Skip Navigation



Bioinformatics Advance Access published online on February 26, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp110
This Article
Right arrow Advance Access manuscript (PDF)
Right arrow All Versions of this Article:
25/9/1137    most recent
btp110v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Kim, J.
Right arrow Articles by Yoon, S.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Kim, J.
Right arrow Articles by Yoon, S.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2009). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A Robust Peak Detection Method for RNA Structure Inference by High-throughput Contact Mapping

Jinkyu Kim 1, Seunghak Yu 1, Byonghyo Shim 2, Hanjoo Kim 1, Hyeyoung Min 3, Eui-Young Chung 4, Rhiju Das 5 and Sungroh Yoon 1,*

School of Electrical Engineering, Korea University, Seoul 136-713, Korea
Department of Computer Science, Korea University, Seoul 136-713, Korea
Department of Statistics, Stanford University, Stanford, CA 94305, USA
Department of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea
Departments of Biochemistry and Physics, Stanford University, Stanford, CA 94305, USA

*To whom correspondence should be addressed. Prof. Sungroh Yoon, E-mail: sryoon{at}korea.ac.kr


  Abstract

Motivation: For high-throughput prediction of the helical arrangements of large RNA molecules, an innovative method termed multiplexed hydroxyl radical (·OH) cleavage analysis (MOHCA) has been proposed (Das et al., 2008). A key step in this promising technique is to detect peaks accurately from noisy radioactivity profiles. Since manual peak finding is laborious and prone to error, an automated peak detection method to improve the accuracy and throughput of MOHCA is required. Existing methods were not applicable to MOHCA due to their high false positive rates.

Results:We developed a two-step computational method that can detect peaks from MOHCA profiles in a robust manner. The first step exploits an ensemble of linear and nonlinear signal processing techniques to find true peak candidates. In the second step, a binary classifier trained with the characteristics of true and false peaks is used to eliminate false peaks out of the peak candidates. We tested the proposed approach with 2002 MOHCA cleavage profiles and obtained the median recall, precision, and F-measure values of 0.917, 0.750, and 0.830, respectively. Compared with the alternatives considered, the proposed method was able to handle false peaks substantially better, thus resulting in 51.0–71.8% higher median values of precision and F-measure.

Availability: The software and supplemental data are available athttp://dna.korea.ac.kr/pub/mohca.

Contact: sryoon{at}korea.ac.kr

Associate Editor: Prof. Anna Tramontano

Received on November 18, 2008; revised on January 30, 2009; accepted on February 22, 2009

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.