Chromatographic Alignment of ESI-LC-MS Proteomics Data Sets by Ordered Bijective Interpolated Warping

Abstract

Mass spectrometry proteomics typically relies upon analyzing outcomes of single analyses; however, comparing raw data across multiple experiments should enhance both peptide/protein identification and quantitation. In the absence of convincing tandem MS identifications, comparing peptide quantities between experiments (or fractions) requires the chromatographic alignment of MS signals. An extension of dynamic time warping (DTW), termed ordered bijective interpolated warping (OBI-Warp), is presented and used to align a variety of electrospray ionization liquid chromatography mass spectrometry (ESI-LC-MS) proteomics data sets. An algorithm to produce a bijective (one-to-one) function from DTW output is coupled with piecewise cubic hermite interpolation to produce a smooth warping function. Data sets were chosen to represent a broad selection of ESI-LC-MS alignment cases. High confidence, overlapping tandem mass spectra are used as standards to optimize and compare alignment parameters. We determine that Pearson's correlation coefficient as a measure of spectra similarity outperforms covariance, dot product, and Euclidean distance in its ability to produce correct alignments with optimal and suboptimal alignment parameters. We demonstrate the importance of penalizing gaps for best alignments. Using optimized parameters, we show that OBI-Warp produces alignments consistent with time standards across these data sets. The source and executables are released under MIT style license at http://obi-warp.sourceforge.net/.

Generic Software Frameworks for GC-MS Based Metabolomics

“…This allows a simultaneous alignment and retention time correction of all peaks. The other available method is based on the Obi-Warp dynamic time warping (Prince & Marcotte, 2006) algorithm and is capable of correcting large non-linear retention time distortions. It uses the peak set with the highest number of features as alignment reference, which is comparable to the approach used by Lange et al (2007).…”

“…Time correction is applied after the peak assignments between the reference chromatogram and the others have been calculated. Signal-based methods include recent variants of correlation optimized warping (Smilde & Horvatovich, 2008), parametric time warping (Christin et al, 2010) and dynamic time warping (Christin et al, 2010;Clifford et al, 2009;Hoffmann & Stoye, 2009;Prince & Marcotte, 2006) and usually consider the complete chromatogram for comparison. However, attempts are made to reduce the computational burden associated with a complete pairwise comparison of mass spectra by partitioning the chromatograms into similar regions (Hoffmann & Stoye, 2009), or by selecting a representative subset of mass traces (Christin et al, 2010).…”

Quantitative strategies to fuel the merger of discovery and hypothesis-driven shotgun proteomics

“…The first class uses full scan MS data to 'align' the retention times of one run to another. This alignment is performed by algorithms such as dynamic time warping or correlation optimized warping which find an optimal mapping of retention times between runs that maximizes their similarity [34][35][36]. The second class matches detected features (such as peptide isotope distributions or MS/MS spectra) between runs, and applies algorithms such as regression to fit a time correction function to the matched markers [29,37,38].…”

Multi-class alignment of LC-MS data using probabilistic-based mixture regression models

“…(5) as: (6) To fit the dataset D into this framework for the spline-based regression models, the regression between y i and x i defined in Eq. (1) can be rewritten as follows: (7) where B i denotes spline basis matrix evaluated at x i . Then, the regression and the error models derived from the class-specific conditional probability density function for y i can be rewritten as follows: (8) Thus, Eq.…”

“…One of the methods that do not utilize landmarks for alignment is dynamic time warping (DTW), which was originally applied in speech recognition [5]. DTW has been applied for aligning chromatographic and LC-MS data [6][7][8]. However, the above approach is limited for a consensus alignment of all pair-wise combinations of spectra.…”