Important Issues in Planning a Proteomics Experiment: Statistical Considerations of Quantitative Proteomic Data.

Mass spectrometry is frequently used in quantitative proteomics to detect differentially regulated proteins. A very important but unfortunately oftentimes neglected part in detecting differential proteins is the statistical analysis. Data from proteomics experiments are usually high-dimensional and hence require profound statistical methods. It is especially important to already correctly design a proteomic experiment before it is conducted in the laboratory. Only this can ensure that the statistical analysis is capable of detecting truly differential proteins afterward. This chapter thus covers aspects of both statistical planning as well as the actual analysis of quantitative proteomic experiments.

Important issues in planning a proteomics experiment: statistical considerations of quantitative proteomic data.

Mass spectrometry is frequently used in quantitative proteomics to detect differentially regulated proteins. A very important but unfortunately oftentimes neglected part in detecting differential proteins is the statistical analysis. Data from proteomics experiments are usually high-dimensional and hence require profound statistical methods. It is especially important to already correctly design a proteomic experiment before it is conducted in the laboratory. Only this can ensure that the statistical analysis is capable of detecting truly differential proteins afterwards. This chapter thus covers aspects of both statistical planning and the actual analysis of quantitative proteomic experiments.

Identification of core components and transient interactors of the peroxisomal importomer by dual-track stable isotope labeling with amino acids in cell culture analysis.

The importomer complex plays an essential role in the biogenesis of peroxisomes by mediating the translocation of matrix proteins across the organellar membrane. A central part of this highly dynamic import machinery is the docking complex consisting of Pex14p, Pex13p, and Pex17p that is linked to the RING finger complex (Pex2p, Pex10p, Pex12p) via Pex8p. To gain detailed knowledge on the molecular players governing peroxisomal matrix protein import and, thus, the integrity and functionality of peroxisomes, we aimed at a most comprehensive investigation of stable and transient interaction partners of Pex14p, the central component of the importomer. To this end, we performed a thorough quantitative proteomics study based on epitope tagging of Pex14p combined with dual-track stable isotope labeling with amino acids in cell culture-mass spectrometry (SILAC-MS) analysis of affinity-purified Pex14p complexes and statistics. The results led to the establishment of the so far most extensive Pex14p interactome, comprising 9 core and further 12 transient components. We confirmed virtually all known Pex14p interaction partners including the core constituents of the importomer as well as Pex5p, Pex11p, Pex15p, and Dyn2p. More importantly, we identified new transient interaction partners (Pex25p, Hrr25p, Esl2p, prohibitin) that provide a valuable resource for future investigations on the functionality, dynamics, and regulation of the peroxisomal importomer.

Peek a peak: a glance at statistics for quantitative label-free proteomics.

Today, label-free mass spectrometry methods are frequently used for quantification of proteins and peptides. There have been several proposals of measurable parameters that best reflect quantities, such as peak areas as well as spectral counts. This review provides a systematic overview of the proposed methods. Owing to the shotgun proteomics approach generally used today for label-free mass spectrometry, any quantitative measure in the first place is a measure of peptide quantity. There has been no systematic research on how to best infer protein quantity from its measured peptides' quantities. The way peptide identifications are assembled to protein lists may especially lead to significantly different results in protein quantification. A further focus of this review will thus be the assembly of measured peptide quantities to a protein quantity.

Using Laboratory Information Management Systems as central part of a proteomics data workflow.

The organization and storage of proteomics data are challenging issues today and even more for the rising amount of information in the future. This review article describes the advantages of using Laboratory Information Management Systems (LIMS) in proteomics laboratories. Seven typical LIMS are explored in detail to describe their role in an even bigger interrelation. They are a central part of the proteomics data workflow, starting with data generation and ending with the publication in journals and repositories. Therefore, they enable community-wide data utilization and further Systems Biology discoveries.

Retention time alignment algorithms for LC/MS data must consider non-linear shifts.

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.