WaVPeak: picking NMR peaks through wavelet-based smoothing and volume-based filtering

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	WaVPeak: picking NMR peaks through wavelet-based smoothing and volume-based filtering
	Liu, Zhi ; Abbas, Ahmed ; Jing, Bing-Yi ; Gao, Xin ; Liu Z(刘志)
刊名	http://dx.doi.org/10.1093/bioinformatics/bts078
	2012-04-01
关键词	RESONANCE ASSIGNMENT NOESY SPECTRA SPECTROSCOPY TRANSFORM PROTEINS DECOMPOSITION MUNIN
英文摘要	King Abdullah University of Science and Technology (KAUST) [GRP-CF-2011-19-P-Gao-Huang]; KAUST GMSV; Hong Kong Research Grant Council [HKUST6019/10P]; National Nature Science Foundation of China [71071155]; Fundamental Research Funds for the Central Universities; Renmin University of China [10XNL007]; NSFC [71071155]; Motivation: Nuclear magnetic resonance (NMR) has been widely used as a powerful tool to determine the 3D structures of proteins in vivo. However, the post- spectra processing stage of NMR structure determination usually involves a tremendous amount of time and expert knowledge, which includes peak picking, chemical shift assignment and structure calculation steps. Detecting accurate peaks from the NMR spectra is a prerequisite for all following steps, and thus remains a key problem in automatic NMR structure determination. Results: We introduce WaVPeak, a fully automatic peak detection method. WaVPeak first smoothes the given NMR spectrum by wavelets. The peaks are then identified as the local maxima. The false positive peaks are filtered out efficiently by considering the volume of the peaks. WaVPeak has two major advantages over the state-of-the-art peak-picking methods. First, through wavelet-based smoothing, WaVPeak does not eliminate any data point in the spectra. Therefore, WaVPeak is able to detect weak peaks that are embedded in the noise level. NMR spectroscopists need the most help isolating these weak peaks. Second, WaVPeak estimates the volume of the peaks to filter the false positives. This is more reliable than intensity-based filters that are widely used in existing methods. We evaluate the performance of WaVPeak on the benchmark set proposed by PICKY (Alipanahi et al., 2009), one of the most accurate methods in the literature. The dataset comprises 32 2D and 3D spectra from eight different proteins. Experimental results demonstrate that WaVPeak achieves an average of 96%, 91%, 88%, 76% and 85% recall on N-15-HSQC, HNCO, HNCA, HNCACB and CBCA(CO)NH, respectively. When the same number of peaks are considered, WaVPeak significantly outperforms PICKY.
语种	英语
出版者	BIOINFORMATICS
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/90129]
专题	经济学院－已发表论文
推荐引用方式 GB/T 7714	Liu, Zhi,Abbas, Ahmed,Jing, Bing-Yi,et al. WaVPeak: picking NMR peaks through wavelet-based smoothing and volume-based filtering[J]. http://dx.doi.org/10.1093/bioinformatics/bts078,2012.
APA	Liu, Zhi,Abbas, Ahmed,Jing, Bing-Yi,Gao, Xin,&刘志.(2012).WaVPeak: picking NMR peaks through wavelet-based smoothing and volume-based filtering.http://dx.doi.org/10.1093/bioinformatics/bts078.
MLA	Liu, Zhi,et al."WaVPeak: picking NMR peaks through wavelet-based smoothing and volume-based filtering".http://dx.doi.org/10.1093/bioinformatics/bts078 (2012).