MOTIVATION: Proteomics has particularly evolved to become of high interest for the field of biomarker discovery and drug development. Especially the combination of liquid chromatography and mass spectrometry (LC/MS) has proven to be a powerful technique for analyzing protein mixtures. Clinically orientated proteomic studies will have to compare hundreds of LC/MS runs at a time. In order to compare different runs, sophisticated preprocessing steps have to be performed. An important step is the retention time (rt) alignment of LC/MS runs. Especially non-linear shifts in the rt between pairs of LC/MS runs make this a crucial and non-trivial problem. RESULTS: For the purpose of demonstrating the particular importance of correcting non-linear rt shifts, we evaluate and compare different alignment algorithms. We present and analyze two versions of a new algorithm that is based on regression techniques, once assuming and estimating only linear shifts and once also allowing for the estimation of non-linear shifts. As an example for another type of alignment method we use an established alignment algorithm based on shifting vectors that we adapted to allow for correcting non-linear shifts also. In a simulation study, we show that rt alignment procedures that can estimate non-linear shifts yield clearly better alignments. This is even true under mild non-linear deviations. AVAILABILITY: R code for the regression-based alignment methods and simulated datasets are available at http://www.statistik.tu-dortmund.de/genetik-publikationen-alignment.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The field of time series analysis and forecasting methods has significantly changed in the last decade due to the influence of new knowledge in non-linear dynamics. New methods such as artificial neural networks replaced traditional approaches which usually were appropriate for linear models only. Nevertheless, there are still applications where accurate estimations of linear processes, such as ARMA models, are sufficient. However, the methods for this class of models were developed more than 20 years ago, with the restrictions of the, then current, computers in mind. The authors describe an attempt to combine the ideas of the widely used Box-Jenkins method (1970) with new approaches to model identification and parameter estimation based on evolutionary algorithms, a class of probabilistic parameter optimization methods.