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...

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Autores principales:	Ahmed Abbas, Xin-Bing Kong, Zhi Liu, Bing-Yi Jing, Xin Gao
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/561fad8551d64e1a90c6b633896dbc78
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spelling	oai:doaj.org-article:561fad8551d64e1a90c6b633896dbc782021-11-18T08:02:33ZAutomatic peak selection by a Benjamini-Hochberg-based algorithm.1932-620310.1371/journal.pone.0053112https://doaj.org/article/561fad8551d64e1a90c6b633896dbc782013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23308147/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203A 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 [Formula: 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.Ahmed AbbasXin-Bing KongZhi LiuBing-Yi JingXin GaoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 1, p e53112 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ahmed Abbas Xin-Bing Kong Zhi Liu Bing-Yi Jing Xin Gao Automatic peak selection by a Benjamini-Hochberg-based algorithm.
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 [Formula: 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	article
author	Ahmed Abbas Xin-Bing Kong Zhi Liu Bing-Yi Jing Xin Gao
author_facet	Ahmed Abbas Xin-Bing Kong Zhi Liu Bing-Yi Jing Xin Gao
author_sort	Ahmed Abbas
title	Automatic peak selection by a Benjamini-Hochberg-based algorithm.
title_short	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_sort	automatic peak selection by a benjamini-hochberg-based algorithm.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/561fad8551d64e1a90c6b633896dbc78
work_keys_str_mv	AT ahmedabbas automaticpeakselectionbyabenjaminihochbergbasedalgorithm AT xinbingkong automaticpeakselectionbyabenjaminihochbergbasedalgorithm AT zhiliu automaticpeakselectionbyabenjaminihochbergbasedalgorithm AT bingyijing automaticpeakselectionbyabenjaminihochbergbasedalgorithm AT xingao automaticpeakselectionbyabenjaminihochbergbasedalgorithm
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Automatic peak selection by a Benjamini-Hochberg-based algorithm.

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