An open-source framework for mass spectrometry
OpenMS offers an infrastructure for the development of mass spectrometry related software.
OpenMS covers a wide range of functionalities which are needed for software development when it comes to the analysis of high throughput protein separation and mass spectrometry related data.
· High throughput analysis of proteins using mass spectrometry requires an efficient signal processing which reduces the amount of data with minimal loss of information. Peaks have to be detected, and important features like their peak centroid position, the height and the area have to be determined.
· For peak picking, we propose a Wavelet-based scheme for the processing of mass spectrometry data that is able to cope with the difficulties posed by applications in proteomics.
· Often several preprocessing steps are applied to raw data before the actual peak picking. OpenMS also supports baseline reduction and noise filtering of raw data.
· After the first data reduction by peak picking, our approach is to reduce the amount of data even further.
· This is done by determining all peaks belonging to the same 'feature' (a chemical entity, for instance a peptide charge variant) and adjusting a theoretical model to them. A quality value is assigned to each feature regarding its measurement in each dimension of characterization, e.g. its elution profile (retention time) and isotope distribution (mass-to-charge).
· The features are then used for label-free and isotope-labeled quantitation.
· OpenMS can visualize HPLC-MS data in several differend views. Single scans can be displayed in a 2-dimensional plot. The more complex maps, can be displayed in a 2D view (birds-eye view with color-coded intensities) and even in a 3D view. The representations of the data are highly configurable and can thus be reused in many applications.
· The images on the right are screenshots of 'TOPPView', a MS data viewer that is part of TOPP.
· Many proteomics experiments consist of several HPLC-MS runs. The runs have to be aligned in order to correct for problems in chromatography. Map mapping is the process whereby maps of peaks or features from different measurements are superimposed.
· The first step is to find a transformation that moves features from one map close to corresponding features in the other one. A standard geometric hashing approach is sufficient in most cases.
· In the second step it determines a combinatorial matching and extract groups of corresponding features for differential analysis.
· The identification of peptides and proteins is one of the main tasks in proteomics research. OpenMS can read and write the data formats of the most popular identification engines, e.g. Sequest, Mascot, OMSSA, X!Tandem. Data structures for that store the data for further analysis of identification results are available.
· OpenMS can e.g. filter the identification results, compute consensus results of several identification engines. Additionally the identifications can be validated using retention time prediction.
· High throughput technologies in modern proteomics result in a lot of data which needs to be annotated, analysed and stored. The huge amount of data makes database support an essential feature of nearly every proteomics application.
OpenMS offers database support through the Qt SQL module, which allows the use of a variety of different SQL databases.
· The database model of OpenMS conforms as far as possible to the HUPO PSI-OM model.
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What's New in This Release:
- fixed bug that caused TOPPAS to crash when a connection between nodes was added under certain circumstances