Z. Liu, A. Abbas, B.-Y. Jing, X. Gao
Bioinformatics, 28(7): 914-920, (2012)
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.
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.
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 15N-HSQC, HNCO, HNCA,
HNCACB and CBCA(CO)NH, respectively. When the same number of peaks are
considered, WaVPeak significantly outperforms PICKY.