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. 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 non-linear 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.
| Original language | English |
|---|---|
| Pages (from-to) | 1137-1144 |
| Number of pages | 8 |
| Journal | Bioinformatics |
| Volume | 25 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 2009 May 7 |
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ASJC Scopus subject areas
- Biochemistry
- Molecular Biology
- Computational Theory and Mathematics
- Computer Science Applications
- Computational Mathematics
- Statistics and Probability
Cite this
A robust peak detection method for RNA structure inference by high-throughput contact mapping. / Kim, Jinkyu; Yu, Seunghak; Shim, Byonghyo; Kim, Hanjoo; Min, Hyeyoung; Chung, Eui Young; Das, Rhiju; Yoon, Sungroh.
In: Bioinformatics, Vol. 25, No. 9, 07.05.2009, p. 1137-1144.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A robust peak detection method for RNA structure inference by high-throughput contact mapping
AU - Kim, Jinkyu
AU - Yu, Seunghak
AU - Shim, Byonghyo
AU - Kim, Hanjoo
AU - Min, Hyeyoung
AU - Chung, Eui Young
AU - Das, Rhiju
AU - Yoon, Sungroh
PY - 2009/5/7
Y1 - 2009/5/7
N2 - 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. 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 non-linear 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.
AB - 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. 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 non-linear 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=65449129993&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btp110
DO - 10.1093/bioinformatics/btp110
M3 - Article
C2 - 19246511
AN - SCOPUS:65449129993
VL - 25
SP - 1137
EP - 1144
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 9
ER -