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

Bioinformatics, vol. 25, no. 1, pp. 83-87, May 2009

Jinkyu Kim, Seunghak Yu, Byonghyo Shim, Hanjoo Kim, Hyeyoung Min, Eui-Young Chung, Rhiju Das and Sungroh Yoon

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.

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