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Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm
A 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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3538655/ https://www.ncbi.nlm.nih.gov/pubmed/23308147 http://dx.doi.org/10.1371/journal.pone.0053112 |
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author | Abbas, Ahmed Kong, Xin-Bing Liu, Zhi Jing, Bing-Yi Gao, Xin |
author_facet | Abbas, Ahmed Kong, Xin-Bing Liu, Zhi Jing, Bing-Yi Gao, Xin |
author_sort | Abbas, Ahmed |
collection | PubMed |
description | A 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 [Image: see text]-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 [1] and PICKY [2]. 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. |
format | Online Article Text |
id | pubmed-3538655 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-35386552013-01-10 Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm Abbas, Ahmed Kong, Xin-Bing Liu, Zhi Jing, Bing-Yi Gao, Xin PLoS One Research Article A 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 [Image: see text]-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 [1] and PICKY [2]. 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. Public Library of Science 2013-01-07 /pmc/articles/PMC3538655/ /pubmed/23308147 http://dx.doi.org/10.1371/journal.pone.0053112 Text en © 2013 Abbas et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Abbas, Ahmed Kong, Xin-Bing Liu, Zhi Jing, Bing-Yi Gao, Xin Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm |
title | Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm |
title_full | Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm |
title_fullStr | Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm |
title_full_unstemmed | Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm |
title_short | Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm |
title_sort | automatic peak selection by a benjamini-hochberg-based algorithm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3538655/ https://www.ncbi.nlm.nih.gov/pubmed/23308147 http://dx.doi.org/10.1371/journal.pone.0053112 |
work_keys_str_mv | AT abbasahmed automaticpeakselectionbyabenjaminihochbergbasedalgorithm AT kongxinbing automaticpeakselectionbyabenjaminihochbergbasedalgorithm AT liuzhi automaticpeakselectionbyabenjaminihochbergbasedalgorithm AT jingbingyi automaticpeakselectionbyabenjaminihochbergbasedalgorithm AT gaoxin automaticpeakselectionbyabenjaminihochbergbasedalgorithm |