KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Structural and Functional Bioinformatics Group
Permanent link to this recordhttp://hdl.handle.net/10754/325309
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AbstractA common issue in bioinformatics is that computational methods often generate a large number of predictions sorted according to certain confidence scores. A key problem is then determining how many predictions must be selected to include most of the true predictions while maintaining reasonably high precision. In nuclear magnetic resonance (NMR)-based protein structure determination, for instance, computational peak picking methods are becoming more and more common, although expert-knowledge remains the method of choice to determine how many peaks among thousands of candidate peaks should be taken into consideration to capture the true peaks. Here, we propose a Benjamini-Hochberg (B-H)-based approach that automatically selects the number of peaks. We formulate the peak selection problem as a multiple testing problem. Given a candidate peak list sorted by either volumes or intensities, we first convert the peaks into p-values and then apply the B-H-based algorithm to automatically select the number of peaks. The proposed approach is tested on the state-of-the-art peak picking methods, including WaVPeak  and PICKY . Compared with the traditional fixed number-based approach, our approach returns significantly more true peaks. For instance, by combining WaVPeak or PICKY with the proposed method, the missing peak rates are on average reduced by 20% and 26%, respectively, in a benchmark set of 32 spectra extracted from eight proteins. The consensus of the B-H-selected peaks from both WaVPeak and PICKY achieves 88% recall and 83% precision, which significantly outperforms each individual method and the consensus method without using the B-H algorithm. The proposed method can be used as a standard procedure for any peak picking method and straightforwardly applied to some other prediction selection problems in bioinformatics. The source code, documentation and example data of the proposed method is available at http://sfb.kaust.edu.sa/pages/software.aspx. © 2013 Abbas et al.
CitationAbbas A, Kong X-B, Liu Z, Jing B-Y, Gao X (2013) Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm. PLoS ONE 8: e53112. doi:10.1371/journal.pone.0053112.
PublisherPublic Library of Science (PLoS)
PubMed Central IDPMC3538655
- WaVPeak: picking NMR peaks through wavelet-based smoothing and volume-based filtering.
- Authors: Liu Z, Abbas A, Jing BY, Gao X
- Issue date: 2012 Apr 1
- PICKY: a novel SVD-based NMR spectra peak picking method.
- Authors: Alipanahi B, Gao X, Karakoc E, Donaldson L, Li M
- Issue date: 2009 Jun 15
- An automated framework for NMR resonance assignment through simultaneous slice picking and spin system forming.
- Authors: Abbas A, Guo X, Jing BY, Gao X
- Issue date: 2014 Jun
- Computer vision-based automated peak picking applied to protein NMR spectra.
- Authors: Klukowski P, Walczak MJ, Gonczarek A, Boudet J, Wider G
- Issue date: 2015 Sep 15
- Bayesian peak picking for NMR spectra.
- Authors: Cheng Y, Gao X, Liang F
- Issue date: 2014 Feb