Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics

Timm W, Scherbart A, Boecker S, Kohlbacher O, Nattkemper TW. Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics. BMC Bioinformatics. 2008;9(1):443.Background: Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides and proteins, however, is the fact that absolute quantification is severely hampered by the unclear relationship between the observed peak intensity and the peptide concentration in the sample. While there are numerous approaches to circumvent this problem experimentally (e. g. labeling techniques), reliable prediction of the peak intensities from peptide sequences could provide a peptide-specific correction factor. Thus, it would be a valuable tool towards label-free absolute quantification. Results: In this work we present machine learning techniques for peak intensity prediction for MALDI mass spectra. Features encoding the peptides' physico-chemical properties as well as string-based features were extracted. A feature subset was obtained from multiple forward feature selections on the extracted features. Based on these features, two advanced machine learning methods (support vector regression and local linear maps) are shown to yield good results for this problem (Pearson correlation of 0.68 in a ten-fold cross validation). Conclusion: The techniques presented here are a useful first step going beyond the binary prediction of proteotypic peptides towards a more quantitative prediction of peak intensities. These predictions in turn will turn out to be beneficial for mass spectrometry-based quantitative proteomics

LC-MSsim – a simulation software for liquid chromatography mass spectrometry data

<p>Abstract</p> <p>Background</p> <p>Mass Spectrometry coupled to Liquid Chromatography (LC-MS) is commonly used to analyze the protein content of biological samples in large scale studies. The data resulting from an LC-MS experiment is huge, highly complex and noisy. Accordingly, it has sparked new developments in Bioinformatics, especially in the fields of algorithm development, statistics and software engineering. In a quantitative label-free mass spectrometry experiment, crucial steps are the detection of peptide features in the mass spectra and the alignment of samples by correcting for shifts in retention time. At the moment, it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists only for peptide identification algorithms but no data that represents a ground truth for the evaluation of feature detection, alignment and filtering algorithms.</p> <p>Results</p> <p>We present <it>LC-MSsim</it>, a simulation software for LC-ESI-MS experiments. It simulates ESI spectra on the MS level. It reads a list of proteins from a FASTA file and digests the protein mixture using a user-defined enzyme. The software creates an LC-MS data set using a predictor for the retention time of the peptides and a model for peak shapes and elution profiles of the mass spectral peaks. Our software also offers the possibility to add contaminants, to change the background noise level and includes a model for the detectability of peptides in mass spectra. After the simulation, <it>LC-MSsim </it>writes the simulated data to mzData, a public XML format. The software also stores the positions (monoisotopic m/z and retention time) and ion counts of the simulated ions in separate files.</p> <p>Conclusion</p> <p><it>LC-MSsim </it>generates simulated LC-MS data sets and incorporates models for peak shapes and contaminations. Algorithm developers can match the results of feature detection and alignment algorithms against the simulated ion lists and meaningful error rates can be computed. We anticipate that <it>LC-MSsim </it>will be useful to the wider community to perform benchmark studies and comparisons between computational tools.</p

A systems biology approach to browning in apple - influence of harvest date on the proteome

During long term storage of apple (Malus x domestica Borkh.), physiological disorders may occur. One major group of internal disorders is characterized by flesh browning. The susceptibility to flesh browning is cultivar, batch and season dependent and is caused by a combination of preharvest and postharvest factors. This can result in considerable economic losses with incidence levels up to 40%. Braeburn and Kanzi are commercial cultivars in Belgium that are prone to browning. Harvest date is known to affect the sensitivity of a batch to browning during subsequent storage. The main objective of this experiment is to investigate how the proteome of apples differs in function of their harvest date. This will be related to browning incidence after prolongued storage. Apples (Malus x domestica Borkh., cv. Braeburn) were picked in the orchard of the Experimental Garden for Pome and Stone Fruits, pcfruit (Sint-Truiden, Belgium) at three different harvest times. Proteins were extracted immediately after harvest, using a phenol extraction (Pedreschi et al. 2007) and quantified using a modified Bradford procedure. For each harvest time five replicate extracts were prepared and analysed after tryptic digestion using a nano-2D_UPLC-MS system, Synapt Q-TOF. Data processing was performed with ProteinLynx and Progenesis software. After optimization of the peptide extraction and two-dimensional separation, 24439 peptides could be detected and 145 different protein families could confidentially be identified via an optimized workflow (Vertommen et al in press). Further results will be presented focusing on the observed proteome differences as a function of harvest date.status: publishe

Influence of preharvest calcium, potassium and triazole application on the proteome of apple at harvest

BACKGROUND: Braeburn browning disorder is a storage disease characterised by flesh browning and lens-shaped cavities. The incidence of this postharvest disorder is known to be affected by pre-harvest application of fertilisers and triazole-based fungicides. Recent work has shown that calcium and potassium reduced the incidence of Braeburn browning disorder, while triazoles had the opposite effect. This study addresses the hypothesis of an early proteomic imprint in the apple fruit at harvest induced by the pre-harvest factors applied. If so, this could be used for an early screening of apple fruit at harvest for their postharvest susceptibility to flesh browning. RESULTS: Calcium and triazole had significant effects, while potassium did not. One hundred and thirty protein families were identified, of which 29 were significantly altered after calcium and 63 after triazole treatment. Up-regulation of important antioxidant enzymeswas correlatedwith calcium fertilisation, while triazole induced alterations in the levels of respiration and ethylene biosynthesis related proteins. CONCLUSION: Pre-harvest fertiliser and fungicide application had considerable effects on the apple proteome at harvest. These changes, together with the applied storage conditionswill determinewhether or not BBD develops. ©2016 Society of Chemical Industrystatus: publishe

Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements

<p>Abstract</p> <p>Background</p> <p>Liquid chromatography coupled to mass spectrometry (LC-MS) has become a prominent tool for the analysis of complex proteomics and metabolomics samples. In many applications multiple LC-MS measurements need to be compared, e. g. to improve reliability or to combine results from different samples in a statistical comparative analysis. As in all physical experiments, LC-MS data are affected by uncertainties, and variability of retention time is encountered in all data sets. It is therefore necessary to estimate and correct the underlying distortions of the retention time axis to search for corresponding compounds in different samples. To this end, a variety of so-called <it>LC-MS map alignment algorithms </it>have been developed during the last four years. Most of these approaches are well documented, but they are usually evaluated on very specific samples only. So far, no publication has been assessing different alignment algorithms using a standard LC-MS sample along with commonly used quality criteria.</p> <p>Results</p> <p>We propose two LC-MS proteomics as well as two LC-MS metabolomics data sets that represent typical alignment scenarios. Furthermore, we introduce a new quality measure for the evaluation of LC-MS alignment algorithms. Using the four data sets to compare six freely available alignment algorithms proposed for the alignment of metabolomics and proteomics LC-MS measurements, we found significant differences with respect to alignment quality, running time, and usability in general.</p> <p>Conclusion</p> <p>The multitude of available alignment methods necessitates the generation of standard data sets and quality measures that allow users as well as developers to benchmark and compare their map alignment tools on a fair basis. Our study represents a first step in this direction. Currently, the installation and evaluation of the "correct" parameter settings can be quite a time-consuming task, and the success of a particular method is still highly dependent on the experience of the user. Therefore, we propose to continue and extend this type of study to a community-wide competition. All data as well as our evaluation scripts are available at <url>http://msbi.ipb-halle.de/msbi/caap</url>.</p