OpenMS – An open-source software framework for mass spectrometry

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry is an essential analytical technique for high-throughput analysis in proteomics and metabolomics. The development of new separation techniques, precise mass analyzers and experimental protocols is a very active field of research. This leads to more complex experimental setups yielding ever increasing amounts of data. Consequently, analysis of the data is currently often the bottleneck for experimental studies. Although software tools for many data analysis tasks are available today, they are often hard to combine with each other or not flexible enough to allow for rapid prototyping of a new analysis workflow.</p> <p>Results</p> <p>We present OpenMS, a software framework for rapid application development in mass spectrometry. OpenMS has been designed to be portable, easy-to-use and robust while offering a rich functionality ranging from basic data structures to sophisticated algorithms for data analysis. This has already been demonstrated in several studies.</p> <p>Conclusion</p> <p>OpenMS is available under the Lesser GNU Public License (LGPL) from the project website at <url>http://www.openms.de</url>.</p

eMZed: an open source framework in Python for rapid and interactive development of LC/MS data analysis workflows

Summary: The Python-based, open-source eMZed framework was developed for mass spectrometry (MS) users to create tailored workflows for liquid chromatography (LC)/MS data analysis. The goal was to establish a unique framework with comprehensive basic functionalities that are easy to apply and allow for the extension and modification of the framework in a straightforward manner. eMZed supports the iterative development and prototyping of individual evaluation strategies by providing a computing environment and tools for inspecting and modifying underlying LC/MS data. The framework specifically addresses non-expert programmers, as it requires only basic knowledge of Python and relies largely on existing successful open-source software, e.g. OpenMS. Availability: The framework eMZed and its documentation are freely available at http://emzed.biol.ethz.ch/. eMZed is published under the GPL 3.0 license, and an online discussion group is available at https://groups.google.com/group/emzed-users. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Evaluation of peak-picking algorithms for protein mass spectrometry

Peak picking is an early key step in MS data analysis. We compare three commonly used approaches to peak picking and discuss their merits by means of statistical analysis. Methods investigated encompass signal-to-noise ratio, continuous wavelet transform, and a correlation-based approach using a Gaussian template. Functionality of the three methods is illustrated and discussed in a practical context using a mass spectral data set created with MALDI-TOF technology. Sensitivity and specificity are investigated using a manually defined reference set of peaks. As an additional criterion, the robustness of the three methods is assessed by a perturbation analysis and illustrated using ROC curves

Quantification and Simulation of Liquid Chromatography-Mass Spectrometry Data

Computational mass spectrometry is a fast evolving field that has attracted increased attention over the last couple of years. The performance of software solutions determines the success of analysis to a great extent. New algorithms are required to reflect new experimental procedures and deal with new instrument generations. One essential component of algorithm development is the validation (as well as comparison) of software on a broad range of data sets. This requires a gold standard (or so-called ground truth), which is usually obtained by manual annotation of a real data set. Comprehensive manually annotated public data sets for mass spectrometry data are labor-intensive to produce and their quality strongly depends on the skill of the human expert. Some parts of the data may even be impossible to annotate due to high levels of noise or other ambiguities. Furthermore, manually annotated data is usually not available for all steps in a typical computational analysis pipeline. We thus developed the most comprehensive simulation software to date, which allows to generate multiple levels of ground truth and features a plethora of settings to reflect experimental conditions and instrument settings. The simulator is used to generate several distinct types of data. The data are subsequently employed to evaluate existing algorithms. Additionally, we employ simulation to determine the influence of instrument attributes and sample complexity on the ability of algorithms to recover information. The results give valuable hints on how to optimize experimental setups. Furthermore, this thesis introduces two quantitative approaches, namely a decharging algorithm based on integer linear programming and a new workflow for identification of differentially expressed proteins for a large in vitro study on toxic compounds. Decharging infers the uncharged mass of a peptide (or protein) by clustering all its charge variants. The latter occur frequently under certain experimental conditions. We employ simulation to show that decharging is robust against missing values even for high complexity data and that the algorithm outperforms other solutions in terms of mass accuracy and run time on real data. The last part of this thesis deals with a new state-of-the-art workflow for protein quantification based on isobaric tags for relative and absolute quantitation (iTRAQ). We devise a new approach to isotope correction, propose an experimental design, introduce new metrics of iTRAQ data quality, and confirm putative properties of iTRAQ data using a novel approach. All tools developed as part of this thesis are implemented in OpenMS, a C++ library for computational mass spectrometry

Mass Spectrometry: An Ideal Method For Rna Modification Analysis

Currently there is no good way to measure and find the exact location of multiple RNA modifications. Existing technology can effectively find single varieties of modifications, but cannot identify co-occurrence. As the field of proteomics has shown, mass spectrometry is a powerful and versatile technique assessing broad ranges of chemical modifications in the context of the cellular environment. In this project I used our expertise in proteomics to build a mass spectrometry based platform for identifying RNA modifications. I have since set up both software and analytical platforms querying RNA modifications, and used this platform to survey human tRNA samples and identify what modifications there are, and where they occur

OpenMS - A Framework for Quantitative HPLC/MS-Based Proteomics

In the talk we describe the freely available software library OpenMS which is currently under development at the Freie Universität Berlin and the Eberhardt-Karls Universität Tübingen. We give an overview of the goals and problems in differential proteomics with HPLC and then describe in detail the implemented approaches for signal processing, peak detection and data reduction currently employed in OpenMS. After this we describe methods to identify the differential expression of peptides and propose strategies to avoid MS/MS identification of peptides of interest. We give an overview of the capabilities and design principles of OpenMS and demonstrate its ease of use. Finally we describe projects in which OpenMS will be or was already deployed and thereby demonstrate its versatility

High-accuracy peak picking of proteomics data

A new peak picking algorithm for the analysis of mass spectrometric (MS) data is presented. It is independent of the underlying machine or ionization method, and is able to resolve highly convoluted and asymmetric signals. The method uses the multiscale nature of spectrometric data by first detecting the mass peaks in the wavelet-transformed signal before a given asymmetric peak function is fitted to the raw data. In an optional third stage, the resulting fit can be further improved using techniques from nonlinear optimization. In contrast to currently established techniques (e.g. SNAP, Apex) our algorithm is able to separate overlapping peaks of multiply charged peptides in ESI-MS data of low resolution. Its improved accuracy with respect to peak positions makes it a valuable preprocessing method for MS-based identification and quantification experiments. The method has been validated on a number of different annotated test cases, where it compares favorably in both runtime and accuracy with currently established techniques. An implementation of the algorithm is freely available in our open source framework OpenMS (www.open-ms.de)