Peak detection using peak tree approach for mass spectrometry data
International Journal of Hybrid Intelligent Systems - Computational Models for Life Sciences
A fast and accurate algorithm for the quantification of peptides from mass spectrometry data
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Bayesian peptide peak detection for high resolution TOF mass spectrometry
IEEE Transactions on Signal Processing
Feature detection techniques for preprocessing proteomic data
Journal of Biomedical Imaging - Special issue on mathematical methods for images and surfaces
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Airshark: detecting non-WiFi RF devices using commodity WiFi hardware
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Gaussian mixture decomposition in the analysis of MALDI-TOF spectra
Expert Systems: The Journal of Knowledge Engineering
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Automatic identification of application I/O signatures from noisy server-side traces
FAST'14 Proceedings of the 12th USENIX conference on File and Storage Technologies
Hi-index | 3.84 |
Motivation: A major problem for current peak detection algorithms is that noise in mass spectrometry (MS) spectra gives rise to a high rate of false positives. The false positive rate is especially problematic in detecting peaks with low amplitudes. Usually, various baseline correction algorithms and smoothing methods are applied before attempting peak detection. This approach is very sensitive to the amount of smoothing and aggressiveness of the baseline correction, which contribute to making peak detection results inconsistent between runs, instrumentation and analysis methods. Results: Most peak detection algorithms simply identify peaks based on amplitude, ignoring the additional information present in the shape of the peaks in a spectrum. In our experience, 'true' peaks have characteristic shapes, and providing a shape-matching function that provides a 'goodness of fit' coefficient should provide a more robust peak identification method. Based on these observations, a continuous wavelet transform (CWT)-based peak detection algorithm has been devised that identifies peaks with different scales and amplitudes. By transforming the spectrum into wavelet space, the pattern-matching problem is simplified and in addition provides a powerful technique for identifying and separating the signal from the spike noise and colored noise. This transformation, with the additional information provided by the 2D CWT coefficients can greatly enhance the effective signal-to-noise ratio. Furthermore, with this technique no baseline removal or peak smoothing preprocessing steps are required before peak detection, and this improves the robustness of peak detection under a variety of conditions. The algorithm was evaluated with SELDI-TOF spectra with known polypeptide positions. Comparisons with two other popular algorithms were performed. The results show the CWT-based algorithm can identify both strong and weak peaks while keeping false positive rate low. Availability: The algorithm is implemented in R and will be included as an open source module in the Bioconductor project. Contact: s-lin2@northwestern.edu Supplementary material:http://basic.northwestern.edu/publications/peakdetection/. Colour versions of the figures in this article can be found at Bioinformatics Online.