Wavelet-based Denoising of Mass Spectrometry Data on the Illinois Bio-Grid

Mass spectrometry (MS) is an analytical method biologists and chemists use to sequence and identify unknown proteins. Although sensitivity and sample analysis modalities differ, mass spectrometers basically consist of an ion source, a mass analyzer, and a detector. The final output is a single mass spectrum or a series of mass spectra depending on the equipment used. Each spectrum consists of pairs of mass-to-charge ratio (m/z) and intensity [1]. In the process of capturing data for each spectrum, noise from the spectrometer appears as m/z and intensity pairs interspersed with those from the analyzed sample (analyte). Electronic noise can come from sensors, light sources, and other internal electrical systems. Chemical noise specifically refers to ions that do not belong to the analyte. Chemical noise can come from analyte preparation or contamination. Noise can have a detrimental effect on the performance of algorithms that assist protein identification such as de novo sequencing, spectral scoring used in comparisons, and database searching. Effective reduction or removal of such noise is critical for improved results. We propose refinement of current wavelet-based techniques to improve noise removal in MS data. These wavelet-based techniques will be incorporated into a workflow being developed on the Illinois Bio-Grid (IBG) [2]. Since noise appears as seemingly ordinary data, removing it can be very difficult. Noise removal based on simple global thresholds would remove noise and less intense peaks, losing potentially useful signals. Multiscale analysis methods such as wavelets have shown promise, removing low intensity noise while preserving medium and higher intensity peaks [3]. The standard procedure for denoising with wavelets starts by performing the transform on the raw data. The wavelet transform decomposes the data into a series of average and detail values which are used during reconstruction of the data. The transformed data is thresholded by attenuating signals below a user-defined noise level. Determining a good threshold value will require experimentation with known samples. An inverse transform is done on the thresholded transform data to reconstruct a good approximation of the denoised data. Measuring the quality of the denoising process will require repeated testing with objective quality measures such as RMS error and signal-to-noise ratio measurements. The guidance of an experienced chemist helps the refinement of the denoising algorithms. Since wavelet transforms can preserve higher intensities very well, significant lower intensity peaks will be watched closely. Lower intensity peaks from raw data, especially those along the baseline, can be unintentionally eliminated during denoising and require further investigation [4]. Using wavelet-based denoising is part of a larger workflow that includes baseline correction, peak detection, and integration. Ongoing research has produced a variety of algorithms for each step in the workflow. The ability to choose different algorithms in the workflow affords the developer, researcher, or technician the chance to experiment and choose the workflow that best suits their data. For larger scale deployments and high-throughput analysis, an established workflow improves consistency. Baseline correction fits MS data to a straight line to ease the integration (area under the curve) of peaks during the quantification process. Williams, et al. correct using a polynomial fitted along medians within windows on the m/z axis [5], while Liu, et al. correct by subtracting the convex hull of the spectrum after smoothing raw data [6]. Peak detection is critical to proper protein identification and can depend on the quality of the denoised signal. It is complicated by false and merged peaks, which can be deconvoluted using peak width constraints or wavelet decomposition.