://doi.org/10.1007/978-3-642-13078-6_22

Local structural alignment of RNA with affine gap model. / Wong, Thomas K.F.; Cheung, Brenda W.Y.; Lam, T. W. et al.
Bioinformatics Research and Applications - 6th International Symposium, ISBRA 2010, Proceedings. 2010. p. 191-202 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6053 LNBI).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › peer-review

TY - GEN

T1 - Local structural alignment of RNA with affine gap model

AU - Wong, Thomas K.F.

AU - Cheung, Brenda W.Y.

AU - Lam, T. W.

AU - Yiu, S. M.

PY - 2010

Y1 - 2010

N2 - Predicting new non-coding RNAs (ncRNAs) of a family can be done by aligning the potential candidate with a member of the family with known sequence and secondary structure. Existing tools either only consider the sequence similarity or cannot handle local alignment with gaps. In this paper, we consider the problem of finding the optimal local structural alignment between a query RNA sequence (with known secondary structure) and a target sequence (with unknown secondary structure) with the affine gap penalty model. We provide the algorithm to solve the problem. Based on a preliminary experiment, we show that there are ncRNA families in which considering local structural alignment with gap penalty model can identify real hits more effectively than using global alignment or local alignment without gap penalty model.

AB - Predicting new non-coding RNAs (ncRNAs) of a family can be done by aligning the potential candidate with a member of the family with known sequence and secondary structure. Existing tools either only consider the sequence similarity or cannot handle local alignment with gaps. In this paper, we consider the problem of finding the optimal local structural alignment between a query RNA sequence (with known secondary structure) and a target sequence (with unknown secondary structure) with the affine gap penalty model. We provide the algorithm to solve the problem. Based on a preliminary experiment, we show that there are ncRNA families in which considering local structural alignment with gap penalty model can identify real hits more effectively than using global alignment or local alignment without gap penalty model.

KW - Affine gap

KW - Local structural alignment

KW - Non-coding RNA

UR - http://www.scopus.com/inward/record.url?scp=77953802882&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-13078-6_22

DO - 10.1007/978-3-642-13078-6_22

M3 - Conference Paper

SN - 3642130771

SN - 9783642130779

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 191

EP - 202

BT - Bioinformatics Research and Applications - 6th International Symposium, ISBRA 2010, Proceedings

T2 - 6th International Symposium on Bioinformatics Research and Applications, ISBRA 2010

Y2 - 23 May 2010 through 26 May 2010

ER -

Wong TKF, Cheung BWY, Lam TW, Yiu SM. Local structural alignment of RNA with affine gap model. In Bioinformatics Research and Applications - 6th International Symposium, ISBRA 2010, Proceedings. 2010. p. 191-202. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-13078-6_22

Local structural alignment of RNA with affine gap model

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this